02_neural_network_classification_in_tensorflow.ipynb

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        "# 02. Neural Network Classification with TensorFlow\n",
        "\n",
        "Okay, we've seen how to deal with a regression problem in TensorFlow, let's look at how we can approach a classification problem.\n",
        "\n",
        "A [classification problem](https://en.wikipedia.org/wiki/Statistical_classification) involves predicting whether something is one thing or another.\n",
        "\n",
        "For example, you might want to:\n",
        "* Predict whether or not someone has heart disease based on their health parameters. This is called **binary classification** since there are only two options.\n",
        "* Decide whether a photo of is of food, a person or a dog. This is called **multi-class classification** since there are more than two options.\n",
        "* Predict what categories should be assigned to a Wikipedia article. This is called **multi-label classification** since a single article could have more than one category assigned.\n",
        "\n",
        "In this notebook, we're going to work through a number of different classification problems with TensorFlow. In other words, taking a set of inputs and predicting what class those set of inputs belong to.\n",
        "\n",
        "## What we're going to cover\n",
        "\n",
        "Specifically, we're going to go through doing the following with TensorFlow:\n",
        "- Architecture of a classification model\n",
        "- Input shapes and output shapes\n",
        "  - `X`: features/data (inputs)\n",
        "  - `y`: labels (outputs) \n",
        "    - \"What class do the inputs belong to?\"\n",
        "- Creating custom data to view and fit\n",
        "- Steps in modelling for binary and mutliclass classification\n",
        "  - Creating a model\n",
        "  - Compiling a model\n",
        "    - Defining a loss function\n",
        "    - Setting up an optimizer\n",
        "      - Finding the best learning rate\n",
        "    - Creating evaluation metrics\n",
        "  - Fitting a model (getting it to find patterns in our data)\n",
        "  - Improving a model\n",
        "- The power of non-linearity\n",
        "- Evaluating classification models\n",
        "  - Visualizng the model (\"visualize, visualize, visualize\")\n",
        "  - Looking at training curves\n",
        "  - Compare predictions to ground truth (using our evaluation metrics)\n",
        "\n",
        "## How you can use this notebook\n",
        "\n",
        "You can read through the descriptions and the code (it should all run, except for the cells which error on purpose), but there's a better option.\n",
        "\n",
        "Write all of the code yourself.\n",
        "\n",
        "Yes. I'm serious. Create a new notebook, and rewrite each line by yourself. Investigate it, see if you can break it, why does it break?\n",
        "\n",
        "You don't have to write the text descriptions but writing the code yourself is a great way to get hands-on experience.\n",
        "\n",
        "Don't worry if you make mistakes, we all do. The way to get better and make less mistakes is to **write more code**."
      ]
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        "## Typical architecture of a classification neural network \n",
        "\n",
        "The word *typical* is on purpose.\n",
        "\n",
        "Because the architecture of a classification neural network can widely vary depending on the problem you're working on.\n",
        "\n",
        "However, there are some fundamentals all deep neural networks contain:\n",
        "* An input layer.\n",
        "* Some hidden layers.\n",
        "* An output layer.\n",
        "\n",
        "Much of the rest is up to the data analyst creating the model.\n",
        "\n",
        "The following are some standard values you'll often use in your classification neural networks.\n",
        "\n",
        "| **Hyperparameter** | **Binary Classification** | **Multiclass classification** |\n",
        "| --- | --- | --- |\n",
        "| Input layer shape | Same as number of features (e.g. 5 for age, sex, height, weight, smoking status in heart disease prediction) | Same as binary classification |\n",
        "| Hidden layer(s) | Problem specific, minimum = 1, maximum = unlimited | Same as binary classification |\n",
        "| Neurons per hidden layer | Problem specific, generally 10 to 100 | Same as binary classification |\n",
        "| Output layer shape | 1 (one class or the other) | 1 per class (e.g. 3 for food, person or dog photo) |\n",
        "| Hidden activation | Usually [ReLU](https://www.kaggle.com/dansbecker/rectified-linear-units-relu-in-deep-learning) (rectified linear unit) | Same as binary classification |\n",
        "| Output activation | [Sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function) | [Softmax](https://en.wikipedia.org/wiki/Softmax_function) |\n",
        "| Loss function | [Cross entropy](https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_loss_function_and_logistic_regression) ([`tf.keras.losses.BinaryCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy) in TensorFlow) | Cross entropy ([`tf.keras.losses.CategoricalCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalCrossentropy) in TensorFlow) |\n",
        "| Optimizer | [SGD](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/SGD) (stochastic gradient descent), [Adam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) | Same as binary classification |\n",
        "\n",
        "Table 1: Typical architecture of a classification network. Source: Adapted from page 295 of [Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow Book by Aurélien Géron](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)\n",
        "\n",
        "Don't worry if not much of the above makes sense right now, we'll get plenty of experience as we go through this notebook.\n",
        "\n",
        "Let's start by importing TensorFlow as the common alias `tf`. For this notebook, make sure you're using version 2.x+."
      ]
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    {
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        },
        "outputId": "fc4b651b-a7f5-416c-afd6-c6a48c2fc120"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "import datetime\n",
        "print(f\"Notebook last run (end-to-end): {datetime.datetime.now()}\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.12.0\n",
            "Notebook last run (end-to-end): 2023-06-10 09:45:45.746434\n"
          ]
        }
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      "cell_type": "markdown",
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      "source": [
        "## Creating data to view and fit\n",
        "\n",
        "We could start by importing a classification dataset but let's practice making some of our own classification data.\n",
        "\n",
        "> 🔑 **Note:** It's a common practice to get you and model you build working on a toy (or simple) dataset before moving to your actual problem. Treat it as a rehersal experiment before the actual experiment(s).\n",
        "\n",
        "Since classification is predicting whether something is one thing or another, let's make some data to reflect that.\n",
        "\n",
        "To do so, we'll use Scikit-Learn's [`make_circles()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles) function.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7dT80sWqLYPf"
      },
      "source": [
        "from sklearn.datasets import make_circles\n",
        "\n",
        "# Make 1000 examples\n",
        "n_samples = 1000\n",
        "\n",
        "# Create circles\n",
        "X, y = make_circles(n_samples, \n",
        "                    noise=0.03, \n",
        "                    random_state=42)"
      ],
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "Wonderful, now we've created some data, let's look at the features (`X`) and labels (`y`)."
      ]
    },
    {
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8961bc05-a75d-4fb3-cc80-b60035c1db9e"
      },
      "source": [
        "# Check out the features\n",
        "X"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.75424625,  0.23148074],\n",
              "       [-0.75615888,  0.15325888],\n",
              "       [-0.81539193,  0.17328203],\n",
              "       ...,\n",
              "       [-0.13690036, -0.81001183],\n",
              "       [ 0.67036156, -0.76750154],\n",
              "       [ 0.28105665,  0.96382443]])"
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          },
          "metadata": {},
          "execution_count": 3
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    {
      "cell_type": "code",
      "metadata": {
        "id": "FkgZlGstK572",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9dd0fd3-6e0f-42a7-ed91-18f66d144c7d"
      },
      "source": [
        "# See the first 10 labels\n",
        "y[:10]"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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      "source": [
        "Okay, we've seen some of our data and labels, how about we move towards visualizing?\n",
        "\n",
        "> 🔑 **Note:** One important step of starting any kind of machine learning project is to [become one with the data](https://karpathy.github.io/2019/04/25/recipe/). And one of the best ways to do this is to visualize the data you're working with as much as possible. The data explorer's motto is \"visualize, visualize, visualize\".\n",
        "\n",
        "We'll start with a DataFrame."
      ]
    },
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        "outputId": "f569b82a-d8d8-4296-96e1-ab2dc45feb29"
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      "source": [
        "# Make dataframe of features and labels\n",
        "import pandas as pd\n",
        "circles = pd.DataFrame({\"X0\":X[:, 0], \"X1\":X[:, 1], \"label\":y})\n",
        "circles.head()"
      ],
      "execution_count": 5,
      "outputs": [
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              "         X0        X1  label\n",
              "0  0.754246  0.231481      1\n",
              "1 -0.756159  0.153259      1\n",
              "2 -0.815392  0.173282      1\n",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ngNwBOxevcv"
      },
      "source": [
        "What kind of labels are we dealing with?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "011b9697-c16b-4869-9f19-843dfd506cc1"
      },
      "source": [
        "# Check out the different labels\n",
        "circles.label.value_counts()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    500\n",
              "0    500\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
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      "source": [
        "Alright, looks like we're dealing with a **binary classification** problem. It's binary because there are only two labels (0 or 1).\n",
        "\n",
        "If there were more label options (e.g. 0, 1, 2, 3 or 4), it would be called **multiclass classification**.\n",
        "\n",
        "Let's take our visualization a step further and plot our data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIPTzrETMemP",
        "colab": {
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          "height": 430
        },
        "outputId": "c5b400f3-45bb-4acc-e2e9-2e16b39f2592"
      },
      "source": [
        "# Visualize with a plot\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdYlBu);"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Jty66H6cbJp"
      },
      "source": [
        "Nice! From the plot, can you guess what kind of model we might want to build?\n",
        "\n",
        "How about we try and build one to classify blue or red dots? As in, a model which is able to distinguish blue from red dots.\n",
        "\n",
        "> 🛠 **Practice:** Before pushing forward, you might want to spend 10 minutes playing around with the [TensorFlow Playground](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=2,2&seed=0.93799&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&batchSize_hide=true). Try adjusting the different hyperparameters you see and click play to see a neural network train. I think you'll find the data very similar to what we've just created."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cv4fEHihOAq4"
      },
      "source": [
        "## Input and output shapes\n",
        "\n",
        "One of the most common issues you'll run into when building neural networks is shape mismatches.\n",
        "\n",
        "More specifically, the shape of the input data and the shape of the output data.\n",
        "\n",
        "In our case, we want to input `X` and get our model to predict `y`.\n",
        "\n",
        "So let's check out the shapes of `X` and `y`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZxjjmsHNt_K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfd3caf3-4fbf-46d3-e62a-19af868da36c"
      },
      "source": [
        "# Check the shapes of our features and labels\n",
        "X.shape, y.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1000, 2), (1000,))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BSq58RogJMa"
      },
      "source": [
        "Hmm, where do these numbers come from?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPr81SsKgHvZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8480a1de-84f1-4954-9f3b-19d0944dc6d8"
      },
      "source": [
        "# Check how many samples we have\n",
        "len(X), len(y)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 1000)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XCBbrNaOgPWC"
      },
      "source": [
        "So we've got as many `X` values as we do `y` values, that makes sense.\n",
        "\n",
        "Let's check out one example of each."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwrSEVubgZNb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08757ca7-bd1b-4475-e4ff-92d4043ad229"
      },
      "source": [
        "# View the first example of features and labels\n",
        "X[0], y[0]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.75424625, 0.23148074]), 1)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OMeqY8JSgg4L"
      },
      "source": [
        "Alright, so we've got two `X` features which lead to one `y` value.\n",
        "\n",
        "This means our neural network input shape will has to accept a tensor with at least one dimension being two and output a tensor with at least one value.\n",
        "\n",
        "> 🤔 **Note:** `y` having a shape of (1000,) can seem confusing. However, this is because all `y` values are actually scalars (single values) and therefore don't have a dimension. For now, think of your output shape as being at least the same value as one example of `y` (in our case, the output from our neural network has to be at least one value)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vSjUbiCBN9fc"
      },
      "source": [
        "## Steps in modelling\n",
        "\n",
        "Now we know what data we have as well as the input and output shapes, let's see how we'd build a neural network to model it.\n",
        "\n",
        "In TensorFlow, there are typically 3 fundamental steps to creating and training a model.\n",
        "\n",
        "1. **Creating a model** - piece together the layers of a neural network yourself (using the [functional](https://www.tensorflow.org/guide/keras/functional) or [sequential API](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential)) or import a previously built model (known as transfer learning).\n",
        "2. **Compiling a model** - defining how a model's performance should be measured (loss/metrics) as well as defining how it should improve (optimizer). \n",
        "3. **Fitting a model** - letting the model try to find patterns in the data (how does `X` get to `y`). \n",
        "\n",
        "Let's see these in action using the Sequential API to build a model for our regression data. And then we'll step through each."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lt4pbcZ7OEif",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53a0f771-d165-417d-851d-63fb50f8cda1"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_1 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_1.compile(loss=tf.keras.losses.BinaryCrossentropy(), # binary since we are working with 2 clases (0 & 1)\n",
        "                optimizer=tf.keras.optimizers.SGD(),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "# 3. Fit the model\n",
        "model_1.fit(X, y, epochs=5)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "32/32 [==============================] - 5s 3ms/step - loss: 6.1300 - accuracy: 0.4870\n",
            "Epoch 2/5\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 5.8850 - accuracy: 0.4780\n",
            "Epoch 3/5\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 5.4667 - accuracy: 0.4890\n",
            "Epoch 4/5\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 4.2173 - accuracy: 0.5200\n",
            "Epoch 5/5\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 3.4511 - accuracy: 0.5100\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff7faffe710>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oa8Tcv9ePRq6"
      },
      "source": [
        "Looking at the accuracy metric, our model performs poorly (50% accuracy on a binary classification problem is the equivalent of guessing), but what if we trained it for longer?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqVVD_IqPHm5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1593941a-afdc-44e6-a2e8-52d4003db078"
      },
      "source": [
        "# Train our model for longer (more chances to look at the data)\n",
        "model_1.fit(X, y, epochs=200, verbose=0) # set verbose=0 to remove training updates\n",
        "model_1.evaluate(X, y)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 0s 2ms/step - loss: 0.6935 - accuracy: 0.5000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6934829950332642, 0.5]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-qaMmKrPegL"
      },
      "source": [
        "Even after 200 passes of the data, it's still performing as if it's guessing.\n",
        "\n",
        "What if we added an extra layer and trained for a little longer?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TED0ZkOuPklW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eec5bed6-bf41-43b5-8cc7-e25e864ec722"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model (same as model_1 but with an extra layer)\n",
        "model_2 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1), # add an extra layer\n",
        "  tf.keras.layers.Dense(1) \n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_2.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.SGD(),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "# 3. Fit the model\n",
        "model_2.fit(X, y, epochs=100, verbose=0) # set verbose=0 to make the output print less"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff7ec6c0e20>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFWWlhByirLa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6efefb94-590b-4035-a2e3-bef5173e3e5a"
      },
      "source": [
        "# Evaluate the model\n",
        "model_2.evaluate(X, y)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 0s 2ms/step - loss: 0.6935 - accuracy: 0.5000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6934892535209656, 0.5]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HC7vhAHKQQRY"
      },
      "source": [
        "Still not even as good as guessing (~50% accuracy)... hmm...?\n",
        "\n",
        "Let's remind ourselves of a couple more ways we can use to improve our models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wyTs6QgijF6_"
      },
      "source": [
        "## Improving a model\n",
        "\n",
        "To improve our model, we can alter almost every part of the 3 steps we went through before.\n",
        "\n",
        "1. **Creating a model** - here you might want to add more layers, increase the number of hidden units (also called neurons) within each layer, change the activation functions of each layer.\n",
        "2. **Compiling a model** - you might want to choose a different optimization function (such as the [Adam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) optimizer, which is usually pretty good for many problems) or perhaps change the learning rate of the optimization function.\n",
        "3. **Fitting a model** - perhaps you could fit a model for more epochs (leave it training for longer).\n",
        "\n",
        "![various options you can use to improve a neural network model](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-improving-a-model-from-model-perspective.png)\n",
        "*There are many different ways to potentially improve a neural network. Some of the most common include: increasing the number of layers (making the network deeper), increasing the number of hidden units (making the network wider) and changing the learning rate. Because these values are all human-changeable, they're referred to as [hyperparameters](https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)) and the practice of trying to find the best hyperparameters is referred to as [hyperparameter tuning](https://en.wikipedia.org/wiki/Hyperparameter_optimization).*\n",
        "\n",
        "How about we try adding more neurons, an extra layer and our friend the Adam optimizer?\n",
        "\n",
        "Surely doing this will result in predictions better than guessing...\n",
        "\n",
        "> **Note:** The following message (below this one) can be ignored if you're running TensorFlow 2.8.0+, the error seems to have been fixed.\n",
        "\n",
        "> **Note:** If you're using TensorFlow 2.7.0+ (but not 2.8.0+) the original code from the following cells may have caused some errors. They've since been updated to fix those errors. You can see explanations on what happened at the following resources:\n",
        "* [Example Colab Notebook](https://colab.research.google.com/drive/1_dlrB_DJOBS9c9foYJs49I0YwN7LTakl?usp=sharing)\n",
        "* [TensorFlow for Deep Learning GitHub Discussion on TensorFlow 2.7.0 breaking changes](https://github.com/mrdbourke/tensorflow-deep-learning/discussions/278)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "46pebMB4Qeth",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99f3e33a-4341-4d12-9d12-2481d9312cb8"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model (this time 3 layers)\n",
        "model_3 = tf.keras.Sequential([\n",
        "  # Before TensorFlow 2.7.0\n",
        "  # tf.keras.layers.Dense(100), # add 100 dense neurons\n",
        "\n",
        "  # With TensorFlow 2.7.0\n",
        "  # tf.keras.layers.Dense(100, input_shape=(None, 1)), # add 100 dense neurons\n",
        "\n",
        "  ## After TensorFlow 2.8.0 ##\n",
        "  tf.keras.layers.Dense(100), # add 100 dense neurons\n",
        "  tf.keras.layers.Dense(10), # add another layer with 10 neurons\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_3.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.Adam(), # use Adam instead of SGD\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "# 3. Fit the model\n",
        "model_3.fit(X, y, epochs=100, verbose=1) # fit for 100 passes of the data"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "32/32 [==============================] - 2s 4ms/step - loss: 2.9793 - accuracy: 0.4640\n",
            "Epoch 2/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7269 - accuracy: 0.4990\n",
            "Epoch 3/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7006 - accuracy: 0.5000\n",
            "Epoch 4/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6955 - accuracy: 0.4450\n",
            "Epoch 5/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6945 - accuracy: 0.4770\n",
            "Epoch 6/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6940 - accuracy: 0.4750\n",
            "Epoch 7/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6942 - accuracy: 0.5000\n",
            "Epoch 8/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6950 - accuracy: 0.5010\n",
            "Epoch 9/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6953 - accuracy: 0.4930\n",
            "Epoch 10/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6944 - accuracy: 0.4530\n",
            "Epoch 11/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6958 - accuracy: 0.4380\n",
            "Epoch 12/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6946 - accuracy: 0.4920\n",
            "Epoch 13/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6956 - accuracy: 0.4990\n",
            "Epoch 14/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6948 - accuracy: 0.5050\n",
            "Epoch 15/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6948 - accuracy: 0.4740\n",
            "Epoch 16/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6942 - accuracy: 0.4910\n",
            "Epoch 17/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6962 - accuracy: 0.4910\n",
            "Epoch 18/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6945 - accuracy: 0.4510\n",
            "Epoch 19/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6951 - accuracy: 0.5120\n",
            "Epoch 20/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6963 - accuracy: 0.4600\n",
            "Epoch 21/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6957 - accuracy: 0.4930\n",
            "Epoch 22/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6947 - accuracy: 0.4650\n",
            "Epoch 23/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6947 - accuracy: 0.4920\n",
            "Epoch 24/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6960 - accuracy: 0.4520\n",
            "Epoch 25/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6981 - accuracy: 0.4880\n",
            "Epoch 26/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6960 - accuracy: 0.4960\n",
            "Epoch 27/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6959 - accuracy: 0.4950\n",
            "Epoch 28/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6945 - accuracy: 0.4640\n",
            "Epoch 29/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6982 - accuracy: 0.4680\n",
            "Epoch 30/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7009 - accuracy: 0.4790\n",
            "Epoch 31/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6946 - accuracy: 0.5040\n",
            "Epoch 32/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6961 - accuracy: 0.4680\n",
            "Epoch 33/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6960 - accuracy: 0.4850\n",
            "Epoch 34/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6981 - accuracy: 0.4610\n",
            "Epoch 35/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6961 - accuracy: 0.4980\n",
            "Epoch 36/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6946 - accuracy: 0.4780\n",
            "Epoch 37/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6952 - accuracy: 0.5040\n",
            "Epoch 38/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6971 - accuracy: 0.4750\n",
            "Epoch 39/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6956 - accuracy: 0.4720\n",
            "Epoch 40/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6961 - accuracy: 0.4950\n",
            "Epoch 41/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6976 - accuracy: 0.5100\n",
            "Epoch 42/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7008 - accuracy: 0.4660\n",
            "Epoch 43/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6959 - accuracy: 0.5020\n",
            "Epoch 44/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6985 - accuracy: 0.4840\n",
            "Epoch 45/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6972 - accuracy: 0.5090\n",
            "Epoch 46/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6950 - accuracy: 0.4730\n",
            "Epoch 47/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6970 - accuracy: 0.4920\n",
            "Epoch 48/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6960 - accuracy: 0.5110\n",
            "Epoch 49/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6965 - accuracy: 0.4840\n",
            "Epoch 50/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6979 - accuracy: 0.4540\n",
            "Epoch 51/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6990 - accuracy: 0.4840\n",
            "Epoch 52/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6962 - accuracy: 0.4950\n",
            "Epoch 53/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6965 - accuracy: 0.4730\n",
            "Epoch 54/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6990 - accuracy: 0.5030\n",
            "Epoch 55/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6963 - accuracy: 0.5030\n",
            "Epoch 56/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6965 - accuracy: 0.4980\n",
            "Epoch 57/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6977 - accuracy: 0.4890\n",
            "Epoch 58/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6970 - accuracy: 0.5090\n",
            "Epoch 59/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7005 - accuracy: 0.4910\n",
            "Epoch 60/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6995 - accuracy: 0.4880\n",
            "Epoch 61/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6989 - accuracy: 0.4750\n",
            "Epoch 62/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6969 - accuracy: 0.4970\n",
            "Epoch 63/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6948 - accuracy: 0.4700\n",
            "Epoch 64/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6958 - accuracy: 0.5080\n",
            "Epoch 65/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6987 - accuracy: 0.4820\n",
            "Epoch 66/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6965 - accuracy: 0.5010\n",
            "Epoch 67/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6977 - accuracy: 0.4550\n",
            "Epoch 68/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6954 - accuracy: 0.5020\n",
            "Epoch 69/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6967 - accuracy: 0.5030\n",
            "Epoch 70/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6979 - accuracy: 0.4870\n",
            "Epoch 71/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6978 - accuracy: 0.4690\n",
            "Epoch 72/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6968 - accuracy: 0.4980\n",
            "Epoch 73/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6966 - accuracy: 0.5000\n",
            "Epoch 74/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6965 - accuracy: 0.5040\n",
            "Epoch 75/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6959 - accuracy: 0.5170\n",
            "Epoch 76/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6977 - accuracy: 0.4770\n",
            "Epoch 77/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6971 - accuracy: 0.4990\n",
            "Epoch 78/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6976 - accuracy: 0.4680\n",
            "Epoch 79/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7007 - accuracy: 0.5020\n",
            "Epoch 80/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6990 - accuracy: 0.4850\n",
            "Epoch 81/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6984 - accuracy: 0.4860\n",
            "Epoch 82/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6969 - accuracy: 0.4990\n",
            "Epoch 83/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6995 - accuracy: 0.4720\n",
            "Epoch 84/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6959 - accuracy: 0.5030\n",
            "Epoch 85/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6964 - accuracy: 0.4440\n",
            "Epoch 86/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6999 - accuracy: 0.4870\n",
            "Epoch 87/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6972 - accuracy: 0.4740\n",
            "Epoch 88/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6973 - accuracy: 0.4420\n",
            "Epoch 89/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6971 - accuracy: 0.4690\n",
            "Epoch 90/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6979 - accuracy: 0.4710\n",
            "Epoch 91/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6987 - accuracy: 0.4960\n",
            "Epoch 92/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6982 - accuracy: 0.4900\n",
            "Epoch 93/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6984 - accuracy: 0.4780\n",
            "Epoch 94/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6998 - accuracy: 0.4930\n",
            "Epoch 95/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6976 - accuracy: 0.4540\n",
            "Epoch 96/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6977 - accuracy: 0.4870\n",
            "Epoch 97/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6974 - accuracy: 0.4520\n",
            "Epoch 98/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6989 - accuracy: 0.4830\n",
            "Epoch 99/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6974 - accuracy: 0.5020\n",
            "Epoch 100/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6963 - accuracy: 0.4910\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff7ec104610>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJJL0YT7RVzv"
      },
      "source": [
        "Still! \n",
        "\n",
        "We've pulled out a few tricks but our model isn't even doing better than guessing.\n",
        "\n",
        "Let's make some visualizations to see what's happening.\n",
        "\n",
        "> 🔑 **Note:** Whenever your model is performing strangely or there's something going on with your data you're not quite sure of, remember these three words: **visualize, visualize, visualize**. Inspect your data, inspect your model, inpsect your model's predictions.\n",
        "\n",
        "To visualize our model's predictions we're going to create a function `plot_decision_boundary()` which:\n",
        "* Takes in a trained model, features (`X`) and labels (`y`).\n",
        "* Creates a [meshgrid](https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html) of the different `X` values.\n",
        "* Makes predictions across the meshgrid.\n",
        "* Plots the predictions as well as a line between the different zones (where each unique class falls).\n",
        "\n",
        "If this sounds confusing, let's see it in code and then see the output.\n",
        "\n",
        "> 🔑 **Note:** If you're ever unsure of what a function does, try unraveling it and writing it line by line for yourself to see what it does. Break it into small parts and see what each part outputs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MuwGjU_XR0aG"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def plot_decision_boundary(model, X, y):\n",
        "  \"\"\"\n",
        "  Plots the decision boundary created by a model predicting on X.\n",
        "  This function has been adapted from two phenomenal resources:\n",
        "   1. CS231n - https://cs231n.github.io/neural-networks-case-study/\n",
        "   2. Made with ML basics - https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/08_Neural_Networks.ipynb\n",
        "  \"\"\"\n",
        "  # Define the axis boundaries of the plot and create a meshgrid\n",
        "  x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
        "  y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
        "  xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),\n",
        "                       np.linspace(y_min, y_max, 100))\n",
        "  \n",
        "  # Create X values (we're going to predict on all of these)\n",
        "  x_in = np.c_[xx.ravel(), yy.ravel()] # stack 2D arrays together: https://numpy.org/devdocs/reference/generated/numpy.c_.html\n",
        "  \n",
        "  # Make predictions using the trained model\n",
        "  y_pred = model.predict(x_in)\n",
        "\n",
        "  # Check for multi-class\n",
        "  if model.output_shape[-1] > 1: # checks the final dimension of the model's output shape, if this is > (greater than) 1, it's multi-class \n",
        "    print(\"doing multiclass classification...\")\n",
        "    # We have to reshape our predictions to get them ready for plotting\n",
        "    y_pred = np.argmax(y_pred, axis=1).reshape(xx.shape)\n",
        "  else:\n",
        "    print(\"doing binary classifcation...\")\n",
        "    y_pred = np.round(np.max(y_pred, axis=1)).reshape(xx.shape)\n",
        "  \n",
        "  # Plot decision boundary\n",
        "  plt.contourf(xx, yy, y_pred, cmap=plt.cm.RdYlBu, alpha=0.7)\n",
        "  plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
        "  plt.xlim(xx.min(), xx.max())\n",
        "  plt.ylim(yy.min(), yy.max())"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OgekXFM2clQW"
      },
      "source": [
        "Now we've got a function to plot our model's decision boundary (the cut off point its making between red and blue dots), let's try it out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIyCVjolTmy4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "a31d7a08-b58e-4fd1-f483-3e99a816a6b9"
      },
      "source": [
        "# Check out the predictions our model is making\n",
        "plot_decision_boundary(model_3, X, y)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 0s 1ms/step\n",
            "doing binary classifcation...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XIzcQrknUJPK"
      },
      "source": [
        "Looks like our model is trying to draw a straight line through the data.\n",
        "\n",
        "What's wrong with doing this?\n",
        "\n",
        "The main issue is our data isn't separable by a straight line.\n",
        "\n",
        "In a regression problem, our model might work. In fact, let's try it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7d9O9subUshC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "outputId": "627f66fb-2396-4ba4-ab17-d9d57042400b"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create some regression data\n",
        "X_regression = np.arange(0, 1000, 5)\n",
        "y_regression = np.arange(100, 1100, 5)\n",
        "\n",
        "# Split it into training and test sets\n",
        "X_reg_train = X_regression[:150]\n",
        "X_reg_test = X_regression[150:]\n",
        "y_reg_train = y_regression[:150]\n",
        "y_reg_test = y_regression[150:]\n",
        "\n",
        "# Fit our model to the data\n",
        "# Note: Before TensorFlow 2.7.0, this line would work\n",
        "# model_3.fit(X_reg_train, y_reg_train, epochs=100)\n",
        "\n",
        "# After TensorFlow 2.7.0, see here for more: https://github.com/mrdbourke/tensorflow-deep-learning/discussions/278\n",
        "model_3.fit(tf.expand_dims(X_reg_train, axis=-1), y_reg_train,epochs=100)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-d2462edf8696>\u001b[0m in \u001b[0;36m<cell line: 19>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# After TensorFlow 2.7.0, see here for more: https://github.com/mrdbourke/tensorflow-deep-learning/discussions/278\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mmodel_3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_reg_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_reg_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                     \u001b[0mretval_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m                 \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1284, in train_function  *\n        return step_function(self, iterator)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1268, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1249, in run_step  **\n        outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1050, in train_step\n        y_pred = self(x, training=True)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/input_spec.py\", line 280, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Exception encountered when calling layer 'sequential_2' (type Sequential).\n    \n    Input 0 of layer \"dense_3\" is incompatible with the layer: expected axis -1 of input shape to have value 2, but received input with shape (None, 1)\n    \n    Call arguments received by layer 'sequential_2' (type Sequential):\n      • inputs=tf.Tensor(shape=(None, 1), dtype=int64)\n      • training=True\n      • mask=None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lFGjyziVn_Zh",
        "outputId": "f7ae07c3-8300-4137-ed89-3af4c9798849"
      },
      "source": [
        "model_3.summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_3 (Dense)             (None, 100)               300       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 10)                1010      \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,321\n",
            "Trainable params: 1,321\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMu37fayVEZI"
      },
      "source": [
        "Oh wait... we compiled our model for a binary classification problem.\n",
        "\n",
        "No trouble, we can recreate it for a regression problem."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUJtmMPZVOZ9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "590d6687-f249-48c4-9e9a-52aa41f7d685"
      },
      "source": [
        "# Setup random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Recreate the model\n",
        "model_3 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(100),\n",
        "  tf.keras.layers.Dense(10),\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# Change the loss and metrics of our compiled model\n",
        "model_3.compile(loss=tf.keras.losses.mae, # change the loss function to be regression-specific\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=['mae']) # change the metric to be regression-specific\n",
        "\n",
        "# Fit the recompiled model\n",
        "model_3.fit(tf.expand_dims(X_reg_train, axis=-1), \n",
        "            y_reg_train, \n",
        "            epochs=100)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "5/5 [==============================] - 1s 7ms/step - loss: 542.3636 - mae: 542.3636\n",
            "Epoch 2/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 406.3517 - mae: 406.3517\n",
            "Epoch 3/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 276.9076 - mae: 276.9076\n",
            "Epoch 4/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 143.3961 - mae: 143.3961\n",
            "Epoch 5/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 52.8697 - mae: 52.8697\n",
            "Epoch 6/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 94.0862 - mae: 94.0862\n",
            "Epoch 7/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 79.7002 - mae: 79.7002\n",
            "Epoch 8/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 45.2805 - mae: 45.2805\n",
            "Epoch 9/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 54.3336 - mae: 54.3336\n",
            "Epoch 10/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 42.9423 - mae: 42.9423\n",
            "Epoch 11/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 48.0703 - mae: 48.0703\n",
            "Epoch 12/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 44.8320 - mae: 44.8320\n",
            "Epoch 13/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 42.4521 - mae: 42.4521\n",
            "Epoch 14/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 42.0532 - mae: 42.0532\n",
            "Epoch 15/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 42.2878 - mae: 42.2878\n",
            "Epoch 16/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.5402 - mae: 41.5402\n",
            "Epoch 17/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.5515 - mae: 41.5515\n",
            "Epoch 18/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 41.2768 - mae: 41.2768\n",
            "Epoch 19/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.1682 - mae: 41.1682\n",
            "Epoch 20/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.2507 - mae: 41.2507\n",
            "Epoch 21/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.1038 - mae: 41.1038\n",
            "Epoch 22/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 41.2770 - mae: 41.2770\n",
            "Epoch 23/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.1049 - mae: 41.1049\n",
            "Epoch 24/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.1117 - mae: 41.1117\n",
            "Epoch 25/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.9811 - mae: 40.9811\n",
            "Epoch 26/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.9428 - mae: 40.9428\n",
            "Epoch 27/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.9315 - mae: 40.9315\n",
            "Epoch 28/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.0729 - mae: 41.0729\n",
            "Epoch 29/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.0471 - mae: 41.0471\n",
            "Epoch 30/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.8692 - mae: 40.8692\n",
            "Epoch 31/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.1844 - mae: 41.1844\n",
            "Epoch 32/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.9293 - mae: 40.9293\n",
            "Epoch 33/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.0351 - mae: 41.0351\n",
            "Epoch 34/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.1809 - mae: 41.1809\n",
            "Epoch 35/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.5908 - mae: 40.5908\n",
            "Epoch 36/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 41.0515 - mae: 41.0515\n",
            "Epoch 37/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.7233 - mae: 40.7233\n",
            "Epoch 38/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.3500 - mae: 40.3500\n",
            "Epoch 39/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.7817 - mae: 40.7817\n",
            "Epoch 40/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.5139 - mae: 40.5139\n",
            "Epoch 41/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 40.4664 - mae: 40.4664\n",
            "Epoch 42/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 40.3865 - mae: 40.3865\n",
            "Epoch 43/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 40.5000 - mae: 40.5000\n",
            "Epoch 44/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.2628 - mae: 40.2628\n",
            "Epoch 45/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.3840 - mae: 40.3840\n",
            "Epoch 46/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.3894 - mae: 40.3894\n",
            "Epoch 47/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 40.3768 - mae: 40.3768\n",
            "Epoch 48/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.1620 - mae: 40.1620\n",
            "Epoch 49/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.8043 - mae: 40.8043\n",
            "Epoch 50/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 40.2675 - mae: 40.2675\n",
            "Epoch 51/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.8095 - mae: 40.8095\n",
            "Epoch 52/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.2644 - mae: 41.2644\n",
            "Epoch 53/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.0419 - mae: 41.0419\n",
            "Epoch 54/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.1137 - mae: 41.1137\n",
            "Epoch 55/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.3421 - mae: 41.3421\n",
            "Epoch 56/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.9778 - mae: 41.9778\n",
            "Epoch 57/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.9889 - mae: 40.9889\n",
            "Epoch 58/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.4767 - mae: 40.4767\n",
            "Epoch 59/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.6950 - mae: 41.6950\n",
            "Epoch 60/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.8925 - mae: 40.8925\n",
            "Epoch 61/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.9653 - mae: 39.9653\n",
            "Epoch 62/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 41.3684 - mae: 41.3684\n",
            "Epoch 63/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.9093 - mae: 39.9093\n",
            "Epoch 64/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.9428 - mae: 39.9428\n",
            "Epoch 65/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.0617 - mae: 40.0617\n",
            "Epoch 66/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.2791 - mae: 40.2791\n",
            "Epoch 67/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.8298 - mae: 39.8298\n",
            "Epoch 68/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.7830 - mae: 39.7830\n",
            "Epoch 69/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.9955 - mae: 39.9955\n",
            "Epoch 70/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.4357 - mae: 40.4357\n",
            "Epoch 71/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 40.0852 - mae: 40.0852\n",
            "Epoch 72/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.2352 - mae: 39.2352\n",
            "Epoch 73/100\n",
            "5/5 [==============================] - 0s 7ms/step - loss: 39.5742 - mae: 39.5742\n",
            "Epoch 74/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 39.4380 - mae: 39.4380\n",
            "Epoch 75/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.7058 - mae: 39.7058\n",
            "Epoch 76/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 39.3266 - mae: 39.3266\n",
            "Epoch 77/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.1765 - mae: 39.1765\n",
            "Epoch 78/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.8100 - mae: 39.8100\n",
            "Epoch 79/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.2923 - mae: 39.2923\n",
            "Epoch 80/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 38.8027 - mae: 38.8027\n",
            "Epoch 81/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 39.2369 - mae: 39.2369\n",
            "Epoch 82/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.4951 - mae: 39.4951\n",
            "Epoch 83/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.1491 - mae: 39.1491\n",
            "Epoch 84/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.1423 - mae: 39.1423\n",
            "Epoch 85/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 38.9542 - mae: 38.9542\n",
            "Epoch 86/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 38.9720 - mae: 38.9720\n",
            "Epoch 87/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 38.6682 - mae: 38.6682\n",
            "Epoch 88/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 38.9467 - mae: 38.9467\n",
            "Epoch 89/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 38.5172 - mae: 38.5172\n",
            "Epoch 90/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 38.4433 - mae: 38.4433\n",
            "Epoch 91/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 38.4717 - mae: 38.4717\n",
            "Epoch 92/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 38.4171 - mae: 38.4171\n",
            "Epoch 93/100\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 39.2786 - mae: 39.2786\n",
            "Epoch 94/100\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 38.8514 - mae: 38.8514\n",
            "Epoch 95/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.4096 - mae: 38.4096\n",
            "Epoch 96/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.7604 - mae: 38.7604\n",
            "Epoch 97/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.3457 - mae: 38.3457\n",
            "Epoch 98/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.6886 - mae: 38.6886\n",
            "Epoch 99/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.5821 - mae: 38.5821\n",
            "Epoch 100/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.0490 - mae: 38.0490\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff7e221cf40>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYHvt7fRWBUL"
      },
      "source": [
        "Okay, it seems like our model is learning something (the `mae` value trends down with each epoch), let's plot its predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qUf5DL_fWMZA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "outputId": "77b2e006-da5d-4bb5-8fd5-4237b0dc58fc"
      },
      "source": [
        "# Make predictions with our trained model\n",
        "y_reg_preds = model_3.predict(y_reg_test)\n",
        "\n",
        "# Plot the model's predictions against our regression data\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.scatter(X_reg_train, y_reg_train, c='b', label='Training data')\n",
        "plt.scatter(X_reg_test, y_reg_test, c='g', label='Testing data')\n",
        "plt.scatter(X_reg_test, y_reg_preds.squeeze(), c='r', label='Predictions')\n",
        "plt.legend();"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 6ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V-LGK_jQYUcA"
      },
      "source": [
        "Okay, the predictions aren't perfect (if the predictions were perfect, the red would line up with the green), but they look better than complete guessing.\n",
        "\n",
        "So this means our model must be learning something...\n",
        "\n",
        "There must be something we're missing out on for our classification problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RZqShArhUh6d"
      },
      "source": [
        "## The missing piece: Non-linearity\n",
        "\n",
        "Okay, so we saw our neural network can model straight lines (with ability a little bit better than guessing).\n",
        "\n",
        "What about non-straight (non-linear) lines?\n",
        "\n",
        "If we're going to model our classification data (the red and clue circles), we're going to need some non-linear lines.\n",
        "\n",
        "> 🔨 **Practice:** Before we get to the next steps, I'd encourage you to play around with the [TensorFlow Playground](https://playground.tensorflow.org/#activation=linear&batchSize=1&dataset=circle&regDataset=reg-plane&learningRate=0.01&regularizationRate=0&noise=0&networkShape=1&seed=0.09561&showTestData=false&discretize=false&percTrainData=70&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularizationRate_hide=true&discretize_hide=true&regularization_hide=true&dataset_hide=true&noise_hide=true&batchSize_hide=true) (check out what the data has in common with our own classification data) for 10-minutes. In particular the tab which says \"activation\". Once you're done, come back.\n",
        "\n",
        "Did you try out the activation options? If so, what did you find?\n",
        "\n",
        "If you didn't, don't worry, let's see it in code.\n",
        "\n",
        "We're going to replicate the neural network you can see at this link: [TensorFlow Playground](https://playground.tensorflow.org/#activation=linear&batchSize=1&dataset=circle&regDataset=reg-plane&learningRate=0.01&regularizationRate=0&noise=0&networkShape=1&seed=0.09561&showTestData=false&discretize=false&percTrainData=70&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularizationRate_hide=true&discretize_hide=true&regularization_hide=true&dataset_hide=true&noise_hide=true&batchSize_hide=true).\n",
        "\n",
        "![simple neural net created with TensorFlow playground](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-tensorflow-playground-simple-net-linear-activation.png)\n",
        "*The neural network we're going to recreate with TensorFlow code. See it live at [TensorFlow Playground](https://playground.tensorflow.org/#activation=linear&batchSize=1&dataset=circle&regDataset=reg-plane&learningRate=0.01&regularizationRate=0&noise=0&networkShape=1&seed=0.09561&showTestData=false&discretize=false&percTrainData=70&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularizationRate_hide=true&discretize_hide=true&regularization_hide=true&dataset_hide=true&noise_hide=true&batchSize_hide=true).*\n",
        "\n",
        "The main change we'll add to models we've built before is the use of the `activation` keyword.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p54Sk5WeYue9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ccad4f60-9f43-4165-efd9-4ff105996ce6"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model\n",
        "model_4 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.linear), # 1 hidden layer with linear activation\n",
        "  tf.keras.layers.Dense(1) # output layer\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_4.compile(loss=tf.keras.losses.binary_crossentropy,\n",
        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), # note: \"lr\" used to be what was used, now \"learning_rate\" is favoured\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "history = model_4.fit(X, y, epochs=100)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "32/32 [==============================] - 1s 3ms/step - loss: 3.3985 - accuracy: 0.5000\n",
            "Epoch 2/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.8290 - accuracy: 0.5000\n",
            "Epoch 3/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.6833 - accuracy: 0.5000\n",
            "Epoch 4/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.5881 - accuracy: 0.5000\n",
            "Epoch 5/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.5133 - accuracy: 0.5000\n",
            "Epoch 6/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.4515 - accuracy: 0.5000\n",
            "Epoch 7/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.4001 - accuracy: 0.5000\n",
            "Epoch 8/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.3553 - accuracy: 0.5000\n",
            "Epoch 9/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.3164 - accuracy: 0.5000\n",
            "Epoch 10/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.2812 - accuracy: 0.5000\n",
            "Epoch 11/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.2498 - accuracy: 0.5000\n",
            "Epoch 12/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.2213 - accuracy: 0.5000\n",
            "Epoch 13/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.1950 - accuracy: 0.5000\n",
            "Epoch 14/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.1710 - accuracy: 0.5000\n",
            "Epoch 15/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.1487 - accuracy: 0.5000\n",
            "Epoch 16/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.1283 - accuracy: 0.5000\n",
            "Epoch 17/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.1090 - accuracy: 0.5000\n",
            "Epoch 18/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.0909 - accuracy: 0.5000\n",
            "Epoch 19/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.0742 - accuracy: 0.5000\n",
            "Epoch 20/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.0582 - accuracy: 0.5000\n",
            "Epoch 21/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.0433 - accuracy: 0.5000\n",
            "Epoch 22/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.0289 - accuracy: 0.5000\n",
            "Epoch 23/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.0155 - accuracy: 0.5000\n",
            "Epoch 24/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.0028 - accuracy: 0.5000\n",
            "Epoch 25/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9907 - accuracy: 0.5000\n",
            "Epoch 26/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9789 - accuracy: 0.5000\n",
            "Epoch 27/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9678 - accuracy: 0.5000\n",
            "Epoch 28/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9571 - accuracy: 0.5000\n",
            "Epoch 29/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9470 - accuracy: 0.5000\n",
            "Epoch 30/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9374 - accuracy: 0.5000\n",
            "Epoch 31/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9280 - accuracy: 0.5000\n",
            "Epoch 32/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9189 - accuracy: 0.5000\n",
            "Epoch 33/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.9105 - accuracy: 0.5000\n",
            "Epoch 34/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9024 - accuracy: 0.5000\n",
            "Epoch 35/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.8944 - accuracy: 0.5000\n",
            "Epoch 36/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8868 - accuracy: 0.5000\n",
            "Epoch 37/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8795 - accuracy: 0.5000\n",
            "Epoch 38/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8726 - accuracy: 0.5000\n",
            "Epoch 39/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8657 - accuracy: 0.5000\n",
            "Epoch 40/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8593 - accuracy: 0.5000\n",
            "Epoch 41/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8529 - accuracy: 0.5000\n",
            "Epoch 42/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8468 - accuracy: 0.5000\n",
            "Epoch 43/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8408 - accuracy: 0.5000\n",
            "Epoch 44/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.8352 - accuracy: 0.5000\n",
            "Epoch 45/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8297 - accuracy: 0.5000\n",
            "Epoch 46/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8244 - accuracy: 0.5000\n",
            "Epoch 47/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8194 - accuracy: 0.5000\n",
            "Epoch 48/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8144 - accuracy: 0.5000\n",
            "Epoch 49/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8096 - accuracy: 0.5000\n",
            "Epoch 50/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8049 - accuracy: 0.5000\n",
            "Epoch 51/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8004 - accuracy: 0.5000\n",
            "Epoch 52/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7962 - accuracy: 0.5000\n",
            "Epoch 53/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7920 - accuracy: 0.5000\n",
            "Epoch 54/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7879 - accuracy: 0.5000\n",
            "Epoch 55/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7839 - accuracy: 0.5000\n",
            "Epoch 56/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7800 - accuracy: 0.5000\n",
            "Epoch 57/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7763 - accuracy: 0.5000\n",
            "Epoch 58/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7728 - accuracy: 0.5000\n",
            "Epoch 59/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7694 - accuracy: 0.5000\n",
            "Epoch 60/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7661 - accuracy: 0.5000\n",
            "Epoch 61/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7629 - accuracy: 0.5000\n",
            "Epoch 62/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7601 - accuracy: 0.5000\n",
            "Epoch 63/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7570 - accuracy: 0.5000\n",
            "Epoch 64/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7542 - accuracy: 0.5000\n",
            "Epoch 65/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7516 - accuracy: 0.5000\n",
            "Epoch 66/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7489 - accuracy: 0.5000\n",
            "Epoch 67/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7463 - accuracy: 0.5000\n",
            "Epoch 68/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7439 - accuracy: 0.5000\n",
            "Epoch 69/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.7415 - accuracy: 0.5000\n",
            "Epoch 70/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7392 - accuracy: 0.5000\n",
            "Epoch 71/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7368 - accuracy: 0.5000\n",
            "Epoch 72/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7348 - accuracy: 0.5000\n",
            "Epoch 73/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7327 - accuracy: 0.5000\n",
            "Epoch 74/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7307 - accuracy: 0.5000\n",
            "Epoch 75/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7289 - accuracy: 0.5000\n",
            "Epoch 76/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7270 - accuracy: 0.5000\n",
            "Epoch 77/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7253 - accuracy: 0.5000\n",
            "Epoch 78/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7236 - accuracy: 0.5000\n",
            "Epoch 79/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7220 - accuracy: 0.5000\n",
            "Epoch 80/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.7204 - accuracy: 0.5000\n",
            "Epoch 81/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7189 - accuracy: 0.5000\n",
            "Epoch 82/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7175 - accuracy: 0.5000\n",
            "Epoch 83/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7162 - accuracy: 0.5000\n",
            "Epoch 84/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7149 - accuracy: 0.5000\n",
            "Epoch 85/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7136 - accuracy: 0.5000\n",
            "Epoch 86/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7125 - accuracy: 0.5000\n",
            "Epoch 87/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7114 - accuracy: 0.5000\n",
            "Epoch 88/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7104 - accuracy: 0.5000\n",
            "Epoch 89/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7095 - accuracy: 0.5000\n",
            "Epoch 90/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7085 - accuracy: 0.5000\n",
            "Epoch 91/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7076 - accuracy: 0.5000\n",
            "Epoch 92/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7068 - accuracy: 0.5000\n",
            "Epoch 93/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7060 - accuracy: 0.5000\n",
            "Epoch 94/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7052 - accuracy: 0.5000\n",
            "Epoch 95/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7044 - accuracy: 0.5000\n",
            "Epoch 96/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7038 - accuracy: 0.5000\n",
            "Epoch 97/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7031 - accuracy: 0.5000\n",
            "Epoch 98/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7025 - accuracy: 0.5000\n",
            "Epoch 99/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7019 - accuracy: 0.5000\n",
            "Epoch 100/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7013 - accuracy: 0.5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTNzKBvwf2uJ"
      },
      "source": [
        "Okay, our model performs a little worse than guessing.\n",
        "\n",
        "Let's remind ourselves what our data looks like."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RcwI7-s1f2a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "7dbccdb8-8fb4-4e94-df68-b0a023553b20"
      },
      "source": [
        "# Check out our data\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdYlBu);"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EmuUVtenj-tC"
      },
      "source": [
        "And let's see how our model is making predictions on it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vFgOI88itVD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "714b4774-d687-4657-fc4f-3d32280e754b"
      },
      "source": [
        "# Check the deicison boundary (blue is blue class, yellow is the crossover, red is red class)\n",
        "plot_decision_boundary(model_4, X, y)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n",
            "doing binary classifcation...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XgNyVx3XkG4K"
      },
      "source": [
        "Well, it looks like we're getting a straight (linear) line prediction again. \n",
        "\n",
        "But our data is non-linear (not a straight line)...\n",
        "\n",
        "What we're going to have to do is add some non-linearity to our model.\n",
        "\n",
        "To do so, we'll use the `activation` parameter in on of our layers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "utJIUKmHkbyV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4eb26b1b-33a8-48cc-d0bc-421fba4a422b"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create a model with a non-linear activation\n",
        "model_5 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.relu), # can also do activation='relu'\n",
        "  tf.keras.layers.Dense(1) # output layer \n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_5.compile(loss=tf.keras.losses.binary_crossentropy,\n",
        "              optimizer=tf.keras.optimizers.Adam(),\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "history = model_5.fit(X, y, epochs=100)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "32/32 [==============================] - 1s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 2/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 3/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 4/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 5/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 6/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 7/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 8/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 9/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 10/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 11/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 12/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 13/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 14/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 15/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 16/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 17/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 18/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 19/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 20/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 21/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 22/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 23/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 24/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 25/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 26/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 27/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 28/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 29/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 30/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 31/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 32/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 33/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 34/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 35/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 36/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 37/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 38/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 39/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 40/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 41/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 42/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 43/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 44/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 45/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 46/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 47/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 48/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 49/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 50/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 51/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 52/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 53/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 54/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 55/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 56/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 57/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 58/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 59/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 60/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 61/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 62/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 63/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 64/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 65/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 66/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 67/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 68/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 69/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 70/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 71/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 72/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 73/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 74/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 75/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 76/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 77/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 78/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 79/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 80/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 81/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 82/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 83/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 84/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 85/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 86/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 87/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 88/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 89/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 90/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 91/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 92/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 93/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 94/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 95/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 96/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 97/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 98/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 99/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 100/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eW3l-KicgLW8"
      },
      "source": [
        "Hmm... still not learning...\n",
        "\n",
        "What we if increased the number of neurons and layers?\n",
        "\n",
        "Say, 2 hidden layers, with [ReLU](https://www.tensorflow.org/api_docs/python/tf/keras/activations/relu), pronounced \"rel-u\", (short for [rectified linear unit](https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/)), activation on the first one, and 4 neurons each?\n",
        "\n",
        "To see this network in action, check out the [TensorFlow Playground demo](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.001&regularizationRate=0&noise=0&networkShape=4,4&seed=0.93799&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&batchSize_hide=true&dataset_hide=true).\n",
        "\n",
        "![multi-layer neural net created with TensorFlow playground](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-tensorflow-playground-two-layer-net-relu-activation.png)\n",
        "*The neural network we're going to recreate with TensorFlow code. See it live at [TensorFlow Playground](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.001&regularizationRate=0&noise=0&networkShape=4,4&seed=0.93799&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&batchSize_hide=true&dataset_hide=true).*\n",
        "\n",
        "Let's try."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXxtQFHwlc9w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00ab8b71-8fd1-49be-f4b8-fb2e4841166f"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create a model\n",
        "model_6 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu), # hidden layer 1, 4 neurons, ReLU activation\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu), # hidden layer 2, 4 neurons, ReLU activation\n",
        "  tf.keras.layers.Dense(1) # ouput layer\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_6.compile(loss=tf.keras.losses.binary_crossentropy,\n",
        "                optimizer=tf.keras.optimizers.Adam(lr=0.001), # Adam's default learning rate is 0.001\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "# Fit the model\n",
        "history = model_6.fit(X, y, epochs=100)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "32/32 [==============================] - 1s 3ms/step - loss: 5.2656 - accuracy: 0.4800\n",
            "Epoch 2/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 4.7414 - accuracy: 0.4800\n",
            "Epoch 3/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 4.6147 - accuracy: 0.4750\n",
            "Epoch 4/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 3.8119 - accuracy: 0.4750\n",
            "Epoch 5/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 2.6134 - accuracy: 0.4760\n",
            "Epoch 6/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 2.3118 - accuracy: 0.4760\n",
            "Epoch 7/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 2.0902 - accuracy: 0.4790\n",
            "Epoch 8/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 1.7976 - accuracy: 0.4780\n",
            "Epoch 9/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.2940 - accuracy: 0.4750\n",
            "Epoch 10/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 1.1589 - accuracy: 0.4760\n",
            "Epoch 11/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.1238 - accuracy: 0.4770\n",
            "Epoch 12/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.0958 - accuracy: 0.4800\n",
            "Epoch 13/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 1.0707 - accuracy: 0.4810\n",
            "Epoch 14/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.0475 - accuracy: 0.4780\n",
            "Epoch 15/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.0259 - accuracy: 0.4780\n",
            "Epoch 16/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.0066 - accuracy: 0.4780\n",
            "Epoch 17/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9880 - accuracy: 0.4770\n",
            "Epoch 18/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9701 - accuracy: 0.4740\n",
            "Epoch 19/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9537 - accuracy: 0.4770\n",
            "Epoch 20/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9377 - accuracy: 0.4750\n",
            "Epoch 21/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9232 - accuracy: 0.4750\n",
            "Epoch 22/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.9089 - accuracy: 0.4740\n",
            "Epoch 23/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8957 - accuracy: 0.4710\n",
            "Epoch 24/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8833 - accuracy: 0.4700\n",
            "Epoch 25/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8709 - accuracy: 0.4720\n",
            "Epoch 26/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8594 - accuracy: 0.4710\n",
            "Epoch 27/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.8487 - accuracy: 0.4720\n",
            "Epoch 28/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8381 - accuracy: 0.4710\n",
            "Epoch 29/100\n",
            "32/32 [==============================] - 0s 8ms/step - loss: 0.8281 - accuracy: 0.4730\n",
            "Epoch 30/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.8184 - accuracy: 0.4730\n",
            "Epoch 31/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8093 - accuracy: 0.4700\n",
            "Epoch 32/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8003 - accuracy: 0.4660\n",
            "Epoch 33/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7922 - accuracy: 0.4700\n",
            "Epoch 34/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7845 - accuracy: 0.4650\n",
            "Epoch 35/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7767 - accuracy: 0.4690\n",
            "Epoch 36/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7696 - accuracy: 0.4660\n",
            "Epoch 37/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7629 - accuracy: 0.4680\n",
            "Epoch 38/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7568 - accuracy: 0.4670\n",
            "Epoch 39/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7508 - accuracy: 0.4660\n",
            "Epoch 40/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7455 - accuracy: 0.4680\n",
            "Epoch 41/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7402 - accuracy: 0.4640\n",
            "Epoch 42/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7352 - accuracy: 0.4530\n",
            "Epoch 43/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7303 - accuracy: 0.4480\n",
            "Epoch 44/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7262 - accuracy: 0.4380\n",
            "Epoch 45/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7220 - accuracy: 0.4310\n",
            "Epoch 46/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7182 - accuracy: 0.4450\n",
            "Epoch 47/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7150 - accuracy: 0.4430\n",
            "Epoch 48/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7115 - accuracy: 0.4370\n",
            "Epoch 49/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7086 - accuracy: 0.4920\n",
            "Epoch 50/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7057 - accuracy: 0.4960\n",
            "Epoch 51/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7030 - accuracy: 0.4940\n",
            "Epoch 52/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7007 - accuracy: 0.4960\n",
            "Epoch 53/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6985 - accuracy: 0.5000\n",
            "Epoch 54/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6964 - accuracy: 0.5010\n",
            "Epoch 55/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6945 - accuracy: 0.5010\n",
            "Epoch 56/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6929 - accuracy: 0.5020\n",
            "Epoch 57/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6911 - accuracy: 0.5040\n",
            "Epoch 58/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6898 - accuracy: 0.5080\n",
            "Epoch 59/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6888 - accuracy: 0.5180\n",
            "Epoch 60/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6873 - accuracy: 0.5150\n",
            "Epoch 61/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6865 - accuracy: 0.5050\n",
            "Epoch 62/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6852 - accuracy: 0.5110\n",
            "Epoch 63/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6841 - accuracy: 0.5120\n",
            "Epoch 64/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6834 - accuracy: 0.5150\n",
            "Epoch 65/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6825 - accuracy: 0.5120\n",
            "Epoch 66/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6816 - accuracy: 0.5180\n",
            "Epoch 67/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6809 - accuracy: 0.5210\n",
            "Epoch 68/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6803 - accuracy: 0.5280\n",
            "Epoch 69/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6794 - accuracy: 0.5300\n",
            "Epoch 70/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6788 - accuracy: 0.5590\n",
            "Epoch 71/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6781 - accuracy: 0.5470\n",
            "Epoch 72/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6777 - accuracy: 0.5540\n",
            "Epoch 73/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6772 - accuracy: 0.5490\n",
            "Epoch 74/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6767 - accuracy: 0.5560\n",
            "Epoch 75/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6762 - accuracy: 0.5500\n",
            "Epoch 76/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6757 - accuracy: 0.5430\n",
            "Epoch 77/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6753 - accuracy: 0.5610\n",
            "Epoch 78/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6748 - accuracy: 0.5550\n",
            "Epoch 79/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6746 - accuracy: 0.5510\n",
            "Epoch 80/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6737 - accuracy: 0.5850\n",
            "Epoch 81/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6736 - accuracy: 0.5870\n",
            "Epoch 82/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6730 - accuracy: 0.5720\n",
            "Epoch 83/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6727 - accuracy: 0.5730\n",
            "Epoch 84/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6718 - accuracy: 0.5810\n",
            "Epoch 85/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6714 - accuracy: 0.5870\n",
            "Epoch 86/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6712 - accuracy: 0.5810\n",
            "Epoch 87/100\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.6702 - accuracy: 0.5810\n",
            "Epoch 88/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6697 - accuracy: 0.5670\n",
            "Epoch 89/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6693 - accuracy: 0.5740\n",
            "Epoch 90/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6689 - accuracy: 0.5660\n",
            "Epoch 91/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6682 - accuracy: 0.5810\n",
            "Epoch 92/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6679 - accuracy: 0.5850\n",
            "Epoch 93/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6679 - accuracy: 0.5650\n",
            "Epoch 94/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6666 - accuracy: 0.5840\n",
            "Epoch 95/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6661 - accuracy: 0.5800\n",
            "Epoch 96/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6656 - accuracy: 0.5850\n",
            "Epoch 97/100\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.6649 - accuracy: 0.5850\n",
            "Epoch 98/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6645 - accuracy: 0.5800\n",
            "Epoch 99/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6638 - accuracy: 0.5920\n",
            "Epoch 100/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6631 - accuracy: 0.5900\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJqimQ0UsiO5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab9a9ff6-4f7b-405a-d6eb-ea7e1a3a94a7"
      },
      "source": [
        "# Evaluate the model\n",
        "model_6.evaluate(X, y)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 0s 2ms/step - loss: 0.6624 - accuracy: 0.5900\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6624451875686646, 0.5899999737739563]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dh6l8egoc-Wr"
      },
      "source": [
        "We're still hitting 50% accuracy, our model is still practically as good as guessing.\n",
        "\n",
        "How do the predictions look?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OtmmuzJkcclw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "469fd617-2b94-4a08-bb7f-6790ddd0a62e"
      },
      "source": [
        "# Check out the predictions using 2 hidden layers\n",
        "plot_decision_boundary(model_6, X, y)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 0s 1ms/step\n",
            "doing binary classifcation...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-hj-lAodLYU"
      },
      "source": [
        "What gives? \n",
        "\n",
        "It seems like our model is the same as the one in the [TensorFlow Playground](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,4&seed=0.93799&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&batchSize_hide=true) but model it's still drawing straight lines... \n",
        "\n",
        "Ideally, the yellow lines go on the inside of the red circle and the blue circle.\n",
        "\n",
        "Okay, okay, let's model this circle once and for all. \n",
        "\n",
        "One more model (I promise... actually, I'm going to have to break that promise... we'll be building plenty more models). \n",
        "\n",
        "This time we'll change the activation function on our output layer too. Remember the architecture of a classification model? For binary classification, the output layer activation is usually the [Sigmoid activation function](https://www.tensorflow.org/api_docs/python/tf/math/sigmoid)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgyJ67E3corL"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create a model\n",
        "model_7 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu), # hidden layer 1, ReLU activation\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu), # hidden layer 2, ReLU activation\n",
        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.sigmoid) # ouput layer, sigmoid activation\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_7.compile(loss=tf.keras.losses.binary_crossentropy,\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "# Fit the model\n",
        "history = model_7.fit(X, y, epochs=100, verbose=0)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5wpRgBVtSRK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5911953-7b59-4c62-a4a0-074bc3943698"
      },
      "source": [
        "# Evaluate our model\n",
        "model_7.evaluate(X, y)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 0s 2ms/step - loss: 0.2472 - accuracy: 0.9760\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.24719198048114777, 0.9760000109672546]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XsB0aZ42eIS2"
      },
      "source": [
        "Woah! It looks like our model is getting some incredible results, let's check them out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSoLyVMmczP4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "143fdd18-2799-4b49-f60a-f9861d4b5dad"
      },
      "source": [
        "# View the predictions of the model with relu and sigmoid activations\n",
        "plot_decision_boundary(model_7, X, y)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 0s 1ms/step\n",
            "doing binary classifcation...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKR2JO4PeTie"
      },
      "source": [
        "Nice! It looks like our model is almost perfectly (apart from a few examples) separating the two circles.\n",
        "\n",
        "> 🤔 **Question:** What's wrong with the predictions we've made? Are we really evaluating our model correctly here? Hint: what data did the model learn on and what did we predict on?\n",
        "\n",
        "Before we answer that, it's important to recognize what we've just covered.\n",
        "\n",
        "> 🔑 **Note:** The combination of **linear (straight lines) and non-linear (non-straight lines) functions** is one of the key fundamentals of neural networks.\n",
        "\n",
        "Think of it like this:\n",
        "\n",
        "If I gave you an unlimited amount of straight lines and non-straight lines, what kind of patterns could you draw?\n",
        "\n",
        "That's essentially what neural networks do to find patterns in data.\n",
        "\n",
        "Now you might be thinking, \"but I haven't seen a linear function or a non-linear function before...\"\n",
        "\n",
        "Oh but you have.\n",
        "\n",
        "We've been using them the whole time.\n",
        "\n",
        "They're what power the layers in the models we just built.\n",
        "\n",
        "To get some intuition about the activation functions we've just used, let's create them and then try them on some toy data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qm-b9aoemQ8A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "151ca0b3-6a7c-4e3e-d5d0-69f5f63aa38f"
      },
      "source": [
        "# Create a toy tensor (similar to the data we pass into our model)\n",
        "A = tf.cast(tf.range(-10, 10), tf.float32)\n",
        "A"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
              "array([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,\n",
              "         1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ti67IeWI9vnP"
      },
      "source": [
        "How does this look?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYWO_14e-Pf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "8d2f349b-993d-43a4-c4ba-92926b988be1"
      },
      "source": [
        "# Visualize our toy tensor\n",
        "plt.plot(A);"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5f9qOgTb-O7L"
      },
      "source": [
        "A straight (linear) line!\n",
        "\n",
        "Nice, now let's recreate the [sigmoid function](https://en.wikipedia.org/wiki/Sigmoid_function) and see what it does to our data. You can also find a pre-built sigmoid function at [`tf.keras.activations.sigmoid`](https://www.tensorflow.org/api_docs/python/tf/keras/activations/sigmoid)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "corG975yiwAP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66130e75-32a3-41c9-f577-5776d789b18b"
      },
      "source": [
        "# Sigmoid - https://www.tensorflow.org/api_docs/python/tf/keras/activations/sigmoid\n",
        "def sigmoid(x):\n",
        "  return 1 / (1 + tf.exp(-x))\n",
        "\n",
        "# Use the sigmoid function on our tensor\n",
        "sigmoid(A)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
              "array([4.5397868e-05, 1.2339458e-04, 3.3535014e-04, 9.1105117e-04,\n",
              "       2.4726230e-03, 6.6928510e-03, 1.7986210e-02, 4.7425874e-02,\n",
              "       1.1920292e-01, 2.6894143e-01, 5.0000000e-01, 7.3105854e-01,\n",
              "       8.8079703e-01, 9.5257413e-01, 9.8201376e-01, 9.9330717e-01,\n",
              "       9.9752742e-01, 9.9908900e-01, 9.9966466e-01, 9.9987662e-01],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3EpZb0F6-cn7"
      },
      "source": [
        "And how does it look?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vz4Pr2Mk-eko",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "f9cd482c-5f3d-44ce-f245-865eeb0b6b96"
      },
      "source": [
        "# Plot sigmoid modified tensor\n",
        "plt.plot(sigmoid(A));"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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al5fX5EjII488IpvNpkceeUSHDh1Snz59NG3aNP3Hf/xHx60FAHSBbQdL9dGuEjnsNt0zaZDV5QBBw2ZaOlfiR1wul2JjY1VaWsopGwCW+el/b9J72wr0jxf30x+nj7G6HMDvtfX3N3fpAYA22FNcrv/9ukBS/a3fAXQcwggAtMFz6/fIGClrZKKGJfawuhwgqBBGAOAcjpSe1NtbDkniqAjQGQgjAHAOL3y0T7Vuo4yBvTQ2tafV5QBBhzACAK04XlGj1zfmSZJ+etUQi6sBghNhBABa8efc/aqsceuC5BhdMTTe6nKAoEQYAYAWVFTXafmn+yXV9xWx2WzWFgQEKcIIALTg9Y15OlFZq4Hx3XXDhX2tLgcIWoQRAGhGdZ1bL3y0T5L04ysGyWHnqAjQWQgjANCMd7ccVoGrSokxTv3jJf2sLgcIaoQRADiD22O0dH39gHh3Xz5IzjAG9QQ6E2EEAM7w/jcF2ltSodiocN2WMcDqcoCgRxgBgNMYY/Tsut2SpNkT0xTt9HlwcwA+IowAwGk+3l2irw+5FBXu0B0T06wuBwgJhBEAOM2za+v7itw6IUW9ukdYXA0QGggjANBgS95x5e49qnCHTfdMGmR1OUDIIIwAQIMXPq6/r8gtY/opOS7K4mqA0EEYAQBJVbVurd1RJEmamZlqcTVAaCGMAICk3L1HVVnjVlJMpEb3i7W6HCCkEEYAQNKabwslSVmjEhgQD+hihBEAIc/jMVqzvSGMjEy0uBog9BBGAIS8rw+XqtBVre4RDmUO7m11OUDIIYwACHmNp2gmD+/DODSABQgjAELe6u31V9FwigawBmEEQEjLP1ap7Udcstukq4YnWF0OEJIIIwBCWk5Dx9Vxab3Uk9u/A5YgjAAIaWsaTtFcN4pTNIBVCCMAQparqlaf7T0qSbqG/iKAZQgjAELWup3FqvMYDUmI1sD47laXA4QswgiAkOW96ypHRQBLEUYAhKRat0drd9b3F7mW/iKApQgjAELS5/uOqayqTvHRERqTEmd1OUBII4wACEmrGy7pvXpEghx2BsYDrEQYARByjDFaTX8RwG8QRgCEnJ2FZTp4/KScYXZdPjTe6nKAkEcYARByGq+imTQ0Xt0iwiyuBgBhBEDIYWA8wL8QRgCElEJXlb7MPyFJunokA+MB/oAwAiCk5DQcFRmTEqeEHpEWVwNAIowACDFrGi7p5UZngP8gjAAIGZU1dfp4d4kkwgjgTwgjAELGh9+VqKbOowG9umloQrTV5QBoQBgBEDIaT9FkjUyUzcZdVwF/QRgBEBLcHqMPdjAwHuCPCCMAQsKWvOM6VlGj2KhwjUvraXU5AE5DGAEQEhrHorlqeB+FO/jqA/wJeySAkNA4Sm8Wp2gAv0MYARD09hSXa29xhcIdNk0e1sfqcgCcgTACIOjlNBwVuXRQb/WIDLe4GgBnIowACHqN/UW4igbwT4QRAEHtaHm1Nh04Lkm6hlF6Ab9EGAEQ1NbuLJbHSKP6xqhfXJTV5QBoBmEEQFBbwykawO8RRgAErapatz7cVSyJMAL4M8IIgKCVu+eoKmvcSoqJ1AXJMVaXA6AFhBEAQevUjc4SGBgP8GOEEQBByeMx3vuLXDsqyeJqALSmXWFk8eLFSktLU2RkpDIyMrRx48ZW2584cUJz585V37595XQ6NWzYML333nvtKhgA2uLrw6UqdFWre4RDlw7qZXU5AFoR5usMK1asUHZ2tpYuXaqMjAwtWrRIU6ZM0c6dO5WQkHBW+5qaGl177bVKSEjQW2+9pX79+unAgQOKi4vriPoBoFmNNzqbPLyPnGEOi6sB0Bqfw8hTTz2le+65R3PmzJEkLV26VCtXrtSyZcv08MMPn9V+2bJlOnbsmD799FOFh9ffhjktLe38qgaAc2gMI1nc6Azwez6dpqmpqdGmTZuUlZV1agF2u7KyspSbm9vsPH/729+UmZmpuXPnKjExURdeeKGeeOIJud3uFj+nurpaLperyQMA2ir/WKV2FJTJYbfpquFnH7EF4F98CiMlJSVyu91KTGz6l0ZiYqIKCgqanWfv3r1666235Ha79d577+nRRx/Vk08+qX//939v8XMWLlyo2NhY7yMlJcWXMgGEuMaOq+NSe6pn9wiLqwFwLp1+NY3H41FCQoL+9Kc/aezYsZo+fbp+9atfaenSpS3OM2/ePJWWlnof+fn5nV0mgCCyejt3XQUCiU99RuLj4+VwOFRYWNhkemFhoZKSmr90rm/fvgoPD5fDcaoD2ciRI1VQUKCamhpFRJz9V4vT6ZTT6fSlNACQJJWerNWGvcckMTAeECh8OjISERGhsWPHKicnxzvN4/EoJydHmZmZzc5z2WWXaffu3fJ4PN5p3333nfr27dtsEAGA87H+u2LVeYyGJERrYHx3q8sB0AY+n6bJzs7W888/rz//+c/avn277rvvPlVUVHivrpk1a5bmzZvnbX/ffffp2LFjuv/++/Xdd99p5cqVeuKJJzR37tyOWwsAaMDAeEDg8fnS3unTp6u4uFjz589XQUGBxowZo1WrVnk7tebl5cluP5VxUlJS9P777+vBBx/URRddpH79+un+++/XQw891HFrAQCSat0erd1ZJIlLeoFAYjPGGKuLOBeXy6XY2FiVlpYqJobBrgA075PdJZrxwgbFR0dowy+z5LAzHg1gpbb+/mZsGgBBo/FGZ1ePSCCIAAGEMAIgKBhjtIaB8YCARBgBEBR2FJTp4PGTcobZdfmQeKvLAeADwgiAoNB4Fc2kofGKimBgPCCQEEYABIXGUzRcRQMEHsIIgIBX6KrSlwdLZbNJV49kYDwg0BBGAAS8nO319xYZkxKnhB6RFlcDwFeEEQABb/W39aOGc4oGCEyEEQABraK6Tp/sOSqJW8ADgYowAiCgfbSrRDV1Hg3o1U1DE6KtLgdAOxBGAAS0Uzc6S5TNxl1XgUBEGAEQsNweow92MDAeEOgIIwAC1ua84zpWUaPYqHCNS+tpdTkA2okwAiBgNd519arhfRTu4OsMCFTsvQAC1moGxgOCAmEEQEDaU1yuvcUVCnfYdMUwBsYDAhlhBEBAajxFc+mg3uoRGW5xNQDOB2EEQEA6/ZJeAIGNMAIg4Bwtr9amA8clSddwSS8Q8AgjAALOBzuK5DHSBckx6hcXZXU5AM4TYQRAwGk8RcONzoDgQBgBEFCqat368LsSSfQXAYIFYQRAQMndc1Qna93qGxupC5JjrC4HQAcgjAAIKKtPO0XDwHhAcCCMAAgYHo/x3l8ki1M0QNAgjAAIGNsOlaqorFrdIxy6dFAvq8sB0EEIIwACRuNVNJOH95EzzGFxNQA6CmEEQMBY/S13XQWCEWEEQEDIP1apHQVlcthtump4gtXlAOhAhBEAAaHxFM241J6K6xZhcTUAOhJhBEBAYGA8IHgRRgD4vdKTtdqw95gkwggQjAgjAPzeup1FqvMYDU2IVmrv7laXA6CDEUYA+L0124skcaMzIFgRRgD4tZo6j9btbAgjjNILBCXCCAC/9vn+YyqrqlN8dIQuTomzuhwAnYAwAsCvNd7o7JoRibLbGRgPCEaEEQB+yxjjDSP0FwGCF2EEgN/aUVCmQydOKjLcrsuHxFtdDoBOQhgB4LfWNBwVuXxIH0VFMDAeEKwIIwD81mrvXVcZiwYIZoQRAH6poLRKXx0slc0mXT2C/iJAMCOMAPBLOTvqj4qMSYlTnx5Oi6sB0JkIIwD8UmN/EcaiAYIfYQSA36mortMne45Kkq7lrqtA0COMAPA7H+0qVk2dR6m9u2lIQrTV5QDoZIQRAH5n9benxqKx2bjrKhDsCCMA/IrbY/TBDvqLAKGEMALAr2w6cFzHK2sVGxWucak9rS4HQBcgjADwK2sabnR29YgEhTn4igJCAXs6AL/SeElvFlfRACGDMALAb+wpLtfekgpFOOyaPLyP1eUA6CKEEQB+Y3XDUZFLB/dWtDPM4moAdBXCCAC/4b3r6kgGxgNCCWEEgF84Wl6tTXnHJUnX0F8ECCmEEQB+4YMdRTJGurBfjJLjoqwuB0AXalcYWbx4sdLS0hQZGamMjAxt3LixTfO98cYbstlsuuWWW9rzsQCC2GquogFCls9hZMWKFcrOztaCBQu0efNmpaena8qUKSoqKmp1vv379+sXv/iFJk2a1O5iAQSnqlq3PtpVIokwAoQin8PIU089pXvuuUdz5szRqFGjtHTpUnXr1k3Lli1rcR63260ZM2boscce06BBg86rYADB59M9JTpZ61ZybKQuSI6xuhwAXcynMFJTU6NNmzYpKyvr1ALsdmVlZSk3N7fF+f7t3/5NCQkJuuuuu9r0OdXV1XK5XE0eAIKXd2C8UQyMB4Qin8JISUmJ3G63EhObHkZNTExUQUFBs/N8/PHHevHFF/X888+3+XMWLlyo2NhY7yMlJcWXMgEEEI/HKGc7/UWAUNapV9OUlZVp5syZev755xUfH9/m+ebNm6fS0lLvIz8/vxOrBGClrw6VqqisWtHOMGUM6mV1OQAs4NMtDuPj4+VwOFRYWNhkemFhoZKSks5qv2fPHu3fv1/Tpk3zTvN4PPUfHBamnTt3avDgwWfN53Q65XQ6fSkNQIBqvNHZ5GF95AxzWFwNACv4dGQkIiJCY8eOVU5Ojneax+NRTk6OMjMzz2o/YsQIbdu2TVu3bvU+brrpJl111VXaunUrp18AeEfpvXYUp2iAUOXz4A/Z2dmaPXu2xo0bpwkTJmjRokWqqKjQnDlzJEmzZs1Sv379tHDhQkVGRurCCy9sMn9cXJwknTUdQOjJP1apHQVlcthtupKB8YCQ5XMYmT59uoqLizV//nwVFBRozJgxWrVqlbdTa15enux2buwK4Nwab3Q2Pq2n4rpFWFwNAKvYjDHG6iLOxeVyKTY2VqWlpYqJ4R4EQLC4/fnP9Omeo3pk6kjdPYl7EAHBpq2/vzmEAcASpZW12rDvmCT6iwChjjACwBLrviuS22M0LDFaqb27W10OAAsRRgBYgoHxADQijADocjV1Hq3fWSyp/hbwAEIbYQRAl9u475jKqusUH+3UmP5xVpcDwGKEEQBdbo13LJoE2e0MjAeEOsIIgC5ljKG/CIAmCCMAutT2I2U6dOKkIsPtumxI2wfQBBC8CCMAulTjKZpJQ/soKoKB8QAQRgB0Me/AeJyiAdCAMAKgyxSUVumrg6Wy2aSrRiRYXQ4AP0EYAdBlGo+KXJwSpz49nBZXA8BfEEYAdBnvKZpRSRZXAsCfEEYAdImK6jp9uvuoJOnaUZyiAXAKYQRAl/jwu2LVuD1K691Ng/tEW10OAD9CGAHQJVZvP3WjM5uNu64COIUwAqDT1bk9WrujSJJ0LQPjATgDYQRAp9ucd0LHK2sV1y1cY1N7Wl0OAD9DGAHQ6VZ/WyBJunp4gsIcfO0AaIpvBQCdqsnAeJyiAdAMwgiATrWnuEL7j1YqwmHXFcP6WF0OAD9EGAHQqRpvdJY5uLeinWEWVwPAHxFGAHQqTtEAOBfCCIBOU1Jerc15xyVJWSO56yqA5hFGAHSaD3YUyRhpdL9Y9Y2NsrocAH6KMAKg06z59tRdVwGgJYQRAJ2iqtatj3aVSJKyGBgPQCsIIwA6xSe7S3Sy1q1+cVEa1TfG6nIA+DHCCIBOscY7MF4CA+MBaBVhBECH83iM1myvHxiPS3oBnAthBECH+/LgCRWXVSvaGaaMgb2tLgeAnyOMAOhwjadoJg/vo4gwvmYAtI5vCQAdbs239adoruMUDYA2IIwA6FB5Ryu1s7BMDrtNVw7jkl4A50YYAdChGk/RTEjrpdhu4RZXAyAQEEYAdCgGxgPgK8IIgA5zorJGG/cfkyRdyy3gAbQRYQRAh3n1swNye4wuSI7RgN7drC4HQIAgjADoECdr3Fr2yX5J0r1XDLK2GAABhTACoEO8+UW+jlXUKKVXlKaO7mt1OQACCGEEwHmrdXv0pw/3SpLuvWKwwhx8tQBoO74xAJy3v209rEMnTio+2qkfjO1vdTkAAgxhBMB58XiMlqzfI0m66/KBigx3WFwRgEBDGAFwXtZsL9TuonL1cIZpxqUDrC4HQAAijABoN2OMnl1Xf1RkZmaqYiK54yoA3xFGALRb7t6j2pp/Qs4wu+68fKDV5QAIUIQRAO22pOGoyPTxKYqPdlpcDYBARRgB0C7bDpbqo10lcthtumcSNzkD0H6EEQDtsmT9bknSTenJSunFrd8BtB9hBIDP9hSX63+/LpAk3XflYIurARDoCCMAfPbc+j0yRsoamahhiT2sLgdAgCOMAPDJkdKTenvLIUkcFQHQMQgjAHzywkf7VOs2yhjYS2NTe1pdDoAgQBgB0GbHK2r0+sY8SdJPrxpicTUAggVhBECb/Tl3vypr3LogOUZXDI23uhwAQYIwAqBNKqrrtPzT/ZLq+4rYbDZrCwIQNNoVRhYvXqy0tDRFRkYqIyNDGzdubLHt888/r0mTJqlnz57q2bOnsrKyWm0PwD+9vjFPJyprNTC+u264sK/V5QAIIj6HkRUrVig7O1sLFizQ5s2blZ6erilTpqioqKjZ9uvWrdNtt92mtWvXKjc3VykpKbruuut06NCh8y4eQNeornPrhY/2SZJ+fMUgOewcFQHQcWzGGOPLDBkZGRo/fryeeeYZSZLH41FKSop+/vOf6+GHHz7n/G63Wz179tQzzzyjWbNmtekzXS6XYmNjVVpaqpiYGF/KBdAB3vw8X//6P18pMcapD//1KjnDHFaXBCAAtPX3t09HRmpqarRp0yZlZWWdWoDdrqysLOXm5rZpGZWVlaqtrVWvXr1abFNdXS2Xy9XkAcAabo/R0vX1A+LdffkgggiADudTGCkpKZHb7VZiYmKT6YmJiSooKGjTMh566CElJyc3CTRnWrhwoWJjY72PlJQUX8oE0IHe/6ZAe0sqFBsVrtsyBlhdDoAg1KVX0/zmN7/RG2+8obfffluRkZEttps3b55KS0u9j/z8/C6sEkAjY4yeXVc/IN7siWmKdoZZXBGAYOTTN0t8fLwcDocKCwubTC8sLFRSUlKr8/7hD3/Qb37zG61Zs0YXXXRRq22dTqecTqcvpQHoBB/vLtHXh1yKCnfojolpVpcDIEj5dGQkIiJCY8eOVU5Ojneax+NRTk6OMjMzW5zvd7/7nR5//HGtWrVK48aNa3+1ALrUs2vr+4rcOiFFvbpHWFwNgGDl8zHX7OxszZ49W+PGjdOECRO0aNEiVVRUaM6cOZKkWbNmqV+/flq4cKEk6be//a3mz5+v1157TWlpad6+JdHR0YqOju7AVQHQkbbkHVfu3qMKs9t0z6RBVpcDIIj5HEamT5+u4uJizZ8/XwUFBRozZoxWrVrl7dSal5cnu/3UAZclS5aopqZG3//+95ssZ8GCBfr1r399ftUD6DTPrqs/KnLLxf2UHBdlcTUAgpnP9xmxAvcZAbrWrsIyXfvHD2WzSasfnKwhCRzFBOC7TrnPCIDQsKThviJTRiURRAB0OsIIgCYOHq/U37YellQ/IB4AdDbCCIAmnv9wr+o8RpcN6a30lDirywEQAggjALxKyqv1xuf1Nxn86ZVDLK4GQKggjADwWv7JflXXeZTeP1YTB/e2uhwAIYIwAkCSVFZVqz/n7pdU31fEZrNZWxCAkEEYASBJ+u8NeSqrqtPgPt113ajWh3cAgI5EGAGgqlq3Xvx4nyTpJ5MHy27nqAiArkMYAaD/2XxQxWXVSo6N1M1j+lldDoAQQxgBQlyd26Pn1u+VJN09aZAiwvhaANC1+NYBQtzKbUeUd6xSPbuF69YJKVaXAyAEEUaAEGaM0ZKGAfHmXDZQ3SJ8HjsTAM4bYQQIYet2FmtHQZm6Rzg0OzPN6nIAhCjCCBDCnl23W5J0e8YAxXYLt7gaAKGKMAKEqM/3H9Pn+48rwmHX3ZMGWV0OgBBGGAFCVGNfke+N7afEmEiLqwEQyggjQAjafsSlD3YUyW6T7r1isNXlAAhxhBEgBDUeFblhdF8NjO9ucTUAQh1hBAgxB45W6O9fHZYk3TeZoyIArEcYAULM0vV75THS5GF9dGG/WKvLAQDCCBBKXsndr9c35kmS7ruSoyIA/ANhBAgRr23I06PvfiNJ+vHkQbp0UG+LKwKAeoQRIAS8+Xm+fvn2NknS3ZcP1MPXj7C4IgA4hTACBLn/2XRQD/31K0nSHRPT9KupI2Wz2SyuCgBOIYwAQeydLYf0i7e+lDHSzEtTtWDaKIIIAL9DGAGC1P/98rCy39wqY+rHnnnspgsIIgD8EmEECELvbTuiB1ZslcdI08el6N9vvlB2O0EEgH8ijABBZtXXBfrn17fI7TH63iX9tfCfRhNEAPg1wggQRNZ8W6ifv75ZdR6jW8Yk63ffv4ggAsDvEUaAILF2R5F++t+bVes2mpaerD/8IF0OggiAAEAYAYLA+u+K9eNXN6nG7dHU0X31xx+mK8zB7g0gMPBtBQS4j3eV6N6Xv1BNnUdTLkjUolvHEEQABBS+sYAA9umeEt398ueqrvMoa2Sinr7tEoUTRAAEGL61gAC1Ye9R3bX8C1XVenT1iAQtnnGxIsLYpQEEHr65gAD0xf5jmrP8c52sdWvysD56dsYlcoY5rC4LANqFMAIEmM15xzV72UZV1rg1aWi8nps5VpHhBBEAgYswAgSQL/NPaPaLG1VR49bEwb31p5njCCIAAh5hBAgQ2w6WauaLG1RWXacJA3vphdnjFBVBEAEQ+AgjQAD45nCpfvTiBrmq6jQutadeumO8ukWEWV0WAHQIwgjg57YfcelHL2xQ6claXTIgTsvvnKDuToIIgOBBGAH82M6CMs14YYOOV9YqPaU+iEQTRAAEGcII4Kd2F5Vpxguf6VhFjUb3i9XLd05QTGS41WUBQIcjjAB+aE9xuW57foNKymt0QXKMXrlrgmKjCCIAghNhBPAzW/KO6/bnP1NxWbVGJPXQq3dlKK5bhNVlAUCn4eQz4Aeqat36+1dH9Erufn15sFSSNDyxh/777gz17E4QARDcCCOAhfKPVerVzw7ozS/ydbyyVpIU4bDrHy7qq19OHane0U6LKwSAzkcYAbqYx2O0flexXsk9oLU7i2RM/fR+cVGacekA/XBciuIJIQBCCGEE6CInKmv05hf5evWzPOUdq/ROnzQ0XrMy03T1iAQ57DYLKwQAaxBGgE721cETeiX3gP725WFV13kkSTGRYfrBuBT96NJUDYzvbnGFAGAtwgjQCapq3Vr51RG9/NkBfZl/wjt9VN8YzcpM1c1j+jGuDAA0IIwAHSj/WKVe3XBAb37etEPqjaOTNDMzTZcMiJPNxqkYADgdYQQ4Tx6P0YcNHVI/OKND6u0ZAzR9PB1SAaA1hBGgnU5U1ugvXxzUqxsO6MDRph1SZ16aqmtGJtIhFQDagDACtIExRkVl1dpdVK7dReX6Mv+EVm474u2Q2iMyTD8Ym6IfXTpAg/pEW1wtAAQWwghwmjq3R/nHT3pDx+6icu0uLtfeonKVVded1b6xQ+pNY5LVLYLdCQDag29PhKSqWrf2Fldod3F94NjTEDz2lVSoxu1pdh6H3abUXt00OCFaQxKilTUyQZcM6EmHVAA4T+0KI4sXL9bvf/97FRQUKD09XU8//bQmTJjQYvu//OUvevTRR7V//34NHTpUv/3tb3XjjTe2u2igrUora7W7uOysIx0Hj5/0djQ9U2S4XYPi6wPH6Y/U3t3kDONyXADoaD6HkRUrVig7O1tLly5VRkaGFi1apClTpmjnzp1KSEg4q/2nn36q2267TQsXLtQ//MM/6LXXXtMtt9yizZs368ILL+yQlUBw83iMymvq5DpZq7KqOpVVNTyvrj31vKpOrqo6lVXVen/mHzupkvLqFpcbGxVeHzT6NA0d/eKiZKfjKQB0GZsxLf192LyMjAyNHz9ezzzzjCTJ4/EoJSVFP//5z/Xwww+f1X769OmqqKjQ3//+d++0Sy+9VGPGjNHSpUvb9Jkul0uxsbEqLS1VTEyML+WiExhj5PYY1XmMatwe1dZ56p83/Kx1e5o8r63zqNZj6n+6Tz2vqnM3CROnB4nTp5fX1LV4FKMt+sZGakhCtAY3hI7Gn/HREZxiAYBO1Nbf3z4dGampqdGmTZs0b9487zS73a6srCzl5uY2O09ubq6ys7ObTJsyZYreeeedFj+nurpa1dWn/qJ1uVy+lNlmL368T/mnjRFyutMz2pm/B0//xWjOeLfpe6dPM973jKmfz/v6tGmNE8xpNZx6/1RtxtvOyOORPMY0POT9aRqnNbxvGt5zn+P9xvndHqO6xvBweqhwe84rHLRXhMOumKgw9YgMV4/IMMU0/Dz1/NTrHpHh6hsbqcEJ0Yp20jUKAPyZT9/SJSUlcrvdSkxMbDI9MTFRO3bsaHaegoKCZtsXFBS0+DkLFy7UY4895ktp7bLyq8PanHei0z8nFDjsNoU7bAp32BseZz8Pc9gVcdpzZ5jdGyRiGgLE6WGjx2nTe0SGKTKc/hoAEIz88k/GefPmNTma4nK5lJKS0uGf872x/TVxcLz39ZlH7Ju8PONNW8tvyaYz2trq29tsanJaoH667aw2jcs4fbk2m+2M9+un2W2NP+uf22022e2nntsapzVpWx8eWnu/cRnNhYswh00Rpz0Pt9vpYwEAaDefwkh8fLwcDocKCwubTC8sLFRSUlKz8yQlJfnUXpKcTqeczs6/ffaMjNRO/wwAANA6uy+NIyIiNHbsWOXk5HineTwe5eTkKDMzs9l5MjMzm7SXpNWrV7fYHgAAhBafT9NkZ2dr9uzZGjdunCZMmKBFixapoqJCc+bMkSTNmjVL/fr108KFCyVJ999/vyZPnqwnn3xSU6dO1RtvvKEvvvhCf/rTnzp2TQAAQEDyOYxMnz5dxcXFmj9/vgoKCjRmzBitWrXK20k1Ly9PdvupAy4TJ07Ua6+9pkceeUS//OUvNXToUL3zzjvcYwQAAEhqx31GrMB9RgAACDxt/f3tU58RAACAjkYYAQAAliKMAAAASxFGAACApQgjAADAUoQRAABgKcIIAACwFGEEAABYijACAAAs5fPt4K3QeJNYl8tlcSUAAKCtGn9vn+tm7wERRsrKyiRJKSkpFlcCAAB8VVZWptjY2BbfD4ixaTwejw4fPqwePXrIZrN12HJdLpdSUlKUn58fEmPehNL6sq7BK5TWl3UNXqGyvsYYlZWVKTk5uckgumcKiCMjdrtd/fv377Tlx8TEBPU/hjOF0vqyrsErlNaXdQ1eobC+rR0RaUQHVgAAYCnCCAAAsFRIhxGn06kFCxbI6XRaXUqXCKX1ZV2DVyitL+savEJtfc8lIDqwAgCA4BXSR0YAAID1CCMAAMBShBEAAGApwggAALBU0IeRxYsXKy0tTZGRkcrIyNDGjRtbbf+Xv/xFI0aMUGRkpEaPHq333nuviyo9PwsXLtT48ePVo0cPJSQk6JZbbtHOnTtbnWf58uWy2WxNHpGRkV1Ucfv9+te/PqvuESNGtDpPoG7XtLS0s9bVZrNp7ty5zbYPtG364Ycfatq0aUpOTpbNZtM777zT5H1jjObPn6++ffsqKipKWVlZ2rVr1zmX6+t+3xVaW9fa2lo99NBDGj16tLp3767k5GTNmjVLhw8fbnWZ7dkXusK5tusdd9xxVt3XX3/9OZfrj9tVOvf6NrcP22w2/f73v29xmf66bTtLUIeRFStWKDs7WwsWLNDmzZuVnp6uKVOmqKioqNn2n376qW677Tbddddd2rJli2655Rbdcsst+vrrr7u4ct+tX79ec+fO1WeffabVq1ertrZW1113nSoqKlqdLyYmRkeOHPE+Dhw40EUVn58LLrigSd0ff/xxi20Debt+/vnnTdZz9erVkqQf/OAHLc4TSNu0oqJC6enpWrx4cbPv/+53v9N//dd/aenSpdqwYYO6d++uKVOmqKqqqsVl+rrfd5XW1rWyslKbN2/Wo48+qs2bN+uvf/2rdu7cqZtuuumcy/VlX+gq59quknT99dc3qfv1119vdZn+ul2lc6/v6et55MgRLVu2TDabTd/73vdaXa4/bttOY4LYhAkTzNy5c72v3W63SU5ONgsXLmy2/Q9/+EMzderUJtMyMjLMj3/8406tszMUFRUZSWb9+vUttnnppZdMbGxs1xXVQRYsWGDS09Pb3D6Ytuv9999vBg8ebDweT7PvB+o2NcYYSebtt9/2vvZ4PCYpKcn8/ve/9047ceKEcTqd5vXXX29xOb7u91Y4c12bs3HjRiPJHDhwoMU2vu4LVmhuXWfPnm1uvvlmn5YTCNvVmLZt25tvvtlcffXVrbYJhG3bkYL2yEhNTY02bdqkrKws7zS73a6srCzl5uY2O09ubm6T9pI0ZcqUFtv7s9LSUklSr169Wm1XXl6u1NRUpaSk6Oabb9Y333zTFeWdt127dik5OVmDBg3SjBkzlJeX12LbYNmuNTU1evXVV3XnnXe2OmBkoG7TM+3bt08FBQVNtl1sbKwyMjJa3Hbt2e/9VWlpqWw2m+Li4lpt58u+4E/WrVunhIQEDR8+XPfdd5+OHj3aYttg2q6FhYVauXKl7rrrrnO2DdRt2x5BG0ZKSkrkdruVmJjYZHpiYqIKCgqanaegoMCn9v7K4/HogQce0GWXXaYLL7ywxXbDhw/XsmXL9O677+rVV1+Vx+PRxIkTdfDgwS6s1ncZGRlavny5Vq1apSVLlmjfvn2aNGmSysrKmm0fLNv1nXfe0YkTJ3THHXe02CZQt2lzGrePL9uuPfu9P6qqqtJDDz2k2267rdVB1HzdF/zF9ddfr5dfflk5OTn67W9/q/Xr1+uGG26Q2+1utn2wbFdJ+vOf/6wePXron/7pn1ptF6jbtr0CYtRe+Gbu3Ln6+uuvz3l+MTMzU5mZmd7XEydO1MiRI/Xcc8/p8ccf7+wy2+2GG27wPr/ooouUkZGh1NRUvfnmm236ayNQvfjii7rhhhuUnJzcYptA3aY4pba2Vj/84Q9ljNGSJUtabRuo+8Ktt97qfT569GhddNFFGjx4sNatW6drrrnGwso637JlyzRjxoxzdiwP1G3bXkF7ZCQ+Pl4Oh0OFhYVNphcWFiopKanZeZKSknxq749+9rOf6e9//7vWrl2r/v37+zRveHi4Lr74Yu3evbuTqusccXFxGjZsWIt1B8N2PXDggNasWaO7777bp/kCdZtK8m4fX7Zde/Z7f9IYRA4cOKDVq1f7PLT8ufYFfzVo0CDFx8e3WHegb9dGH330kXbu3OnzfiwF7rZtq6ANIxERERo7dqxycnK80zwej3Jycpr85Xi6zMzMJu0lafXq1S229yfGGP3sZz/T22+/rQ8++EADBw70eRlut1vbtm1T3759O6HCzlNeXq49e/a0WHcgb9dGL730khISEjR16lSf5gvUbSpJAwcOVFJSUpNt53K5tGHDhha3XXv2e3/RGER27dqlNWvWqHfv3j4v41z7gr86ePCgjh492mLdgbxdT/fiiy9q7NixSk9P93neQN22bWZ1D9rO9MYbbxin02mWL19uvv32W3PvvfeauLg4U1BQYIwxZubMmebhhx/2tv/kk09MWFiY+cMf/mC2b99uFixYYMLDw822bdusWoU2u++++0xsbKxZt26dOXLkiPdRWVnpbXPm+j722GPm/fffN3v27DGbNm0yt956q4mMjDTffPONFavQZv/yL/9i1q1bZ/bt22c++eQTk5WVZeLj401RUZExJri2qzH1Vw0MGDDAPPTQQ2e9F+jbtKyszGzZssVs2bLFSDJPPfWU2bJli/cKkt/85jcmLi7OvPvuu+arr74yN998sxk4cKA5efKkdxlXX321efrpp72vz7XfW6W1da2pqTE33XST6d+/v9m6dWuTfbi6utq7jDPX9Vz7glVaW9eysjLzi1/8wuTm5pp9+/aZNWvWmEsuucQMHTrUVFVVeZcRKNvVmHP/OzbGmNLSUtOtWzezZMmSZpcRKNu2swR1GDHGmKefftoMGDDAREREmAkTJpjPPvvM+97kyZPN7Nmzm7R/8803zbBhw0xERIS54IILzMqVK7u44vaR1OzjpZde8rY5c30feOAB7/+bxMREc+ONN5rNmzd3ffE+mj59uunbt6+JiIgw/fr1M9OnTze7d+/2vh9M29UYY95//30jyezcufOs9wJ9m65du7bZf7eN6+TxeMyjjz5qEhMTjdPpNNdcc81Z/x9SU1PNggULmkxrbb+3Smvrum/fvhb34bVr13qXcea6nmtfsEpr61pZWWmuu+4606dPHxMeHm5SU1PNPffcc1aoCJTtasy5/x0bY8xzzz1noqKizIkTJ5pdRqBs285iM8aYTj30AgAA0Iqg7TMCAAACA2EEAABYijACAAAsRRgBAACWIowAAABLEUYAAIClCCMAAMBShBEAAGApwggAALAUYQQAAFiKMAIAACxFGAEAAJb6/xQL2S9JmchSAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGT2qtUV-k5f"
      },
      "source": [
        "A non-straight (non-linear) line!\n",
        "\n",
        "Okay, how about the [ReLU function](https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/#:~:text=The%20rectified%20linear%20activation%20function,otherwise%2C%20it%20will%20output%20zero.) (ReLU turns all negatives to 0 and positive numbers stay the same)?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_PUMn--Hiwyz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3a698f1-0b45-4b47-d965-81b06c3a856b"
      },
      "source": [
        "# ReLU - https://www.tensorflow.org/api_docs/python/tf/keras/activations/relu\n",
        "def relu(x):\n",
        "  return tf.maximum(0, x)\n",
        "\n",
        "# Pass toy tensor through ReLU function\n",
        "relu(A)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
              "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 2., 3., 4., 5., 6.,\n",
              "       7., 8., 9.], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xAkmlNeZ-90e"
      },
      "source": [
        "How does the ReLU-modified tensor look?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXWCO2R4mlk7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "490ad23d-bc42-462e-e8b7-5e88204fb4e3"
      },
      "source": [
        "# Plot ReLU-modified tensor\n",
        "plt.plot(relu(A));"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-NLdoxJr_Grk"
      },
      "source": [
        "Another non-straight line!\n",
        "\n",
        "Well, how about TensorFlow's [linear activation function](https://www.tensorflow.org/api_docs/python/tf/keras/activations/linear)? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cg_kBQLVoXRB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "347b8add-95f0-40b3-af81-7fc0b89d08fb"
      },
      "source": [
        "# Linear - https://www.tensorflow.org/api_docs/python/tf/keras/activations/linear (returns input non-modified...)\n",
        "tf.keras.activations.linear(A)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
              "array([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,\n",
              "         1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0NnpC_N_uWT"
      },
      "source": [
        "Hmm, it looks like our inputs are unmodified..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwlgKpco_7V2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "138695ba-6de3-49d6-bc72-c1f38ab60d2f"
      },
      "source": [
        "# Does the linear activation change anything?\n",
        "A == tf.keras.activations.linear(A)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=bool, numpy=\n",
              "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True])>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KcCT_ipvADpR"
      },
      "source": [
        "Okay, so it makes sense now the model doesn't really learn anything when using only linear activation functions, because the linear activation function doesn't change our input data in anyway.\n",
        "\n",
        "Where as, with our non-linear functions, our data gets manipulated. A neural network uses these kind of transformations at a large scale to figure draw patterns between its inputs and outputs.\n",
        "\n",
        "Now rather than dive into the guts of neural networks, we're going to keep coding applying what we've learned to different problems but if you want a more in-depth look at what's going on behind the scenes, check out the Extra Curriculum section below. \n",
        "\n",
        "> 📖  **Resource:** For more on activation functions, check out the [machine learning cheatsheet page](https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html#) on them."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BPCpA90uQBjk"
      },
      "source": [
        "## Evaluating and improving our classification model\n",
        "\n",
        "If you answered the question above, you might've picked up what we've been doing wrong.\n",
        "\n",
        "We've been evaluating our model on the same data it was trained on.\n",
        "\n",
        "A better approach would be to split our data into training, validation (optional) and test sets.\n",
        "\n",
        "Once we've done that, we'll train our model on the training set (let it find patterns in the data) and then see how well it learned the patterns by using it to predict values on the test set.\n",
        "\n",
        "Let's do it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NjOviFscgl4S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3174fb2a-63e6-446a-e6a4-a2d24f95fb29"
      },
      "source": [
        "# How many examples are in the whole dataset?\n",
        "len(X)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNvKa8QrkSWR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82374782-b4d6-463d-9cae-e84110cd26e7"
      },
      "source": [
        "# Split data into train and test sets\n",
        "X_train, y_train = X[:800], y[:800] # 80% of the data for the training set\n",
        "X_test, y_test = X[800:], y[800:] # 20% of the data for the test set\n",
        "\n",
        "# Check the shapes of the data\n",
        "X_train.shape, X_test.shape # 800 examples in the training set, 200 examples in the test set "
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((800, 2), (200, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtNge18ahgqa"
      },
      "source": [
        "Great, now we've got training and test sets, let's model the training data and evaluate what our model has learned on the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RUOBhA1g8Kx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1bcee017-8ff6-4280-f670-c74d1c3e94f6"
      },
      "source": [
        "# Let's recreate a model to fit on the training data and evaluate on the testing data\n",
        "\n",
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model (same as model_7)\n",
        "model_8 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(1, activation=\"sigmoid\")                            \n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_8.compile(loss=\"binary_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.01),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "history = model_8.fit(X_train, y_train, epochs=25)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "25/25 [==============================] - 1s 3ms/step - loss: 0.6894 - accuracy: 0.5238\n",
            "Epoch 2/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6742 - accuracy: 0.5800\n",
            "Epoch 3/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6609 - accuracy: 0.6237\n",
            "Epoch 4/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6442 - accuracy: 0.7300\n",
            "Epoch 5/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6260 - accuracy: 0.6888\n",
            "Epoch 6/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5987 - accuracy: 0.7538\n",
            "Epoch 7/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5592 - accuracy: 0.8313\n",
            "Epoch 8/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5124 - accuracy: 0.8500\n",
            "Epoch 9/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.4418 - accuracy: 0.8988\n",
            "Epoch 10/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3742 - accuracy: 0.9538\n",
            "Epoch 11/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3165 - accuracy: 0.9650\n",
            "Epoch 12/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2639 - accuracy: 0.9825\n",
            "Epoch 13/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2134 - accuracy: 0.9912\n",
            "Epoch 14/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1787 - accuracy: 0.9987\n",
            "Epoch 15/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1460 - accuracy: 0.9962\n",
            "Epoch 16/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1266 - accuracy: 0.9987\n",
            "Epoch 17/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1085 - accuracy: 0.9962\n",
            "Epoch 18/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0973 - accuracy: 0.9975\n",
            "Epoch 19/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0852 - accuracy: 0.9987\n",
            "Epoch 20/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0822 - accuracy: 0.9962\n",
            "Epoch 21/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0701 - accuracy: 0.9950\n",
            "Epoch 22/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0631 - accuracy: 0.9975\n",
            "Epoch 23/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0595 - accuracy: 0.9975\n",
            "Epoch 24/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0564 - accuracy: 0.9987\n",
            "Epoch 25/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0509 - accuracy: 0.9962\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aAnKqoDxloAA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "275eb568-652b-4861-ee98-aa1ad9493f5c"
      },
      "source": [
        "# Evaluate our model on the test set\n",
        "loss, accuracy = model_8.evaluate(X_test, y_test)\n",
        "print(f\"Model loss on the test set: {loss}\")\n",
        "print(f\"Model accuracy on the test set: {100*accuracy:.2f}%\")"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 6ms/step - loss: 0.0459 - accuracy: 0.9950\n",
            "Model loss on the test set: 0.0459492951631546\n",
            "Model accuracy on the test set: 99.50%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c1lWpsNwjD5C"
      },
      "source": [
        "100% accuracy? Nice!\n",
        "\n",
        "Now, when we started to create `model_8` we said it was going to be the same as `model_7` but you might've found that to be a little lie.\n",
        "\n",
        "That's because we changed a few things: \n",
        "* **The `activation` parameter** - We used strings (`\"relu\"` & `\"sigmoid\"`) instead of using library paths (`tf.keras.activations.relu`), in TensorFlow, they both offer the same functionality.\n",
        "* **The `learning_rate` (also `lr`) parameter** - We increased the **learning rate** parameter in the [Adam optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) to `0.01` instead of `0.001` (an increase of 10x).\n",
        "  * You can think of the learning rate as how quickly a model learns. The higher the learning rate, the faster the model's capacity to learn, however, there's such a thing as a *too high* learning rate, where a model tries to learn too fast and doesn't learn anything. We'll see a trick to find the ideal learning rate soon.\n",
        "* **The number of epochs** - We lowered the number of epochs (using the `epochs` parameter) from 100 to 25 but our model still got an incredible result on both the training and test sets.\n",
        "  * One of the reasons our model performed well in even less epochs (remember a single epoch is the model trying to learn patterns in the data by looking at it once, so 25 epochs means the model gets 25 chances) than before is because we increased the learning rate.\n",
        "\n",
        "We know our model is performing well based on the evaluation metrics but let's see how it performs visually.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LRvAIBfAmtdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "outputId": "c2299705-9a19-448b-8aa5-9a92e7dc8235"
      },
      "source": [
        "# Plot the decision boundaries for the training and test sets\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_decision_boundary(model_8, X=X_train, y=y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_decision_boundary(model_8, X=X_test, y=y_test)\n",
        "plt.show()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 0s 1ms/step\n",
            "doing binary classifcation...\n",
            "313/313 [==============================] - 1s 2ms/step\n",
            "doing binary classifcation...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "42KDFaHonmGN"
      },
      "source": [
        "Check that out! How cool. With a few tweaks, our model is now predicting the blue and red circles almost perfectly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EqaFRxFaiklC"
      },
      "source": [
        "### Plot the loss curves\n",
        "\n",
        "Looking at the plots above, we can see the outputs of our model are very good.\n",
        "\n",
        "But how did our model go whilst it was learning?\n",
        "\n",
        "As in, how did the performance change everytime the model had a chance to look at the data (once every epoch)?\n",
        "\n",
        "To figure this out, we can check the **loss curves** (also referred to as the **learning curves**).\n",
        "\n",
        "You might've seen we've been using the variable `history` when calling the `fit()` function on a model ([`fit()` returns a `History` object](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit)).\n",
        "\n",
        "This is where we'll get the information for how our model is performing as it learns.\n",
        "\n",
        "Let's see how we might use it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBHuMm9mpoOz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "outputId": "50fc00c9-9992-46a3-8ee6-0aaebb3df8b1"
      },
      "source": [
        "# You can access the information in the history variable using the .history attribute\n",
        "pd.DataFrame(history.history)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        loss  accuracy\n",
              "0   0.689398   0.52375\n",
              "1   0.674188   0.58000\n",
              "2   0.660881   0.62375\n",
              "3   0.644166   0.73000\n",
              "4   0.626045   0.68875\n",
              "5   0.598718   0.75375\n",
              "6   0.559194   0.83125\n",
              "7   0.512439   0.85000\n",
              "8   0.441817   0.89875\n",
              "9   0.374190   0.95375\n",
              "10  0.316489   0.96500\n",
              "11  0.263874   0.98250\n",
              "12  0.213365   0.99125\n",
              "13  0.178737   0.99875\n",
              "14  0.145954   0.99625\n",
              "15  0.126606   0.99875\n",
              "16  0.108517   0.99625\n",
              "17  0.097273   0.99750\n",
              "18  0.085247   0.99875\n",
              "19  0.082191   0.99625\n",
              "20  0.070125   0.99500\n",
              "21  0.063128   0.99750\n",
              "22  0.059500   0.99750\n",
              "23  0.056382   0.99875\n",
              "24  0.050893   0.99625"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.689398</td>\n",
              "      <td>0.52375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.674188</td>\n",
              "      <td>0.58000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.660881</td>\n",
              "      <td>0.62375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.644166</td>\n",
              "      <td>0.73000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.626045</td>\n",
              "      <td>0.68875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.598718</td>\n",
              "      <td>0.75375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.559194</td>\n",
              "      <td>0.83125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.512439</td>\n",
              "      <td>0.85000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.441817</td>\n",
              "      <td>0.89875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.374190</td>\n",
              "      <td>0.95375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.316489</td>\n",
              "      <td>0.96500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.263874</td>\n",
              "      <td>0.98250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.213365</td>\n",
              "      <td>0.99125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.178737</td>\n",
              "      <td>0.99875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.145954</td>\n",
              "      <td>0.99625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.126606</td>\n",
              "      <td>0.99875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.108517</td>\n",
              "      <td>0.99625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.097273</td>\n",
              "      <td>0.99750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.085247</td>\n",
              "      <td>0.99875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.082191</td>\n",
              "      <td>0.99625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.070125</td>\n",
              "      <td>0.99500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.063128</td>\n",
              "      <td>0.99750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.059500</td>\n",
              "      <td>0.99750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.056382</td>\n",
              "      <td>0.99875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.050893</td>\n",
              "      <td>0.99625</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c62abfa-de4e-45ea-a32d-ffd749777db7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2c62abfa-de4e-45ea-a32d-ffd749777db7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2c62abfa-de4e-45ea-a32d-ffd749777db7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mblFGnDap48J"
      },
      "source": [
        "Inspecting the outputs, we can see the loss values going down and the accuracy going up. \n",
        "\n",
        "How's it look (visualize, visualize, visualize)?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqlwnZGJpYNF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "outputId": "df489cac-bcfd-45d9-9c10-e45b4ce09dda"
      },
      "source": [
        "# Plot the loss curves\n",
        "pd.DataFrame(history.history).plot()\n",
        "plt.title(\"Model_8 training curves\")"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Model_8 training curves')"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mofXV4znqmNh"
      },
      "source": [
        "Beautiful. This is the ideal plot we'd be looking for when dealing with a classification problem, loss going down, accuracy going up.\n",
        "\n",
        "> 🔑 **Note:** For many problems, the loss function going down means the model is improving (the predictions it's making are getting closer to the ground truth labels)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QyZA0CYLir5-"
      },
      "source": [
        "### Finding the best learning rate\n",
        "\n",
        "Aside from the architecture itself (the layers, number of neurons, activations, etc), the most important hyperparameter you can tune for your neural network models is the **learning rate**.\n",
        "\n",
        "In `model_8` you saw we lowered the Adam optimizer's learning rate from the default of `0.001` (default) to `0.01`.\n",
        "\n",
        "And you might be wondering why we did this.\n",
        "\n",
        "Put it this way, it was a lucky guess.\n",
        "\n",
        "I just decided to try a lower learning rate and see how the model went.\n",
        "\n",
        "Now you might be thinking, \"Seriously? You can do that?\"\n",
        "\n",
        "And the answer is yes. You can change any of the hyperparamaters of your neural networks.\n",
        "\n",
        "With practice, you'll start to see what kind of hyperparameters work and what don't.\n",
        "\n",
        "That's an important thing to understand about machine learning and deep learning in general. It's very experimental. You build a model and evaluate it, build a model and evaluate it.\n",
        "\n",
        "That being said, I want to introduce you a trick which will help you find the optimal learning rate (at least to begin training with) for your models going forward.\n",
        "\n",
        "To do so, we're going to use the following:\n",
        "* A [learning rate **callback**](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/LearningRateScheduler).\n",
        "   * You can think of a callback as an extra piece of functionality you can add to your model *while* its training.\n",
        "* Another model (we could use the same ones as above, we we're practicing building models here).\n",
        "* A modified loss curves plot.\n",
        "\n",
        "We'll go through each with code, then explain what's going on.\n",
        "\n",
        "> 🔑 **Note:** The default hyperparameters of many neural network building blocks in TensorFlow are setup in a way which usually work right out of the box (e.g. the [Adam optimizer's](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) default settings can usually get good results on many datasets). So it's a good idea to try the defaults first, then adjust as needed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3g05waDawhsZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05feebb1-1a13-43e6-8740-4fae9bfc1c26"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create a model (same as model_8)\n",
        "model_9 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_9.compile(loss=\"binary_crossentropy\", # we can use strings here too\n",
        "              optimizer=\"Adam\", # same as tf.keras.optimizers.Adam() with default settings\n",
        "              metrics=[\"accuracy\"]) \n",
        "\n",
        "# Create a learning rate scheduler callback\n",
        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-4 * 10**(epoch/20)) # traverse a set of learning rate values starting from 1e-4, increasing by 10**(epoch/20) every epoch\n",
        "\n",
        "# Fit the model (passing the lr_scheduler callback)\n",
        "history = model_9.fit(X_train, \n",
        "                      y_train, \n",
        "                      epochs=100,\n",
        "                      callbacks=[lr_scheduler])"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "25/25 [==============================] - 2s 3ms/step - loss: 0.7049 - accuracy: 0.5075 - lr: 1.0000e-04\n",
            "Epoch 2/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7043 - accuracy: 0.5075 - lr: 1.1220e-04\n",
            "Epoch 3/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7037 - accuracy: 0.5063 - lr: 1.2589e-04\n",
            "Epoch 4/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7030 - accuracy: 0.5050 - lr: 1.4125e-04\n",
            "Epoch 5/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7023 - accuracy: 0.5050 - lr: 1.5849e-04\n",
            "Epoch 6/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7017 - accuracy: 0.5050 - lr: 1.7783e-04\n",
            "Epoch 7/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7009 - accuracy: 0.4988 - lr: 1.9953e-04\n",
            "Epoch 8/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7002 - accuracy: 0.4925 - lr: 2.2387e-04\n",
            "Epoch 9/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6995 - accuracy: 0.4837 - lr: 2.5119e-04\n",
            "Epoch 10/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6987 - accuracy: 0.4525 - lr: 2.8184e-04\n",
            "Epoch 11/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6980 - accuracy: 0.4888 - lr: 3.1623e-04\n",
            "Epoch 12/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6971 - accuracy: 0.4963 - lr: 3.5481e-04\n",
            "Epoch 13/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6965 - accuracy: 0.5000 - lr: 3.9811e-04\n",
            "Epoch 14/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6957 - accuracy: 0.5000 - lr: 4.4668e-04\n",
            "Epoch 15/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6953 - accuracy: 0.5038 - lr: 5.0119e-04\n",
            "Epoch 16/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6945 - accuracy: 0.5050 - lr: 5.6234e-04\n",
            "Epoch 17/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6940 - accuracy: 0.5013 - lr: 6.3096e-04\n",
            "Epoch 18/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6934 - accuracy: 0.5063 - lr: 7.0795e-04\n",
            "Epoch 19/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6929 - accuracy: 0.5063 - lr: 7.9433e-04\n",
            "Epoch 20/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6925 - accuracy: 0.4737 - lr: 8.9125e-04\n",
            "Epoch 21/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6918 - accuracy: 0.4762 - lr: 0.0010\n",
            "Epoch 22/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6910 - accuracy: 0.4900 - lr: 0.0011\n",
            "Epoch 23/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6904 - accuracy: 0.4963 - lr: 0.0013\n",
            "Epoch 24/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6897 - accuracy: 0.5100 - lr: 0.0014\n",
            "Epoch 25/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6884 - accuracy: 0.5138 - lr: 0.0016\n",
            "Epoch 26/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6870 - accuracy: 0.5263 - lr: 0.0018\n",
            "Epoch 27/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6851 - accuracy: 0.5175 - lr: 0.0020\n",
            "Epoch 28/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6826 - accuracy: 0.5200 - lr: 0.0022\n",
            "Epoch 29/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6807 - accuracy: 0.5475 - lr: 0.0025\n",
            "Epoch 30/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6772 - accuracy: 0.6137 - lr: 0.0028\n",
            "Epoch 31/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6735 - accuracy: 0.5337 - lr: 0.0032\n",
            "Epoch 32/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6687 - accuracy: 0.5250 - lr: 0.0035\n",
            "Epoch 33/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6621 - accuracy: 0.6000 - lr: 0.0040\n",
            "Epoch 34/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6546 - accuracy: 0.6175 - lr: 0.0045\n",
            "Epoch 35/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6450 - accuracy: 0.6762 - lr: 0.0050\n",
            "Epoch 36/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6364 - accuracy: 0.6150 - lr: 0.0056\n",
            "Epoch 37/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6167 - accuracy: 0.6850 - lr: 0.0063\n",
            "Epoch 38/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6000 - accuracy: 0.6938 - lr: 0.0071\n",
            "Epoch 39/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5782 - accuracy: 0.7425 - lr: 0.0079\n",
            "Epoch 40/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5459 - accuracy: 0.7538 - lr: 0.0089\n",
            "Epoch 41/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.4846 - accuracy: 0.7962 - lr: 0.0100\n",
            "Epoch 42/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.4177 - accuracy: 0.8662 - lr: 0.0112\n",
            "Epoch 43/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3558 - accuracy: 0.9125 - lr: 0.0126\n",
            "Epoch 44/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2758 - accuracy: 0.9588 - lr: 0.0141\n",
            "Epoch 45/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2315 - accuracy: 0.9525 - lr: 0.0158\n",
            "Epoch 46/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1820 - accuracy: 0.9712 - lr: 0.0178\n",
            "Epoch 47/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1542 - accuracy: 0.9675 - lr: 0.0200\n",
            "Epoch 48/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1227 - accuracy: 0.9762 - lr: 0.0224\n",
            "Epoch 49/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1084 - accuracy: 0.9737 - lr: 0.0251\n",
            "Epoch 50/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0981 - accuracy: 0.9762 - lr: 0.0282\n",
            "Epoch 51/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1140 - accuracy: 0.9638 - lr: 0.0316\n",
            "Epoch 52/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0725 - accuracy: 0.9812 - lr: 0.0355\n",
            "Epoch 53/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0567 - accuracy: 0.9837 - lr: 0.0398\n",
            "Epoch 54/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0695 - accuracy: 0.9800 - lr: 0.0447\n",
            "Epoch 55/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0455 - accuracy: 0.9912 - lr: 0.0501\n",
            "Epoch 56/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1560 - accuracy: 0.9500 - lr: 0.0562\n",
            "Epoch 57/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2902 - accuracy: 0.8925 - lr: 0.0631\n",
            "Epoch 58/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2339 - accuracy: 0.9212 - lr: 0.0708\n",
            "Epoch 59/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1639 - accuracy: 0.9438 - lr: 0.0794\n",
            "Epoch 60/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3406 - accuracy: 0.8900 - lr: 0.0891\n",
            "Epoch 61/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1991 - accuracy: 0.9225 - lr: 0.1000\n",
            "Epoch 62/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.0945 - accuracy: 0.9712 - lr: 0.1122\n",
            "Epoch 63/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.0424 - accuracy: 0.9925 - lr: 0.1259\n",
            "Epoch 64/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.0487 - accuracy: 0.9837 - lr: 0.1413\n",
            "Epoch 65/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.0634 - accuracy: 0.9762 - lr: 0.1585\n",
            "Epoch 66/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.5936 - accuracy: 0.8425 - lr: 0.1778\n",
            "Epoch 67/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.2098 - accuracy: 0.9200 - lr: 0.1995\n",
            "Epoch 68/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.3636 - accuracy: 0.8525 - lr: 0.2239\n",
            "Epoch 69/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.2313 - accuracy: 0.9100 - lr: 0.2512\n",
            "Epoch 70/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.5824 - accuracy: 0.7400 - lr: 0.2818\n",
            "Epoch 71/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.4876 - accuracy: 0.7763 - lr: 0.3162\n",
            "Epoch 72/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.4564 - accuracy: 0.8012 - lr: 0.3548\n",
            "Epoch 73/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.6045 - accuracy: 0.6388 - lr: 0.3981\n",
            "Epoch 74/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.6803 - accuracy: 0.5188 - lr: 0.4467\n",
            "Epoch 75/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.6969 - accuracy: 0.5038 - lr: 0.5012\n",
            "Epoch 76/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7014 - accuracy: 0.5013 - lr: 0.5623\n",
            "Epoch 77/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7068 - accuracy: 0.5088 - lr: 0.6310\n",
            "Epoch 78/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7154 - accuracy: 0.5138 - lr: 0.7079\n",
            "Epoch 79/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7120 - accuracy: 0.5113 - lr: 0.7943\n",
            "Epoch 80/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7040 - accuracy: 0.4963 - lr: 0.8913\n",
            "Epoch 81/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7118 - accuracy: 0.5088 - lr: 1.0000\n",
            "Epoch 82/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7132 - accuracy: 0.5412 - lr: 1.1220\n",
            "Epoch 83/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7262 - accuracy: 0.4812 - lr: 1.2589\n",
            "Epoch 84/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7054 - accuracy: 0.5038 - lr: 1.4125\n",
            "Epoch 85/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7296 - accuracy: 0.4963 - lr: 1.5849\n",
            "Epoch 86/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7638 - accuracy: 0.5038 - lr: 1.7783\n",
            "Epoch 87/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7673 - accuracy: 0.5013 - lr: 1.9953\n",
            "Epoch 88/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7591 - accuracy: 0.5088 - lr: 2.2387\n",
            "Epoch 89/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7668 - accuracy: 0.5312 - lr: 2.5119\n",
            "Epoch 90/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7854 - accuracy: 0.4938 - lr: 2.8184\n",
            "Epoch 91/100\n",
            "25/25 [==============================] - 0s 5ms/step - loss: 0.7992 - accuracy: 0.5163 - lr: 3.1623\n",
            "Epoch 92/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7398 - accuracy: 0.4963 - lr: 3.5481\n",
            "Epoch 93/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7391 - accuracy: 0.5088 - lr: 3.9811\n",
            "Epoch 94/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7574 - accuracy: 0.4938 - lr: 4.4668\n",
            "Epoch 95/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.8169 - accuracy: 0.4863 - lr: 5.0119\n",
            "Epoch 96/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.7793 - accuracy: 0.4638 - lr: 5.6234\n",
            "Epoch 97/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.8047 - accuracy: 0.5013 - lr: 6.3096\n",
            "Epoch 98/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.9616 - accuracy: 0.4963 - lr: 7.0795\n",
            "Epoch 99/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.9511 - accuracy: 0.4913 - lr: 7.9433\n",
            "Epoch 100/100\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.8580 - accuracy: 0.4613 - lr: 8.9125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KKln6dnbNPVR"
      },
      "source": [
        "Now our model has finished training, let's have a look at the training history."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCd12upnyO4y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "outputId": "0d170294-048d-429a-eead-7500434da927"
      },
      "source": [
        "# Checkout the history\n",
        "pd.DataFrame(history.history).plot(figsize=(10,7), xlabel=\"epochs\");"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9OkUFgZSx6hP"
      },
      "source": [
        "As you you see the learning rate exponentially increases as the number of epochs increases.\n",
        "\n",
        "And you can see the model's accuracy goes up (and loss goes down) at a specific point when the learning rate slowly increases.\n",
        "\n",
        "To figure out where this infliction point is, we can plot the loss versus the log-scale learning rate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fnEklbYyGBG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "outputId": "df02ed46-b3f0-4633-9c48-87c53052d07c"
      },
      "source": [
        "# Plot the learning rate versus the loss\n",
        "lrs = 1e-4 * (10 ** (np.arange(100)/20))\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.semilogx(lrs, history.history[\"loss\"]) # we want the x-axis (learning rate) to be log scale\n",
        "plt.xlabel(\"Learning Rate\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Learning rate vs. loss\");"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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1a9dK8seNr127Vnv27JHkH7O75JJLgvf/0Y9+pB07dujnP/+5Nm3apD/+8Y/6xz/+oZ/+9KfRWD4AAACAQSKqhdOqVas0a9YszZo1S5K0ePFizZo1S7fddpsk6cCBA8EiSpLGjBmjV199VW+99ZZmzJih++67T4899ljIZzgBAAAAQF/EzDlOA4VznAAAAABICXyOEwAAAABEA4UTAAAAAPSAwgkAAAAAekDhBAAAAAA9oHACAAAAgB5QOAEAAABADyicAAAAAKAHFE4AAAAA0AMKJwAAAADoAYUTAAAAAPSAwgkAAAAAekDhBAAAAAA9oHACAAAA0Gv/3XJQOw42RHsZA4bCCQAAAECvbC2v16VPrNTX/9/7+senpdFezoCgcAIAAADQK7uqmiRJLo9PP3/hc9384hdyerxRXlVkUTgBAAAA6JXqRqckaUhakiwW6e8r9+j8hz/W/prmKK8sciicAAAAAPRKZYNLkrRgUqH+fNnRyk5N0rrSGp35+w/00fbKKK8uMiicAAAAAPRKVaBwystw6KQjh+rf1x2vyUVZqmp06buPfaJH3tsuwzCivMrwonACAAAA0CtVgVG9/IxkSVJxbppevPpYnXvUSPkM6e7XNunWf66P5hLDjsIJAAAAQK+0dpySg7elJNl077em685vTpEkPfPJHrW4EycwgsIJAAAAQK9UNfoLp9x0R7vbLRaLLj5mtDJT7DIMaXcgfS8RUDgBAAAA6JWqBv+oXl56cofPWSwWjclPlyTtqmoc0HVFEoUTAAAAgJD5fIaqAx2n/AxHp/cZnRconCopnAAAAAAMQnUtbnl8/sS83E46TpI0Ji9NEh0nAAAAAIOUeYZTVopdyfbOy4kSc1Svkj1OAAAAAAYhc0wvr4sxPanNqB4dJwAAAACDUXfBECYzHOJAbYuaXYkRSU7hBAAAACBklY0dz3A63JC0JGWl2CVJu6sTo+tE4QQAAAAgZMGOUzejeu0iyRNknxOFEwAAAICQVQXCIbob1ZMSb58ThRMAAACAkAXDIXoonFqT9SicAAAAAAwylSGM6knSmHz/WU47KZwAAAAADDZVIYRDSFJJYFRvdxV7nAAAAAAMMmY4RH4PHSezcCqrS4xIcgonAAAAACHxeH2qaXZLknJ72OM0JD1Z2alJkhIjIILCCQAAAEBIDjW5ZRiSxSINSeu+cJISKyCCwgkAAABASKoa/WN6uWnJslktPd5/TJ4/IGJXAuxzonACAAAAEJLgGU49BEOYgmc50XECAAAAMFgEo8jTuw+GMI0JjOrtZI8TAAAAgMHCPPw2N8SOk7nHaTeFEwAAAIDBwhzVy+8hUc9UEtjjVF7nVJPLE7F1DQQKJwAAAAAhMcMh8no4w8mUk5asnLRAJHllfAdEUDgBAAAACEllL8MhpNaDcOP9LCcKJwAAAAAhqQqGQ4ReOJkBERROAAAAAAYFMxwi1FE9SRptnuUU55HkFE4AAAAAQhI8x6kvHSf2OAEAAABIdC1ur+qd/mS83nSczD1O8X6WE4UTAAAAgB6ZY3pJNouyUuwhP84snA7WO9XojN9IcgonAAAAAD0yx/Ry05NlsVhCflx2WpKGmJHkcdx1onACAAAA0KPgGU7poY/pmUoSYJ8ThRMAAACAHlX14Qwn05gEOMuJwgkAAABAj8yOU34vgiFMrR0nCicAAAAACaztHqfeCp7lRMcJAAAAQCKr7M+oXqDjtJM9TgAAAAASWbU5qteHcIjRgT1OlQ1ONcRpJDmFEwAAAIAeVTX2veOUnZoUHPGL131OFE4AAAAAetSaqtf7jpMklcT5PicKJwAAAADdMgxDlQ3mOU697zhJ8Z+sR+EEAAAAoFtNLq+cHp+kvo3qSW3PcorPgAgKJwAAAADdMsf0UpNsSku29+k5RtNxAgAAAJDIKgOJen3tNkltO04UTgAAAAASUH+DISSpJN8fDlHZ4FJ9izss6xpIFE4AAAAAulXVz2AIScpMSVJ+oGO1Ow73OVE4AQAAAOhW8AynfhROUutBuDvjcJ8ThRMAAACAboVjVE+SSvLiNyCCwgkAAABAt6oC4RD5/QiHkKQx+eYhuIzqAQAAAEgwZscpt5+jesFDcOMwWY/CCQAAAEC3Ks1wCEb1AAAAAKBz1WEKhzA7TlWNLtXFWSR53479BQAAADAo+HxGsHDK72fHKcNhV0lemtKS7appdCsrJSkcSxwQFE4AAAAAulTX4pbHZ0jq/x4nSXr3Zyf3+zmigVE9AAAAAF2qDARDZKbYlWwfvOXD4P3KAQAAAPSoqsGMIu/fmF68o3ACAAAA0KVwBUPEOwonAAAAAF2qNAunfh5+G+8onAAAAAB0yRzVy01nVA8AAAAAOlXVYEaR03ECAAAAgE5VNfo7TuxxAgAAAIAumB2nPFL1AAAAAKBzVYRDSKJwAgAAANANMxwij3AIAAAAAOjI4/XpUJNbEh0nCicAAAAAnTKLJotFGpJG4QQAAAAAHZiJerlpybJZLVFeTXRROAEAAADoVGui3uDuNkkUTgAAAAC6UBkIhsgd5Gc4SRROAAAAALrAGU6tKJwAAAAAdKo6cIZTPh0nCicAAAAAnTPDIeg4UTgBAAAACc3j9WlzWb0Mw+j1YysDo3rscaJwAgAAABLao+/v1MIH3tNTH+3q9WOrAuEQ+aTqUTgBAAAAieztjeWSpGc+2dPrrlNVI+EQJgonAAAAIEE5PV59sa9WkrS1okEbDtT16vHVZqoeo3oUTgAAAECi+nJ/nVweX/DP/1y7P+THtri9qnd6JNFxkiicAAAAgIS1evchSVJWil2S9K+1++X1hTauZ0aRJ9kswccPZlEvnB588EGVlJQoJSVF8+bN08qVK7u9/wMPPKAjjzxSqampKi4u1k9/+lO1tLQM0GoBAACA+LF6j79wuvy4McpKsausrkWf7KwK6bFVbRL1LBZLxNYYL6JaOC1dulSLFy/WkiVLtHr1as2YMUMLFy5URUVFp/f/29/+pptuuklLlizRxo0b9fjjj2vp0qX6n//5nwFeOQAAABD7Vu+ukSQdMzZPp08rkiT9c01o43qV5hlO6YzpSVEunO6//35dccUVuvzyyzV58mQ99NBDSktL0xNPPNHp/T/66CMdd9xx+va3v62SkhKdeuqpuuiii3rsUgEAAACDzf6aZpXVtchmtWhGcba+OXOEJOm19QfU4vb2+Pg31pdJkkblpkV0nfEiaoWTy+XSZ599pgULFrQuxmrVggULtGLFik4fc+yxx+qzzz4LFko7duzQa6+9ptNPP31A1gwAAADEi88C+5smFWUqLdmueWNyVZSdovoWj97d3PmEl2nHwQY999leSdIVXxkT8bXGg6jt8qqsrJTX61VhYWG72wsLC7Vp06ZOH/Ptb39blZWVOv7442UYhjwej370ox91O6rndDrldDqDf66r610EIwAAABAuzS6vbnn5C6Um2fSrRVMjunfI3N80e9QQSZLVatFZM4br4fd26OU1+/X1qUVdPvb+t7bI6zN0ysShmj06N2JrjCdRD4fojXfffVd33323/vjHP2r16tV68cUX9eqrr+qXv/xll4+55557lJ2dHfwoLi4ewBUDAAAAfi1ur658epVeXL1Pz3yyR6XVzRF9PTNR76jRQ4K3meN6/9lUodpmd6ePW7+vVv/+/IAk6YaFR0Z0jfEkaoVTfn6+bDabysvL291eXl6uYcOGdfqYW2+9VRdffLF+8IMfaNq0aTr77LN1991365577pHP5+v0MTfffLNqa2uDH6WlpWH/WgAAAIDuuDw+Xf3Mar2/tTJ422d7qiP2ei1ur77c75+0OmpUa+E0qShTEwoz5PL6tGz9gU4fe9+bmyVJZ80YrklFWRFbY7yJWuGUnJys2bNna/ny5cHbfD6fli9frvnz53f6mKamJlmt7Zdss9kkSYbReR69w+FQVlZWuw8AAABgoLi9Pl3399X6z6YKOexWHTPWP/pmJt5Fwud7a+XxGSrIdGjkkNTg7RaLJdh1ermTdL1Pd1Xrnc0HZbNa9NOvTYjY+uJRVEf1Fi9erEcffVRPPfWUNm7cqKuuukqNjY26/PLLJUmXXHKJbr755uD9zzzzTP3pT3/Ss88+q507d+qtt97SrbfeqjPPPDNYQAEAAACxwusztPgf6/TGl+VKtln16CVzdMn8Ekmt4Q2RYO5vOmpUTod9VGfNGC5J+nhnlcpqW89DNQxDv13mzxo4f06xxuSnR2x98SiqRwBfcMEFOnjwoG677TaVlZVp5syZWrZsWTAwYs+ePe06TL/4xS9ksVj0i1/8Qvv27VNBQYHOPPNM3XXXXdH6EgAAAIBO+XyGfvb8Or2ybr+SbBb96btH6SsTClRe5y9WNpXVqdHpUboj/D+Sm/ubZrfZ32Qqzk3TnNFDtGr3If1r3T5d+ZUjJEnvbjmoT3cdUrLdqh+fMi7sa4p3FqOrGbcEVVdXp+zsbNXW1jK2BwAAgIjw+Qz9z0tf6NlPS2WzWvTgt2e1S7E77tf/0b6aZv3tB/N07Lj8sL62YRg6+q63Vdng0vM/mq85JR1T8Z7+eLdufXm9pgzP0qs/PkE+n6Ez//CBvtxfpytOGKNbzpgc1jXFqt7UBnGVqgcAAADEOsMwdMcrX+rZT0tltUi/u2Bmh+hvM+kuEuN6pdXNqmxwKclm0dQR2Z3e54xpRbJbLfpyf522VdTrtfUH9OX+OmU47LrqJLpNnaFwAgAAAMLouVV79dSK3bJYpP89b0ZwT1FbR43KkdS6FymczLS+KcOzlZLUeQ5AbnqyTpxQIEl6/rN9uv/NLZKkH5wwRrnpyWFfUyKgcAIAAADCZGdlo25/5UtJ0g2nHqlzZ4/s9H7m3qPVe2rk84V354yZ1tfZ/qa2vjnLn6732Ps7tKOyUUPSkvT948eEdS2JhMIJAAAACAOXx6frn12jJpdXx4zN1Y9OPKLL+04qylJKklW1zW7tqGwM6zrM8b+25zd15muTCpWebJMnULhdc/I4ZaYkhXUtiYTCCQAAAAiD+9/aos/31io7NUm/u2CmbFZLl/dNslk1fWSOpNYEvHBodHq0qSxw8O3onG7vm5ps08IpwyRJw7JS9N1jRodtHYmIwgkAAADop4+2V+rh97ZLkn5z7jQVZaf28IjWjlA49zmt21sjnyENz04JaQ1XnzxOc8fk6jfnTe9yPxT8onqOEwAAABDvDjW6tHjpOhmGdNHc4g4Jel2ZHYFkPbN7dVQP+5tM44Zm6B8/nB+2109kdJwAAACAPjIMQze9+LnK6lo0tiBdt34j9POPzGS9rRUNqm12h2U9q/fUBJ47tMIJoaNwAgAAAPro2U9L9caX5UqyWfR/F85SWnLoA115GQ6V5KVJktaEYVzPMIzg2F+oHSeEjsIJAAAA6INtFQ2685UNkqSfLTyyy8Nmu3NUm1jy/tpR2aiaJrccdqsmF2X1+/nQHnucAAAAMOjsq2nWh9sq9eG2Su042KjTpxXpByeMUZIttL5CdaNL1z+7Rs1ur44fl68fHD+2T+s4atQQvbh6X1iS9cy9UtNHZivZTn8k3CicAAAAkPBqm9xasaNSH2yr1IfbqrTzsLOTvthXq5fW7NVdZ0/T0SW5XT6P2+vTX1bs1v97e4vqWjwakpak+86fIWs30ePdMQMi1uw5JK/P6DbCvCdrGNOLKAonAAAAJLS/r9yjW176QoFzXiVJVos0ozhHx4/LV256sn7/n23aUt6gbz20QhfMKdZNp03UkPTkds/zzuYK/fLfG7TjoL/omlSUpd+cO02FWSl9XtuEwkxlOOxqcHq0pbxek/oxYrd6d40kgiEihcIJAAAACcvnM/SH/2yTz5DG5qfrKxMKdNy4fM0bm6uslKTg/c6eNUK/WbZJf19ZqqWrSvXmhjL9z+mTdN7skdp+sFG/enWD3t18UJKUl56sGxYeqfPnFPerQyRJNqtFM4tz9MG2Sn22+1CfC6e6Fre2VNRLonCKFAonAAAAJKwPt1dqX02zslLseu36E7o85DUnLVn3nDNd5x41Ure8tF6by+v1s+c/1+Mf7NS2igZ5fIaSbBZddmyJrjtlfLuiq7+OGuUvnFbvOaTvHjO6T8+xdk+NDEMalZumgkxH2NaGVhROAAAASFjPfloqyd9R6qpoamtOSa7+/ePj9cQHO/XA21u1qczfxTll4lDdcsYkjS3ICPsag8l6/QiIMIMhZrO/KWIonAAAAJCQqhtdevPLMknS+UcXh/y4JJtVPzzxCJ0xvUjPfLJH88fm6SsTCiK1TM0q9hc7u6qaVNXgVF5G7zpGNU0u/WOVv0CkcIoccgoBAACQkF5cvVdur6FpI7I1ZXjvz1gaOSRNN359YkSLJknKTkvS+KH+TlZvz3MyDEM/f/5zHaht0dj8dJ09a0QEVgiJwgkAAAAJyDAMLQ2M6V3Qi25TtJiBDqv39G5c768f79abG8qVbLPq/y6apXQHA2WRQuEEAACAhLN6T422VjQoJcmqs2YOj/ZyemSO2H3Wi31OGw/U6ZevbpQk3XTaRE0d0fuuGkJH4QQAAICEs/TTPZKkM6YND2sCXqQcNTpHkvT53hq5vb4e79/k8ui6v6+Ry+PTKROH6vLjSiK7QFA4AQAAILHUt7j1yroDkqQL58b+mJ4kjc3PUHZqklrcPm08UNfj/e98ZYO2VTSoMMuh//3WDFks/TtPCj2jcAIAAEBC+ffnB9Ts9mpsQbrmxEnKnNVq0VGjciT1PK73yrr9evbTUlks0u8umKnc9OQBWCEonAAAAJBQzLObLjy6OK46Ma0BETVd3qe0ukn/8+IXkqRrTx6nY4/IH4ilQRROAAAASCAbD9RpXWmN7FaLzjlqZLSX0yvBgIhd1TpQ26y6Fre8PiP4ebfXp+v+vkb1To/mjB6i608ZH62lDkrkFQIAACBhmBHkX5tcqPxeHiQbbTOKc2S1SPtrWzT/nv8Eb09NsindYZPNalF5nVNZKXY9cOFM2W30QAYS320AAAAkhBa3Vy+t2ScpPs5uOly6w65Ljy1RdmqS7NbWEcNmt1eVDS6V1zklSb85d7pGDkmL1jIHLTpOAAAASAhvfFmm2ma3hmen6ITxBdFeTp8sOXOKlpw5RYZhyOX1qdHpVaPTowanR41OjzJTknTksMxoL3NQonACAABAQjDH9M6bUyybNX5CITpjsVjksNvksNtIzYsRjOoBAAAg7u2uatRH26tksUjfmh1foRCIDxROAAAAiHtmt+n4cfkqzmX/D8KPwgkAAABxrarBqadX7JYkXTR3VJRXg0RF4QQAAICYUNfi1pV/WaWLHvlYDU5PyI/7/X+2qd7p0eSiLH19yrAIrhCDGYUTAAAAoq62ya3vPvaJ3txQrhU7qnTvG5tDetzOykb99WN/t+mWMybJGuehEIhdFE4AAACIqupGly569GN9vrdWmQ5/6PNTK3bps92Henzsb5dtksdn6KQjC3TcuPxILxWDGIUTAAAAoqaywamLHvlYGw7UKT8jWc9fdazOPWqkDEO68YXP5fR4u3zsZ7ur9fr6Mlkt0s2nTRrAVWMwonACAABAVFTUtejCRz7W5vJ6Dc106Nkr5+vIYZn6xRmTlJeerG0VDfrTu9s7faxhGLrr1Y2SpG/NLuZQWEQchRMAAMAgZhiGKupb5PH6BvR1D9Q264JHPta2igYVZado6Q/na9zQDEnSkPRkLTlriiTpwXe2aWt5fYfHL1tfptV7apSaZNPiUycM6NoxONmjvQAAAABEx+6qRt384hf6aHuVMh12HT0mV8eMzdX8sfmaPDxLtggFLew91KRvP/qJ9lQ3aUROqp698pgOZy+dOb1I/1yzT8s3VejGFz7X8z86Nhj84PL49JtlmyRJV5wwRoVZKRFZJ9AWhRMAAMAg4/H69PgHO/W7t7eoxe3vNNU7PfrPpgr9Z1OFJCkzxa55Y3J1zNg8LZo1QvkZjl69xvp9tdpcVq/y+hZV1DlVXteisrrW33t8hkblpulvV8zTyCEdD6y1WCz65aKp+vj+/2r1nhr99ZPdumR+iSTpb5/s1q6qJuVnJOvKE4/o3zcDCJHFMAwj2osYSHV1dcrOzlZtba2ysrKivRwAAIABtX5frW568XOt31cnSTpuXJ5+tWiaGp0erdhepY93VGnlzmrVtzlHKTPFrhtOPVLfmTdKdlv3Oz1Kq5t016sbtezLsm7vN3FYpp68fK6GZXffLfrLil267Z9fKj3ZprcWn6iMFLtO/O07OtTk1q8WTdV3jxkd4lcOdNSb2oDCCQAAYBBocXv1wNtb9ej7O+T1GcpOTdItZ0zSt2aPlMXSfiTP6zO0YX+dVuyo1Mtr9mvDAX+RNakoS7/85hTNKcnt8PzNLq/+9N/tevi/2+X0+GSzWnTM2FwNy0pVYZZDhVkpgQ//74dlpYR05pLPZ+hbD6/QZ7sP6ZSJQzW+MFMP/Xe7jihI1xs/+UqPhRzQHQqnblA4AQCAwcTl8emNL8t035ubtauqSZJ0xvQiLTlzsoZm9rw3yOsz9LeVe3TvG5tV2+yWJJ171EjddNpEFWQ6ZBiGXvuiTHe9ukH7a1skSccekafbz5qiCYXhSbrbWl6vM/7vA7m8Plktks+QHr1kjr42uTAsz4/Bi8KpG7FUOL36+QG9s7lC6ck2pTns/l+T7Up3tP81LdkW+LAHf022868rAACga7urGvW3lXv0/Kq9qmp0SZKGZaXol4um9qngqGpw6rfLNmvpqlJJ/vG9q046Qu9tOaiPd1RLkkbkpOoXZ0zS16cO69DF6q//9/ZW/e7tLZKkeWNy9eyVx4T9NTD4UDh1I5YKp7tf26hH3tvRp8farRalO+zKcNiVmWIP/j4jxa5Mh//P6ck2/6+Bzx1+W7oj8Ptke8RScwAAwMBxe316a0O5/vbJHn2wrTJ4e2GWQxccPUo/OGGMslKS+vUaq/cc0m3/XB/cIyVJDrtVV510hH74lSOUmmzr1/N3xeXxadGDH2pbRYOev2q+po/MicjrYHChcOpGLBVOK7ZXaU3pITU5vWp0eVp/dXnV6PT/2uQyf/X/3u2NzH+ulCRrsLjKTLEr05Hk/zUlSVmp/l8zA4WZvxCzKcORpHSHzV+wBT7nsEfmYgkAADpnGIY+31urf3++Xy+t2a/KBqckyWKRvjK+QN+eN0qnTBwa1r1AXp+hv6/coz+9u10zi3N08+kTO03GC7dGp0f1LZ4eAyWAUFE4dSOWCqe+cHt9wSLKvHg0Or1qcLpV3+JRg9OjhhaPGgKfb3J61eD0qNHlUYMzUJA5PYHbvPL6wvufP9lmVUaKvV0xZRZc/u6YWZC1/tnsmrW9jVFEAAC6ZhiGvtxfp39/fkCvfrFfpdXNwc/lZzh0wdEjdeHRozqcjQSgvd7UBpzjFGeSbFZlp1qVndq/Nrvkv+g6Pb5gh6vR5S+66ls8qmvxF2L+D3fwV7P4anB6gr82BLpjkuTy+lTd6FJ1YJa6r5LtVmWl2JWVkqSs1CRlBz6yUu3B35sdr7Rk/whiamAMMS3ZFuyeJZG0AwBIIPtqmvW3T3br1c8PBIMeJCk1yaZTJg3VN6YP1ymThvL3HxABFE6DmMViUUqSTSlJNuWmJ/frubw+I1h4NQQ6YWb3y19wtd7W9s/1h30+WIB5fKpscKmyoX8FmH8EMSnYzcoIFFbm152SZFVKkk2pgd+nJrfvkLXtmKU77EpLshF7CgCIin01zTrr9x8Egx5Skqz66sShOmPacH114tCI7S0C4EfhhLCwWS3+7lA/N5x6vD41Or3Bjlddi1u1zf6PusCH+ecGp1fNbv+oon90MfCryyuXx38Keovbpxa3MzjvHQ7JNqvSHDalJfm7XGnJ9sCvNqW3+X1q4M9pyTY57FY5kgK/2m1yJFmDvzeLt5Qkm1Lsrb8nsAMAYGp2eXXlX1apqtGlCYUZuubkcVowqVDpDn6UAwYK/7chpthtVmWnWZWd1r8CzO31BfeAmeOEDYFCrNnlVYvbqxaPz/+r2/zVH8IR7JQ5PWpwuoPji57AfjCX1ydXk081cofjS+5Sks2i1CT/2GFam0REM6o+w2FXVmqbEI/AaGNmSqBDFija0h12OexWIlsBIE4ZhqGbX/xcX+6vU156sv58+VyNyEmN9rKAQYfCCQkpyWZVTlqyctL6N4JoMveDNbu8anJ71dwm7bC5Teph623+zpf5e6fHF/jwF2pOj1dOt08t5q+BQs7slEmS22vI7fWorsXT7/VbLfIXUoEI+nbjiG1GGbNTk1SQ6dDQTIeGZjlUkJmirBQ7RRcARNHjH+zUy2v3y2a16MHvHEXRBEQJhRMQgrb7wYZE8HV8Pn+B1uL2qtndWpA1OFvj6hsDAR31ztbgjrrmQICH0626Zk8w7KPF7S/EfIb893d6JPVubDElyaqCTIcKM1NUmJWioVkODctq//th2SlKS+ZyAgDh9sHWSt392kZJ0q1nTNIxY/OivCJg8OInHSCGWK0WpQb2R4WjQPP6DH8B5jQ7YP4xxEZXazhHMMLe6dGhJpcq6pyqqG9RRb1T9S3+4qu0urld1G1n8jMcKslLU0l+ukry0jQ6L11j8tM1Oi9Nmf3c+wYAg1FpdZOu/ftq+QzpvNkjdemxJdFeEjCoUTgBCcxmtQTTBPui2eXVwXp/IVVe51R5XYvK61tUEfh9WZ3/9w1Ojyob/CEcq3Yf6vA8I3JSNXl4liYXZQV/HTkklRFAAOhCk8ujK5/+TDVNbs0Yma1fLZrKNROIMgonAF1KTbZpVF6aRuV1f4BiXYtbuyubtKuqUburGrWzskm7qxq1q6pJlQ1O7atp1r6aZr21oTz4mMwUuyYXZWlsQUagQ5WmUbn+DhUpUQAGM8Mw9PPnP9fGA3XKz3DooYtnKyWJqHEg2vjpBEC/ZaUkadrIbE0bmd3hc7XNbm06UKcNB+r05f46bdhfp60V9apv8eiTndX6ZGd1h8eYY3/TRmbr+HH5mjc2r89dMwCINw+/t0P//vyA7FaL/vTdo1SUTRgEEAsshmEY0V7EQKqrq1N2drZqa2uVlZUV7eUAg5LL49O2igZtPFAX6FI1aXe1v0tV09Qx5t1utWjWqBwdNy5fx4/L14ziHCVxEDGABNTs8mrGHW/K5fXpV4um6rvHjI72koCE1pvagH/CBTDgku1W/16n4R0vULXNbu2patKOygZ9vKNaH26r1J7qJn2665A+3XVID7y9VRkOu44Zm6uvTCjQV8YXqCQ/PQpfBQCE35byerm8PuVnJOs780ZFezkA2qBwAhBTslNbx/6+OXOEJGlPVZM+3F6pD7ZV6qNtlTrU5NbbGyv09sYKSdKo3DR9ZUK+vjK+QMeOy2esD0Dc2lxWL0k6clgmYRBAjOGnCwAxzx9QMUoXzR0ln8/QhgN1em/rQb235aA+231Ie6qb9NeP9+ivH++R3WrRvLG5WjRzhE6bVkQRBSCubDILp0K2EwCxhp8oAMQVq9WiqSOyNXVEtq4+aZwanB59vL1K/91yUO9tPajdVU36cFuVPtxWpVv/uV5fmzxM58waoRPG58vOvigAMW5zeZ0kaeKwzCivBMDhKJwAxLUMh10LJhdqweRCSdKuykb9+/P9enHNPu042KhX1u3XK+v2Kz8jWWfOGK7zZo/UlOEd0/8AIBa0HdUDEFtI1QOQkAzD0Od7a/XSmn16Zd1+VTW6gp87cUKBfnzKOM0enRvFFQJAewfrnTr6rrdlsUgb7vi6UpM5uwmINFL1AAx6FotFM4pzNKM4R7ecMUkfbK3U86v3atn6Mv13y0H9d8tBHXtEnq776ngdMzaXTdgAos7sNpXkpVM0ATGIwglAwkuyWXXyxKE6eeJQ7alq0h/f3aYXVu/VR9ur9NH2Kh1dMkTXfXW8ThifTwEFIGo2lfn3Nx1ZyJgeEIvYKQ1gUBmVl6Zfnztd7/7sZF18zGgl26z6dNchXfLESp3zp4+0cmd1tJcIYJBifxMQ2yicAAxKI3JS9ctFU/Xez0/W5ceVyGG3as2eGp3/8Apd9dfPtLuqMdpLBDDIbC73F04k6gGxicIJwKA2LDtFS86covdvPFnfnjdKVov0+voyLbj/v/rVvzeotskd7SUCGAS8PkNbyuk4AbGMwgkAJA3NTNHdZ0/T69d/RV+ZUCC319BjH+zUife+oyc/3Cm31xftJQJIYHuqm9Ti9iklyarReenRXg6ATlA4AUAbRw7L1F++N1dPXn60JhRmqKbJrdtf2aCFD7ynz3YfivbyACSozYFgiPFDM2WzElIDxCIKJwDoxElHDtVrPz5Bd509VXnpydpxsFHnP7xC/7d8q7y+QXX8HYABsIlgCCDmUTgBQBfsNqu+M2+03vnZSTp71gh5fYbuf2uLLnxkhfbVNEd7eQASyKYDBEMAsY7CCQB6kJWSpN9dMFO/u2CGMhx2fbrrkL7+wHv69+f7o700AAmiNVEvK8orAdAVCicACNHZs0bqtR+foJnFOapv8ejav63Rz55bp0anJ9pLAxDHml1e7QocgcCoHhC7KJwAoBdG5aXpuR/N13VfHSeLRXrus736xu8/IDgCQJ9traiXYUh56ckqyHREezkAukDhBAC9lGSz6v879Uj9/YpjVJSdop2VjTrvoY90xytf0n0C0GsEQwDxgcIJAPromLF5ev36E3TuUSNlGNKfP9ylU3/3nt7bcjDaSwMQRzZTOAFxgcIJAPohJy1Z950/Q099b65G5KRqX02zLnlipf6/f6xTTZMr2ssDEAfMwolEPSC2UTgBQBicOKFAb/70K7rs2BJZLNILq/dqwf3/1WtfHJBhcO4TgK61juqRqAfEMgonAAiTdIddt581Rc//6FiNG5qhygaXrn5mtX739tZoLw1AjKpscKqywSmLRZpQmBHt5QDoBoUTAITZ7NFD9OqPj9e1J4+TJP3hP1u1eg+pewA6Msf0RuWmKS3ZHuXVAOgOhRMARIDDbtMNC4/U2bNGyGdIN/xjnZpd3mgvC0CMCY7pFbK/CYh1FE4AEEG3nzlFhVkO7ahs1P++sTnaywEQYzaX1UmSJhaxvwmIdRROABBB2WlJ+s250yVJT3y4Ux/vqIryigDEEhL1gPhB4QQAEXbSkUN14dHFkqSfPb9ODRySC0CSz2doS3mDJM5wAuIBhRMADIBbzpikETmpKq1u1t2vbYz2cgDEgD3VTWp2e+WwW1WSlx7t5QDoAYUTAAyAzJQk/e+3/CN7f/tkj97bcjDKKwIQbWYwxPjCDNmsliivBkBPKJwAYIAce0S+Lp0/WpJ04wufq7bZHeUVAYimzcFEPYIhgHhA4QQAA+jG0yaqJC9NB2pbdOcrG6K9HABRtLk8kKjH/iYgLlA4AcAASku2695vzZDFIr2weq/e2lAe7SUBiJJNBwIdJwonIC5QOAHAAJtTkqsrThgrSbrhuXXaU9UU5RUBGGgtbq92VTVKouMExAsKJwCIgsVfm6AZxTmqbXbryqdXqclFRDkwmGwtb5DPkIakJakg0xHt5QAIAYUTAERBSpJND333KOVnJGtTWb1ufOELGYYR7WUBGCCbysz9TVmyWEjUA+IBhRMARElRdqr++J3ZslstemXdfj32/s5oLwnAAAkm6jGmB8QNCicAiKK5Y3J125mTJUn3vL5RH2ytjPKKAAyEzeX+won9TUD8iHrh9OCDD6qkpEQpKSmaN2+eVq5c2e39a2pqdM0116ioqEgOh0MTJkzQa6+9NkCrBYDwu/iY0Tpv9kj5DOnav69WaTVhEUCi20THCYg7US2cli5dqsWLF2vJkiVavXq1ZsyYoYULF6qioqLT+7tcLn3ta1/Trl279Pzzz2vz5s169NFHNWLEiAFeOQCEj8Vi0a8WTdX0kdmqaXLrh09/pmaXN9rLAhAh1Y0uHax3SpImFFI4AfEiqoXT/fffryuuuEKXX365Jk+erIceekhpaWl64oknOr3/E088oerqar388ss67rjjVFJSohNPPFEzZswY4JUDQHj5wyJmKy89WRsO1OmmFz8nLAJIUPsONUuShmY6lO6wR3k1AEIVtcLJ5XLps88+04IFC1oXY7VqwYIFWrFiRaeP+de//qX58+frmmuuUWFhoaZOnaq7775bXm/X/zLrdDpVV1fX7gMAYtHwnFQ9+J2jZLda9M+1+/X4B4RFAImossHfbcrPIIYciCdRK5wqKyvl9XpVWFjY7vbCwkKVlZV1+pgdO3bo+eefl9fr1WuvvaZbb71V9913n371q191+Tr33HOPsrOzgx/FxcVh/ToAIJyOGZunX5wxSZL02zc2a1dlY5RXBCDcDpqFE+c3AXEl6uEQveHz+TR06FA98sgjmj17ti644ALdcssteuihh7p8zM0336za2trgR2lp6QCuGAB679JjS3TC+Hy5PD7d9q8vGdkDEkxrxyk5yisB0BtRK5zy8/Nls9lUXl7e7vby8nINGzas08cUFRVpwoQJstlswdsmTZqksrIyuVyuTh/jcDiUlZXV7gMAYpnFYtGd35yqZLtV7205qNe+6LwLDwxG1Y0u7T0U38mTlfX+n1kKGNUD4krUCqfk5GTNnj1by5cvD97m8/m0fPlyzZ8/v9PHHHfccdq2bZt8Pl/wti1btqioqEjJyfyrDYDEMSY/XVedeIQk6c5/f6n6FneUVwTEhvMe+kgL7v9vXP8/YXacChjVA+JKVEf1Fi9erEcffVRPPfWUNm7cqKuuukqNjY26/PLLJUmXXHKJbr755uD9r7rqKlVXV+v666/Xli1b9Oqrr+ruu+/WNddcE60vAQAi5qqTjtDovDSV1zn1u7e2Rns5QEzYU9WkFrdPFYE473hEOAQQn6KagXnBBRfo4MGDuu2221RWVqaZM2dq2bJlwcCIPXv2yGptre2Ki4v1xhtv6Kc//ammT5+uESNG6Prrr9eNN94YrS8BACImJcmmO785VZc+sVJPfrRT584eoSnDs6O9LCBqfD5DHp9/z1+LO37POqNwAuJT1A8PuPbaa3Xttdd2+rl33323w23z58/Xxx9/HOFVAUBsOHFCgc6YVqRXvzigW19er+d/dKysVku0lwVEhcvbOqrf4vZ1c8/YVtng3+OUn8k2AyCexFWqHgAMRrd+Y7LSk21avadGS1eRDIrBy92mcHLGacfJ4/XpUFOgcKLjBMQVCicAiHHDslO0+NQjJUm/fn2Tqhrid28H0B9ub2s0f4tnYAonwzC08UBdu6KtP6obXTIMyWqRhqTRcQLiCYUTAMSBS+eP1qSiLNU2u/Xr1zdFezlAVLg8bTtOAzOq9/xne3Xa/3tfD727PSzPZx5+m5vukI2xWyCuUDgBQByw26z61aKpkqTnPturT3dVR3lFwMBr2/UZqI7TrqpGSdKa0pqwPF9wfxOH3wJxh8IJAOLE7NFDdNHcYknSrS+vl89n9PAIILE4PQMfDtHs8r+OWUD1V2U9ZzgB8YrCCQDiyI1fn6gMh12byur10faqaC8HGFDtOk4DFA7RHHid0uomecPwjxUHiSIH4haFEwDEkZy0ZJ1z1AhJ0tMf74ruYoAB5opCx8lM73N7De2vae7385kdJ0b1gPhD4QQAcea7x4yWJL21oVwHavv/gxwQL6LZcZKk3VVN/X4+Dr8F4heFEwDEmQmFmZo3Jlc+Q/r7J3uivRxgwLTrOA1QOETbwikc+5xawyEonIB4Q+EEAHHo4vn+rtPfPy1t98MkkMhc3oGPI292tSmcKsNROAU6ToRDAHGHwgkA4tCpk4epINOhg/VOvbmhLNrLAQZEuwNwB2hUr6XNP0zsCuuoHnucgHhD4QQAcSjZbtVFR/ujyZ9esTvKqwEGRvtwiAEqnFxt9zj1r+Pk9RmqbvSP6hUwqgfEHQonAIhTF80bJZvVok92VmtLeX20lwNEXNtwCOcAjai2C4eoburX+WnVjS75DMlikXLT6TgB8YbCCQDiVFF2qhZMGipJ+uvHdJ2Q+KLRcWpbOLk8PpXVtfT5ucwxvdy0ZNlt/AgGxBv+rwWAOHbxMSWSpBdX71OD0xPdxQAR5vIO/DlOZoGWZLNI6l+yHlHkQHyjcAKAOHbsEXkam5+uBqdHL6/ZF+3lABEVjThys3A6oiBDUv/OcmpN1GNMD4hHFE4AEMesVou+EzgQ968f75Zh9H3/BRDr3APccfJ4fcEkv4nDMiX1s+NUzxlOQDyjcAKAOHfe7JFKSbJqU1m9Vu0+FO3lABHTtuPkHIA9Tm2jyCcWZUmSdleGoeNE4QTEJQonAIhz2alJWjRzhCSiyZHY2necIl84mYffWizSkYX97zgdpHAC4hqFEwAkgO8GxvVeX39AB+udUV4NEBmutgfgDkAcuVmcpdhtKslPl+Tf49TXkdjKBnNUjz1OQDyicAKABDB1RLZmjcqR22voH6tKo70cICIGOo7cjCJPTbZpRE6qbFaLmt3ePv/jhPm4/Ew6TkA8onACgARxcaDr9MzHu+XtxyGdQKw6fFQv0mEoZnGWmmRTst2qETmpkqRdfUzWM/c4FTCqB8QlCicASBCnTytShsOu/bUt2lxWH+3lAGHXtuPkMxRMvIsUc4+TI8n/49LovDRJfdvn5PMZqm4kVQ+IZxROAJAgUpJsmlmcI0laU0q6HhJP246TJDkjfJZTc5uOkySV5Jn7nHpfOB1qcgU7wXnscQLiEoUTACSQYOG0pyaq6wAiwXlY4RTps5xaDiucWjtOvR/VM4MhctKSlGTjxy8gHvF/LgAkkFmjciRJa0troroOIBLcnsMLp8h2nMzCLDW5/x0nznAC4h+FEwAkELPjtK2iQbXN7uguBggzV5RG9Rz2QOGU7+847a7sfSR5a+HEmB4QryicACCB5GU4guNE6+g6IcEcvscp0qN6ZjiE2XEaOSRNFotU7/QEgx5CFYwip+MExC0KJwBIMOxzQqJye9p3eSI9qtcaDuH/cSklyabh2X2LJG89/JbCCYhXFE4AkGBmBQqntSTrIcEMdDiE87BwCKlNQERl7/Y5Bc9w4vBbIG5ROAFAgpk1aogkaU1pTcQPCAUG0kCHQ5gdp5R2hVPfAiI4/BaIfxROAJBgJhVlKdluVU2Tu0+xyUCsOjwcomWAwiHaFk4lfYwkD4ZDZBIOAcQrCicASDDJdqumDs+SJK3Zw7geEocZDpHpsEsaiHOc2seRS/3oONWzxwmIdxROAJCAguN6BEQggbgCo3qZKWbhNFDhEG06Tvm97zj5fIaqGknVA+Jdnwqn0tJS7d27N/jnlStX6ic/+YkeeeSRsC0MANB3HISLRBTsOKUkSZKcngh3nFzmqF7rj0ujcv2FU22zWzVNoUWS1za75fb69xvmcY4TELf6VDh9+9vf1jvvvCNJKisr09e+9jWtXLlSt9xyi+68886wLhAA0Htmx2njgbrgWTRAvHNGqePUdo9TWrJdhVn+rlGoXSdzf1NWij14mC6A+NOnwmn9+vWaO3euJOkf//iHpk6dqo8++kjPPPOMnnzyyXCuDwDQB8OzU1SQ6ZDHZ2j9/tpoLwcIC7PjlJUa6DhFuHBq6WRUT+r9PqeDwWAIxvSAeNanwsntdsvh8P/P//bbb+uss86SJE2cOFEHDhwI3+oAAH1isViC5zkREIFEYY67BTtOER7Vaw6EQ6QcVjgFk/UqQ+04EQwBJII+FU5TpkzRQw89pPfff19vvfWWvv71r0uS9u/fr7y8vLAuEADQN+a4HvuckAi8PkNen79wygrscYr0qF6w45R8WOGU37uOU2U9ZzgBiaBPhdNvfvMbPfzwwzrppJN00UUXacaMGZKkf/3rX8ERPgBAdJkBESTrIRG425zhNGB7nFydj+qVBEb1doVaOJmjegRDAHHN3pcHnXTSSaqsrFRdXZ2GDBkSvP3KK69UWlpa2BYHAOi7aSOyZbVIB2pbdKC2WUXZqdFeEtBnbRP0zD1OET/HydMxHEKSRgdG9Xb3MhyCUT0gvvWp49Tc3Cyn0xksmnbv3q0HHnhAmzdv1tChQ8O6QABA36Q77DpymP8g3LV0nRDn2nacMhwD23FqG0cutYZDVDW6VNfi7vF5gnucCIcA4lqfCqdvfvOb+stf/iJJqqmp0bx583Tfffdp0aJF+tOf/hTWBQIA+i44rsc+J8Q58/DbJJslODoXyXAIn88IdrkOH9XLcNiD3aM9IXSd6DgBiaFPhdPq1at1wgknSJKef/55FRYWavfu3frLX/6i//u//wvrAgEAfWcm69FxQrwzO05JNmtwdC6SHSdzTE/qGA4htUnWC2GfkxkOwR4nIL71qXBqampSZmamJOnNN9/UOeecI6vVqmOOOUa7d+8O6wIBAH1ndpw+31fTbtQJiDfm+zfZbpXD7v/xxRnBjlPb/VMpnRxa23qWU/cdJ8MwiCMHEkSfCqdx48bp5ZdfVmlpqd544w2deuqpkqSKigplZWWFdYEAgL4bm5+hzBS7Wtw+bS6rj/ZygD5zejp2nCJ5AG5z4LmT7VZZrZYOn289y6n7jlNdi0euQNFXwB4nIK71qXC67bbbdMMNN6ikpERz587V/PnzJfm7T7NmzQrrAgEAfWe1WjSTg3CRAMzDb5Nt1mBYQyRH9bqKIjeNzg+t42Tub8pw2Duk8wGIL30qnM477zzt2bNHq1at0htvvBG8/ZRTTtHvfve7sC0OANB/5kG4BEQgnpnhEMn2tnucIjmq133hFOoep+Dht3SbgLjXp3OcJGnYsGEaNmyY9u7dK0kaOXIkh98CQAwiIAKJILjHqW3HyRPBcAh351HkptG5/o5TRb1TTS6P0pI7/5GqdX8TwRBAvOtTx8nn8+nOO+9Udna2Ro8erdGjRysnJ0e//OUv5fOx+RgAYok5qrejslGHGl3RXQzQR8E4crtFDnvkU/Wa3Z0ffmvKTkvSkDT/Qby7Krse1ztY3yKJYAggEfSpcLrlllv0hz/8Qb/+9a+1Zs0arVmzRnfffbd+//vf69Zbbw33GgEA/TAkPVljAvsx1u6tie5igD5ytes4tY7qGYYRkdcL7nHqJIrc1Jqs1/W4Hol6QOLo06jeU089pccee0xnnXVW8Lbp06drxIgRuvrqq3XXXXeFbYEAgP6bVZyjnZWNWrOnRicfOTTaywF6zdUuVa/1332dHl9EQhdaujj8tq2SvDStLa3Rrm4CIjj8Fkgcfeo4VVdXa+LEiR1unzhxoqqrq/u9KABAeM0MnOe0loAIxKm25zi1LZScEQqIaHF1P6onhdpxChROmexxAuJdnwqnGTNm6A9/+EOH2//whz9o+vTp/V4UACC8ZhX7k/XW7jkkny8yo01AJLUNh0iyWWULnK0UqYCI5h5S9SRpQmGmJOn9rZXydvH/1UFG9YCE0adRvd/+9rc644wz9PbbbwfPcFqxYoVKS0v12muvhXWBAID+m1iUKYfdqroWj3ZUNmrc0IxoLwnolbajepLksFvV5PJGLCCip3AISTpl0lANSUvSvppmLd9YrlOnDOtwHzOOnMIJiH996jideOKJ2rJli84++2zV1NSopqZG55xzjr788ks9/fTT4V4jAKCfkmxWTRmeJUn6Yl9NdBcD9IHLPADX7v/RxSxonJ4IjeqZHafkrn9USkmy6cK5oyRJT360q8PnDcMIjuoVUDgBca9PhZMkDR8+XHfddZdeeOEFvfDCC/rVr36lQ4cO6fHHHw/n+gAAYTJtRLYk6Yu9dVFeCdB7h3ecUgIFVMQ7Tvbugye+e8xoWS3SR9urtLmsvt3nGpyeYGHHHicg/vW5cAIAxJepgcJp/b7aKK8E6L224RCS2kWSR0JLCHHkkjQiJ1ULAyN6T63Y1e5zZhR5WrKtywNyAcQPCicAGCSmjfQXTl/uryUgAnHH7Dgl2/yhEI6kyB6CG8oeJ9Nlx5ZIkl5avU+1Te7g7USRA4mFwgkABolxBRlKSbKq0eXVjsqu45OBWNSx4xTZUT2zk9Vdqp5p7phcTRyWqWa3V/9YVRq8vTUYgjE9IBH0qm98zjnndPv5mpqa/qwFABBBdptVk4uytHpPjdbvqyVZD3HF2WGPU6DjFKFwiN50nCwWiy4/rkQ3vvCFnlqxS987foxsVgsdJyDB9KrjlJ2d3e3H6NGjdckll0RqrQCAfgoGRLDPCXHG7DgFC6eId5x6TtVr65szRygnLUl7DzXrP5sqJLU5wymTwglIBL3qOP35z3+O1DoAAANgKoUT4lRX4RDOSBdOIXSczPVcePQoPfTf7Xryo5362uRCOk5AgmGPEwAMImZAxIb9dQREIK60hkMMTKqeOarnCLFwkqTvHjNKVov04bYqbS2v18HAHqcCOk5AQqBwAoBBxAyIaHB6tLOKgAjED/dhB+A6In2Ok6t3HSdJGjkkTadO9keTP/nRrjaH3xIOASQCCicAGETsNqsmFWVJ4jwnxJcO4RBmx8kT/VS9ti47rkSS9OLqfSqtbpLEqB6QKCicAGCQCQZE7KVwQvw4fI+TIxAO4YzUAbju0A7APdy8NtHk5gG4FE5AYqBwAoBBhoAIxCNXsOPkPwC3NY48wgfg2ntXOFksluCBuCZS9YDEQOEEAIOM2XH6koAIxBGz4+SwRz4cwjCM1sIpxDjytr45c4SyU5P8j0+yKr2XXSsAsYnCCQAGmfFDM+Sw+wMidhEQgTjhGsBznJwen4zAvyn0do+T5B/vu3BusST/mJ7FYgnn8gBECYUTAAwybQMiGNdDvHB1FQ4RgY5T231TKX0onCTpe8eN0eSiLJ03e2S4lgUgyiicAGAQMsf1SNZDvOh4AG4gHCICe5zMMT271RIs1HqrMCtFr11/gn6yYEI4lwYgiiicAGAQmkZABOJMh1E9MxwiAqN6ZuHUlzE9AImLwgkABiEzWe/LfQREID64Pf736UCEQ5iH36YQ6gCgDQonABiExhdmKNluVb3To92BQzqBWHZ4x8ksoCLRcTIjzuk4AWiLwgkABqEkAiIQZ9yeww/Ajdw5Ti1mxymJH5MAtOKKAACD1LQR/sKJgAjEA6f3sANwzXCISIzqsccJQCconABgkAoGROylcEJsMwyjk1S9yIdD9DWKHEBionACgEHKDIhYv79WhkFABGKXx2cED6RNPvwcJ0/4O05m4ASFE4C2KJwAYJCaUJjpD4ho8Wh3FQERiF1mt0lqG0fu/9Xl8YU9GZJRPQCdoXACgEEqyWbVpGGZkgiIQGwzo8iljqN6kuQMc9fJDIdIJY4cQBsUTgAwiAXH9SicEMOc3tZ9THarGQ7RWtSEe59TC3ucAHSCwgkABrFgQASFE2KY2+vvOCXbrbJY/IWTzWoJJuyFO5K8NRyCH5MAtOKKAACDWNuOEwERiFUu8wwnW/sfW1LsZrJeeEf12OMEoDMUTgAwiE0ozFSyzaq6Fo/2VBMQgdh0eBS5yRHoCEVqVI/CCUBbFE4AMIgl262aWERABGKb2XEyR/NMDntkznIyO1iEQwBoi8IJAAY59jkh1rm66DilBDtOYR7VC6TqOeg4AWiDwgkABrlpJOshxrV2nA4vnPyFjTNC4RCM6gFoi8IJAAa51oCIOgIiEJOCe5y6KJwIhwAwECicAGCQMwMiapvdKq1ujvZygA66CocwR/XC3XFymoVTMj8mAWjFFQEABrm2ARGf76uJ7mKATnQ5qhehcIjgOU52Ok4AWlE4AQA0Y2SOJGntnpqorgPojMs8AHeAR/VSSNUD0AaFEwBAM4tzJElrS2uiug6gM8GO0wCd49TsCsSRs8cJQBsUTgAAzRyVI8kfSW7uJwFixUCHQ5h7nFIonAC0QeEEANCYvHRlpybJ6fFp04H6aC8HaMfsOCXbDz8AN9BxIo4cwACgcAIAyGq1aEZwXO9QdBcDHKbnjlP4Cie31yePz7+nisIJQFsxUTg9+OCDKikpUUpKiubNm6eVK1eG9Lhnn31WFotFixYtiuwCAWAQMPc5rWGfE2KMs8dUvfCN6rUtwlKIIwfQRtSvCEuXLtXixYu1ZMkSrV69WjNmzNDChQtVUVHR7eN27dqlG264QSeccMIArRQAEtsss+NEsh5ijNlxOjwcIniOUxg7TuaYnsXSscMFYHCL+hXh/vvv1xVXXKHLL79ckydP1kMPPaS0tDQ98cQTXT7G6/XqO9/5ju644w6NHTt2AFcLAInL7DjtqGxUbZM7uosB2uhpVM/sSIVDS5tEPYvF0sO9AQwmUS2cXC6XPvvsMy1YsCB4m9Vq1YIFC7RixYouH3fnnXdq6NCh+v73v9/jazidTtXV1bX7AAB0NCQ9WSV5aZKktXtrorsYoI3WcIjOO07h3ONEMASArkS1cKqsrJTX61VhYWG72wsLC1VWVtbpYz744AM9/vjjevTRR0N6jXvuuUfZ2dnBj+Li4n6vGwAS1UzG9RCD3D0dgBvGVL0WosgBdCHqo3q9UV9fr4svvliPPvqo8vPzQ3rMzTffrNra2uBHaWlphFcJAPGrNSCCZD3Ejq7CIRwRCIdoDhZOcfUjEoABYI/mi+fn58tms6m8vLzd7eXl5Ro2bFiH+2/fvl27du3SmWeeGbzN5/NfLO12uzZv3qwjjjii3WMcDoccDkcEVg8AiWfWqCGSpHWlNTIMgz0eiAnBPU4DOaqXTMcJQHtR/eeU5ORkzZ49W8uXLw/e5vP5tHz5cs2fP7/D/SdOnKgvvvhCa9euDX6cddZZOvnkk7V27VrG8ACgnyYVZSnZbtWhJrd2VzVFezmApNY9Tkm29oV8JM5xanGxxwlA56LacZKkxYsX69JLL9WcOXM0d+5cPfDAA2psbNTll18uSbrkkks0YsQI3XPPPUpJSdHUqVPbPT4nJ0eSOtwOAOi9ZLtVU4Znac2eGq0trVFJfnq0lwQEO04O++GjembHKYypeh72OAHoXNQLpwsuuEAHDx7UbbfdprKyMs2cOVPLli0LBkbs2bNHVitzxgAwUGYW52jNnhqt2XNIi2aNiPZygDYdp67iyMM4qheII6dwAnC4qBdOknTttdfq2muv7fRz7777brePffLJJ8O/IAAYxGaNGqI/f7hLa0tror0UQJLk8nZfOEUiHIJRPQCHo5UDAGhnViBZb8OBurDuHQH6aiDDIVoonAB0gcIJANDOyCGpyktPlttraMMBDg1H9HU5qheII/f4DHm84ek6tRBHDqALXBUAAO1YLJbW85w4CBcxwDwA9/BwiLb7kMyznvqrOZCql0IcOYDDUDgBADqYNSpHktjnhJjQVcepbSEVrnE99jgB6AqFEwCgg5nF/oNw15YeivJKgK73OFmtluBtLWHqOJlBExROAA5H4QQA6GB6cbYsFqm0ulmVDc5oLweDnLOLA3AlKcUe3oCI1j1OFE4A2qNwAgB0kJWSpCMKMiRJa9nnhCjrquMkSY5gJDmjegAii8IJANApM5acfU6INvMcp2Rbxx9bWiPJCYcAEFkUTgCATs0kIAIxwt1FOITUGknuDNeonoeOE4DOUTgBADplRpKvK62Rz2dEdzEY1Mw48s5G9cy9SGbB01/BjhPnOAE4DFcFAECnjizMVGqSTfVOj7YfbIj2cjBIGYYRHNXrtOMU5lG9FvY4AegChRMAoFN2m1XTRmZLktYwrocoMbtNUg8dpx5G9b7YW6vfLtukJpen2/s1k6oHoAsUTgCALhEQgWgzu01S5+EQDnOPUw/nON375mb98d3tevPL8m7vZ3auKJwAHI7CCQDQJXOf0xoiyREl7jYFUecdp9DOcSqrbZEk7atp7vZ+wThyUvUAHIbCCQDQJTNZb3NZXY8jTkAkmB0nq0WyWTs5ADc4qtd9x6mq0X+Qc0VdS5f38foMuQKFGnucAByOwgkA0KWi7FQNy0qRz/DvEQEGmlnIdNZtkkLrOHl9hqobXZKk8jpnl/dr+xwUTgAOR+EEAOiWOa73OYUToqC7RD2pdY9Td3HkNU0umYn65fVdd5zaFk6OLgo1AIMXVwUAQLcmFmVKkraU10d5JRiM3IHCqbNgCKm14+TsZlSvssEV/H1FNx0nc3+Tw26VtZOxQACDG4UTAKBb44cGCqcKznLCwHN7uj78VpJS7D3HkVc1tBZLFfUtMozOD3RuIRgCQDconAAA3ZpQmCFJ2lZe3+UPnECkuLz+YqarUb1QznGqbGztOLm9hg41uTu9X7OLYAgAXaNwAgB0qyQ/XUk2ixpdXu2v7Xp/CBAJrp46TsFwiK5H9dp2nCSpvItkPXOfFGc4AegMhRMAoFtJNqvG5KdLYp8TBl6P4RBJPYdDVLXZ4yR1XTg1uyicAHSNwgkA0CNzn9NWCicMMHePceQhjOod1nHqKiAiePhtEj8eAeiIKwMAoEfjA/uctpYTEJGIapvd+s+mcnm83R8iGw2uYKpe5yl3KYGCyunpOVXPPEC3y1E9wiEAdIPCCQDQowmFJOslsnvf2KzvPblK//78QLSX0kEwjrzHjlM3e5wa/R2mcQX+fwDo6iwns3Ayk/oAoC0KJwBAj8YPJVkvke2qapQk7TgYe4Wx2UnqKVXP2W0cub/jNHl4lqRuRvXMPU50nAB0gsIJANAjkvUS26Emf2FRUd/14bDR4u4pHMJupur1vMdpcpG/cCrv4utsdhNHDqBrFE4AgB6RrJfYDjX6zzU6GIuFU6jhEF3scWpyedQU6CS1dpx6GNUjHAJAJ7gyAABCMr6QZL1EZXacDjbEXuHUGg7R0zlOnXeczDG9ZLtVYwv8xX9FvVM+X8eR02A4BB0nAJ2gcAIAhMTc57SFZL2E4vR4gx2ZmOw4eQMH4Pawx6nF7e10/11Vo79wyk9PVkGGQxaL5PUZwdvbaqZwAtANCicAQEjMZL2tJOsllJomd/D3lQ2dd2KiKRgOYe8qjtxf5PiM1iKrrapAFy0/0yG7zar8DIekziPJCYcA0B0KJwBASCYUkqyXiKrbdF7cXkO1ze5u7j3wgnHkts6LGUeb/Ugtno7jemYwRF56siRpaKa/cOqsu2bukyKOHEBnKJwAACEZndearLevpjnay0GYmPubTLGWrOfqoePksFtlCXyqs31O5uG3eYFOU2FWiqTuO04cgAugMxROAICQtE3WY1wvcZiJeqZY2+dkdpwcXexxslgswUhyZyeH4FYFCyd/x6kwyxzV66TjxB4nAN2gcAIAhIxkvcRzeMfpYENsndPl6uEAXKnNIbidjOpVNfoLpIJAx2loZqDjVN9JxykYR07hBKAjCicAQMgmDPUXTiTrJY6awwunGOs4mXHkSV2c4yS17klqCanj5C+cOjvLiXOcAHSHKwMAIGTjAwERdJwSR3XMj+p1H0cutQZEdL7HyQyHMPc4+X/tbC8XceQAukPhBAAImZmst7WigWS9BGF2nMyY7lgrnFyB8bu+dpwqD+s4BUf1Ous4EQ4BoBsUTgCAkJnJek0k6yUMc4+TWRTHWqqe2XHqKhxCah2tO7zj5PMZqg7sccrPaN9xOljvlPewM6vMOHI6TgA6Q+EEAAhZu2Q99jklhOrAAbjmAcex13HqPo5ckhyBQufwc5xqmt0ya6PcwDlOeRkOWS3+A3PNw3FNwQNwKZwAdILCCQDQK8FkvQr2OSWCmmDHKVA4NcRY4dTDAbhSa6Fz+Kieub8pJy0pmMpns1pUkNkxktwwDFL1AHSLwgkA0Csk6yWWQ43tR/VqmtydxnpHS2scedcdpxR756N6rcEQye1u7+wQXKentehijxOAzlA4AQB6ZQLJegnD4/WprsUjSSrJTw8WJ2aEdywwD8BN7i4cIthxal84tUaRO9rdPjSzY7Je28emdPNaAAYvrgwAgF4Z3yZZz+cjWS+e1TT79zdZLFJOalLwkNhY2udkdpy6iyM3wyHado2k1j1MBYcXTp10nMwxvSSbRfZuXgvA4MWVAQDQK22T9fbXkqwXz8z9TVkpSbLbrMG9P7GUrOcO5QDcQMfJeXjHqbF9FLmpMBBJXlHfpnAiGAJADyicAAC9kmSzamy+Oa7HPqd4Zh5+OyQtSZKChVMsdZxCOQA3OKrn6Twcwjz81mRGkrcNh+DwWwA9oXACAPTauMC43hb2OcU18wynIYHwhFgsnJzBcIiuf2RxdBkO0UXHqZNRPTORj44TgK5QOAEAes1M1ttaQccpnpmjekPSAoWTucepoaXLxwy0/oVDtD/81jS0k45TCx0nAD2gcAIA9BrJeonBHNXLieFRvVDCIVo7ToeFQwT2OOUf1nEaGtjjVNXolCdQmAX3OBFFDqALFE4AgF5rPQSXZL14Znaccs2OU6CgiKXCqT8dp8rA13F4HHleerJsVosMo3Wcr3WPEz8aAegcVwcAQK+NzksjWS9K3ttyUG9vKA/Lc3W1xylWUvV8PkOeQGHe7QG4nYRDNLu8agx0kQ7f42S1WoJnOZn7nMyiiz1OALpC4QQA6DWS9aLD7fXph09/piufXtUu2KCvDh/VG9pmVM8wot9JdHlbC6HuO04dwyGqGv3FX7LNqkyHvcNjDj/LiT1OAHpC4QQA6JPxJOsNuMoGp5rdXvkMaeXO6n4/3+GjemaIgtPjU73T0+/n7y93m8Kpu1S9FHvHc5yqGlr3N1ksHbtVhWbHKdBdI44cQE8onAAAfTI+kKy3hY7TgGmbArdqV/8LJ3NULydQOKUm24LdmVjY5+RqM3oX0jlObcIhzI7T4fubTGYkeUWw4+R/rIPCCUAXKJwAAH1iJuttq6DjNFAq2oznrdx1qN/Pd6gpcABuelLwtlhK1jMPv7VbLbJau9vjFBjV87R2nCrrOz/DyWSOJVbU0XECEBoKJwBAn5CsN/DK2xQzm8rqVNvs7vNz+XxGh1E9KbYKJ1cIh99KrR0nZ5uOU6XZcUrvvuNUXu8vRs048tRkfjQC0DmuDgCAPilpk6y3r4ZkvYFwsE3HyTCk1Xv63nWqb/HIrHdzOimcYiFZzxVCFLnU5hwnTyd7nDK76Dgddggu4RAAekLhBADoE3ubZL1tFexzGght9zhJ0qf9CIioDnSb0pNt7QqTeO44tUvVa/CvP7+HjlMFceQAQkThBADoM5L1BlZFYKxs9ughkqRP+xEQcfgZTqZYKpzMVD1HTx2nYBy5LxijXtXY/R4ns3CqanTJ5fEF9zhROAHoCoUTAKDPpgzPlhSeaGz0zByfO2NakSRpXWltuy5LbxwKFBZD0g4rnAIpdAcbol84maN63R1+K7UvdpyBLpVZ+HWVqjckLSn4vAcbnGoO7I9iVA9AVyicAAB9dtKRBZKkD7ZVBjfXI3LMUb25Y3KVn5Esl9enL/bV9um5zEQ98/BbU0x1nDyh7XEyz3GSWgMizI5TfhcdJ4vFoqGZreN6LcFwCAonAJ2jcAIA9NnEYZkakZMqp8enD7dVRns5Cc3j9QXPJirMStHRJbmS+t7tCybqHTaqZxYTsVA4tXacuv9xJclmkZlW3uLxyuczVB0snDrvOEntAyLMYAkz2hwADsfVAQDQZxaLRQsmDZUkLd9UHuXVJLbKBpcMQ7JZLcpLT9acQOHU131O1V2N6gU6TlWNTnm8vg6PG0ihhkNYLJZ2ARG1zW55A5GBh399bRWaHaf6lmDHlD1OALpC4QQA6JdTJhVKkpZvrOA8pwgygyEKMhyyWi2aGyicPtt9KFgk9EZXo3q56cmyWvxx52ZxFS3mAbg9jepJbZP1fKoM7M/KTk3q9rGFwY5TCwfgAugRhRMAoF/mjc1VerJNFfVOrd/ft/026Jm5v8n8YX9SUabSk22qb/Foc1nvUw27GtWzWS3BQIVon+Xk8vqLmeQeOk6SlGKe5eT2qrKh+/1NpqHmIbh1ztZznNjjBKALFE4AgH5x2G36ygR/SMTbGxjXi5RgxykwXma3WXVUP2LJzW5STiejbLGSrOf29L7j5PS07gXrKlHPNDSztePUEgiVaBs0AQBtUTgBAPrNHNd7e2NFlFeSuA7vOElqDYjoQ+FUExjVG3LYqJ4UO8l6zhDjyKXW4qrF7VVViB2n1kNwna2jenScAHSBwgkA0G8nH1kgi0XacKBO+2uao72chHQw0HEyU++k1sJp1a7q4MGvoQoegNtJx2lojBROrXHkPRczbcMhqgKdsrz07jtOZuG0r6Y5uE+McAgAXaFwAgD0W16GQ7NH+cfGlm+i6xQJZsdpaJuO08ziHCXZLCqvc6q0OvSC1TCM1sIpvZNRvRgpnEI9AFdqjRFv8fh0MNBxyuux4+T/Ohucng7PAwCH4+oAAAiL1nQ99jlFgrnHqe2oXmqyTVNHZEvq3bheo8sbTKyL5VE9s+Pk6FWqXmvHqbsznKSOqXtWS2hBFAAGJ64OAICwMM9z+mhblRrb/As+wiPYcWozqicpGEu+qheF06FAMITDbu00fjtmCqcQD8CVWkMdnG6vqhpD2+NksVjaF6JJNlksPXe3AAxOFE4AgLAYNzRDo3LT5PL69P7WymgvJ6F4vL5gF6XtqJ7Ut4CItvubOisUYiVVz9mbwskc1XO3fq96StWT2heiBEMA6A6FEwAgLCwWi04JdJ0Y1wuvqkaXfIZ/lOzwwIM5Jf69ZTsONgYPfu1JV4ffmmKm49SHOPK25zjldbJ/63BtO04OosgBdIPCCQAQNgsC+5ze2Vwhn693KW/oWkVgTK8g0yGbtX2HKCctWRMKMySFPq7X1eG3JvNg2AanR02u6I1dmgfghtZx8hc9tc3uYNhDfiYdJwDhQ+EEAAibuWNylZliV2WDS2v31kR7OQmjvK5jFHlb5rjep7sOhfR85uG3nUWRS1J6si249ymaXSez4xRKOIQjMKq3LxCHn2yzKtNh7/FxZiS5pE73ewGAicIJABA2STarTpxQIIlxvXCqqO94+G1bc8eYhVNoHaeeRvUsFktMjOv1Jo7cHLMzC6e8jM73bx3u8HAIAOgKhRMAIKzMcb23N3CeU7iYHaeCLjpOcwIdpy/314WUaNjTqJ7/tWKncAolItwMh9jfpnAKRdsunoMznAB0gysEACCsTjqyQDarRZvL61Va3RTt5SSEnjpOI3JSNSInVV6foTV7anp8PnNUL6eLUT0pNpL1XIFznJJCCYcIdJzMYIieznAy0XECECoKJwBAWOWkJWv2aH/SG+N64VHRwx4nSTo6kK4XSix5TWBUr7PDb02x0HFy96rj1L7oOTx9sCtDswiHABAaCicAQNiZh+Eu38S4Xjj01HGSpKPNfU47ey6cguc4dTOqNzSWCqeQ4sjb36enw29NWSn24GPpOAHoDoUTACDszH1OH++oUn2LO8qriX89pepJrcl6a0oPBUfcunKoh1Q9KTY6TubX0aeOU4iFk8ViCSbrHf4cANAWhRMAIOzGFmRobH663F5D722pjPZy4prXZwQPtu2u4zSuIEM5aUlqcfu08UBdt895qBejehVRDYfwx5GHdo5T+/uEOqonSYWZFE4AekbhBACIiFOC43rsc+qPqganfIZktUh53QQeWK0WTRuRLcmfrteVFrdXzW7/wbLdjerFUsepN+EQplAOvzUVBArSw4svAGiLKwQAICJOPtJfOH2wtVKGYUR5NfHL7PjkZzhks3Z/LtHk4VmSpC/313Z5H3N/k91q6faAWLNwqmxwyueLzn+/3oRDODqEQ4Q2qidJZ0wr0oicVJ0wvqB3CwQwqPR8pDYAAH0wu2SIUpKsqqh3akt5g44clhntJcWl4P6mbsb0TJOL/IXThm5G9Q41th5+290Bseaom8dnqKbZ3e2ZT5ES3ONk7/kg247hEKF3nE6fVqTTpxX1bnEABh06TgCAiHDYbZo7Jk+S9P7Wg1FeTfwKJup1EwxhmjLcP6q36UC9vF10iYKJet0EQ0j+JDuzWIrWuF5rx6nnvUeOw0b1olHoAUhsFE4AgIg5YVy+JOmDbQRE9FVFnb9oCaXjNCY/XalJNjW7vdpZ2dDpfUItnKQ2h+BGqXBq3ePUu45TdmpSSBHmANAbXFUAABFz/Hh/4fTJjmo5Pd4oryY+ldf3HEVuslktmlTkH4nsKiDCTNTL6SZRz9SarNcS0lrDzdXHA3BDjSIHgN6gcAIARMzEYZnKz3Co2e3V6t010V5OXOpNx0lqHdfrsnAKnOEUyihbtJP1zFG90OLIWwun/F5EkQNAqCicAAARY7FYdPw49jn1h9ntCWWPk9SarLehy46Tv3DKCWVUL8qFkzmq5wgpjrz1PnScAEQChRMAIKKOD0Q8s8+pb3rfcWqNJO8sBr4mhMNvTcE9Tg0DXzh5fYbMfItQOk52m1X2QFx7bxL1ACBUFE4AgIg6PhAQ8cW+2uCYGELj9RnBoqUwK7SO04TCTNmsFh1qcutAbce9SdWB/wbdHX5rMou1aHSczG6TFNoBuFLruB4dJwCRQOEEAIioYdkpGj80Q4YhfbS9KtrLiSvVjS55fYYsltAPdE1Jsmn80AxJne9zqomTVD0zGEIKLRxCak3Wy6PjBCACKJwAABFnput9sI19Tr1hHn6bn+GQPcTiQWrd5/Tl/toOnzvUm1G9YKpelDtOtp7jyKXWs5zyOcMJQARQOAEAIu6EQOH0/tbKTvfdoHNmp2doZu86KGayXmcBEYd6MapnFk61ze4Bj5N3t4kit1hCK5zM0cJReWkRWxeAwSsmCqcHH3xQJSUlSklJ0bx587Ry5cou7/voo4/qhBNO0JAhQzRkyBAtWLCg2/sDAKJv3pg8Jdks2nuoWburmqK9nLhhdpxC3d9kmlxkdpzaF05ur0/1To+k0Eb1slOTgt2eyoaB3Z8WPPw2xG6TJN1//kw9cvHsYOEIAOEU9cJp6dKlWrx4sZYsWaLVq1drxowZWrhwoSoqKjq9/7vvvquLLrpI77zzjlasWKHi4mKdeuqp2rdv3wCvHAAQqnSHXUeNGiJJep90vZBV9LHjZI7q7atpDu5pkloT9SwWf1HUE4vFErV9TsGOU4jBEJI0Jj9dp04ZFqklARjkol443X///briiit0+eWXa/LkyXrooYeUlpamJ554otP7P/PMM7r66qs1c+ZMTZw4UY899ph8Pp+WL18+wCsHAPSGOa73Aec5hczsOPW2cMpOTVJxbqqk9uN65hlO2alJsllD6+QUBLpdA104uXpx+C0ADISoXo1cLpc+++wzLViwIHib1WrVggULtGLFipCeo6mpSW63W7m5uZFaJgAgDMzznD7aXiVPm8Q0dC3YcerlqJ4kTSnyj6u1HdcL7m8KYUzPFK2Okzmq15uOEwBEUlSvRpWVlfJ6vSosLGx3e2FhocrKykJ6jhtvvFHDhw9vV3y15XQ6VVdX1+4DADDwpo3IVnZqkupbPPp8X8e0N3RU0ceOk9R6EO6GA207Tv5RvZwQEvVMrcl6Hc+EiiS31x8iEmoUOQBEWlxfjX7961/r2Wef1UsvvaSUlM7/Ne6ee+5RdnZ28KO4uHiAVwkAkCSb1aJjj8iTJL2/hX1OoTA7Tr0Nh5A6jyQ3R/Vye9FxGhZ47QM1A1s4tYZDxPWPKgASSFSvRvn5+bLZbCovL293e3l5uYYN635z57333qtf//rXevPNNzV9+vQu73fzzTertrY2+FFaWhqWtQMAeo/znELn8xmtceRZfek4+Uf1th9sVIvbHyVuFk45vSicRg7x75XaWzOwaYh9CYcAgEiK6tUoOTlZs2fPbhfsYAY9zJ8/v8vH/fa3v9Uvf/lLLVu2THPmzOn2NRwOh7Kystp9AACi44Rx/n1Oa/bUqCEQi43OVTe55PEZslj8B+D2VmGWQ3npyfL6DG0qq5fUmqoXyuG3puJc/5lIpdXNvV5Dfzj7EEcOAJEU9X/GWbx4sR599FE99dRT2rhxo6666io1Njbq8ssvlyRdcskluvnmm4P3/81vfqNbb71VTzzxhEpKSlRWVqaysjI1NDRE60sAAIRoVF6aRuWmyeMz9PH2qmgvJ6aZiXp56cl9GlezWCwdxvWqe3H4rclM59tf0yyvb+AOL6bjBCDWRP1qdMEFF+jee+/VbbfdppkzZ2rt2rVatmxZMDBiz549OnDgQPD+f/rTn+RyuXTeeeepqKgo+HHvvfdG60sAAPRC67ge+5y603qGU+/3N5nMcT0zktw806k3qXpDM1OUZLPI4zNUVjdw+5zY4wQg1tijvQBJuvbaa3Xttdd2+rl333233Z937doV+QUBACLmhHH5+tsne/Q+5zl1K5io14f9TaYpwY6Tv3A61IdRPZvVohE5qdpV1aTS6iaNyEnt83p6w+w4Oeg4AYgRXI0AAAPq2CPyZbX4QwsO1A7svpl4UlEXSNTrR8fJHNXbVFYnr89oPcepF6N6kjRyiLnPaeACItwcgAsgxnA1AgAMqOy0JE0bmSNJen8r43pdKa/vf8dpTF660pJtanH7tONgQzBVrzejelLrPqe9hwau0HVyAC6AGMPVCAAw4E4Y59/nROHUNbPjNLQPZziZrFaLJhX5u05f7KtVTXPvR/WkNh2nQwPZcfIHUdBxAhAruBoBAAbcyROHSpLe+LIsuJcH7ZUHwyH63nGSWvc5rdheJSMQitebc5ykNmc5DWDHiXAIALGGqxEAYMAdNSpHR43Kkcvj02Mf7Iz2cmLSwUBBWdiPjpPUWjh9GEgxzHDYez3+Zp7ltDcKe5wIhwAQK7gaAQAGnMVi0bVfHSdJ+uvHu4OhBfDz+Yw2ceT96zhNLvJHku+v9RdiOb0c05Ok4sCo3oG6lmAnKNJcXg7ABRBbKJwAAFFx8pFDNakoS00ur/780a5oLyemHGpyyRM4bLagn4XThGEZsltbi4/cXibqSVJ+RrJSkqwyDA1YEqKLcAgAMYarEQAgKiwWi645+QhJ0pMf7lSD0xPlFcUOs9uUl57c7z0+DrtN44ZmBP/c2/1Nkv+/VWsk+QAVTsSRA4gxXI0AAFFz2tQijS1IV12LR3/9eHe0lxMzyoOH3/Zvf5NpyvDs4O97m6hnKg4ERAxUsp6bjhOAGMPVCAAQNTarRVed6O86Pfb+TrW4vVFeUWwI1/4mkxkQIfX+DCeT2XHaO1CFU6DjlEzHCUCM4GoEAIiqRbNGaEROqiobnFr6aWm0lxMTKoKJerFTOJmH4A70qB4dJwCxgqsRACCqkmxW/fDEsZKkh/+7fcBS22JZa8cpPKN6k9oWTul9HdUb2ENwXR4OwAUQW7gaAQCi7vw5xcrPcGh/bYteXrsv2suJuvIwd5yyUpI0KnAWU1/CIaS2o3qEQwAYnLgaAQCiLiXJpitOGCNJ+tO72+UNRHEPVmbHqSBMHSdJuuKEMZpZnKNjj8jr0+PNUb2D9c4B2YtGOASAWMPVCAAQE75zzGhlpyZpZ2WjXvviQLSXE1UVdf7CKVwdJ0m6eH6JXr7mOOVn9O05s1OTlOGwSxqYgIjgHicOwAUQIyicAAAxIcNh1+XHlUiSHnxnmwxjcHadDMNQRX1448jDwX+WkxlJHvlxPTfhEABiDFcjAEDMuOzYEqUn27SprF7LN1ZEezlRcajJLbfXXzQW9LE7FCnFgX1Se6sHoOPkYY8TgNjC1QgAEDNy0pL13WNGS5IefHdblFcTHftr/N2c/IzkmOu2DGTHycU5TgBiDFcjAEBM+f4JY5Rss2rNnhqtK62J9nIG3L5A4TQiJzXKK+moeAAPwTVH9ZJirHgEMHhxNQIAxJShmSk6Y3qRJOmpFbuiu5goMDtOI4bEYOEUGNUbiENwzVE9Ok4AYgVXIwBAzLlkvn9c79/rDqiywRnl1QysfYExuOHZsVc4maN6A9Nx8u/zirVxRQCDF1cjAEDMmTVqiGaMzJbL69PST0ujvZwBtS8OOk6HmtxqcHoi+lqEQwCINVyNAAAx6ZL5JZKkv368W57AfpfBIJb3OGU47BqSliRJKo1wsp6LOHIAMYarEQAgJp0xvUi56ck6UNuitzaUR3s5A8Yc1YvFjpMkjQwGRERun5NhGG06ThyACyA2UDgBAGJSSpJNF80tljR4QiKaXV5VNbokSSNz0qK8ms4V5wYiySPYcfL4Wg8/dthsEXsdAOgNCicAQMz6zrzRslkt+nhHtTaV1UV7ORFnjullOOzKSrVHeTWdMztOpREMiDC7TZKUZKfjBCA2UDgBAGLW8JxUnTq5UJL0lxW7o7yayGu7v8liic2CoTiYrBe5UT13mz1txJEDiBVcjQAAMc0MiXhp9T7VNruju5gIi/X9TZI0MniWUwQ7ToHCyWKRbNbYLCABDD4UTgCAmHbM2FwdWZipZrdXz61K7GjyfTX+YiQWE/VMbTtOhmH0cO++aXv4bax23gAMPhROAICYZrFYdMmx/gNxn/54t3y+yPywHgviouMU2OPU4PRErAMYPPyWMT0AMYQrEgAg5p09a4QyU+zaXdWk/245GO3lREwsn+FkSkmyqSDTIUkqrY7MPqdgFDlnOAGIIVyRAAAxLy3ZrvPnJH40+f6aFkmx3XGSpJGB9UUqWc8Mh6DjBCCWcEUCAMSFi48ZLYtFenfzQe2qbIz2csLO4/WprC5QOMVwx0mSioOH4EamcHIGO07sbwIQOyicAABxoSQ/XSdNKJCUmNHkZXUt8voMJdusKshwRHs53Wo9BDcyo3p0nADEIq5IAIC4ccmxJZKkpZ/u0cYDiXUgrhkMUZSTImuMR3BH+hDc4B4nCicAMYQrEgAgbpw4vkBzx+Sq0eXVxY+v1M4EGtmLh2AIU+uoXmQ7Tg7CIQDEEK5IAIC4YbVa9OglczSpKEuVDU5997FPdKA2Mj+8D7RgFHk8FE655llOTRE5y8ksnOg4AYglXJEAAHElOzVJf/neXI3JT9e+mmZd/PhKVTe6or2sfgt2nGI8UU+SirJTZbFILW6fKhvC/703wyGS6TgBiCFckQAAcacg06Gnvz9XRdkp2lbRoEufWKn6lr4fxrqprE5/+M9Wtbi9YVxl78TTqF6y3aqirBRJkdnnZB6AS8cJQCzhigQAiEsjh6Tp6e/PU256sr7YV6sfPLWqT4XP7qpGXfjIx7r3zS165pM9EVhpaIKjenHQcZLaBERUh79wIhwCQCziigQAiFvjhmboqcvnKsNh1yc7q3XNM6uD+2NCUdfi1vee/FQ1Tf5u1bL1ByK11G4ZhhHsOI3MSYvKGnprZHCfU/j3mBEOASAWcUUCAMS1aSOz9dilc+SwW7V8U4X+v3+sC3YsuuPx+nTNM6u1/WCjCjL95yat2n1IFfUtkV5yB5UNLjk9Plks0rDslAF//b6I5CG4rR2n2I5lBzC4UDgBAOLeMWPz9MfvHCW71aJ/rduvCx9Z0WPa3i//vUHvb61UapJNf77saM0ozpFhSG9+WT5Aq25ldpsKM1PiJhBh5JDIHYLr8hIOASD2cEUCACSEUyYV6uGLZyszxa7Ve2r0jf/7QB9uq+z0vk+v2KWnVuyWJP3ugpmaOiJbp00dJkl648uyAVuzKd72N0lSce5AdJz4MQVA7OCKBABIGKdMKtS/rztek4qyVNXo0sWPf6IH39kmn6/1rKH3tx7U7a9skCT9/OtH6uuBgmnhFP+vK7ZXqaZpYOPN98dRop7JLJz21TTL6wvvWU5uOk4AYhBXJABAQhmdl66Xrj5W580eKZ8h/e8bm3Xl06tU2+TWtooGXf3Manl9hs45aoSuOvGI4OPG5Kdr4rBMeXyG3t5YMaBrNkf1hsdR4TQsK0V2q0Vur6HyuvDuCwsWTnScAMQQrkgAgISTkmTT/543Xb8+Z5qS7Va9vbFC3/jD+/r+U5+qvsWjOaOH6J5zpsliaR8+YHadlq0f2HG9vXE4qmezWoKFXriT9VwcgAsgBnFFAgAkJIvFogvnjtILPzpWI4ekqrS6WburmjRySKoevni2HHZbh8ecNs1fOL239aAanJ4BW2trFHn8FE6SVJxrBkSEd5+TiwNwAcQgrkgAgIQ2bWS2/n3d8Tp92jCNyU/X45cerbwMR6f3PbIwUyV5aXJ5fHp388CN6+0LBCzEU8dJaj1zqjTMARGEQwCIRVyRAAAJLyctWX/8zmy9c8NJOnJYZpf3s1gsWjh1YMf16lvcqmvxd7fiKRxCau047akKb+FEOASAWMQVCQCANk6bWiRJemdThVrc3oi/njmml5OWpHSHPeKvF06TirIkSav3HArr8wb3OHEALoAYQuEEAEAb00dkqyg7RY0urz7Y2vk5UOEUPMMpzrpNknT0mFxZLdKuqqZgpHo40HECEIu4IgEA0IbVamlN1xuAw3D3xeEZTqaslCRNG5kjyX/+Vbi4vOxxAhB7uCIBAHAY81DctzeWB7sfkbIvDqPI2zr2iDxJ0kfhLJyIIwcQg7giAQBwmKNLcpWXnqyaJrc+2VEd0dfaG8cdJ0maP9ZfOK3YXinDMMLynG46TgBiEFckAAAOY7Na9LXJhZKkZV8eiOhrmR2nkXHacZpTMkRJNov217ZoT5jOc3KxxwlADOKKBABAJ8xY8je+LJfPF55OSmda9zilRew1Iikt2a5ZxUMkhW9cz+3xf7+T6TgBiCFckQAA6MRxR+Qr02HXwXpn2OO2TU6PVwfrnZKk4TkpEXmNgXDMEea4XngKJ8IhAMQirkgAAHQi2W7VKZOGSorcYbgHalokSSlJVuWmJ0fkNQZC24CIcOxzIhwCQCziigQAQBfMdL1lX5aFLfigrbZR5BZL/B72OmtUjhx2qyobnNpW0dDv52vtOMXv9wRA4qFwAgCgCydOGKqUJKv2HmrWl/vrwv78rVHk8bm/yeSw2zSnxL/PacWO/o/rmal6DjpOAGIIVyQAALqQmmzTSRP843q/WbZJnjCf6RTvUeRtHXtEviTpo21hKJw87HECEHu4IgEA0I0fnzJeqUk2vb+1Ure/8mXII3sb9tfp0idW6t+f7+/yPvEeRd7WMYHznD7eWdXvFELiyAHEIq5IAAB0Y/LwLD1w4UxZLNJfP96jP3+4q8fHrNlzSBc+skL/3XJQP3/+c1XUt3R6v301/nOPEqHjNH1kttKTbappcmtjWd/HGg3DkNvrL7zoOAGIJVyRAADowcIpw3TzaRMlSb96dYOWbyzv8r4f76jSdx/7RHUtHlktUpPLq9+9taXT+wbDIRKg45Rks2rumFxJ/Ysld7UZh6TjBCCWcEUCACAEV5wwVhceXSyfIV339zXa0ElYxLubK3TpEyvV6PLquHF5eup7cyVJSz8t1eay+nb39fqMYBx5InScJGl+GM5zMrtNEgfgAogtXJEAAAiBxWLRLxdN1bFH5KnJ5dX3n/pUFXWtI3jL1h/QFX9ZJafHp1MmDtXjlx6tE8YX6PRpw+QzpLte29ju+SrqW+TxGbJbLSrMit/Db9syAyI+2Vnd5yCNJz7YKUlKS7YxqgcgpnBFAgAgREk2q/70ndkaW5CuA7Ut+sFfVqnZ5dVLa/bqmr+tkdtr6IzpRXro4tlKSbJJkm78+kQl2Sx6b8tB/XfLweBzmcEQw7JTZLMmxnlFk4qylJ2apAanR1/sq+3145/5ZLfuD4w13nTaxIT5vgBIDBROAAD0QnZakp649GgNSUvS53trdd5DH2nxP9bJ6zN03uyR+r8LZ7XrlIzOS9cl80skSXe/ulHeQOLcvgSKIjfZrBbNM/c59fI8p9e/OKBbX14vSbruq+OC3zMAiBUUTgAA9FJJfroevniOkmwWfbm/ToYhXTJ/tH577vROuyTXfXWcslOTtLm8Xv9YVSopsYIh2jq2D/ucPtpeqeufXSufIV00d5QWf21CpJYHAH1G4QQAQB/MHZOre781Q3npybruq+N0x1lTZO1itCwnLVk/PmW8JOm+N7eo0ekJjuolUsdJko4d59/n9Omuajk93h7vv35fra78y2dyeX36+pRh+tWiqbJYGNEDEHvs0V4AAADx6pszR+isGcND+kH/4mNG6+kVu7SrqkkP/3d7Qo7qSdL4oRnKz0hWZYNL60prgxHlndld1ajL/vypGpweHTM2Vw9cOJN9TQBiFh0nAAD6IdTuSLLdqpsCZ0E98v6OYJx5oo3qWSwWHTPWP6730fbKLu9XUd+iix9fqcoGpyYVZemRS+YEAzUAIBZROAEAMEAWThmmo0uGqMXtU0W9U1LidZyk1ljyzvY5GYahj7ZX6uLHVmpPdZNG5abpqe8drayUpIFeJgD0CqN6AAAMEIvFolvOmKxFD34YvG14AhZO5kG4a/bUqNnlVWqyTT6fobc2lutP727X2tIaSVJ+RrKe/v5cDc1MjHOsACQ2CicAAAbQzOIcnTVjuP61br/yMxwJOZ5WkpemouwUHaht0cc7q1Td4NJD/92urRUNkiSH3aoLji7WVScdoaLsxCscASQmCicAAAbYjadN1MYDdfrqpKHRXkpEWCwWzT8iTy+u3qcr/7JKbq//7KpMh10Xzx+ty48bo4JMR5RXCQC9Q+EEAMAAG5GTqrcWnxjtZUTUsUfk68XV++T2GsrPcOj7x4/Rd44ZxV4mAHGLwgkAAITdN2cO167KRhXlpOjco0Ym5EgigMGFwgkAAIRdks2qGxYeGe1lAEDYEEcOAAAAAD2gcAIAAACAHlA4AQAAAEAPKJwAAAAAoAcUTgAAAADQAwonAAAAAOgBhRMAAAAA9IDCCQAAAAB6QOEEAAAAAD2gcAIAAACAHlA4AQAAAEAPKJwAAAAAoAcUTgAAAADQAwonAAAAAOgBhRMAAAAA9IDCCQAAAAB6EBOF04MPPqiSkhKlpKRo3rx5WrlyZbf3f+655zRx4kSlpKRo2rRpeu211wZopQAAAAAGo6gXTkuXLtXixYu1ZMkSrV69WjNmzNDChQtVUVHR6f0/+ugjXXTRRfr+97+vNWvWaNGiRVq0aJHWr18/wCsHAAAAMFhYDMMwormAefPm6eijj9Yf/vAHSZLP51NxcbGuu+463XTTTR3uf8EFF6ixsVH//ve/g7cdc8wxmjlzph566KEeX6+urk7Z2dmqra1VVlZW+L4QAAAAAHGlN7VBVDtOLpdLn332mRYsWBC8zWq1asGCBVqxYkWnj1mxYkW7+0vSwoULu7y/0+lUXV1duw8AAAAA6I2oFk6VlZXyer0qLCxsd3thYaHKyso6fUxZWVmv7n/PPfcoOzs7+FFcXByexQMAAAAYNOzRXkCk3XzzzVq8eHHwz7W1tRo1ahSdJwAAAGCQM2uCUHYvRbVwys/Pl81mU3l5ebvby8vLNWzYsE4fM2zYsF7d3+FwyOFwBP9sfnPoPAEAAACQpPr6emVnZ3d7n6gWTsnJyZo9e7aWL1+uRYsWSfKHQyxfvlzXXnttp4+ZP3++li9frp/85CfB29566y3Nnz8/pNccPny4SktLlZmZKYvFIkk6+uij9emnn3b7uP7ep6vP1dXVqbi4WKWlpTEfVhHK9yBWXqOvz9Obx4V6357ul+jvGynx3zu9fUw4rjl9+Tzvm8i9Rqxcc/i7ivdNX+/L31WJ/97h76rOGYah+vp6DR8+vMf7Rn1Ub/Hixbr00ks1Z84czZ07Vw888IAaGxt1+eWXS5IuueQSjRgxQvfcc48k6frrr9eJJ56o++67T2eccYaeffZZrVq1So888khIr2e1WjVy5Mh2t9lsth7/w/T3Pj09PisrK+YvKqF8D2LlNfr6PL15XKj37el+if6+kRL/vdPbx4TjmtOfz/O+Cf9rxMo1h7+reN/09b78XZX47x3+rupaT50mU9QLpwsuuEAHDx7UbbfdprKyMs2cOVPLli0LBkDs2bNHVmtrhsWxxx6rv/3tb/rFL36h//mf/9H48eP18ssva+rUqX1ewzXXXBPx+4Ty+Fg3EF9DuF6jr8/Tm8eFet+e7pfo7xsp8d87vX1MOK45/f18PEj0901vH8ffVaHhfdO3+/J3VeK/d/i7qv+ifo7TYMe5UugL3jfoC9436CveO+gL3jfoi1h+30Q1jhz+8IolS5a0C7AAesL7Bn3B+wZ9xXsHfcH7Bn0Ry+8bOk4AAAAA0AM6TgAAAADQAwonAAAAAOgBhRMAAAAA9IDCCQAAAAB6QOEEAAAAAD2gcIozTU1NGj16tG644YZoLwVxoKamRnPmzNHMmTM1depUPfroo9FeEuJEaWmpTjrpJE2ePFnTp0/Xc889F+0lIU6cffbZGjJkiM4777xoLwUx7N///reOPPJIjR8/Xo899li0l4M4Es1rDHHkceaWW27Rtm3bVFxcrHvvvTfay0GM83q9cjqdSktLU2Njo6ZOnapVq1YpLy8v2ktDjDtw4IDKy8s1c+ZMlZWVafbs2dqyZYvS09OjvTTEuHfffVf19fV66qmn9Pzzz0d7OYhBHo9HkydP1jvvvKPs7GzNnj1bH330EX83ISTRvMbQcYojW7du1aZNm3TaaadFeymIEzabTWlpaZIkp9MpwzDEv5UgFEVFRZo5c6YkadiwYcrPz1d1dXV0F4W4cNJJJykzMzPay0AMW7lypaZMmaIRI0YoIyNDp512mt58881oLwtxIprXGAqnMHnvvfd05plnavjw4bJYLHr55Zc73OfBBx9USUmJUlJSNG/ePK1cubJXr3HDDTfonnvuCdOKEQsG4n1TU1OjGTNmaOTIkfrZz36m/Pz8MK0e0TQQ7x3TZ599Jq/Xq+Li4n6uGtE2kO8bJK7+vo/279+vESNGBP88YsQI7du3byCWjiiL92sQhVOYNDY2asaMGXrwwQc7/fzSpUu1ePFiLVmyRKtXr9aMGTO0cOFCVVRUBO9j7kM5/GP//v365z//qQkTJmjChAkD9SVhAET6fSNJOTk5WrdunXbu3Km//e1vKi8vH5CvDZE1EO8dSaqurtYll1yiRx55JOJfEyJvoN43SGzheB9hcIr7946BsJNkvPTSS+1umzt3rnHNNdcE/+z1eo3hw4cb99xzT0jPedNNNxkjR440Ro8ebeTl5RlZWVnGHXfcEc5lI8oi8b453FVXXWU899xz/VkmYlCk3jstLS3GCSecYPzlL38J11IRQyJ5zXnnnXeMc889NxzLRIzry/voww8/NBYtWhT8/PXXX28888wzA7JexI7+XIOidY2h4zQAXC6XPvvsMy1YsCB4m9Vq1YIFC7RixYqQnuOee+5RaWmpdu3apXvvvVdXXHGFbrvttkgtGTEgHO+b8vJy1dfXS5Jqa2v13nvv6cgjj4zIehE7wvHeMQxDl112mb761a/q4osvjtRSEUPC8b4BQnkfzZ07V+vXr9e+ffvU0NCg119/XQsXLozWkhEj4uEaZI/2AgaDyspKeb1eFRYWtru9sLBQmzZtitKqEOvC8b7ZvXu3rrzyymAoxHXXXadp06ZFYrmIIeF473z44YdaunSppk+fHpxBf/rpp3n/JLBw/V21YMECrVu3To2NjRo5cqSee+45zZ8/P9zLRYwK5X1kt9t133336eSTT5bP59PPf/5zEvUQ8jUomtcYCqc4dNlll0V7CYgTc+fO1dq1a6O9DMSh448/Xj6fL9rLQBx6++23o70ExIGzzjpLZ511VrSXgTgUzWsMo3oDID8/XzabrcOm/PLycg0bNixKq0Ks432DvuK9g77gfYNw4H2EvoqH9w6F0wBITk7W7NmztXz58uBtPp9Py5cvZ3wBXeJ9g77ivYO+4H2DcOB9hL6Kh/cOo3ph0tDQoG3btgX/vHPnTq1du1a5ubkaNWqUFi9erEsvvVRz5szR3Llz9cADD6ixsVGXX355FFeNaON9g77ivYO+4H2DcOB9hL6K+/fOgOf4Jah33nnHkNTh49JLLw3e5/e//70xatQoIzk52Zg7d67x8ccfR2/BiAm8b9BXvHfQF7xvEA68j9BX8f7esRiGYQxEgQYAAAAA8Yo9TgAAAADQAwonAAAAAOgBhRMAAAAA9IDCCQAAAAB6QOEEAAAAAD2gcAIAAACAHlA4AQAAAEAPKJwAAAAAoAcUTgCAhFRSUqIHHngg2ssAACQICicAQJ9ddtllWrRoUbSX0alPP/1UV155ZcRfp6SkRBaLRRaLRWlpaZo2bZoee+yxXj+PxWLRyy+/HP4FAgDCgsIJABBX3G53SPcrKChQWlpahFfjd+edd+rAgQNav369vvvd7+qKK67Q66+/PiCvDQAYGBROAICIWb9+vU477TRlZGSosLBQF198sSorK4OfX7ZsmY4//njl5OQoLy9P3/jGN7R9+/bg53ft2iWLxaKlS5fqxBNPVEpKip555plgp+vee+9VUVGR8vLydM0117Qrqg4f1bNYLHrsscd09tlnKy0tTePHj9e//vWvduv917/+pfHjxyslJUUnn3yynnrqKVksFtXU1HT7dWZmZmrYsGEaO3asbrzxRuXm5uqtt94Kfv7TTz/V1772NeXn5ys7O1snnniiVq9e3W6tknT22WfLYrEE/yxJ//znP3XUUUcpJSVFY8eO1R133CGPxxPKtx8AEEYUTgCAiKipqdFXv/pVzZo1S6tWrdKyZctUXl6u888/P3ifxsZGLV68WKtWrdLy5ctltVp19tlny+fztXuum266Sddff702btyohQsXSpLeeecdbd++Xe+8846eeuopPfnkk3ryySe7XdMdd9yh888/X59//rlOP/10fec731F1dbUkaefOnTrvvPO0aNEirVu3Tj/84Q91yy239Opr9vl8euGFF3To0CElJycHb6+vr9ell16qDz74QB9//LHGjx+v008/XfX19ZL8hZUk/fnPf9aBAweCf37//fd1ySWX6Prrr9eGDRv08MMP68knn9Rdd93Vq3UBAMLAAACgjy699FLjm9/8Zqef++Uvf2mceuqp7W4rLS01JBmbN2/u9DEHDx40JBlffPGFYRiGsXPnTkOS8cADD3R43dGjRxsejyd427e+9S3jggsuCP559OjRxu9+97vgnyUZv/jFL4J/bmhoMCQZr7/+umEYhnHjjTcaU6dObfc6t9xyiyHJOHToUOffgMDrJCcnG+np6YbdbjckGbm5ucbWrVu7fIzX6zUyMzONV155pd36XnrppXb3O+WUU4y777673W1PP/20UVRU1OVzAwAig44TACAi1q1bp3feeUcZGRnBj4kTJ0pScBxv69atuuiiizR27FhlZWUFR9T27NnT7rnmzJnT4fmnTJkim80W/HNRUZEqKiq6XdP06dODv09PT1dWVlbwMZs3b9bRRx/d7v5z584N6Wv92c9+prVr1+o///mP5s2bp9/97ncaN25c8PPl5eW64oorNH78eGVnZysrK0sNDQ0dvs7DrVu3TnfeeWe77+EVV1yhAwcOqKmpKaS1AQDCwx7tBQAAElNDQ4POPPNM/eY3v+nwuaKiIknSmWeeqdGjR+vRRx/V8OHD5fP5NHXqVLlcrnb3T09P7/AcSUlJ7f5ssVg6jPiF4zGhyM/P17hx4zRu3Dg999xzmjZtmubMmaPJkydLki699FJVVVX9/+3cPUgjQRjG8SdBiHZKwEKIiYUIokIWAlrYRrC0ERELKwvFz0ICEljQKojYRFFstAmigiiCimLhFqKCjcT4UW1jY4Jgu+SK84ScxV7w7tbi/4NpZpnhfavlYWdHi4uLCofDCgQC6ujo+NTn797e3mSapnp6ej49q6ys/HLdAIA/R3ACAPwThmFoe3tbkUhEFRWfXzcvLy/K5XJaXV1VZ2enJOn8/Px/l/mhqalJBwcHJXO//jUqRygUUm9vrxKJhHZ3dyVJlmUpnU6ru7tbkmTbdsklGdLPUOc4TsmcYRjK5XIlX68AAN7gqB4A4EteX191c3NTMmzb1vDwsPL5vPr6+nR5eamnpycdHh5qcHBQjuOopqZGwWBQKysrenx81OnpqSYnJz3rY2hoSHd3d5qentb9/b02Nzc/Lpvw+Xxl7TU2Nqa9vT1dXV1JkhobG7WxsaFsNquLiwv19/erqqqqZE0kEtHJyYmen59VKBQkSclkUuvr6zJNU7e3t8pms8pkMpqZmfl6wwCAshCcAABfcnZ2pmg0WjJM01RdXZ0sy5LjOIrH42ptbdX4+Liqq6vl9/vl9/uVyWR0fX2tlpYWTUxMKJVKedZHQ0ODtra2tLOzo7a2Ni0tLX3cqhcIBMraq7m5WfF4XMlkUpK0tramQqEgwzA0MDCg0dFR1dbWlqyZn5/X8fGxQqGQotGoJKmrq0v7+/s6OjpSLBZTe3u7FhYWFA6H/0LHAIBy+IrFYtHrIgAA+I7m5ua0vLws27a9LgUA4DH+cQIA4F06nVYsFlMwGJRlWUqlUhoZGfG6LADAN0BwAgDg3cPDg2ZnZ5XP51VfX6+pqSklEgmvywIAfAMc1QMAAAAAF1wOAQAAAAAuCE4AAAAA4ILgBAAAAAAuCE4AAAAA4ILgBAAAAAAuCE4AAAAA4ILgBAAAAAAuCE4AAAAA4ILgBAAAAAAufgA92Wb8XHuv2QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0kMFKLvOF8s"
      },
      "source": [
        "To figure out the ideal value of the learning rate (at least the ideal value to *begin* training our model), the rule of thumb is to take the learning rate value where the loss is still decreasing but not quite flattened out (usually about 10x smaller than the bottom of the curve).\n",
        "\n",
        "In this case, our ideal learning rate ends up between `0.01` ($10^{-2}$) and `0.02`.\n",
        "\n",
        "![finding the ideal learning rate by plotting learning rate vs. loss](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-learning-rate-vs-loss.png)\n",
        "\n",
        "*The ideal learning rate at the start of model training is somewhere just before the loss curve bottoms out (a value where the loss is still decreasing).*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJo-nDw4zFfx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d62f4f89-c41b-4ca7-ce07-9c51a87f5cd2"
      },
      "source": [
        "# Example of other typical learning rate values\n",
        "10**0, 10**-1, 10**-2, 10**-3, 1e-4"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 0.1, 0.01, 0.001, 0.0001)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iALar-WvPlEc"
      },
      "source": [
        "Now we've estimated the ideal learning rate (we'll use `0.02`) for our model, let's refit it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJ9wbXblzPPL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8f59fd8-bda6-43d1-8dad-630dbb692422"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model\n",
        "model_10 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
        "])\n",
        "\n",
        "# Compile the model with the ideal learning rate\n",
        "model_10.compile(loss=\"binary_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.02), # to adjust the learning rate, you need to use tf.keras.optimizers.Adam (not \"adam\")\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model for 20 epochs (5 less than before)\n",
        "history = model_10.fit(X_train, y_train, epochs=20)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "25/25 [==============================] - 2s 4ms/step - loss: 0.6831 - accuracy: 0.6037\n",
            "Epoch 2/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.6705 - accuracy: 0.6025\n",
            "Epoch 3/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.6525 - accuracy: 0.6100\n",
            "Epoch 4/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.6175 - accuracy: 0.6762\n",
            "Epoch 5/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.5739 - accuracy: 0.7163\n",
            "Epoch 6/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.5204 - accuracy: 0.7850\n",
            "Epoch 7/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.4874 - accuracy: 0.7875\n",
            "Epoch 8/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.4448 - accuracy: 0.8175\n",
            "Epoch 9/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.4119 - accuracy: 0.8475\n",
            "Epoch 10/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.3944 - accuracy: 0.8425\n",
            "Epoch 11/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.3729 - accuracy: 0.8650\n",
            "Epoch 12/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3622 - accuracy: 0.8637\n",
            "Epoch 13/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3799 - accuracy: 0.8512\n",
            "Epoch 14/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3431 - accuracy: 0.8712\n",
            "Epoch 15/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3444 - accuracy: 0.8662\n",
            "Epoch 16/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3333 - accuracy: 0.8712\n",
            "Epoch 17/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3282 - accuracy: 0.8725\n",
            "Epoch 18/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3153 - accuracy: 0.8838\n",
            "Epoch 19/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3190 - accuracy: 0.8825\n",
            "Epoch 20/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3252 - accuracy: 0.8788\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qb9xwISc0pnF"
      },
      "source": [
        "Nice! With a little higher learning rate (`0.02` instead of `0.01`) we reach a higher accuracy than `model_8` in less epochs (`20` instead of `25`).\n",
        "\n",
        "> 🛠 **Practice:** Now you've seen an example of what can happen when you change the learning rate, try changing the learning rate value in the [TensorFlow Playground](https://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.03154&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&problem_hide=true) and see what happens. What happens if you increase it? What happens if you decrease it?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZCYAqitKUk2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f2ba555-6947-4d5e-abc8-3141c2e92e83"
      },
      "source": [
        "# Evaluate model on the test dataset\n",
        "model_10.evaluate(X_test, y_test)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 4ms/step - loss: 0.4014 - accuracy: 0.8200\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4013669192790985, 0.8199999928474426]"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWqQShzZKT7R"
      },
      "source": [
        "\n",
        "\n",
        "Let's see how the predictions look."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJbzQ5kQ2HSh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "outputId": "4ba07423-21ec-4125-d795-5a9aac309c89"
      },
      "source": [
        "# Plot the decision boundaries for the training and test sets\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_decision_boundary(model_10, X=X_train, y=y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_decision_boundary(model_10, X=X_test, y=y_test)\n",
        "plt.show()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n",
            "doing binary classifcation...\n",
            "313/313 [==============================] - 0s 1ms/step\n",
            "doing binary classifcation...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ppVLzAao2Hkl"
      },
      "source": [
        "And as we can see, almost perfect again.\n",
        "\n",
        "These are the kind of experiments you'll be running often when building your own models.\n",
        "\n",
        "Start with default settings and see how they perform on your data.\n",
        "\n",
        "And if they don't perform as well as you'd like, improve them.\n",
        "\n",
        "Let's look at a few more ways to evaluate our classification models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0bNdy1EbftYp"
      },
      "source": [
        "### More classification evaluation methods\n",
        "\n",
        "Alongside the visualizations we've been making, there are a number of different evaluation metrics we can use to evaluate our classification models.\n",
        "\n",
        "| **Metric name/Evaluation method** | **Defintion** | **Code** |\n",
        "| --- | --- | --- |\n",
        "| Accuracy | Out of 100 predictions, how many does your model get correct? E.g. 95% accuracy means it gets 95/100 predictions correct. | [`sklearn.metrics.accuracy_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html) or [`tf.keras.metrics.Accuracy()`](tensorflow.org/api_docs/python/tf/keras/metrics/Accuracy) |\n",
        "| Precision | Proportion of true positives over total number of samples. Higher precision leads to less false positives (model predicts 1 when it should've been 0). | [`sklearn.metrics.precision_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) or [`tf.keras.metrics.Precision()`](tensorflow.org/api_docs/python/tf/keras/metrics/Precision) |\n",
        "| Recall | Proportion of true positives over total number of true positives and false negatives (model predicts 0 when it should've been 1). Higher recall leads to less false negatives. | [`sklearn.metrics.recall_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html) or [`tf.keras.metrics.Recall()`](tensorflow.org/api_docs/python/tf/keras/metrics/Recall) |\n",
        "| F1-score | Combines precision and recall into one metric. 1 is best, 0 is worst. | [`sklearn.metrics.f1_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) |\n",
        "| [Confusion matrix](https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/)  | Compares the predicted values with the true values in a tabular way, if 100% correct, all values in the matrix will be top left to bottom right (diagnol line). | Custom function or [`sklearn.metrics.plot_confusion_matrix()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html) |\n",
        "| Classification report | Collection of some of the main classification metrics such as precision, recall and f1-score. | [`sklearn.metrics.classification_report()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html) |\n",
        "\n",
        "> 🔑 **Note:** Every classification problem will require different kinds of evaluation methods. But you should be familiar with at least the ones above.\n",
        "\n",
        "Let's start with accuracy.\n",
        "\n",
        "Because we passed `[\"accuracy\"]` to the `metrics` parameter when we compiled our model, calling `evaluate()` on it will return the loss as well as accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUvEwzqp4zVW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0adef01f-812c-4cea-e0ae-620063a913a6"
      },
      "source": [
        "# Check the accuracy of our model\n",
        "loss, accuracy = model_10.evaluate(X_test, y_test)\n",
        "print(f\"Model loss on test set: {loss}\")\n",
        "print(f\"Model accuracy on test set: {(accuracy*100):.2f}%\")"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 3ms/step - loss: 0.4014 - accuracy: 0.8200\n",
            "Model loss on test set: 0.4013669192790985\n",
            "Model accuracy on test set: 82.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5fXPTGi5aRX"
      },
      "source": [
        "How about a confusion matrix?\n",
        "\n",
        "![anatomy of a confusion matrix](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-anatomy-of-a-confusion-matrix.png)\n",
        "*Anatomy of a confusion matrix (what we're going to be creating). Correct predictions appear down the diagonal (from top left to bottom right).*\n",
        "\n",
        "We can make a confusion matrix using [Scikit-Learn's `confusion_matrix`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html) method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_Zee4lI5vi2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "c176acc6-b07a-47f8-b7af-43fc8cd3663b"
      },
      "source": [
        "# Create a confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Make predictions\n",
        "y_preds = model_10.predict(X_test)\n",
        "\n",
        "# Create confusion matrix\n",
        "confusion_matrix(y_test, y_preds)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-94-f9843efd97f5>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Create confusion matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_preds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[1;32m    315\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m     \"\"\"\n\u001b[0;32m--> 317\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    318\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s is not supported\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m     96\u001b[0m             \"Classification metrics can't handle a mix of {0} and {1} targets\".format(\n\u001b[1;32m     97\u001b[0m                 \u001b[0mtype_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of binary and continuous targets"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4GLOPxx9fx__"
      },
      "source": [
        "Ahh, it seems our predictions aren't in the format they need to be.\n",
        "\n",
        "Let's check them out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aLBs249Gfu-W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0253795-31a6-44de-9eae-48c7ece62f6a"
      },
      "source": [
        "# View the first 10 predictions\n",
        "y_preds[:10]"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.82973593],\n",
              "       [0.82973593],\n",
              "       [0.82973593],\n",
              "       [0.82973593],\n",
              "       [0.744222  ],\n",
              "       [0.78953826],\n",
              "       [0.82973593],\n",
              "       [0.45766044],\n",
              "       [0.82973593],\n",
              "       [0.00916267]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zacw9BNegVkY"
      },
      "source": [
        "What about our test labels?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3BH8XHAegYMd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56d40e1b-066d-4238-9a57-7ff4d89560e8"
      },
      "source": [
        "# View the first 10 test labels\n",
        "y_test[:10]"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 0, 0, 1, 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vd8EA95ggks"
      },
      "source": [
        "It looks like we need to get our predictions into the binary format (0 or 1).\n",
        "\n",
        "But you might be wondering, what format are they currently in?\n",
        "\n",
        "In their current format (`9.8526537e-01`), they're in a form called **prediction probabilities**.\n",
        "\n",
        "You'll see this often with the outputs of neural networks. Often they won't be exact values but more a probability of how *likely* they are to be one value or another.\n",
        "\n",
        "So one of the steps you'll often see after making predicitons with a neural network is converting the prediction probabilities into labels.\n",
        "\n",
        "In our case, since our ground truth labels (`y_test`) are binary (0 or 1), we can convert the prediction probabilities using to their binary form using [`tf.round()`](https://www.tensorflow.org/api_docs/python/tf/math/round)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YaS_yvGA6KV3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8fb2cc8-834d-4c7e-edf4-4724f7ab1eef"
      },
      "source": [
        "# Convert prediction probabilities to binary format and view the first 10\n",
        "tf.round(y_preds)[:10]"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 1), dtype=float32, numpy=\n",
              "array([[1.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       [0.],\n",
              "       [1.],\n",
              "       [0.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mLvWYgFIh_A9"
      },
      "source": [
        "Wonderful! Now we can use the `confusion_matrix` function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GEZx1WHCiC8n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af992c1d-bb6f-4585-dd6e-84679e9b9d81"
      },
      "source": [
        "# Create a confusion matrix\n",
        "confusion_matrix(y_test, tf.round(y_preds))"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[65, 36],\n",
              "       [ 0, 99]])"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VY1LmNeTiKHl"
      },
      "source": [
        "Alright, we can see the highest numbers are down the diagonal (from top left to bottom right) so this a good sign, but the rest of the matrix doesn't really tell us much.\n",
        "\n",
        "How about we make a function to make our confusion matrix a little more visual?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IIPSs9ERi78w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "outputId": "edce2cc7-3605-4d6f-958b-4307e92b3b6c"
      },
      "source": [
        "# Note: The following confusion matrix code is a remix of Scikit-Learn's \n",
        "# plot_confusion_matrix function - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html\n",
        "# and Made with ML's introductory notebook - https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/08_Neural_Networks.ipynb \n",
        "import itertools\n",
        "\n",
        "figsize = (10, 10)\n",
        "\n",
        "# Create the confusion matrix\n",
        "cm = confusion_matrix(y_test, tf.round(y_preds))\n",
        "cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis] # normalize it\n",
        "n_classes = cm.shape[0]\n",
        "\n",
        "# Let's prettify it\n",
        "fig, ax = plt.subplots(figsize=figsize)\n",
        "# Create a matrix plot\n",
        "cax = ax.matshow(cm, cmap=plt.cm.Blues) # https://matplotlib.org/3.2.0/api/_as_gen/matplotlib.axes.Axes.matshow.html\n",
        "fig.colorbar(cax)\n",
        "\n",
        "# Create classes\n",
        "classes = False\n",
        "\n",
        "if classes:\n",
        "  labels = classes\n",
        "else:\n",
        "  labels = np.arange(cm.shape[0])\n",
        "\n",
        "# Label the axes\n",
        "ax.set(title=\"Confusion Matrix\",\n",
        "       xlabel=\"Predicted label\",\n",
        "       ylabel=\"True label\",\n",
        "       xticks=np.arange(n_classes),\n",
        "       yticks=np.arange(n_classes),\n",
        "       xticklabels=labels,\n",
        "       yticklabels=labels)\n",
        "\n",
        "# Set x-axis labels to bottom\n",
        "ax.xaxis.set_label_position(\"bottom\")\n",
        "ax.xaxis.tick_bottom()\n",
        "\n",
        "# Adjust label size\n",
        "ax.xaxis.label.set_size(20)\n",
        "ax.yaxis.label.set_size(20)\n",
        "ax.title.set_size(20)\n",
        "\n",
        "# Set threshold for different colors\n",
        "threshold = (cm.max() + cm.min()) / 2.\n",
        "\n",
        "# Plot the text on each cell\n",
        "for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "  plt.text(j, i, f\"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)\",\n",
        "           horizontalalignment=\"center\",\n",
        "           color=\"white\" if cm[i, j] > threshold else \"black\",\n",
        "           size=15)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0EaKOeeNnWbq"
      },
      "source": [
        "That looks much better. It seems our model has made almost perfect predictions on the test set except for two false positives (top right corner)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZvNQjM-rk7uE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe681bb3-ce07-4fe6-b1ca-8f70dc6eba0b"
      },
      "source": [
        "# What does itertools.product do? Combines two things into each combination\n",
        "import itertools\n",
        "for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "  print(i, j)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 0\n",
            "0 1\n",
            "1 0\n",
            "1 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2WLKnUi6fI6B"
      },
      "source": [
        "## Working with a larger example (multiclass classification)\n",
        "\n",
        "We've seen a binary classification example (predicting if a data point is part of a red circle or blue circle) but what if you had multiple different classes of things?\n",
        "\n",
        "For example, say you were a fashion company and you wanted to build a neural network to predict whether a piece of clothing was a shoe, a shirt or a jacket (3 different options).\n",
        "\n",
        "When you have more than two classes as an option, this is known as **multiclass classification**.\n",
        "\n",
        "The good news is, the things we've learned so far (with a few tweaks) can be applied to multiclass classification problems as well.\n",
        "\n",
        "Let's see it in action.\n",
        "\n",
        "To start, we'll need some data. The good thing for us is TensorFlow has a multiclass classication dataset known as [Fashion MNIST built-in](https://github.com/zalandoresearch/fashion-mnist). Meaning we can get started straight away.\n",
        "\n",
        "We can import it using the [`tf.keras.datasets`](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) module.\n",
        "\n",
        "> 📖 **Resource:** The following multiclass classification problem has been adapted from the [TensorFlow classification guide](https://www.tensorflow.org/tutorials/keras/classification). A good exercise would be to once you've gone through the following example, replicate the TensorFlow guide."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zl50sxPTqpw4"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "\n",
        "# The data has already been sorted into training and test sets for us\n",
        "(train_data, train_labels), (test_data, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D6qJforMrCZy"
      },
      "source": [
        "Now let's check out an example."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PWdrQsyrBcy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5a53fb9-0450-4cf8-a2b3-56d2f6674c2b"
      },
      "source": [
        "# Show the first training example\n",
        "print(f\"Training sample:\\n{train_data[0]}\\n\") \n",
        "print(f\"Training label: {train_labels[0]}\")"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training sample:\n",
            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0\n",
            "    0   1   4   0   0   0   0   1   1   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   3   0  36 136 127  62\n",
            "   54   0   0   0   1   3   4   0   0   3]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   6   0 102 204 176 134\n",
            "  144 123  23   0   0   0   0  12  10   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 155 236 207 178\n",
            "  107 156 161 109  64  23  77 130  72  15]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   1   0  69 207 223 218 216\n",
            "  216 163 127 121 122 146 141  88 172  66]\n",
            " [  0   0   0   0   0   0   0   0   0   1   1   1   0 200 232 232 233 229\n",
            "  223 223 215 213 164 127 123 196 229   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 183 225 216 223 228\n",
            "  235 227 224 222 224 221 223 245 173   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 193 228 218 213 198\n",
            "  180 212 210 211 213 223 220 243 202   0]\n",
            " [  0   0   0   0   0   0   0   0   0   1   3   0  12 219 220 212 218 192\n",
            "  169 227 208 218 224 212 226 197 209  52]\n",
            " [  0   0   0   0   0   0   0   0   0   0   6   0  99 244 222 220 218 203\n",
            "  198 221 215 213 222 220 245 119 167  56]\n",
            " [  0   0   0   0   0   0   0   0   0   4   0   0  55 236 228 230 228 240\n",
            "  232 213 218 223 234 217 217 209  92   0]\n",
            " [  0   0   1   4   6   7   2   0   0   0   0   0 237 226 217 223 222 219\n",
            "  222 221 216 223 229 215 218 255  77   0]\n",
            " [  0   3   0   0   0   0   0   0   0  62 145 204 228 207 213 221 218 208\n",
            "  211 218 224 223 219 215 224 244 159   0]\n",
            " [  0   0   0   0  18  44  82 107 189 228 220 222 217 226 200 205 211 230\n",
            "  224 234 176 188 250 248 233 238 215   0]\n",
            " [  0  57 187 208 224 221 224 208 204 214 208 209 200 159 245 193 206 223\n",
            "  255 255 221 234 221 211 220 232 246   0]\n",
            " [  3 202 228 224 221 211 211 214 205 205 205 220 240  80 150 255 229 221\n",
            "  188 154 191 210 204 209 222 228 225   0]\n",
            " [ 98 233 198 210 222 229 229 234 249 220 194 215 217 241  65  73 106 117\n",
            "  168 219 221 215 217 223 223 224 229  29]\n",
            " [ 75 204 212 204 193 205 211 225 216 185 197 206 198 213 240 195 227 245\n",
            "  239 223 218 212 209 222 220 221 230  67]\n",
            " [ 48 203 183 194 213 197 185 190 194 192 202 214 219 221 220 236 225 216\n",
            "  199 206 186 181 177 172 181 205 206 115]\n",
            " [  0 122 219 193 179 171 183 196 204 210 213 207 211 210 200 196 194 191\n",
            "  195 191 198 192 176 156 167 177 210  92]\n",
            " [  0   0  74 189 212 191 175 172 175 181 185 188 189 188 193 198 204 209\n",
            "  210 210 211 188 188 194 192 216 170   0]\n",
            " [  2   0   0   0  66 200 222 237 239 242 246 243 244 221 220 193 191 179\n",
            "  182 182 181 176 166 168  99  58   0   0]\n",
            " [  0   0   0   0   0   0   0  40  61  44  72  41  35   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]]\n",
            "\n",
            "Training label: 9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MMqbRp8jrdtv"
      },
      "source": [
        "Woah, we get a large list of numbers, followed (the data) by a single number (the class label).\n",
        "\n",
        "What about the shapes?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gN5-jr6arj19",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10187c2d-8704-4f9a-930b-ed4983dbed2f"
      },
      "source": [
        "# Check the shape of our data\n",
        "train_data.shape, train_labels.shape, test_data.shape, test_labels.shape"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNfIOUUEsJRt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ee330e7-797c-4566-92d8-513564e2bf74"
      },
      "source": [
        "# Check shape of a single example\n",
        "train_data[0].shape, train_labels[0].shape"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((28, 28), ())"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2wW0cEfsAve"
      },
      "source": [
        "Okay, 60,000 training examples each with shape (28, 28) and a label each as well as 10,000 test examples of shape (28, 28).\n",
        "\n",
        "But these are just numbers, let's visualize."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmC2VsWOscKP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "b90ad917-c182-416c-ac7d-8aea5d173429"
      },
      "source": [
        "# Plot a single example\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(train_data[7]);"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sOqirdtfstdQ"
      },
      "source": [
        "Hmm, but what about its label?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzTDEpaYsxga",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9c3472f-c92f-4778-f6d0-59e28b998822"
      },
      "source": [
        "# Check our samples label\n",
        "train_labels[7]"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZHdVBrCUs10A"
      },
      "source": [
        "It looks like our labels are in numerical form. And while this is fine for a neural network, you might want to have them in human readable form.\n",
        "\n",
        "Let's create a small list of the class names (we can find them on [the dataset's GitHub page](https://github.com/zalandoresearch/fashion-mnist#labels)).\n",
        "\n",
        "> 🔑 **Note:** Whilst this dataset has been prepared for us and ready to go, it's important to remember many datasets won't be ready to go like this one. Often you'll have to do a few preprocessing steps to have it ready to use with a neural network (we'll see more of this when we work with our own data later)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGOi32T8s1ai",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "512efff5-cddc-46bb-fe37-4af308ad7735"
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
        "\n",
        "# How many classes are there (this'll be our output shape)?\n",
        "len(class_names)"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zOiwINQQtzys"
      },
      "source": [
        "Now we have these, let's plot another example.\n",
        "\n",
        "> 🤔 **Question:** Pay particular attention to what the data we're working with *looks* like. Is it only straight lines? Or does it have non-straight lines as well? Do you think if we wanted to find patterns in the photos of clothes (which are actually collections of pixels), will our model need non-linearities (non-straight lines) or not?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_qD40id2tytn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "a6c6da14-8cb1-4d6a-d769-79500cee5b9a"
      },
      "source": [
        "# Plot an example image and its label\n",
        "plt.imshow(train_data[17], cmap=plt.cm.binary) # change the colours to black & white\n",
        "plt.title(class_names[train_labels[17]]);"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rtz9__w3s4JF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "d55a1527-23cf-44b1-adae-7365924acfbd"
      },
      "source": [
        "# Plot multiple random images of fashion MNIST\n",
        "import random\n",
        "plt.figure(figsize=(7, 7))\n",
        "for i in range(4):\n",
        "  ax = plt.subplot(2, 2, i + 1)\n",
        "  rand_index = random.choice(range(len(train_data)))\n",
        "  plt.imshow(train_data[rand_index], cmap=plt.cm.binary)\n",
        "  plt.title(class_names[train_labels[rand_index]])\n",
        "  plt.axis(False)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLqZif3Rv0Aq"
      },
      "source": [
        "Alright, let's build a model to figure out the relationship between the pixel values and their labels.\n",
        "\n",
        "Since this is a multiclass classification problem, we'll need to make a few changes to our architecture (inline with Table 1 above):\n",
        "\n",
        "* The **input shape** will have to deal with 28x28 tensors (the height and width of our images).\n",
        "  * We're actually going to squash the input into a tensor (vector) of shape `(784)`.\n",
        "* The **output shape** will have to be 10 because we need our model to predict for 10 different classes.\n",
        "  * We'll also change the `activation` parameter of our output layer to be [`\"softmax\"`](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax) instead of `'sigmoid'`. As we'll see the `\"softmax\"` activation function outputs a series of values between 0 & 1 (the same shape as **output shape**, which together add up to ~1. The index with the highest value is predicted by the model to be the most *likely* class.\n",
        "* We'll need to change our loss function from a binary loss function to a multiclass loss function. \n",
        "  * More specifically, since our labels are in integer form, we'll use [`tf.keras.losses.SparseCategoricalCrossentropy()`](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/losses/SparseCategoricalCrossentropy), if our labels were one-hot encoded (e.g. they looked something like `[0, 0, 1, 0, 0...]`), we'd use [`tf.keras.losses.CategoricalCrossentropy()`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalCrossentropy).\n",
        "* We'll also use the `validation_data` parameter when calling the `fit()` function. This will give us an idea of how the model performs on the test set during training.\n",
        "\n",
        "You ready? Let's go."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qUFHuzIpv30K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92fff40c-463b-47db-92ed-212ecf27d0ec"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model\n",
        "model_11 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(10, activation=tf.keras.activations.softmax)\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_11.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "                 optimizer=tf.keras.optimizers.Adam(),\n",
        "                 metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "non_norm_history = model_11.fit(train_data,\n",
        "                                tf.one_hot(train_labels, depth=10),\n",
        "                                epochs=10,\n",
        "                                validation_data=(test_data, tf.one_hot(test_labels, depth=10)))"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 8s 3ms/step - loss: 1.4328 - accuracy: 0.4478 - val_loss: 1.0988 - val_accuracy: 0.5670\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.9787 - accuracy: 0.6252 - val_loss: 0.9259 - val_accuracy: 0.6440\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.8544 - accuracy: 0.6770 - val_loss: 0.8354 - val_accuracy: 0.6971\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7808 - accuracy: 0.7176 - val_loss: 0.7759 - val_accuracy: 0.7182\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.7393 - accuracy: 0.7345 - val_loss: 0.7440 - val_accuracy: 0.7280\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7152 - accuracy: 0.7433 - val_loss: 0.7415 - val_accuracy: 0.7380\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.7002 - accuracy: 0.7484 - val_loss: 0.7196 - val_accuracy: 0.7412\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6881 - accuracy: 0.7518 - val_loss: 0.7108 - val_accuracy: 0.7478\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6777 - accuracy: 0.7563 - val_loss: 0.7034 - val_accuracy: 0.7497\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6716 - accuracy: 0.7604 - val_loss: 0.6984 - val_accuracy: 0.7524\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hzYWEgoVJ_p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca78ead7-b9a6-4fb7-8aa2-15096ec29986"
      },
      "source": [
        "# Check the shapes of our model\n",
        "# Note: the \"None\" in (None, 784) is for batch_size, we'll cover this in a later module\n",
        "model_11.summary()"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_23\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_5 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense_64 (Dense)            (None, 4)                 3140      \n",
            "                                                                 \n",
            " dense_65 (Dense)            (None, 4)                 20        \n",
            "                                                                 \n",
            " dense_66 (Dense)            (None, 10)                50        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,210\n",
            "Trainable params: 3,210\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XRfkre59zSto"
      },
      "source": [
        "Alright, our model gets to about ~35% accuracy after 10 epochs using a similar style model to what we used on our binary classification problem.\n",
        "\n",
        "Which is better than guessing (guessing with 10 classes would result in about 10% accuracy) but we can do better.\n",
        "\n",
        "Do you remember when we talked about neural networks preferring numbers between 0 and 1? (if not, treat this as a reminder)\n",
        "\n",
        "Well, right now, the data we have isn't between 0 and 1, in other words, it's not normalized (hence why we used the `non_norm_history` variable when calling `fit()`). It's pixel values are between 0 and 255.\n",
        "\n",
        "Let's see."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGiweanwz82_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7ba8fd6-a29a-4e89-f674-5b1938fc0ef8"
      },
      "source": [
        "# Check the min and max values of the training data\n",
        "train_data.min(), train_data.max()"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "syB7LOk30H0_"
      },
      "source": [
        "We can get these values between 0 and 1 by dividing the entire array by the maximum: `255.0` (dividing by a float also converts to a float).\n",
        "\n",
        "\n",
        "Doing so will result in all of our data being between 0 and 1 (known as **scaling** or **normalization**)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABRKp5U8voV_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "083b3598-8695-43f5-f64f-627a8f5e1f80"
      },
      "source": [
        "# Divide train and test images by the maximum value (normalize it)\n",
        "train_data = train_data / 255.0\n",
        "test_data = test_data / 255.0\n",
        "\n",
        "# Check the min and max values of the training data\n",
        "train_data.min(), train_data.max()"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 0.00392156862745098)"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LeYQEXOf06oo"
      },
      "source": [
        "Beautiful! Now our data is between 0 and 1. Let's see what happens when we model it.\n",
        "\n",
        "We'll use the same model as before (`model_11`) except this time the data will be normalized."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1QRy7y_K_87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf464ef5-5f3b-4fac-b1ee-7c21cd716b9d"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model\n",
        "model_12 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)), # input layer (we had to reshape 28x28 to 784)\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(10, activation=\"softmax\") # output shape is 10, activation is softmax\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_12.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                 optimizer=tf.keras.optimizers.Adam(),\n",
        "                 metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model (to the normalized data)\n",
        "norm_history = model_12.fit(train_data,\n",
        "                            train_labels,\n",
        "                            epochs=10,\n",
        "                            validation_data=(test_data, test_labels))"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 1.8323 - accuracy: 0.2312 - val_loss: 1.5601 - val_accuracy: 0.3152\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.3739 - accuracy: 0.4692 - val_loss: 1.1972 - val_accuracy: 0.5608\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.0978 - accuracy: 0.5811 - val_loss: 1.0421 - val_accuracy: 0.5992\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.9914 - accuracy: 0.6165 - val_loss: 0.9660 - val_accuracy: 0.6302\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.9259 - accuracy: 0.6448 - val_loss: 0.9106 - val_accuracy: 0.6486\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.8860 - accuracy: 0.6589 - val_loss: 0.8811 - val_accuracy: 0.6559\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.8627 - accuracy: 0.6702 - val_loss: 0.8617 - val_accuracy: 0.6704\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.8460 - accuracy: 0.6764 - val_loss: 0.8481 - val_accuracy: 0.6813\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.8320 - accuracy: 0.6844 - val_loss: 0.8358 - val_accuracy: 0.6876\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.8210 - accuracy: 0.6885 - val_loss: 0.8265 - val_accuracy: 0.6884\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C_I2KNJiMWZ8"
      },
      "source": [
        "Woah, we used the exact same model as before but we with normalized data we're now seeing a much higher accuracy value!\n",
        "\n",
        "Let's plot each model's history (their loss curves)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmRcYU7xN1wQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "outputId": "600d1352-fd9d-499a-ca5a-5f013bd861f6"
      },
      "source": [
        "import pandas as pd\n",
        "# Plot non-normalized data loss curves\n",
        "pd.DataFrame(non_norm_history.history).plot(title=\"Non-normalized Data\")\n",
        "# Plot normalized data loss curves\n",
        "pd.DataFrame(norm_history.history).plot(title=\"Normalized data\");"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKm8MsACOm4j"
      },
      "source": [
        "Wow. From these two plots, we can see how much quicker our model with the normalized data (`model_12`) improved than the model with the non-normalized data (`model_11`).\n",
        "\n",
        "> 🔑 **Note:** The same model with even *slightly* different data can produce *dramatically* different results. So when you're comparing models, it's important to make sure you're comparing them on the same criteria (e.g. same architecture but different data or same data but different architecture).\n",
        "\n",
        "How about we find the ideal learning rate and see what happens?\n",
        "\n",
        "We'll use the same architecture we've been using."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LcR_wb4nPSb2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "786011ab-2456-46b7-8d75-a900409ea30a"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model\n",
        "model_13 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)), # input layer (we had to reshape 28x28 to 784)\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(10, activation=\"softmax\") # output shape is 10, activation is softmax\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_13.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                 optimizer=tf.keras.optimizers.Adam(),\n",
        "                 metrics=[\"accuracy\"])\n",
        "\n",
        "# Create the learning rate callback\n",
        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-3 * 10**(epoch/20))\n",
        "\n",
        "# Fit the model\n",
        "find_lr_history = model_13.fit(train_data,\n",
        "                               train_labels,\n",
        "                               epochs=40, # model already doing pretty good with current LR, probably don't need 100 epochs\n",
        "                               validation_data=(test_data, test_labels),\n",
        "                               callbacks=[lr_scheduler])"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 1.6535 - accuracy: 0.3828 - val_loss: 1.1865 - val_accuracy: 0.5720 - lr: 0.0010\n",
            "Epoch 2/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.0159 - accuracy: 0.6455 - val_loss: 0.9266 - val_accuracy: 0.6625 - lr: 0.0011\n",
            "Epoch 3/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.8720 - accuracy: 0.6811 - val_loss: 0.8496 - val_accuracy: 0.6857 - lr: 0.0013\n",
            "Epoch 4/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.8162 - accuracy: 0.7040 - val_loss: 0.8123 - val_accuracy: 0.7024 - lr: 0.0014\n",
            "Epoch 5/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7836 - accuracy: 0.7173 - val_loss: 0.7847 - val_accuracy: 0.7223 - lr: 0.0016\n",
            "Epoch 6/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7602 - accuracy: 0.7268 - val_loss: 0.7674 - val_accuracy: 0.7233 - lr: 0.0018\n",
            "Epoch 7/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7419 - accuracy: 0.7340 - val_loss: 0.7640 - val_accuracy: 0.7216 - lr: 0.0020\n",
            "Epoch 8/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7259 - accuracy: 0.7394 - val_loss: 0.7391 - val_accuracy: 0.7346 - lr: 0.0022\n",
            "Epoch 9/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7124 - accuracy: 0.7452 - val_loss: 0.7272 - val_accuracy: 0.7406 - lr: 0.0025\n",
            "Epoch 10/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6995 - accuracy: 0.7489 - val_loss: 0.7182 - val_accuracy: 0.7423 - lr: 0.0028\n",
            "Epoch 11/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6883 - accuracy: 0.7520 - val_loss: 0.7124 - val_accuracy: 0.7497 - lr: 0.0032\n",
            "Epoch 12/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6766 - accuracy: 0.7567 - val_loss: 0.6958 - val_accuracy: 0.7549 - lr: 0.0035\n",
            "Epoch 13/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6647 - accuracy: 0.7605 - val_loss: 0.6883 - val_accuracy: 0.7555 - lr: 0.0040\n",
            "Epoch 14/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6536 - accuracy: 0.7637 - val_loss: 0.6803 - val_accuracy: 0.7585 - lr: 0.0045\n",
            "Epoch 15/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6430 - accuracy: 0.7681 - val_loss: 0.6716 - val_accuracy: 0.7634 - lr: 0.0050\n",
            "Epoch 16/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6354 - accuracy: 0.7690 - val_loss: 0.6592 - val_accuracy: 0.7664 - lr: 0.0056\n",
            "Epoch 17/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6259 - accuracy: 0.7723 - val_loss: 0.6474 - val_accuracy: 0.7678 - lr: 0.0063\n",
            "Epoch 18/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6168 - accuracy: 0.7738 - val_loss: 0.6447 - val_accuracy: 0.7678 - lr: 0.0071\n",
            "Epoch 19/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6102 - accuracy: 0.7734 - val_loss: 0.6314 - val_accuracy: 0.7716 - lr: 0.0079\n",
            "Epoch 20/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6050 - accuracy: 0.7752 - val_loss: 0.6279 - val_accuracy: 0.7722 - lr: 0.0089\n",
            "Epoch 21/40\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6019 - accuracy: 0.7764 - val_loss: 0.6226 - val_accuracy: 0.7708 - lr: 0.0100\n",
            "Epoch 22/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5991 - accuracy: 0.7792 - val_loss: 0.6331 - val_accuracy: 0.7726 - lr: 0.0112\n",
            "Epoch 23/40\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.5995 - accuracy: 0.7770 - val_loss: 0.6303 - val_accuracy: 0.7601 - lr: 0.0126\n",
            "Epoch 24/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6005 - accuracy: 0.7768 - val_loss: 0.6172 - val_accuracy: 0.7783 - lr: 0.0141\n",
            "Epoch 25/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5991 - accuracy: 0.7787 - val_loss: 0.6256 - val_accuracy: 0.7757 - lr: 0.0158\n",
            "Epoch 26/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6005 - accuracy: 0.7761 - val_loss: 0.6695 - val_accuracy: 0.7584 - lr: 0.0178\n",
            "Epoch 27/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6006 - accuracy: 0.7784 - val_loss: 0.6315 - val_accuracy: 0.7719 - lr: 0.0200\n",
            "Epoch 28/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6003 - accuracy: 0.7781 - val_loss: 0.6666 - val_accuracy: 0.7571 - lr: 0.0224\n",
            "Epoch 29/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6011 - accuracy: 0.7782 - val_loss: 0.6508 - val_accuracy: 0.7527 - lr: 0.0251\n",
            "Epoch 30/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6011 - accuracy: 0.7771 - val_loss: 0.6440 - val_accuracy: 0.7646 - lr: 0.0282\n",
            "Epoch 31/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5993 - accuracy: 0.7778 - val_loss: 0.6345 - val_accuracy: 0.7697 - lr: 0.0316\n",
            "Epoch 32/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6061 - accuracy: 0.7754 - val_loss: 0.6244 - val_accuracy: 0.7770 - lr: 0.0355\n",
            "Epoch 33/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6067 - accuracy: 0.7756 - val_loss: 0.6432 - val_accuracy: 0.7690 - lr: 0.0398\n",
            "Epoch 34/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6118 - accuracy: 0.7722 - val_loss: 0.6402 - val_accuracy: 0.7734 - lr: 0.0447\n",
            "Epoch 35/40\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6130 - accuracy: 0.7704 - val_loss: 0.6773 - val_accuracy: 0.7583 - lr: 0.0501\n",
            "Epoch 36/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6187 - accuracy: 0.7712 - val_loss: 0.6369 - val_accuracy: 0.7747 - lr: 0.0562\n",
            "Epoch 37/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6223 - accuracy: 0.7717 - val_loss: 0.6929 - val_accuracy: 0.7448 - lr: 0.0631\n",
            "Epoch 38/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6337 - accuracy: 0.7657 - val_loss: 0.7518 - val_accuracy: 0.7320 - lr: 0.0708\n",
            "Epoch 39/40\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6375 - accuracy: 0.7665 - val_loss: 0.6864 - val_accuracy: 0.7563 - lr: 0.0794\n",
            "Epoch 40/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6556 - accuracy: 0.7593 - val_loss: 0.7107 - val_accuracy: 0.7393 - lr: 0.0891\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "5-ZCGMTRS9md",
        "outputId": "bfc718fc-d494-4c26-9605-39484c9ca8e5"
      },
      "source": [
        "# Plot the learning rate decay curve\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "lrs = 1e-3 * (10**(tf.range(40)/20))\n",
        "plt.semilogx(lrs, find_lr_history.history[\"loss\"])\n",
        "plt.xlabel(\"Learning rate\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Finding the ideal learning rate\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Finding the ideal learning rate')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GqtOjggqWcfS"
      },
      "source": [
        "In this case, it looks like somewhere close to the default learning rate of the [Adam optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) (`0.001`) is the ideal learning rate.\n",
        "\n",
        "Let's refit a model using the ideal learning rate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9KFhAwKXjWd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14eabaef-f846-4593-f694-bd09a80e6b97"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model\n",
        "model_14 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)), # input layer (we had to reshape 28x28 to 784)\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(10, activation=\"softmax\") # output shape is 10, activation is softmax\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_14.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                 optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=0.001), # ideal learning rate (same as default)\n",
        "                 metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "history = model_14.fit(train_data,\n",
        "                       train_labels,\n",
        "                       epochs=20,\n",
        "                       validation_data=(test_data, test_labels))"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.6538 - accuracy: 0.4141 - val_loss: 1.1606 - val_accuracy: 0.5891\n",
            "Epoch 2/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.0047 - accuracy: 0.6330 - val_loss: 0.9291 - val_accuracy: 0.6533\n",
            "Epoch 3/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.8721 - accuracy: 0.6771 - val_loss: 0.8558 - val_accuracy: 0.6808\n",
            "Epoch 4/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.8192 - accuracy: 0.7018 - val_loss: 0.8200 - val_accuracy: 0.7030\n",
            "Epoch 5/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.7877 - accuracy: 0.7149 - val_loss: 0.7923 - val_accuracy: 0.7150\n",
            "Epoch 6/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.7650 - accuracy: 0.7240 - val_loss: 0.7754 - val_accuracy: 0.7191\n",
            "Epoch 7/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.7476 - accuracy: 0.7302 - val_loss: 0.7590 - val_accuracy: 0.7232\n",
            "Epoch 8/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.7332 - accuracy: 0.7349 - val_loss: 0.7479 - val_accuracy: 0.7321\n",
            "Epoch 9/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.7210 - accuracy: 0.7409 - val_loss: 0.7368 - val_accuracy: 0.7332\n",
            "Epoch 10/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.7106 - accuracy: 0.7449 - val_loss: 0.7271 - val_accuracy: 0.7385\n",
            "Epoch 11/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.7020 - accuracy: 0.7489 - val_loss: 0.7210 - val_accuracy: 0.7415\n",
            "Epoch 12/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6942 - accuracy: 0.7519 - val_loss: 0.7142 - val_accuracy: 0.7455\n",
            "Epoch 13/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.6869 - accuracy: 0.7552 - val_loss: 0.7084 - val_accuracy: 0.7478\n",
            "Epoch 14/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.6806 - accuracy: 0.7575 - val_loss: 0.7031 - val_accuracy: 0.7496\n",
            "Epoch 15/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6751 - accuracy: 0.7598 - val_loss: 0.6985 - val_accuracy: 0.7525\n",
            "Epoch 16/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.6699 - accuracy: 0.7618 - val_loss: 0.6931 - val_accuracy: 0.7563\n",
            "Epoch 17/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6636 - accuracy: 0.7641 - val_loss: 0.6876 - val_accuracy: 0.7560\n",
            "Epoch 18/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6570 - accuracy: 0.7659 - val_loss: 0.6812 - val_accuracy: 0.7573\n",
            "Epoch 19/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6519 - accuracy: 0.7674 - val_loss: 0.6831 - val_accuracy: 0.7576\n",
            "Epoch 20/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6472 - accuracy: 0.7693 - val_loss: 0.6751 - val_accuracy: 0.7587\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ODWXAlnWqri"
      },
      "source": [
        "Now we've got a model trained with a close-to-ideal learning rate and performing pretty well, we've got a couple of options.\n",
        "\n",
        "We could:\n",
        "* Evaluate its performance using other classification metrics (such as a [confusion matrix](https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py) or [classification report](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html)).\n",
        "* Assess some of its predictions (through visualizations).\n",
        "* Improve its accuracy (by training it for longer or changing the architecture).\n",
        "* Save and export it for use in an application.\n",
        "\n",
        "Let's go through the first two options.\n",
        "\n",
        "First we'll create a classification matrix to visualize its predictions across the different classes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jK4zA47sYVp5"
      },
      "source": [
        "# Note: The following confusion matrix code is a remix of Scikit-Learn's \n",
        "# plot_confusion_matrix function - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html\n",
        "# and Made with ML's introductory notebook - https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/08_Neural_Networks.ipynb\n",
        "import itertools\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Our function needs a different name to sklearn's plot_confusion_matrix\n",
        "def make_confusion_matrix(y_true, y_pred, classes=None, figsize=(10, 10), text_size=15): \n",
        "  \"\"\"Makes a labelled confusion matrix comparing predictions and ground truth labels.\n",
        "\n",
        "  If classes is passed, confusion matrix will be labelled, if not, integer class values\n",
        "  will be used.\n",
        "\n",
        "  Args:\n",
        "    y_true: Array of truth labels (must be same shape as y_pred).\n",
        "    y_pred: Array of predicted labels (must be same shape as y_true).\n",
        "    classes: Array of class labels (e.g. string form). If `None`, integer labels are used.\n",
        "    figsize: Size of output figure (default=(10, 10)).\n",
        "    text_size: Size of output figure text (default=15).\n",
        "  \n",
        "  Returns:\n",
        "    A labelled confusion matrix plot comparing y_true and y_pred.\n",
        "\n",
        "  Example usage:\n",
        "    make_confusion_matrix(y_true=test_labels, # ground truth test labels\n",
        "                          y_pred=y_preds, # predicted labels\n",
        "                          classes=class_names, # array of class label names\n",
        "                          figsize=(15, 15),\n",
        "                          text_size=10)\n",
        "  \"\"\"  \n",
        "  # Create the confustion matrix\n",
        "  cm = confusion_matrix(y_true, y_pred)\n",
        "  cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis] # normalize it\n",
        "  n_classes = cm.shape[0] # find the number of classes we're dealing with\n",
        "\n",
        "  # Plot the figure and make it pretty\n",
        "  fig, ax = plt.subplots(figsize=figsize)\n",
        "  cax = ax.matshow(cm, cmap=plt.cm.Blues) # colors will represent how 'correct' a class is, darker == better\n",
        "  fig.colorbar(cax)\n",
        "\n",
        "  # Are there a list of classes?\n",
        "  if classes:\n",
        "    labels = classes\n",
        "  else:\n",
        "    labels = np.arange(cm.shape[0])\n",
        "  \n",
        "  # Label the axes\n",
        "  ax.set(title=\"Confusion Matrix\",\n",
        "         xlabel=\"Predicted label\",\n",
        "         ylabel=\"True label\",\n",
        "         xticks=np.arange(n_classes), # create enough axis slots for each class\n",
        "         yticks=np.arange(n_classes), \n",
        "         xticklabels=labels, # axes will labeled with class names (if they exist) or ints\n",
        "         yticklabels=labels)\n",
        "  \n",
        "  # Make x-axis labels appear on bottom\n",
        "  ax.xaxis.set_label_position(\"bottom\")\n",
        "  ax.xaxis.tick_bottom()\n",
        "\n",
        "  # Set the threshold for different colors\n",
        "  threshold = (cm.max() + cm.min()) / 2.\n",
        "\n",
        "  # Plot the text on each cell\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    plt.text(j, i, f\"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)\",\n",
        "             horizontalalignment=\"center\",\n",
        "             color=\"white\" if cm[i, j] > threshold else \"black\",\n",
        "             size=text_size)"
      ],
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mi5kNRyZdyXA"
      },
      "source": [
        "Since a confusion matrix compares the truth labels (`test_labels`) to the predicted labels, we have to make some predictions with our model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HhxrXhrjbjja",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b322a9fa-2f10-4c2c-95b2-07fee851f8df"
      },
      "source": [
        "# Make predictions with the most recent model\n",
        "y_probs = model_14.predict(test_data) # \"probs\" is short for probabilities\n",
        "\n",
        "# View the first 5 predictions\n",
        "y_probs[:5]"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 0s 1ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[8.8410567e-16, 2.7062191e-10, 2.1381426e-18, 5.9045515e-09,\n",
              "        3.4053493e-11, 2.9043210e-01, 1.0286372e-14, 1.5452971e-01,\n",
              "        4.7737401e-04, 5.5456084e-01],\n",
              "       [3.4300897e-03, 3.2169187e-09, 5.4931837e-01, 1.5544817e-04,\n",
              "        2.3162240e-01, 1.4976830e-06, 2.1184100e-01, 3.7634305e-15,\n",
              "        3.6311189e-03, 1.4645942e-12],\n",
              "       [2.5087815e-05, 9.9891138e-01, 1.2961644e-10, 1.0580971e-03,\n",
              "        4.2421119e-10, 1.7840865e-08, 2.9939443e-08, 5.2730898e-06,\n",
              "        1.9568226e-11, 1.8306384e-13],\n",
              "       [1.7384674e-06, 9.9802667e-01, 4.8881381e-12, 8.1873697e-04,\n",
              "        2.0193766e-10, 8.5180392e-07, 3.1061642e-09, 1.1518502e-03,\n",
              "        2.2215770e-10, 9.2720102e-11],\n",
              "       [1.8438330e-01, 1.2302928e-04, 2.0476623e-01, 4.4732567e-02,\n",
              "        1.0718283e-01, 2.3890880e-05, 4.5752469e-01, 6.5311680e-11,\n",
              "        1.2634498e-03, 2.2060539e-11]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eP5zslXbf5ZY"
      },
      "source": [
        "Our model outputs a list of **prediction probabilities**, meaning, it outputs a number for how likely it thinks a particular class is to be the label.\n",
        "\n",
        "The higher the number in the prediction probabilities list, the more likely the model believes that is the right class.\n",
        "\n",
        "To find the highest value we can use the [`argmax()`](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html) method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQvrJbbWf4Es",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2a8fb4e-3581-460e-e694-4d2e7c0ceb1c"
      },
      "source": [
        "# See the predicted class number and label for the first example\n",
        "y_probs[0].argmax(), class_names[y_probs[0].argmax()]"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9, 'Ankle boot')"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFPgnBCghrTz"
      },
      "source": [
        "Now let's do the same for all of the predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozUpeZU6g2An",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3ba9490-ef81-4d38-d012-a47b0d55a92a"
      },
      "source": [
        "# Convert all of the predictions from probabilities to labels\n",
        "y_preds = y_probs.argmax(axis=1)\n",
        "\n",
        "# View the first 10 prediction labels\n",
        "y_preds[:10]"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1, 1, 6, 1, 2, 6, 5, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "47g1wNk6iYTd"
      },
      "source": [
        "Wonderful, now we've got our model's predictions in label form, let's create a confusion matrix to view them against the truth labels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBMSVSRqcU_m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ae5a87e-427b-49fd-83d0-9532f3ea654d"
      },
      "source": [
        "# Check out the non-prettified confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix(y_true=test_labels, \n",
        "                 y_pred=y_preds)"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[764,   4,  12, 121,  18,   2,  73,   0,   6,   0],\n",
              "       [ 10, 917,   6,  55,   2,   1,   8,   0,   1,   0],\n",
              "       [ 35,   0, 584,  13, 191,   0, 169,   0,   8,   0],\n",
              "       [ 51,  20,   4, 841,  23,   5,  46,   0,  10,   0],\n",
              "       [  2,   0, 144,  71, 681,   1,  82,   0,  19,   0],\n",
              "       [  0,   1,   0,   1,   0, 904,   0,  50,  10,  34],\n",
              "       [204,   0, 145,  73, 312,   1, 257,   0,   8,   0],\n",
              "       [  0,   0,   0,   0,   0,  69,   0, 861,   0,  70],\n",
              "       [  0,   1,   3,  19,  54,  24,  12,   3, 883,   1],\n",
              "       [  0,   0,   0,   0,   1,  49,   0,  55,   0, 895]])"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ql-TQxuYiqPl"
      },
      "source": [
        "That confusion matrix is hard to comprehend, let's make it prettier using the function we created before."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLr6daZAbzRi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "aa6433d6-d0bc-41f1-bd76-71d693427c95"
      },
      "source": [
        "# Make a prettier confusion matrix\n",
        "make_confusion_matrix(y_true=test_labels, \n",
        "                      y_pred=y_preds,\n",
        "                      classes=class_names,\n",
        "                      figsize=(15, 15),\n",
        "                      text_size=10)"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tH3FhuGEjTu9"
      },
      "source": [
        "That looks much better! (one of my favourites sights in the world is a confusion matrix with dark squares down the diagonal)\n",
        "\n",
        "Except the results aren't as good as they could be...\n",
        "\n",
        "It looks like our model is getting confused between the `Shirt` and `T-shirt/top` classes (e.g. predicting `Shirt` when it's actually a `T-shirt/top`).\n",
        "\n",
        "> 🤔 **Question:** Does it make sense that our model is getting confused between the `Shirt` and `T-shirt/top` classes? Why do you think this might be? What's one way you could investigate?\n",
        "\n",
        "We've seen how our models predictions line up to the truth labels using a confusion matrix, but how about we visualize some?\n",
        "\n",
        "Let's create a function to plot a random image along with its prediction.\n",
        "\n",
        "> 🔑 **Note:** Often when working with images and other forms of visual data, it's a good idea to visualize as much as possible to develop a further understanding of the data and the outputs of your model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2XIuAjgJri9e"
      },
      "source": [
        "import random\n",
        "\n",
        "# Create a function for plotting a random image along with its prediction\n",
        "def plot_random_image(model, images, true_labels, classes):\n",
        "  \"\"\"Picks a random image, plots it and labels it with a predicted and truth label.\n",
        "\n",
        "  Args:\n",
        "    model: a trained model (trained on data similar to what's in images).\n",
        "    images: a set of random images (in tensor form).\n",
        "    true_labels: array of ground truth labels for images.\n",
        "    classes: array of class names for images.\n",
        "  \n",
        "  Returns:\n",
        "    A plot of a random image from `images` with a predicted class label from `model`\n",
        "    as well as the truth class label from `true_labels`.\n",
        "  \"\"\" \n",
        "  # Setup random integer\n",
        "  i = random.randint(0, len(images))\n",
        "  \n",
        "  # Create predictions and targets\n",
        "  target_image = images[i]\n",
        "  pred_probs = model.predict(target_image.reshape(1, 28, 28)) # have to reshape to get into right size for model\n",
        "  pred_label = classes[pred_probs.argmax()]\n",
        "  true_label = classes[true_labels[i]]\n",
        "\n",
        "  # Plot the target image\n",
        "  plt.imshow(target_image, cmap=plt.cm.binary)\n",
        "\n",
        "  # Change the color of the titles depending on if the prediction is right or wrong\n",
        "  if pred_label == true_label:\n",
        "    color = \"green\"\n",
        "  else:\n",
        "    color = \"red\"\n",
        "\n",
        "  # Add xlabel information (prediction/true label)\n",
        "  plt.xlabel(\"Pred: {} {:2.0f}% (True: {})\".format(pred_label,\n",
        "                                                   100*tf.reduce_max(pred_probs),\n",
        "                                                   true_label),\n",
        "             color=color) # set the color to green or red"
      ],
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAAIrpcEumyE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "772d7c52-49a6-491d-de32-b0283025c0ba"
      },
      "source": [
        "# Check out a random image as well as its prediction\n",
        "plot_random_image(model=model_14, \n",
        "                  images=test_data, \n",
        "                  true_labels=test_labels, \n",
        "                  classes=class_names)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 26ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNebpPbCw52S"
      },
      "source": [
        "After running the cell above a few times you'll start to get a visual understanding of the relationship between the model's predictions and the true labels.\n",
        "\n",
        "Did you figure out which predictions the model gets confused on?\n",
        "\n",
        "It seems to mix up classes which are similar, for example, `Sneaker` with `Ankle boot`.\n",
        "\n",
        "Looking at the images, you can see how this might be the case.\n",
        "\n",
        "The overall shape of a `Sneaker` and an `Ankle Boot` are similar.\n",
        "\n",
        "The overall shape might be one of the patterns the model has learned and so therefore when two images have a similar shape, their predictions get mixed up."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pOwM1rqhx6p3"
      },
      "source": [
        "### What patterns is our model learning?\n",
        "\n",
        "We've been talking a lot about how a neural network finds patterns in numbers, but what exactly do these patterns look like?\n",
        "\n",
        "Let's crack open one of our models and find out.\n",
        "\n",
        "First, we'll get a list of layers in our most recent model (`model_14`) using the `layers` attribute."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kcwMsgFuySTi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3090d2f-bdd5-4bea-db3f-a1451d96a3ac"
      },
      "source": [
        "# Find the layers of our most recent model\n",
        "model_14.layers"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.layers.reshaping.flatten.Flatten at 0x7ff75d723370>,\n",
              " <keras.layers.core.dense.Dense at 0x7ff75d340580>,\n",
              " <keras.layers.core.dense.Dense at 0x7ff75d341f00>,\n",
              " <keras.layers.core.dense.Dense at 0x7ff75d341c30>]"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9si0o-h4oO1"
      },
      "source": [
        "We can access a target layer using indexing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DXuQmsNX1mGR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bf3c8eb-a407-4cf8-c5a8-166ae79da655"
      },
      "source": [
        "# Extract a particular layer\n",
        "model_14.layers[1]"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.layers.core.dense.Dense at 0x7ff75d340580>"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6Cftaib4uG-"
      },
      "source": [
        "And we can find the patterns learned by a particular layer using the `get_weights()` method.\n",
        "\n",
        "The `get_weights()` method returns the **weights** (also known as a weights matrix) and biases (also known as a bias vector) of a particular layer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WdmZy5xi1srE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78910b6e-ef0f-4fe0-b0c6-9d19f7523eb9"
      },
      "source": [
        "# Get the patterns of a layer in our network\n",
        "weights, biases = model_14.layers[1].get_weights()\n",
        "\n",
        "# Shape = 1 weight matrix the size of our input data (28x28) per neuron (4)\n",
        "weights, weights.shape"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[-0.21522604,  0.49712637, -0.42709765, -0.00931738],\n",
              "        [-0.24937236,  0.07402322, -1.0969763 ,  0.07291749],\n",
              "        [-0.9731768 , -1.0194852 , -1.1511643 ,  0.06833975],\n",
              "        ...,\n",
              "        [ 0.06003708, -4.855139  , -0.8982618 ,  0.03423125],\n",
              "        [ 0.8904201 , -1.8922633 , -0.7969605 , -0.0800999 ],\n",
              "        [ 0.03137114, -1.7429879 , -1.2349681 , -0.0071042 ]],\n",
              "       dtype=float32),\n",
              " (784, 4))"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yCjuNSD73oG0"
      },
      "source": [
        "The weights matrix is the same shape as the input data, which in our case is 784 (28x28 pixels). And there's a copy of the weights matrix for each neuron the in the selected layer (our selected layer has 4 neurons).\n",
        "\n",
        "Each value in the weights matrix corresponds to how a particular value in the input data influences the network's decisions.\n",
        "\n",
        "These values start out as random numbers (they're set by the [`kernel_initializer` parameter](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense) when creating a layer, the default is [`\"glorot_uniform\"`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotUniform)) and are then updated to better representative values of the data (non-random) by the neural network during training.\n",
        "\n",
        "![neural network supervised learning weight updates](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-fashion-mnist-learning.png)\n",
        "*Example workflow of how a supervised neural network starts with random weights and updates them to better represent the data by looking at examples of ideal outputs.*\n",
        "\n",
        "Now let's check out the bias vector."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndG-h2yz1z2_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff495834-2751-4517-c52d-4db32d7ee8e1"
      },
      "source": [
        "# Shape = 1 bias per neuron (we use 4 neurons in the first layer)\n",
        "biases, biases.shape"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0.26203355,  0.30276534,  0.30909157, -0.008352  ], dtype=float32),\n",
              " (4,))"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3JRQFh3U374U"
      },
      "source": [
        "Every neuron has a bias vector. Each of these is paired with a weight matrix.\n",
        "\n",
        "The bias values get initialized as zeroes by default (using the [`bias_initializer` parameter](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense)).\n",
        "\n",
        "The bias vector dictates how much the patterns within the corresponding weights matrix should influence the next layer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QCUb7GeSGYF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d86e41c-471c-4c06-d5c4-b928dfe165b8"
      },
      "source": [
        "# Can now calculate the number of paramters in our model\n",
        "model_14.summary()"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_27\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_9 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense_76 (Dense)            (None, 4)                 3140      \n",
            "                                                                 \n",
            " dense_77 (Dense)            (None, 4)                 20        \n",
            "                                                                 \n",
            " dense_78 (Dense)            (None, 10)                50        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,210\n",
            "Trainable params: 3,210\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "trRRZLIRyLXe"
      },
      "source": [
        "Now we've built a few deep learning models, it's a good time to point out the whole concept of inputs and outputs not only relates to a model as a whole but to *every* layer within a model.\n",
        "\n",
        "You might've already guessed this, but starting from the input layer, each subsequent layer's input is the output of the previous layer.\n",
        "\n",
        "We can see this clearly using the utility [`plot_model()`](https://www.tensorflow.org/api_docs/python/tf/keras/utils/plot_model)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJD0GqGl3NY0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "8b9a9ebf-0244-449a-d99b-36f887ee6d1d"
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "\n",
        "# See the inputs and outputs of each layer\n",
        "plot_model(model_14, show_shapes=True)"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OY5HO72ATJR4"
      },
      "source": [
        "## How a model learns (in brief)\n",
        "\n",
        "Alright, we've trained a bunch of models, but we've never really discussed what's going on under the hood. So how exactly does a model learn?\n",
        "\n",
        "A model learns by updating and improving its weight matrices and biases values every epoch (in our case, when we call the `fit()` fucntion).\n",
        "\n",
        "It does so by comparing the patterns its learned between the data and labels to the actual labels.\n",
        "\n",
        "If the current patterns (weight matrices and bias values) don't result in a desirable decrease in the loss function (higher loss means worse predictions), the optimizer tries to steer the model to update its patterns in the right way (using the real labels as a reference).\n",
        "\n",
        "This process of using the real labels as a reference to improve the model's predictions is called [**backpropagation**](https://en.wikipedia.org/wiki/Backpropagation).\n",
        "\n",
        "In other words, data and labels pass through a model (**forward pass**) and it attempts to learn the relationship between the data and labels. \n",
        "\n",
        "And if this learned relationship isn't close to the actual relationship or it could be improved, the model does so by going back through itself (**backward pass**) and tweaking its weights matrices and bias values to better represent the data.\n",
        "\n",
        "If all of this sounds confusing (and it's fine if it does, the above is a very succinct description), check out the resources in the extra-curriculum section for more."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LXmMG6fcpoIp"
      },
      "source": [
        "## Exercises 🛠\n",
        "\n",
        "1. Play with neural networks in the [TensorFlow Playground](https://playground.tensorflow.org/) for 10-minutes. Especially try different values of the learning, what happens when you decrease it? What happens when you increase it?\n",
        "2. Replicate the model pictured in the [TensorFlow Playground diagram](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.001&regularizationRate=0&noise=0&networkShape=6,6,6,6,6&seed=0.51287&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&discretize_hide=true&regularizationRate_hide=true&percTrainData_hide=true&dataset_hide=true&problem_hide=true&noise_hide=true&batchSize_hide=true) below using TensorFlow code. Compile it using the Adam optimizer, binary crossentropy loss and accuracy metric. Once it's compiled check a summary of the model.\n",
        "![tensorflow playground example neural network](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-tensorflow-playground-replication-exercise.png)\n",
        "*Try this network out for yourself on the [TensorFlow Playground website](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.001&regularizationRate=0&noise=0&networkShape=6,6,6,6,6&seed=0.51287&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&discretize_hide=true&regularizationRate_hide=true&percTrainData_hide=true&dataset_hide=true&problem_hide=true&noise_hide=true&batchSize_hide=true). Hint: there are 5 hidden layers but the output layer isn't pictured, you'll have to decide what the output layer should be based on the input data.*\n",
        "3. Create a classification dataset using Scikit-Learn's [`make_moons()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html) function, visualize it and then build a model to fit it at over 85% accuracy.\n",
        "4. Create a function (or write code) to visualize multiple image predictions for the fashion MNIST at the same time. Plot at least three different images and their prediciton labels at the same time. Hint: see the [classifcation tutorial in the TensorFlow documentation](https://www.tensorflow.org/tutorials/keras/classification) for ideas.\n",
        "5. Recreate [TensorFlow's](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax) [softmax activation function](https://en.wikipedia.org/wiki/Softmax_function) in your own code. Make sure it can accept a tensor and return that tensor after having the softmax function applied to it.\n",
        "6. Train a model to get 88%+ accuracy on the fashion MNIST test set. Plot a confusion matrix to see the results after.\n",
        "7. Make a function to show an image of a certain class of the fashion MNIST dataset and make a prediction on it. For example, plot 3 images of the `T-shirt` class with their predictions.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oksgPs-meGHj"
      },
      "source": [
        "## Extra curriculum 📖\n",
        "* Watch 3Blue1Brown's neural networks video 2: [*Gradient descent, how neural networks learn*](https://www.youtube.com/watch?v=IHZwWFHWa-w). After you're done, write 100 words about what you've learned.\n",
        "  * If you haven't already, watch video 1: [*But what is a Neural Network?*](https://youtu.be/aircAruvnKk). Note the activation function they talk about at the end.\n",
        "* Watch [MIT's introduction to deep learning lecture 1](https://youtu.be/njKP3FqW3Sk) (if you haven't already) to get an idea of the concepts behind using linear and non-linear functions.\n",
        "* Spend 1-hour reading [Michael Nielsen's Neural Networks and Deep Learning book](http://neuralnetworksanddeeplearning.com/index.html).\n",
        "* Read the [ML-Glossary documentation on activation functions](https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html). Which one is your favourite?\n",
        "  * After you've read the ML-Glossary, see which activation functions are available in TensorFlow by searching \"tensorflow activation functions\"."
      ]
    }
  ]
}