Deep_Learning_with_Python_Foundations_Part1_Git.ipynb

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      "name": "Deep Learning with Python Foundations_Part1-Git.ipynb",
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      "cell_type": "markdown",
      "source": [
        "# **Introduction:**\n",
        "\n",
        "In this tutorial project, we are going to learn how to; Build, Plot & Define Artificial Neural Networks with Google TensorFlow. If you are new into Deep Learning, then this is highly recommended.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "z1UExji4i9LT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Some Simple Definitions:**\n",
        "\n",
        "**1. What is Deep Learning?**\n",
        "\n",
        "The human is super with brilliant cognitive abilities, humans have a special way of learning. Also, the human brain is primarily a collection of interconnected neurons with a special way of learning. The ability of a machine to mimic this process in a complex way of some sort leads to Deep Learning.\n",
        "\n",
        "\n",
        "---\n",
        "**Deep Learning** is a subset of machine learning that imitates the way humans learn (acquire some form of knowledge), Neural Network is synonymous with Deep Learning. Unlike traditionally algorithms which are linear in nature, Deep Learning models are quite the opposite with a form of hierarchy and complexity.\n",
        "\n",
        "---\n",
        "\n",
        "**2. What is Neural Network?**\n",
        "\n",
        "\"In 1943, neuroscientists came up with the concept of an artificial neural network as a proof-of-concept attempt to mimic how biological neurons worked. Ten years later, psychologist Frank Rosenblatt further evolved the idea by creating a single-layer neural network for supervised learning called the Perceptron.\n",
        "\n",
        "It could \"learn\" from good examples of data that would train the network, and then the trained network could apply that learning to new data it had never seen before.\" These came with limitations with non-linear functions.\n",
        "\n",
        "---\n",
        "**Neural Networks** can be defined as a collection/aggregation of connected neurons which has the ability to transmit a signal across the network of neurons. Like the biological brain cells/neurons, they can learn, make decisions like humans.\n",
        "\n"
      ],
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      "source": [
        "**Frameworks for Deep Learning:**\n",
        "\n",
        "I am going to explore the some of them.\n",
        "\n",
        "---\n",
        "\n",
        "To start with, **what is a** deep learning framework?\n",
        "\n",
        "\n",
        "Deep learning framework is a software package used by data science & AI researchers to design and train deep learning models. How? The concept inherent here, is to allow people train their models without having to go in depth into the algorithms underlying neural networks/deep learning.\n",
        "\n",
        "Mostly used are;   PyTorch, TensorFlow, MXNet, Keras among others. \n",
        "\n",
        "GPU-accelerated libraries such as cuDNN and NCCL can also be used to achieve high performance accelerated training.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "1. Tensorflow: \n",
        "\n",
        "Developed by Google, TensorFlow’s machine learning models are easy to build, can be used for robust machine learning production, and allow powerful experimentation for research.\n",
        "\n",
        "2. Pytorch:\n",
        "\n",
        "Facebook's PyTorch is based on Python. It is the successor of Torch, which was based on the Lua programming language. Extremely flexible for research\n",
        "\n",
        "3. Keras:\n",
        "\n",
        "Developed by Francois Chollet, today one of the fastest growing deep learning frameworks.\n",
        "\n",
        "Keras supports high-level neural network API, written in Python. **What makes Keras interesting is that it runs on top of TensorFlow, Theano, and CNTK.**\n",
        "\n",
        "4. Theano\n",
        "\n",
        "Developed by The University de Montreal, written in Python and centers around NVIDIA CUDA, allowing users to integrate it with GPS. The Python library allows users to define, optimise, and evaluate mathematical expressions involving multi-dimensional arrays. \n",
        "\n",
        "---\n",
        "\n",
        "**Others include;** \n",
        "\n",
        "Deeplearning4j (DL4J), Caffe, Chainer, Microsoft CNTK\n",
        " "
      ],
      "metadata": {
        "id": "XjXGQr7hpkC3"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Setup**"
      ],
      "metadata": {
        "id": "7MgZiMzLwLFb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Two main libraries for building Neural Networks: TensorFlow and PyTorch. They perform similar task though Tensorflow is more production ready while PyTorch is better for building rapid prototypes being easy to learn.\n",
        "\n",
        "We are going to use;\n",
        "\n",
        "**TensorFlow and Keras**, a higher-level module way more user-friendly than pure TensorFlow and PyTorch, although a bit slower.\n"
      ],
      "metadata": {
        "id": "0jibhCPxwepg"
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    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nn45kg7ok656",
        "outputId": "ae4aac61-1a94-4484-fb11-a2e635354186"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To enable GPU support, check out the [official documentation](https://www.tensorflow.org/install/gpu) or [this guide](https://towardsdatascience.com/installing-tensorflow-with-cuda-cudnn-and-gpu-support-on-windows-10-60693e46e781). \n",
        "\n",
        "After setting it up, your Python instructions will be translated into **CUDA** by your machine and processed by the GPUs, for faster models running."
      ],
      "metadata": {
        "id": "qZ2UnZbmxkhN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install shap #SHAP is a Python library that uses Shapley values to explain the output of any machine learning model."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "za5KEBfnSgRr",
        "outputId": "636634a8-f33c-4235-e7d6-9649d52c1ce7"
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    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "O0d1h3HWiohY"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras import models, layers, utils, backend as K #required libraries\n",
        "import matplotlib.pyplot as plt #for plots\n",
        "import shap"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **What is ANN? Artificial Neural Network**\n",
        "\n"
      ],
      "metadata": {
        "id": "OvQjhfr7yw-A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "What we also know is that a Neural Network is a set of Layers composed of “neurons” (numbers) linked together by weighted links.\n",
        "\n",
        "**ANN** are usually made of layers with input, hidden and output dimensions. The output dimension is determined by the number of neurons called nodes. \n",
        "\n",
        "\n",
        "There is a computational unit that connects the weighted inputs through an activation function (which helps the neuron to switch on/off). \n",
        "\n",
        "\"The weights, like in most of the machine learning algorithms, are randomly initialized and optimized during the training to minimize a loss function.\"\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "**The 3 layers:**\n",
        "\n",
        "![layer1.jfif](data:image/jpeg;base64,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)\n",
        "\n",
        "\n",
        "Source: [link](https://www.google.com/imgres?imgurl=https%3A%2F%2Fwww.researchgate.net%2Fpublication%2F321066887%2Ffigure%2Ffig1%2FAS%3A560898498625536%401510740031477%2FArtificial-neural-network-There-are-three-layers-an-input-layer-hidden-layers-and-an.png&imgrefurl=https%3A%2F%2Fwww.researchgate.net%2Ffigure%2FArtificial-neural-network-There-are-three-layers-an-input-layer-hidden-layers-and-an_fig1_321066887&tbnid=horHRdzA5Qh5BM&vet=12ahUKEwjZwNuqrPT0AhUx-YUKHeJkDAcQMygAegUIARCqAQ..i&docid=j8S-JXhjrSnlqM&w=850&h=591&itg=1&q=3%20layers%20in%20aNN&ved=2ahUKEwjZwNuqrPT0AhUx-YUKHeJkDAcQMygAegUIARCqAQ)\n",
        "\n",
        "---\n",
        "\n",
        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)\n",
        "\n",
        "Source: [Link](https://www.google.com/imgres?imgurl=https%3A%2F%2Fwww.researchgate.net%2Fpublication%2F325831461%2Ffigure%2Ffig2%2FAS%3A639036561567745%401529369597778%2FA-3-layer-neural-network-with-three-inputs-two-hidden-layers-consisting-of-four-neurons.png&imgrefurl=https%3A%2F%2Fwww.researchgate.net%2Ffigure%2FA-3-layer-neural-network-with-three-inputs-two-hidden-layers-consisting-of-four-neurons_fig2_325831461&tbnid=vI937GUOc67cwM&vet=12ahUKEwjZwNuqrPT0AhUx-YUKHeJkDAcQMygEegUIARCyAQ..i&docid=Mfr5VefytrmY5M&w=791&h=388&itg=1&q=3%20layers%20in%20aNN&ved=2ahUKEwjZwNuqrPT0AhUx-YUKHeJkDAcQMygEegUIARCyAQ)\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "1. Input layer: The Input Layer has as many neurons as there are inputs. For example, if the input is an image of size 28x28 (pixels), there will be 28x28 = 784 neurons in the input layer. This layer feeds the input vector to the Neural Network. Say we have a matrix of 3 features (shape N x 3), this layer takes 3 numbers as the input and passes the same 3 numbers to the next layer.\n",
        "\n",
        "\n",
        "2. Hidden layers represent the intermediary nodes, they apply several transformations to the numbers in order to improve the accuracy of the final result, and the output is defined by the number of neurons. The Hidden Layer has an arbitrary number of neurons, determined by the scientist/developer. The number of neurons depends on the \"complexity of your task ( for example, large-sized Image classification tasks requires a large number of neurons in their hidden layers).\"\n",
        "\n",
        "3. Output layer that returns the final output of the Neural Network. For a simple binary classification or regression, the output layer shall have only 1 neuron (so that it returns only 1 number). In the case of a multiclass classification with 5 different classes, the output layer shall have 5 neurons. In other words, the Output Layer has as many neurons as there are outputs required. For classification tasks, there will be as many neurons as there are classes (If there are 2 classes cats and dogs, there will be 2 neurons in the output layer).\n",
        "\n",
        "\n",
        "The simplest form of ANN is the Perceptron, a model with one layer only, very similar to the linear regression model."
      ],
      "metadata": {
        "id": "IyxCdMs2zgbd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Deep Neural Networks**"
      ],
      "metadata": {
        "id": "G8Lrz1Mv_bRA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Deep Neural Networks**\n",
        "\n",
        "\"Deep Learning models are Neural Networks but not all the Neural Networks are Deep Learning models. Generally speaking, “**Deep” Learning applies when the algorithm has at least 2 hidden layers** (so 4 layers in total including input and output).\"\n",
        "\n",
        "---\n",
        "PS: The layers can have a different number of neurons and a different activation function, and in each node, weights are trained to optimise the final result. Meaning, the more layers you add, the bigger the number of trainable parameters.\n",
        "\n",
        "\n",
        "**Things to note:**\n",
        "\n",
        "Bias, Backpropagation, Gradient Descent\n"
      ],
      "metadata": {
        "id": "plxdMuQv_lck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Exercise:** Let's build a Neural Network with Tensorflow using Sequential class of Keras\n",
        "\n",
        "\n",
        "**Model Design** - **Perception**\n",
        "\n",
        "This Perception model is with only one Dense layer. It feeds all its inputs to all the neurons, each neuron providing one output.\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "rnxCB41OD4Uq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modelP = models.Sequential(name=\"Perceptron\", layers=[\n",
        "    layers.Dense(             #a fully connected layer\n",
        "          name=\"dense\",\n",
        "          input_dim=3,        #with 3 features as the input\n",
        "          units=1,            #and 1 node because we want 1 output\n",
        "          activation='linear' #f(x)=x\n",
        "    )\n",
        "])\n",
        "modelP.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NJc_AjYH_M6y",
        "outputId": "7e1724ae-ce95-43c9-e478-0d59573a7fe9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Perceptron\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 1)                 4         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4\n",
            "Trainable params: 4\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Examining the structure and the size of the above model (in terms of parameters to train). \n",
        "\n",
        "We have 4 (3 weights and 1 bias).\n",
        "\n",
        "*To use an activation function that is not already included in Keras, like a binary step function we need to use raw TensorFlow: as shown below*"
      ],
      "metadata": {
        "id": "5e8tlggCE7zc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define the function\n",
        "import tensorflow as tf\n",
        "def binary_step_activation(x):\n",
        "    ##return 1 if x>0 else 0 \n",
        "    return K.switch(x>0, tf.math.divide(x,x), tf.math.multiply(x,0))\n",
        "\n",
        "# build the model\n",
        "modelPi = models.Sequential(name=\"Perceptron\", layers=[\n",
        "      layers.Dense(             \n",
        "          name=\"dense\",\n",
        "          input_dim=3,        \n",
        "          units=1,            \n",
        "          activation=binary_step_activation\n",
        "      )\n",
        "])"
      ],
      "metadata": {
        "id": "PNfIZDFyH4ED"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modelPi.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GRJjX5hKS5jO",
        "outputId": "befd2d91-9466-4913-a29d-167c0821a062"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Perceptron\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, None)              4         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4\n",
            "Trainable params: 4\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Model Design with Deep Neural Network Using Sequential Class**\n",
        "\n",
        "If we recall we first took a look at Perception model."
      ],
      "metadata": {
        "id": "JAR69_bTIKll"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Things to note when building a DNN:**\n",
        "\n",
        "\n",
        "\n",
        "*   **How many layers?:** Try different variants and see what works.  **2 Dense hidden layers** with Dropout is a good start, a technique that reduces overfitting by randomly setting inputs to 0. Hidden layers are useful to overcome the non-linearity of data, so if you don’t need non-linearity then you can avoid hidden layers. Too many hidden layers will lead to overfitting.\n",
        "\n",
        "*   **How many neurons?** \"The number of hidden neurons should be between the size of the input layer and the size of the output layer. My rule of thumb is *(number of inputs + 1 output)/2*.\"\n",
        "\n",
        "\n",
        "*   **What activation function?** Among several types of activation functions, the most used one is *ReLU*, a piecewise linear function that returns the output only if it’s positive, and it is mainly used for hidden layers. PS: The output layer must have an activation compatible with the expected output. For example, the linear function is suited for regression problems while the *Sigmoid* is frequently used for classification.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "f7Ffn2cmIwYJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this **exercise** we assume an input **dataset of N features** and 1 binary target variable (most likely a classification use case)."
      ],
      "metadata": {
        "id": "eaigftxIM0PE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_features = 10\n",
        "modeld = models.Sequential(name=\"DeepNN\", layers=[\n",
        "    ### hidden layer 1\n",
        "    layers.Dense(name=\"h1\", input_dim=n_features,\n",
        "                 units=int(round((n_features+1)/2)), \n",
        "                 activation='relu'),\n",
        "    layers.Dropout(name=\"drop1\", rate=0.2),\n",
        "    \n",
        "    ### hidden layer 2\n",
        "    layers.Dense(name=\"h2\", units=int(round((n_features+1)/4)), \n",
        "                 activation='relu'),\n",
        "    layers.Dropout(name=\"drop2\", rate=0.2),\n",
        "    \n",
        "    ### layer output\n",
        "    layers.Dense(name=\"output\", units=1, activation='sigmoid')\n",
        "])\n",
        "modeld.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xaEK7WJhIe4o",
        "outputId": "31d0d66b-969e-4a66-d13f-5077db33e056"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"DeepNN\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " h1 (Dense)                  (None, 6)                 66        \n",
            "                                                                 \n",
            " drop1 (Dropout)             (None, 6)                 0         \n",
            "                                                                 \n",
            " h2 (Dense)                  (None, 3)                 21        \n",
            "                                                                 \n",
            " drop2 (Dropout)             (None, 3)                 0         \n",
            "                                                                 \n",
            " output (Dense)              (None, 1)                 4         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 91\n",
            "Trainable params: 91\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The **Model class** usually offers some form of flexibility and control over the layers, and it can be used to build more complex models with multiple inputs/outputs. \n",
        "\n",
        "\"Two major differences:\n",
        "\n",
        "1. The Input layer needs to be specified while in the Sequential class it’s implied in the input dimension of the first Dense layer.\n",
        "\n",
        "\n",
        "2. The layers are saved like objects and can be applied to the outputs of other layers like: output = layer(…)(input)\"\n",
        "\n",
        "***Let's use the Model class to build our Perceptron and DeepNN:***\n"
      ],
      "metadata": {
        "id": "I_Rzymx9Ny-W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Perceptron\n",
        "inputs = layers.Input(name=\"input\", shape=(3,))\n",
        "outputs = layers.Dense(name=\"output\", units=1, \n",
        "                       activation='linear')(inputs)\n",
        "modelPj = models.Model(inputs=inputs, outputs=outputs, \n",
        "                     name=\"Perceptron\")\n",
        "\n",
        "# DeepNN\n",
        "### layer input\n",
        "inputs = layers.Input(name=\"input\", shape=(n_features,))\n",
        "### hidden layer 1\n",
        "h1 = layers.Dense(name=\"h1\", units=int(round((n_features+1)/2)), activation='relu')(inputs)\n",
        "h1 = layers.Dropout(name=\"drop1\", rate=0.2)(h1)\n",
        "### hidden layer 2\n",
        "h2 = layers.Dense(name=\"h2\", units=int(round((n_features+1)/4)), activation='relu')(h1)\n",
        "h2 = layers.Dropout(name=\"drop2\", rate=0.2)(h2)\n",
        "### layer output\n",
        "outputs = layers.Dense(name=\"output\", units=1, activation='sigmoid')(h2)\n",
        "modeldi = models.Model(inputs=inputs, outputs=outputs, name=\"DeepNN\")"
      ],
      "metadata": {
        "id": "hcZIOvTBM178"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can always check if the number of parameters in the model summary is the same as the one from Sequential."
      ],
      "metadata": {
        "id": "DVaAt-5OQk5T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modelPj.summary()\n",
        "modeldi.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jOGa7pHdQqgc",
        "outputId": "1d5d10de-578b-4f50-8e4a-b3ccb73475a2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Perceptron\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input (InputLayer)          [(None, 3)]               0         \n",
            "                                                                 \n",
            " output (Dense)              (None, 1)                 4         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4\n",
            "Trainable params: 4\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"DeepNN\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input (InputLayer)          [(None, 10)]              0         \n",
            "                                                                 \n",
            " h1 (Dense)                  (None, 6)                 66        \n",
            "                                                                 \n",
            " drop1 (Dropout)             (None, 6)                 0         \n",
            "                                                                 \n",
            " h2 (Dense)                  (None, 3)                 21        \n",
            "                                                                 \n",
            " drop2 (Dropout)             (None, 3)                 0         \n",
            "                                                                 \n",
            " output (Dense)              (None, 1)                 4         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 91\n",
            "Trainable params: 91\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Deep Learning Visualisation**\n",
        "\n",
        "We are going to visualise some of our exercise models above"
      ],
      "metadata": {
        "id": "UgtF1EH4Uo6m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Artificial Neural Network using TensorFlow model plot**\n",
        "\n",
        "Thanks to Mauro Di Pietro"
      ],
      "metadata": {
        "id": "rqJWDhKjXoQv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "Extract info for each layer in a keras model.\n",
        "'''\n",
        "def utils_nn_config(model):\n",
        "    lst_layers = []\n",
        "    if \"Sequential\" in str(model): #-> Sequential doesn't show the input layer\n",
        "        layer = model.layers[0]\n",
        "        lst_layers.append({\"name\":\"input\", \"in\":int(layer.input.shape[-1]), \"neurons\":0, \n",
        "                           \"out\":int(layer.input.shape[-1]), \"activation\":None,\n",
        "                           \"params\":0, \"bias\":0})\n",
        "    for layer in model.layers:\n",
        "        try:\n",
        "            dic_layer = {\"name\":layer.name, \"in\":int(layer.input.shape[-1]), \"neurons\":layer.units, \n",
        "                         \"out\":int(layer.output.shape[-1]), \"activation\":layer.get_config()[\"activation\"],\n",
        "                         \"params\":layer.get_weights()[0], \"bias\":layer.get_weights()[1]}\n",
        "        except:\n",
        "            dic_layer = {\"name\":layer.name, \"in\":int(layer.input.shape[-1]), \"neurons\":0, \n",
        "                         \"out\":int(layer.output.shape[-1]), \"activation\":None,\n",
        "                         \"params\":0, \"bias\":0}\n",
        "        lst_layers.append(dic_layer)\n",
        "    return lst_layers\n",
        "\n",
        "\n",
        "\n",
        "'''\n",
        "Plot the structure of a keras neural network.\n",
        "'''\n",
        "def visualize_nn(model, description=False, figsize=(10,8)):\n",
        "    ## get layers info\n",
        "    lst_layers = utils_nn_config(model)\n",
        "    layer_sizes = [layer[\"out\"] for layer in lst_layers]\n",
        "    \n",
        "    ## fig setup\n",
        "    fig = plt.figure(figsize=figsize)\n",
        "    ax = fig.gca()\n",
        "    ax.set(title=model.name)\n",
        "    ax.axis('off')\n",
        "    left, right, bottom, top = 0.1, 0.9, 0.1, 0.9\n",
        "    x_space = (right-left) / float(len(layer_sizes)-1)\n",
        "    y_space = (top-bottom) / float(max(layer_sizes))\n",
        "    p = 0.025\n",
        "    \n",
        "    ## nodes\n",
        "    for i,n in enumerate(layer_sizes):\n",
        "        top_on_layer = y_space*(n-1)/2.0 + (top+bottom)/2.0\n",
        "        layer = lst_layers[i]\n",
        "        color = \"green\" if i in [0, len(layer_sizes)-1] else \"blue\"\n",
        "        color = \"red\" if (layer['neurons'] == 0) and (i > 0) else color\n",
        "        \n",
        "        ### add description\n",
        "        if (description is True):\n",
        "            d = i if i == 0 else i-0.5\n",
        "            if layer['activation'] is None:\n",
        "                plt.text(x=left+d*x_space, y=top, fontsize=10, color=color, s=layer[\"name\"].upper())\n",
        "            else:\n",
        "                plt.text(x=left+d*x_space, y=top, fontsize=10, color=color, s=layer[\"name\"].upper())\n",
        "                plt.text(x=left+d*x_space, y=top-p, fontsize=10, color=color, s=layer['activation']+\" (\")\n",
        "                plt.text(x=left+d*x_space, y=top-2*p, fontsize=10, color=color, s=\"Σ\"+str(layer['in'])+\"[X*w]+b\")\n",
        "                out = \" Y\"  if i == len(layer_sizes)-1 else \" out\"\n",
        "                plt.text(x=left+d*x_space, y=top-3*p, fontsize=10, color=color, s=\") = \"+str(layer['neurons'])+out)\n",
        "        \n",
        "        ### circles\n",
        "        for m in range(n):\n",
        "            color = \"limegreen\" if color == \"green\" else color\n",
        "            circle = plt.Circle(xy=(left+i*x_space, top_on_layer-m*y_space-4*p), radius=y_space/4.0, color=color, ec='k', zorder=4)\n",
        "            ax.add_artist(circle)\n",
        "            \n",
        "            ### add text\n",
        "            if i == 0:\n",
        "                plt.text(x=left-4*p, y=top_on_layer-m*y_space-4*p, fontsize=10, s=r'$X_{'+str(m+1)+'}$')\n",
        "            elif i == len(layer_sizes)-1:\n",
        "                plt.text(x=right+4*p, y=top_on_layer-m*y_space-4*p, fontsize=10, s=r'$y_{'+str(m+1)+'}$')\n",
        "            else:\n",
        "                plt.text(x=left+i*x_space+p, y=top_on_layer-m*y_space+(y_space/8.+0.01*y_space)-4*p, fontsize=10, s=r'$H_{'+str(m+1)+'}$')\n",
        "    \n",
        "    ## links\n",
        "    for i, (n_a, n_b) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):\n",
        "        layer = lst_layers[i+1]\n",
        "        color = \"green\" if i == len(layer_sizes)-2 else \"blue\"\n",
        "        color = \"red\" if layer['neurons'] == 0 else color\n",
        "        layer_top_a = y_space*(n_a-1)/2. + (top+bottom)/2. -4*p\n",
        "        layer_top_b = y_space*(n_b-1)/2. + (top+bottom)/2. -4*p\n",
        "        for m in range(n_a):\n",
        "            for o in range(n_b):\n",
        "                line = plt.Line2D([i*x_space+left, (i+1)*x_space+left], \n",
        "                                  [layer_top_a-m*y_space, layer_top_b-o*y_space], \n",
        "                                  c=color, alpha=0.5)\n",
        "                if layer['activation'] is None:\n",
        "                    if o == m:\n",
        "                        ax.add_artist(line)\n",
        "                else:\n",
        "                    ax.add_artist(line)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "DS22rJWSQWvP"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We apply our defined function on the two models (Perception & ANN)"
      ],
      "metadata": {
        "id": "FXvje7RGYlb1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "visualize_nn(modelPj, description=True, figsize=(10,8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "icS7GVqgYXVO",
        "outputId": "7dcabe4e-715c-4518-cb8e-a370daf993c5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "visualize_nn(modeldi, description=True, figsize=(10,8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "xrHc7oC_ZNqD",
        "outputId": "5cc88a0a-039c-4b69-e9d2-07a04b61d6b3"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using Tensorflow for plotting Model Structure"
      ],
      "metadata": {
        "id": "OLplfYujZu21"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "utils.plot_model(modelPj, to_file='modelPj.png', show_shapes=True, show_layer_names=True) #the perception"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "id": "f6udlObMZ_UO",
        "outputId": "be1f91a9-5825-43c5-a99d-3d4ed90060cd"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "utils.plot_model(modeldi, to_file='modeldi.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "V7WWNs62aTpm",
        "outputId": "a6aaf281-eda7-4fa7-c861-9eef385907c3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os   #run this to delete the saved model png file on your pc which savesto your pc when you run the code in the previous cell\n",
        "os.remove('modelPj.png')\n",
        "os.remove('modeldi.png')"
      ],
      "metadata": {
        "id": "rtlMRSFsZzHU"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train & Test our Deep Learning Model"
      ],
      "metadata": {
        "id": "8MguvAcEbZch"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Steps for the Model to Run:**\n",
        "\n",
        "**Compile:** Here we define the the Optimizer, the Loss function, and the Metrics.\n",
        "\n",
        "1. **Adam Optimiser **(a replacement optimisation algorithm for gradient descent), have used this in previous projects. When solving (binary) classification tasks, use a binary Cross-Entropy loss which compares each of the predicted probabilities to the actual class output. You can research for other ML areas.\n",
        "\n",
        "2. **Monitoring your Metrics:** Both the Accuracy and the F1-score (it combines Precision and Recall (ensure you attempt to implement as it is not already included in TensorFlow)."
      ],
      "metadata": {
        "id": "zNuO7UpVbtBd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define metrics\n",
        "def Recall(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "    recall = true_positives / (possible_positives + K.epsilon())\n",
        "    return recall\n",
        "\n",
        "def Precision(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + K.epsilon())\n",
        "    return precision\n",
        "\n",
        "def F1(y_true, y_pred):\n",
        "    precision = Precision(y_true, y_pred)\n",
        "    recall = Recall(y_true, y_pred)\n",
        "    return 2*((precision*recall)/(precision+recall+K.epsilon()))\n",
        "\n",
        "# compile the neural network\n",
        "modeldi.compile(optimizer='adam', loss='binary_crossentropy', \n",
        "              metrics=['accuracy',F1])"
      ],
      "metadata": {
        "id": "mQpQVISPbNV5"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Some things to Note:**\n",
        "\n",
        "**Batches:** Sometimes, the dataset might be too large to be processed all at once, so we split into batches (the higher the batch size, the more memory space you need). Backpropagation and the consequent parameters update happen every batch.\n",
        "\n",
        "---\n",
        "\n",
        "**Epoch:** Simply mean number of training cylcle/set. An epoch is one pass over the full training set. Say you have have 100 observations and the batch size is 20, it will take 5 batches to complete 1 epoch. \n",
        "\n",
        "PS: The **batch size should be a multiple of 2** (common: 32, 64, 128, 256) because computers usually organize the memory in power of 2.\n",
        "\n",
        "You can start with 100 epochs with a batch size of 32.\n",
        "\n",
        "---\n",
        "\n",
        "It’s good practice to keep a portion of the data (20%-30%) for validation. \"In other words, the model will set apart this fraction of data to evaluate the loss and metrics at the end of each epoch, outside the training.\"\n",
        "\n",
        "Let's say you got your data ready into some X and y arrays (if not you can simply generate random data using np.random"
      ],
      "metadata": {
        "id": "ONBG7aoQgBcj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "X = np.random.rand(1000,10)\n",
        "y = np.random.choice([1,0], size=1000)"
      ],
      "metadata": {
        "id": "rbgECsLteyfL"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ub1RyilWgRCR",
        "outputId": "f2f7b95b-1596-48bf-e86d-941a9f9eca64"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Classification problem"
      ],
      "metadata": {
        "id": "P9vf721nr4k7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# train/validation\n",
        "training = modeldi.fit(x=X, y=y, batch_size=32, epochs=100, shuffle=True, verbose=0, validation_split=0.3)\n",
        "\n",
        "# plot\n",
        "metrics = [k for k in training.history.keys() if (\"loss\" not in k) and (\"val\" not in k)]    \n",
        "fig, ax = plt.subplots(nrows=1, ncols=2, sharey=True, figsize=(15,3))\n",
        "       \n",
        "## training    \n",
        "ax[0].set(title=\"Training\")    \n",
        "ax11 = ax[0].twinx()    \n",
        "ax[0].plot(training.history['loss'], color='black'), ax[0].set_xlabel('Epochs'), ax[0].set_ylabel('Loss', color='black')    \n",
        "for metric in metrics:        \n",
        "    ax11.plot(training.history[metric], label=metric),  ax11.set_ylabel(\"Score\", color='steelblue')    \n",
        "ax11.legend()\n",
        "        \n",
        "## validation    \n",
        "ax[1].set(title=\"Validation\")    \n",
        "ax22 = ax[1].twinx()    \n",
        "ax[1].plot(training.history['val_loss'], color='black'), ax[1].set_xlabel('Epochs'), ax[1].set_ylabel('Loss', color='black')    \n",
        "for metric in metrics:          \n",
        "    ax22.plot(training.history['val_'+metric], label=metric), ax22.set_ylabel(\"Score\", color=\"steelblue\")    \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "mRKWQdlte0Ch",
        "outputId": "de966fa5-ae9c-4be7-8d4d-6f8fbe507e15"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x216 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Say we have a **regression problem**, we could set's the MAE as the loss and the R-squared as the metric as shown below"
      ],
      "metadata": {
        "id": "AkuXNMm-sXDi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define metrics #am not running this cell, \n",
        "#def R2(y, y_hat):\n",
        "    ss_res =  K.sum(K.square(y - y_hat)) \n",
        "    ss_tot = K.sum(K.square(y - K.mean(y))) \n",
        "    return ( 1 - ss_res/(ss_tot + K.epsilon()) )\n",
        "\n",
        "# compile the neural network\n",
        "modeldi.compile(optimizer='adam', loss='mean_absolute_error', \n",
        "              metrics=[R2])"
      ],
      "metadata": {
        "id": "9QJ6ACh_sSSx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Let's Explain our DL Model**\n",
        "\n",
        "Here's a good [resource link ](https://github.com/slundberg/shap#deep-learning-example-with-deepexplainer-tensorflowkeras-models) on how to use SHAP explainer for your Machine Learning models"
      ],
      "metadata": {
        "id": "vFFt9AiYosX4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "WUfDa2bYdchN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "References:\n",
        "\n",
        "https://www.techtarget.com/searchenterpriseai/feature/How-neural-network-training-methods-are-modeled-after-the-human-brain\n",
        "\n",
        "---\n",
        "https://www.kdnuggets.com/2021/09/a-breakdown-deep-learning-frameworks.html \n",
        "\n",
        "---\n",
        "\n",
        "https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-frameworks\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "https://www.freecodecamp.org/news/building-a-3-layer-neural-network-from-scratch-99239c4af5d3/\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "https://medium.com/swlh/mathematics-behind-basic-feed-forward-neural-network-3-layers-python-from-scratch-df88085c8049 \n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "https://towardsdatascience.com/deep-learning-with-python-neural-networks-complete-tutorial-6b53c0b06af0 \n",
        "\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "QLY372Xdp27V"
      }
    }
  ]
}