Matthew_Purvis_04_AIT_MLP_CIFAR10_exercise.ipynb

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  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Matthew-Z-Purvis/InfoChallenge2023/blob/main/Matthew_Purvis_04_AIT_MLP_CIFAR10_exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cW-EyI5Lxpqi"
      },
      "source": [
        "# Copyright\n",
        "\n",
        "<PRE>\n",
        "Copyright (c) 2024 Bálint Gyires-Tóth - All Rights Reserved\n",
        "You may use and modify this code for research and development purpuses.\n",
        "Using this code for educational purposes (self-paced or instructor led) without the permission of the author is prohibited.\n",
        "</PRE>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Example of data exploration, modeling, regulartion and prediction\n",
        "Our exercise today involves loading a standard dataset using TensorFlow Keras API, exploring the data, building a simple neural network, and evaluating the effects of basic methods (activation functions, regularization, weight initialization, etc.). We then perform predictions on the test set and inspect the results."
      ],
      "metadata": {
        "id": "3EX4cCa4WKJu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Data\n",
        "The dataset we will use is the well-known CIFAR10 (Canadian Institute For Advanced Research). Explore the details on the Keras website first: https://keras.io/api/datasets/cifar10/\n",
        "\n",
        "After you explored the basic features of the data, let's load it into the memory and explore the shapes:"
      ],
      "metadata": {
        "id": "yY9fztIeWIKt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.datasets import cifar10\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "giRZKTjzX_7l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()"
      ],
      "metadata": {
        "id": "bez7q0eiWQCK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(Y_train.shape)\n",
        "print(Y_test.shape)"
      ],
      "metadata": {
        "id": "2tJoRq5wXywi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa3ac981-0209-48fe-d2f0-de1d2fe4b319"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(50000, 32, 32, 3)\n",
            "(10000, 32, 32, 3)\n",
            "(50000, 1)\n",
            "(10000, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1. Exercise\n",
        "Display the first ten images of the training data. Hints:\n",
        "* Axis 0 refers to the separate images, e.g. X_train[0]\n",
        "* You can use the [Matplotlib Pyplot imshow function](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html) for displaying the image. Just don't forget to import Plotly first!\n",
        "* A corresponding example, but you have to modify the code for your purpose: https://stackoverflow.com/questions/46615554/how-to-display-multiple-images-in-one-figure-correctly"
      ],
      "metadata": {
        "id": "zTCSGFyDlNvf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import plotly\n",
        "\n",
        "fig = plt.figure(figsize=(8, 8))\n",
        "columns = 5\n",
        "rows = 2\n",
        "for i in range(0,10):\n",
        "  img = X_train[i]\n",
        "  fig.add_subplot(rows, columns, i+1)\n",
        "  plt.imshow(img)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "hIQhHTppmBdO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "ed27d8c5-7583-4140-fa71-37da768393f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Print the corresponding labels of the displayed images from the target variable (Y_train):"
      ],
      "metadata": {
        "id": "FAQkUWlmm-bc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(0,10):\n",
        "  print(Y_train[i])"
      ],
      "metadata": {
        "id": "AkOrWzghnJ78",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7dadefa7-acde-4faa-e62e-88530426574d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[6]\n",
            "[9]\n",
            "[9]\n",
            "[4]\n",
            "[1]\n",
            "[1]\n",
            "[2]\n",
            "[7]\n",
            "[8]\n",
            "[3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2. Exercise\n",
        "We will train a Multi-Layer Perceptron (MLP), which requires a 2 dimensional input: 0th axis refers to the datapoints (i.e. images), the 1th dimens to the input. As images are 3 dimensional (width, height, color channels), you have to reshape the images into vectors. We also call this flattening.\n",
        "\n",
        "In order to do so, calculate the size of the equivalent 1D vector of the image:"
      ],
      "metadata": {
        "id": "VX24g5FstsRU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "flattened_dim = X_train.shape[1]*X_train.shape[2]*X_train.shape[3]\n",
        "print(flattened_dim)"
      ],
      "metadata": {
        "id": "hdoMyxHzuXZG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "149d7140-c9ce-4e96-9c2b-dcc327f9115b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3072\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After you calculated it, we can reshape the images, and covert the integer arrays into float arrays -- which are needed for the neural networks as input. Hint:\n",
        "* in the [reshape() function of Numpy](https://www.w3schools.com/python/numpy/numpy_array_reshape.asp) you can use -1 for one axis, to automatically calculate that value."
      ],
      "metadata": {
        "id": "WbqXJUSFubeC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# reshape 3D tensors to 2D tensors\n",
        "X_train = X_train.reshape(-1,flattened_dim)\n",
        "X_test = X_test.reshape(-1,flattened_dim)\n",
        "\n",
        "# it is in int8 format, the neural network requires float32\n",
        "X_train = X_train.astype(float)\n",
        "X_test = X_test.astype(float)\n",
        "print(X_train)"
      ],
      "metadata": {
        "id": "vukOcBM0XzSj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4192608-287d-40ec-c3d5-797431fef923"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 59.  62.  63. ... 123.  92.  72.]\n",
            " [154. 177. 187. ... 143. 133. 144.]\n",
            " [255. 255. 255. ...  80.  86.  84.]\n",
            " ...\n",
            " [ 35. 178. 235. ...  12.  31.  50.]\n",
            " [189. 211. 240. ... 195. 190. 171.]\n",
            " [229. 229. 239. ... 163. 163. 161.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3. Exercise\n",
        "As the next step, let's split the training data into training and validation data. 80% of the original training data should be the final training data, and 20% the validation.\n",
        "You should use Numpy indexing to select the first 80% of X_train as final X_train, and the last 20% as X_valid. Hints:\n",
        "* https://datascienceparichay.com/article/numpy-array-first-n-rows/\n",
        "* https://datascienceparichay.com/article/numpy-array-last-n-rows/"
      ],
      "metadata": {
        "id": "DO79AdIdvVfD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_ratio  = 0.8\n",
        "train_length = len(X_train)# length of the training data\n",
        "train_split  = int(train_length*train_ratio) # where to split the training and validation data\n",
        "\n",
        "X_valid, Y_valid = X_train[train_split:, :], Y_train[train_split:, :]\n",
        "X_train, Y_train = X_train[:train_split, :], Y_train[:train_split, :]\n",
        "print(X_train)\n",
        "print(X_valid)"
      ],
      "metadata": {
        "id": "mRwuWxxIxLgN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d018d798-17b5-4bab-c70b-4fc77f1d4a80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 59.  62.  63. ... 123.  92.  72.]\n",
            " [154. 177. 187. ... 143. 133. 144.]\n",
            " [255. 255. 255. ...  80.  86.  84.]\n",
            " ...\n",
            " [167. 141. 110. ... 161. 134. 130.]\n",
            " [ 45.  76. 127. ...  62.  53.  24.]\n",
            " [235. 252. 252. ... 163. 169. 174.]]\n",
            "[[255. 255. 254. ... 247. 248. 248.]\n",
            " [127. 145. 167. ...  82. 100. 112.]\n",
            " [116.  71.  26. ...  38.  19.   5.]\n",
            " ...\n",
            " [ 35. 178. 235. ...  12.  31.  50.]\n",
            " [189. 211. 240. ... 195. 190. 171.]\n",
            " [229. 229. 239. ... 163. 163. 161.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.4. Exercise\n",
        "As the last step of input data preparation, the data should be standardized. Calculate the mean and variance of the training data (elementvise -- so for each value of the flattened image you should get a mean and variance for the training data along 0th axis). Hint:\n",
        "* https://stackoverflow.com/questions/70626231/how-to-calculate-mean-variance-standard-deviation-per-index-of-array"
      ],
      "metadata": {
        "id": "z05y_QWDxWER"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean = np.mean(X_train, axis=0)\n",
        "std  = np.std(X_train, axis=0)\n"
      ],
      "metadata": {
        "id": "TSZbvWCBx8Ea"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, use the resulting values to standardize the training, validation and test data by substracting the mean and dividing the result with the standard deviation."
      ],
      "metadata": {
        "id": "6WRHKkkjyN1S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = (X_train-mean)/std\n",
        "X_valid = (X_valid-mean)/std\n",
        "X_test  = (X_test-mean)/std"
      ],
      "metadata": {
        "id": "2ESLP1EIyB2J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.5. Exercise\n",
        "And as the last step, convert the dense representation of the classes (i.e. 0,1,2,3...9) to one-hot encoding (0 = [1 0 0 0 0 0 0 0 0 0], 1 = [0 1 0 0 0 0 0 0 0 0] ... 9 = [0 0 0 0 0 0 0 0 0 1]). To do this, first, calculate the number of unique elements in the target training data. Hints:\n",
        "* use the [unique() function](https://numpy.org/doc/stable/reference/generated/numpy.unique.html) of Numpy to list the unique elements\n",
        "* you can count the number of elements in a list with the [len() function](https://www.w3schools.com/python/ref_func_len.asp)"
      ],
      "metadata": {
        "id": "ooHCnUin7r4o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nb_classes = len(np.unique(Y_train))\n"
      ],
      "metadata": {
        "id": "i0LAO9cs8KEE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Doublecheck, if the same number of classes exists in the validation and test target data:"
      ],
      "metadata": {
        "id": "BvM6XhGe8JU9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Validation data has the same number of classes, as the training data:\", nb_classes == len(np.unique(Y_valid)))\n",
        "print(\"Test data has the same number of classes, as the training data:\", nb_classes == len(np.unique(Y_test)))"
      ],
      "metadata": {
        "id": "eZFNSsyx_Py2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48269ada-2855-47f0-86f2-ed1b8767f2b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation data has the same number of classes, as the training data: True\n",
            "Test data has the same number of classes, as the training data: True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And convert the dense representation into one-hot encoding. Hint:\n",
        "* use the [to_categorical function](https://www.tensorflow.org/api_docs/python/tf/keras/utils/to_categorical) with the calculated nb_classes\n"
      ],
      "metadata": {
        "id": "mJM-iBmR8dLe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_train = to_categorical(\n",
        "    Y_train, num_classes=nb_classes, dtype='float32'\n",
        ")\n",
        "Y_valid = to_categorical(\n",
        "    Y_valid, num_classes=nb_classes, dtype='float32'\n",
        ")\n",
        "Y_test  = to_categorical(\n",
        "    Y_test, num_classes=nb_classes, dtype='float32'\n",
        ")"
      ],
      "metadata": {
        "id": "xwZ4OJ6I8lba"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Final check of data preparation\n",
        "Now, lets check the shapes and mean and standard deviation of the training, validation and test data."
      ],
      "metadata": {
        "id": "LshN2OSsv3TZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Shapes of the training, validation and test input data:\", X_train.shape, X_valid.shape, X_test.shape)\n",
        "print(\"Shapes of the training, validation and test output data:\", Y_train.shape, Y_valid.shape, Y_test.shape)\n",
        "print(\"Mean values of the training, validation and test input data:\", X_train.mean(), X_valid.mean(), X_test.mean())\n",
        "print(\"Standard deviation of the training, validation and test input data:\", X_train.std(), X_valid.std(), X_test.std())"
      ],
      "metadata": {
        "id": "O_jdJVcvX0vi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f76c75a9-189b-413b-baf7-acc7925c92a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shapes of the training, validation and test input data: (40000, 3072) (10000, 3072) (10000, 3072)\n",
            "Shapes of the training, validation and test output data: (40000, 10) (10000, 10) (10000, 10)\n",
            "Mean values of the training, validation and test input data: 6.208922265216188e-18 0.0023437140466796656 0.013331171092908317\n",
            "Standard deviation of the training, validation and test input data: 1.0000000000000009 0.9965601589924457 0.9978282721027735\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Don't worry, if the mean and standard deviation of the validation and test data are not exactly 0 and 1, but these might be very mear to it (e.g. 0.01 mean, 0.99 variance)."
      ],
      "metadata": {
        "id": "5GtAOOoezmZY"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cJ14oyZExpqj"
      },
      "source": [
        "# 2. Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4ZXYRMBxpql"
      },
      "source": [
        "Let us begin with a simple example of creating a small neural network without regularization and training it with actual data. The purpose of this is to provide you with an example as to how to proceed with the next exercise."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# imports\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Embedding, Flatten, Dropout\n",
        "from tensorflow.keras.initializers import HeNormal\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow import keras\n",
        "from keras.layers import Dense\n",
        "from keras.models import Sequential, load_model\n"
      ],
      "metadata": {
        "id": "6-8Q6ll7ZDgq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model definition\n",
        "model = Sequential()\n",
        "model.add(Dense(128, activation='tanh', input_shape=(flattened_dim,)))\n",
        "model.add(Dense(128, activation='tanh'))\n",
        "model.add(Dense(nb_classes, activation='softmax'))\n",
        "\n",
        "# loss function and optimizer\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "pke6w3CD8DHp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CZbOR76QxprC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd6c33a1-215d-472a-f7fe-723dbb7d8e82"
      },
      "source": [
        "# training\n",
        "network_history = model.fit(X_train, Y_train,\n",
        "                            validation_data=(X_valid,Y_valid),\n",
        "                            batch_size=128,\n",
        "                            epochs=40,\n",
        "                            verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "313/313 [==============================] - 5s 9ms/step - loss: 1.8132 - accuracy: 0.3665 - val_loss: 1.7490 - val_accuracy: 0.3875\n",
            "Epoch 2/40\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.6841 - accuracy: 0.4148 - val_loss: 1.7275 - val_accuracy: 0.3995\n",
            "Epoch 3/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.6392 - accuracy: 0.4322 - val_loss: 1.6985 - val_accuracy: 0.4101\n",
            "Epoch 4/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.5904 - accuracy: 0.4508 - val_loss: 1.6838 - val_accuracy: 0.4199\n",
            "Epoch 5/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.5593 - accuracy: 0.4612 - val_loss: 1.6674 - val_accuracy: 0.4205\n",
            "Epoch 6/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.5267 - accuracy: 0.4734 - val_loss: 1.6580 - val_accuracy: 0.4225\n",
            "Epoch 7/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4994 - accuracy: 0.4809 - val_loss: 1.6438 - val_accuracy: 0.4307\n",
            "Epoch 8/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4697 - accuracy: 0.4913 - val_loss: 1.6271 - val_accuracy: 0.4394\n",
            "Epoch 9/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4398 - accuracy: 0.4996 - val_loss: 1.6353 - val_accuracy: 0.4365\n",
            "Epoch 10/40\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.4211 - accuracy: 0.5078 - val_loss: 1.6169 - val_accuracy: 0.4461\n",
            "Epoch 11/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4028 - accuracy: 0.5159 - val_loss: 1.6176 - val_accuracy: 0.4419\n",
            "Epoch 12/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.3737 - accuracy: 0.5241 - val_loss: 1.6245 - val_accuracy: 0.4414\n",
            "Epoch 13/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.3510 - accuracy: 0.5319 - val_loss: 1.6185 - val_accuracy: 0.4442\n",
            "Epoch 14/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.3292 - accuracy: 0.5393 - val_loss: 1.6133 - val_accuracy: 0.4430\n",
            "Epoch 15/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.3049 - accuracy: 0.5456 - val_loss: 1.6065 - val_accuracy: 0.4456\n",
            "Epoch 16/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.2816 - accuracy: 0.5556 - val_loss: 1.6198 - val_accuracy: 0.4421\n",
            "Epoch 17/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.2675 - accuracy: 0.5590 - val_loss: 1.6235 - val_accuracy: 0.4417\n",
            "Epoch 18/40\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.2451 - accuracy: 0.5676 - val_loss: 1.6238 - val_accuracy: 0.4497\n",
            "Epoch 19/40\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.2337 - accuracy: 0.5727 - val_loss: 1.6300 - val_accuracy: 0.4485\n",
            "Epoch 20/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.2154 - accuracy: 0.5785 - val_loss: 1.6461 - val_accuracy: 0.4411\n",
            "Epoch 21/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.2017 - accuracy: 0.5803 - val_loss: 1.6371 - val_accuracy: 0.4459\n",
            "Epoch 22/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.1750 - accuracy: 0.5947 - val_loss: 1.6380 - val_accuracy: 0.4484\n",
            "Epoch 23/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.1606 - accuracy: 0.5953 - val_loss: 1.6587 - val_accuracy: 0.4400\n",
            "Epoch 24/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.1498 - accuracy: 0.6007 - val_loss: 1.6641 - val_accuracy: 0.4454\n",
            "Epoch 25/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.1360 - accuracy: 0.6080 - val_loss: 1.6633 - val_accuracy: 0.4466\n",
            "Epoch 26/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.1143 - accuracy: 0.6133 - val_loss: 1.6748 - val_accuracy: 0.4442\n",
            "Epoch 27/40\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.0936 - accuracy: 0.6218 - val_loss: 1.6733 - val_accuracy: 0.4459\n",
            "Epoch 28/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.0754 - accuracy: 0.6284 - val_loss: 1.6855 - val_accuracy: 0.4404\n",
            "Epoch 29/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.0630 - accuracy: 0.6317 - val_loss: 1.6867 - val_accuracy: 0.4472\n",
            "Epoch 30/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.0353 - accuracy: 0.6428 - val_loss: 1.6953 - val_accuracy: 0.4506\n",
            "Epoch 31/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.0225 - accuracy: 0.6477 - val_loss: 1.7129 - val_accuracy: 0.4493\n",
            "Epoch 32/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.0115 - accuracy: 0.6482 - val_loss: 1.7398 - val_accuracy: 0.4448\n",
            "Epoch 33/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.9918 - accuracy: 0.6578 - val_loss: 1.7235 - val_accuracy: 0.4502\n",
            "Epoch 34/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.9778 - accuracy: 0.6632 - val_loss: 1.7441 - val_accuracy: 0.4483\n",
            "Epoch 35/40\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 0.9660 - accuracy: 0.6650 - val_loss: 1.7369 - val_accuracy: 0.4478\n",
            "Epoch 36/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.9469 - accuracy: 0.6712 - val_loss: 1.7756 - val_accuracy: 0.4430\n",
            "Epoch 37/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.9296 - accuracy: 0.6779 - val_loss: 1.7912 - val_accuracy: 0.4464\n",
            "Epoch 38/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.9204 - accuracy: 0.6821 - val_loss: 1.8004 - val_accuracy: 0.4495\n",
            "Epoch 39/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.9109 - accuracy: 0.6866 - val_loss: 1.8065 - val_accuracy: 0.4434\n",
            "Epoch 40/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.8992 - accuracy: 0.6909 - val_loss: 1.8189 - val_accuracy: 0.4409\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uFqhzV85xprJ"
      },
      "source": [
        "Let's plot the training and validation loss and accuracy curves:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYmXGkLCxprK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "44ea5d53-1b14-436c-c9ea-8292df8e6fe3"
      },
      "source": [
        "def plot_history(network_history):\n",
        "    plt.figure()\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.plot(network_history.history['loss'])\n",
        "    plt.plot(network_history.history['val_loss'])\n",
        "    plt.legend(['Training', 'Validation'])\n",
        "\n",
        "    plt.figure()\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.plot(network_history.history['accuracy'])\n",
        "    plt.plot(network_history.history['val_accuracy'])\n",
        "    plt.legend(['Training', 'Validation'], loc='lower right')\n",
        "    plt.show()\n",
        "\n",
        "plot_history(network_history)"
      ],
      "execution_count": null,
      "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",
      "source": [
        "What do you think? Does it overfit?"
      ],
      "metadata": {
        "id": "tgG7QeXF-dx6"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Q9rIeBBxprO"
      },
      "source": [
        "# Inspecting the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6r2AmmoMxprP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3afb2abb-d032-4ac9-f75f-e769863c9315"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 128)               393344    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 411146 (1.57 MB)\n",
            "Trainable params: 411146 (1.57 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3-bVDVuxprS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f477030d-4378-4ead-f995-042fdc53c88e"
      },
      "source": [
        "print('Input: ', model.input)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input:  KerasTensor(type_spec=TensorSpec(shape=(None, 3072), dtype=tf.float32, name='dense_input'), name='dense_input', description=\"created by layer 'dense_input'\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Da2OJ3DmxprX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5698a3a0-aba1-4ece-c5fd-c2064120a0bd"
      },
      "source": [
        "print('Layers:\\n')\n",
        "for layer in model.layers:\n",
        "    print(\"Layer's name: \", layer.name, ', trainable: ', layer.trainable)\n",
        "    print(layer.get_config(),'\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Layers:\n",
            "\n",
            "Layer's name:  dense , trainable:  True\n",
            "{'name': 'dense', 'trainable': True, 'dtype': 'float32', 'batch_input_shape': (None, 3072), 'units': 128, 'activation': 'tanh', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None} \n",
            "\n",
            "Layer's name:  dense_1 , trainable:  True\n",
            "{'name': 'dense_1', 'trainable': True, 'dtype': 'float32', 'units': 128, 'activation': 'tanh', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None} \n",
            "\n",
            "Layer's name:  dense_2 , trainable:  True\n",
            "{'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units': 10, 'activation': 'softmax', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None} \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AtVX3M3lxprc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81bea6d5-80c4-400c-f92d-997065c828a0"
      },
      "source": [
        "print('Output: ', model.output)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Output:  KerasTensor(type_spec=TensorSpec(shape=(None, 10), dtype=tf.float32, name=None), name='dense_2/Softmax:0', description=\"created by layer 'dense_2'\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1. Exercise\n",
        "Introduce [early stopping](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping) and [dropout](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout) with a rate between 0.1..0.5. Tips:\n",
        "\n",
        "* Insert the dropout layer between the two Dense layers.\n",
        "* Set the patience of early stopping to 5.\n",
        "* Set the number of epochs to a very high number.\n",
        "* Don't forget to restore best weights after early stopping.\n",
        "* And also set early stopping to monitor validation accuracy (the default value is validation loss -- which is categorical crossentropy now).\n",
        "\n",
        "Compile and train the model. Attempt to increase the validation accuracy as much as possible by making changes to the dropout rate. Inspect the effects of the modifications."
      ],
      "metadata": {
        "id": "E-hHfs9qA_iO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "es = 5"
      ],
      "metadata": {
        "id": "NwXrPzOgEBG-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model definition with dropout\n",
        "model = Sequential()\n",
        "model.add(Dense(128, activation='tanh', input_shape=(flattened_dim,)))\n",
        "model.add(Dropout(.33))\n",
        "model.add(Dense(128, activation='tanh'))\n",
        "model.add(Dense(nb_classes, activation='softmax'))\n",
        "\n",
        "# loss function and optimizer\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "7hZEGzj8GFwP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training with early stopping\n",
        "network_history = model.fit(X_train, Y_train,\n",
        "                            validation_data=(X_valid,Y_valid),\n",
        "                            batch_size=128,\n",
        "                            epochs=99999,\n",
        "                            verbose=1,\n",
        "                            callbacks=[EarlyStopping(monitor='val_accuracy', patience=5, restore_best_weights=True)])"
      ],
      "metadata": {
        "id": "GCG54R-vGGWj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef7a8043-06b1-4055-b3ef-4a19317e67a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/99999\n",
            "313/313 [==============================] - 6s 7ms/step - loss: 1.9002 - accuracy: 0.3283 - val_loss: 1.7808 - val_accuracy: 0.3749\n",
            "Epoch 2/99999\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.7912 - accuracy: 0.3699 - val_loss: 1.7385 - val_accuracy: 0.3943\n",
            "Epoch 3/99999\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.7515 - accuracy: 0.3868 - val_loss: 1.7329 - val_accuracy: 0.3969\n",
            "Epoch 4/99999\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.7231 - accuracy: 0.3957 - val_loss: 1.7041 - val_accuracy: 0.4100\n",
            "Epoch 5/99999\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.6959 - accuracy: 0.4084 - val_loss: 1.6750 - val_accuracy: 0.4202\n",
            "Epoch 6/99999\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.6741 - accuracy: 0.4163 - val_loss: 1.6693 - val_accuracy: 0.4236\n",
            "Epoch 7/99999\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.6575 - accuracy: 0.4225 - val_loss: 1.6470 - val_accuracy: 0.4288\n",
            "Epoch 8/99999\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.6371 - accuracy: 0.4279 - val_loss: 1.6531 - val_accuracy: 0.4332\n",
            "Epoch 9/99999\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.6239 - accuracy: 0.4326 - val_loss: 1.6355 - val_accuracy: 0.4332\n",
            "Epoch 10/99999\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.6058 - accuracy: 0.4385 - val_loss: 1.6196 - val_accuracy: 0.4420\n",
            "Epoch 11/99999\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.5985 - accuracy: 0.4412 - val_loss: 1.6208 - val_accuracy: 0.4384\n",
            "Epoch 12/99999\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.5827 - accuracy: 0.4496 - val_loss: 1.6137 - val_accuracy: 0.4436\n",
            "Epoch 13/99999\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.5761 - accuracy: 0.4466 - val_loss: 1.6019 - val_accuracy: 0.4448\n",
            "Epoch 14/99999\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.5574 - accuracy: 0.4535 - val_loss: 1.5919 - val_accuracy: 0.4471\n",
            "Epoch 15/99999\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.5484 - accuracy: 0.4592 - val_loss: 1.5921 - val_accuracy: 0.4461\n",
            "Epoch 16/99999\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.5362 - accuracy: 0.4631 - val_loss: 1.5834 - val_accuracy: 0.4536\n",
            "Epoch 17/99999\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.5291 - accuracy: 0.4636 - val_loss: 1.5740 - val_accuracy: 0.4538\n",
            "Epoch 18/99999\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.5172 - accuracy: 0.4660 - val_loss: 1.5620 - val_accuracy: 0.4580\n",
            "Epoch 19/99999\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.5114 - accuracy: 0.4688 - val_loss: 1.5584 - val_accuracy: 0.4589\n",
            "Epoch 20/99999\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.5025 - accuracy: 0.4730 - val_loss: 1.5452 - val_accuracy: 0.4578\n",
            "Epoch 21/99999\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.4924 - accuracy: 0.4765 - val_loss: 1.5366 - val_accuracy: 0.4618\n",
            "Epoch 22/99999\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4810 - accuracy: 0.4791 - val_loss: 1.5495 - val_accuracy: 0.4627\n",
            "Epoch 23/99999\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.4801 - accuracy: 0.4792 - val_loss: 1.5352 - val_accuracy: 0.4685\n",
            "Epoch 24/99999\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.4724 - accuracy: 0.4807 - val_loss: 1.5328 - val_accuracy: 0.4671\n",
            "Epoch 25/99999\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.4670 - accuracy: 0.4828 - val_loss: 1.5273 - val_accuracy: 0.4699\n",
            "Epoch 26/99999\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.4579 - accuracy: 0.4891 - val_loss: 1.5233 - val_accuracy: 0.4674\n",
            "Epoch 27/99999\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.4538 - accuracy: 0.4892 - val_loss: 1.5158 - val_accuracy: 0.4720\n",
            "Epoch 28/99999\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4419 - accuracy: 0.4941 - val_loss: 1.5155 - val_accuracy: 0.4711\n",
            "Epoch 29/99999\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4432 - accuracy: 0.4911 - val_loss: 1.5094 - val_accuracy: 0.4686\n",
            "Epoch 30/99999\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.4321 - accuracy: 0.4946 - val_loss: 1.5139 - val_accuracy: 0.4752\n",
            "Epoch 31/99999\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.4293 - accuracy: 0.4947 - val_loss: 1.5173 - val_accuracy: 0.4684\n",
            "Epoch 32/99999\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.4177 - accuracy: 0.5024 - val_loss: 1.5071 - val_accuracy: 0.4704\n",
            "Epoch 33/99999\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.4173 - accuracy: 0.5035 - val_loss: 1.5046 - val_accuracy: 0.4707\n",
            "Epoch 34/99999\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.4078 - accuracy: 0.4999 - val_loss: 1.5074 - val_accuracy: 0.4734\n",
            "Epoch 35/99999\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4010 - accuracy: 0.5052 - val_loss: 1.5009 - val_accuracy: 0.4730\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2 Exercise\n",
        "Change the [activation function of the dense layers (except the last one)](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense) to rectified linear unit (ReLU) and the [weight initialization](https://www.tensorflow.org/api_docs/python/tf/keras/initializers) to the theoretically best one. Use the original model's code.\n",
        "\n",
        "Compile and train the model. Inspect the effects of the modifications."
      ],
      "metadata": {
        "id": "zQCDM1o1BBtF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# model definition with relu\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(128, activation='relu', kernel_initializer=HeNormal(), input_shape=(flattened_dim,)))\n",
        "model.add(Dense(128, activation='relu', kernel_initializer=HeNormal()))\n",
        "model.add(Dense(nb_classes, activation='softmax'))\n",
        "\n",
        "# loss function and optimizer\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "YjHAAh-wGu9O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training\n",
        "network_history = model.fit(X_train, Y_train,\n",
        "                            validation_data=(X_valid,Y_valid),\n",
        "                            batch_size=128,\n",
        "                            epochs=40,\n",
        "                            verbose=1)"
      ],
      "metadata": {
        "id": "rarwKM1wGxMz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "568c24db-8b14-482e-c625-aff124ca3d42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "313/313 [==============================] - 4s 7ms/step - loss: 1.8356 - accuracy: 0.3815 - val_loss: 1.6876 - val_accuracy: 0.4153\n",
            "Epoch 2/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.5495 - accuracy: 0.4588 - val_loss: 1.5759 - val_accuracy: 0.4572\n",
            "Epoch 3/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4215 - accuracy: 0.5027 - val_loss: 1.5141 - val_accuracy: 0.4734\n",
            "Epoch 4/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.3267 - accuracy: 0.5350 - val_loss: 1.5202 - val_accuracy: 0.4723\n",
            "Epoch 5/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.2594 - accuracy: 0.5565 - val_loss: 1.5012 - val_accuracy: 0.4905\n",
            "Epoch 6/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.2011 - accuracy: 0.5761 - val_loss: 1.4974 - val_accuracy: 0.4873\n",
            "Epoch 7/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.1500 - accuracy: 0.5953 - val_loss: 1.5159 - val_accuracy: 0.4918\n",
            "Epoch 8/40\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.1006 - accuracy: 0.6095 - val_loss: 1.5254 - val_accuracy: 0.4905\n",
            "Epoch 9/40\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.0530 - accuracy: 0.6301 - val_loss: 1.5464 - val_accuracy: 0.4924\n",
            "Epoch 10/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.0044 - accuracy: 0.6475 - val_loss: 1.5679 - val_accuracy: 0.4978\n",
            "Epoch 11/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.9629 - accuracy: 0.6598 - val_loss: 1.6245 - val_accuracy: 0.4892\n",
            "Epoch 12/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.9255 - accuracy: 0.6730 - val_loss: 1.6344 - val_accuracy: 0.4926\n",
            "Epoch 13/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.8901 - accuracy: 0.6869 - val_loss: 1.6775 - val_accuracy: 0.4965\n",
            "Epoch 14/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.8445 - accuracy: 0.7025 - val_loss: 1.6907 - val_accuracy: 0.4907\n",
            "Epoch 15/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.8073 - accuracy: 0.7155 - val_loss: 1.7362 - val_accuracy: 0.4864\n",
            "Epoch 16/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.7810 - accuracy: 0.7265 - val_loss: 1.8005 - val_accuracy: 0.4898\n",
            "Epoch 17/40\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 0.7515 - accuracy: 0.7359 - val_loss: 1.8353 - val_accuracy: 0.4849\n",
            "Epoch 18/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.7190 - accuracy: 0.7471 - val_loss: 1.8519 - val_accuracy: 0.4864\n",
            "Epoch 19/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.6872 - accuracy: 0.7599 - val_loss: 1.8949 - val_accuracy: 0.4859\n",
            "Epoch 20/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.6633 - accuracy: 0.7670 - val_loss: 2.0179 - val_accuracy: 0.4799\n",
            "Epoch 21/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.6345 - accuracy: 0.7749 - val_loss: 2.0063 - val_accuracy: 0.4762\n",
            "Epoch 22/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.6095 - accuracy: 0.7849 - val_loss: 2.0914 - val_accuracy: 0.4736\n",
            "Epoch 23/40\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 0.5803 - accuracy: 0.7952 - val_loss: 2.1386 - val_accuracy: 0.4794\n",
            "Epoch 24/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.5564 - accuracy: 0.8026 - val_loss: 2.2050 - val_accuracy: 0.4789\n",
            "Epoch 25/40\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 0.5375 - accuracy: 0.8111 - val_loss: 2.2936 - val_accuracy: 0.4734\n",
            "Epoch 26/40\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 0.5270 - accuracy: 0.8134 - val_loss: 2.3094 - val_accuracy: 0.4757\n",
            "Epoch 27/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.4936 - accuracy: 0.8265 - val_loss: 2.3789 - val_accuracy: 0.4772\n",
            "Epoch 28/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.4786 - accuracy: 0.8314 - val_loss: 2.4609 - val_accuracy: 0.4671\n",
            "Epoch 29/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.4600 - accuracy: 0.8380 - val_loss: 2.4987 - val_accuracy: 0.4726\n",
            "Epoch 30/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.4322 - accuracy: 0.8479 - val_loss: 2.5083 - val_accuracy: 0.4817\n",
            "Epoch 31/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.4301 - accuracy: 0.8489 - val_loss: 2.6391 - val_accuracy: 0.4775\n",
            "Epoch 32/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.4199 - accuracy: 0.8526 - val_loss: 2.7440 - val_accuracy: 0.4715\n",
            "Epoch 33/40\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 0.3965 - accuracy: 0.8602 - val_loss: 2.7577 - val_accuracy: 0.4717\n",
            "Epoch 34/40\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 0.3863 - accuracy: 0.8636 - val_loss: 2.8136 - val_accuracy: 0.4712\n",
            "Epoch 35/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.3655 - accuracy: 0.8717 - val_loss: 2.8560 - val_accuracy: 0.4720\n",
            "Epoch 36/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.3560 - accuracy: 0.8758 - val_loss: 2.9507 - val_accuracy: 0.4625\n",
            "Epoch 37/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.3588 - accuracy: 0.8735 - val_loss: 3.0811 - val_accuracy: 0.4608\n",
            "Epoch 38/40\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.3401 - accuracy: 0.8810 - val_loss: 3.0805 - val_accuracy: 0.4716\n",
            "Epoch 39/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.3261 - accuracy: 0.8849 - val_loss: 3.1830 - val_accuracy: 0.4689\n",
            "Epoch 40/40\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.3160 - accuracy: 0.8912 - val_loss: 3.2062 - val_accuracy: 0.4719\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.3. Exercise\n",
        "Based on the modifications above, and by any further modifications (e.g. more layers, less layers, more neurons/layer, etc.) to the model design, find a combination that is able to achieve **validation accuracy, that is higher than 53%**.\n"
      ],
      "metadata": {
        "id": "j1tGVPdD-pp3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "es = 5"
      ],
      "metadata": {
        "id": "5dBQxJHIHrbd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# Define initializers with seeds\n",
        "initializer_1 = HeNormal(seed=1)\n",
        "initializer_2 = HeNormal(seed=2)\n",
        "initializer_3 = HeNormal(seed=3)\n",
        "initializer_4 = HeNormal(seed=4)\n",
        "\n",
        "# Define the model\n",
        "model = Sequential()\n",
        "model.add(Dense(512, activation='relu', kernel_initializer=initializer_1, input_shape=(flattened_dim,)))\n",
        "model.add(Dropout(0.5))  # Add dropout for regularization\n",
        "model.add(Dense(nb_classes, activation='softmax'))\n",
        "\n",
        "# Compile the model with categorical crossentropy loss function and Adam optimizer\n",
        "model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.001), metrics=['accuracy'])\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "PuxHOqLt-3Nd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training\n",
        "early_stopping = EarlyStopping(monitor='val_accuracy', patience=20, restore_best_weights=True)\n",
        "\n",
        "\n",
        "# Train the model with data augmentation and early stopping\n",
        "network_history = model.fit(X_train, Y_train,\n",
        "                            validation_data=(X_valid,Y_valid),\n",
        "                            batch_size=128,\n",
        "                            epochs=400000,\n",
        "                            verbose=1,\n",
        "                            callbacks=[early_stopping]\n",
        ")"
      ],
      "metadata": {
        "id": "u8tFNCNVHyt9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e78982d-fce2-4de0-8d02-fa975e01e253"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/400000\n",
            "313/313 [==============================] - 3s 6ms/step - loss: 2.8662 - accuracy: 0.3166 - val_loss: 1.7023 - val_accuracy: 0.4279\n",
            "Epoch 2/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.7667 - accuracy: 0.4114 - val_loss: 1.5403 - val_accuracy: 0.4602\n",
            "Epoch 3/400000\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.5963 - accuracy: 0.4442 - val_loss: 1.4939 - val_accuracy: 0.4818\n",
            "Epoch 4/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.5703 - accuracy: 0.4523 - val_loss: 1.5276 - val_accuracy: 0.4707\n",
            "Epoch 5/400000\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.5579 - accuracy: 0.4570 - val_loss: 1.5070 - val_accuracy: 0.4768\n",
            "Epoch 6/400000\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.5461 - accuracy: 0.4579 - val_loss: 1.5178 - val_accuracy: 0.4756\n",
            "Epoch 7/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.5329 - accuracy: 0.4667 - val_loss: 1.4768 - val_accuracy: 0.4896\n",
            "Epoch 8/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.5205 - accuracy: 0.4735 - val_loss: 1.5021 - val_accuracy: 0.4736\n",
            "Epoch 9/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.5182 - accuracy: 0.4734 - val_loss: 1.5242 - val_accuracy: 0.4726\n",
            "Epoch 10/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.5095 - accuracy: 0.4810 - val_loss: 1.4943 - val_accuracy: 0.4829\n",
            "Epoch 11/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.4986 - accuracy: 0.4797 - val_loss: 1.4644 - val_accuracy: 0.4986\n",
            "Epoch 12/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4886 - accuracy: 0.4866 - val_loss: 1.4616 - val_accuracy: 0.4946\n",
            "Epoch 13/400000\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.4757 - accuracy: 0.4927 - val_loss: 1.4504 - val_accuracy: 0.4992\n",
            "Epoch 14/400000\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.4751 - accuracy: 0.4938 - val_loss: 1.4826 - val_accuracy: 0.4911\n",
            "Epoch 15/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4556 - accuracy: 0.4967 - val_loss: 1.4647 - val_accuracy: 0.5018\n",
            "Epoch 16/400000\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.4412 - accuracy: 0.5026 - val_loss: 1.5023 - val_accuracy: 0.4837\n",
            "Epoch 17/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4302 - accuracy: 0.5088 - val_loss: 1.5286 - val_accuracy: 0.4831\n",
            "Epoch 18/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.4247 - accuracy: 0.5110 - val_loss: 1.4468 - val_accuracy: 0.5013\n",
            "Epoch 19/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.4088 - accuracy: 0.5166 - val_loss: 1.4369 - val_accuracy: 0.5121\n",
            "Epoch 20/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.3951 - accuracy: 0.5224 - val_loss: 1.4255 - val_accuracy: 0.5104\n",
            "Epoch 21/400000\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.3931 - accuracy: 0.5210 - val_loss: 1.4654 - val_accuracy: 0.4940\n",
            "Epoch 22/400000\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.3801 - accuracy: 0.5280 - val_loss: 1.4586 - val_accuracy: 0.5039\n",
            "Epoch 23/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.3587 - accuracy: 0.5363 - val_loss: 1.4739 - val_accuracy: 0.5021\n",
            "Epoch 24/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.3551 - accuracy: 0.5378 - val_loss: 1.4238 - val_accuracy: 0.5160\n",
            "Epoch 25/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.3394 - accuracy: 0.5451 - val_loss: 1.4439 - val_accuracy: 0.5095\n",
            "Epoch 26/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.3293 - accuracy: 0.5465 - val_loss: 1.4198 - val_accuracy: 0.5224\n",
            "Epoch 27/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.3144 - accuracy: 0.5515 - val_loss: 1.4436 - val_accuracy: 0.5080\n",
            "Epoch 28/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.2994 - accuracy: 0.5558 - val_loss: 1.4481 - val_accuracy: 0.5075\n",
            "Epoch 29/400000\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.3059 - accuracy: 0.5544 - val_loss: 1.5153 - val_accuracy: 0.5045\n",
            "Epoch 30/400000\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.2920 - accuracy: 0.5560 - val_loss: 1.4751 - val_accuracy: 0.5050\n",
            "Epoch 31/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.2737 - accuracy: 0.5628 - val_loss: 1.4602 - val_accuracy: 0.5162\n",
            "Epoch 32/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.2687 - accuracy: 0.5662 - val_loss: 1.4857 - val_accuracy: 0.5033\n",
            "Epoch 33/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.2599 - accuracy: 0.5698 - val_loss: 1.4666 - val_accuracy: 0.5075\n",
            "Epoch 34/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.2524 - accuracy: 0.5715 - val_loss: 1.4702 - val_accuracy: 0.5156\n",
            "Epoch 35/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.2318 - accuracy: 0.5784 - val_loss: 1.5021 - val_accuracy: 0.5074\n",
            "Epoch 36/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.2282 - accuracy: 0.5813 - val_loss: 1.4494 - val_accuracy: 0.5179\n",
            "Epoch 37/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.2102 - accuracy: 0.5865 - val_loss: 1.4470 - val_accuracy: 0.5248\n",
            "Epoch 38/400000\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.2038 - accuracy: 0.5897 - val_loss: 1.4725 - val_accuracy: 0.5149\n",
            "Epoch 39/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.1948 - accuracy: 0.5909 - val_loss: 1.4821 - val_accuracy: 0.5149\n",
            "Epoch 40/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.1910 - accuracy: 0.5974 - val_loss: 1.4871 - val_accuracy: 0.5153\n",
            "Epoch 41/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.1688 - accuracy: 0.6013 - val_loss: 1.4682 - val_accuracy: 0.5258\n",
            "Epoch 42/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.1777 - accuracy: 0.6004 - val_loss: 1.4791 - val_accuracy: 0.5201\n",
            "Epoch 43/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.1461 - accuracy: 0.6073 - val_loss: 1.4815 - val_accuracy: 0.5230\n",
            "Epoch 44/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.1553 - accuracy: 0.6053 - val_loss: 1.5042 - val_accuracy: 0.5128\n",
            "Epoch 45/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.1335 - accuracy: 0.6130 - val_loss: 1.4865 - val_accuracy: 0.5176\n",
            "Epoch 46/400000\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.1344 - accuracy: 0.6128 - val_loss: 1.4913 - val_accuracy: 0.5283\n",
            "Epoch 47/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.1216 - accuracy: 0.6165 - val_loss: 1.4866 - val_accuracy: 0.5209\n",
            "Epoch 48/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.1130 - accuracy: 0.6210 - val_loss: 1.5011 - val_accuracy: 0.5289\n",
            "Epoch 49/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.0972 - accuracy: 0.6253 - val_loss: 1.5257 - val_accuracy: 0.5253\n",
            "Epoch 50/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.1052 - accuracy: 0.6245 - val_loss: 1.4782 - val_accuracy: 0.5364\n",
            "Epoch 51/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.0898 - accuracy: 0.6291 - val_loss: 1.4730 - val_accuracy: 0.5304\n",
            "Epoch 52/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.0777 - accuracy: 0.6316 - val_loss: 1.5614 - val_accuracy: 0.5187\n",
            "Epoch 53/400000\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.0824 - accuracy: 0.6309 - val_loss: 1.4933 - val_accuracy: 0.5270\n",
            "Epoch 54/400000\n",
            "313/313 [==============================] - 3s 8ms/step - loss: 1.0653 - accuracy: 0.6387 - val_loss: 1.5155 - val_accuracy: 0.5321\n",
            "Epoch 55/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.0500 - accuracy: 0.6442 - val_loss: 1.4925 - val_accuracy: 0.5309\n",
            "Epoch 56/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.0599 - accuracy: 0.6409 - val_loss: 1.5581 - val_accuracy: 0.5179\n",
            "Epoch 57/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.0435 - accuracy: 0.6460 - val_loss: 1.5287 - val_accuracy: 0.5278\n",
            "Epoch 58/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.0369 - accuracy: 0.6480 - val_loss: 1.5316 - val_accuracy: 0.5260\n",
            "Epoch 59/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.0342 - accuracy: 0.6493 - val_loss: 1.5682 - val_accuracy: 0.5211\n",
            "Epoch 60/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.0067 - accuracy: 0.6586 - val_loss: 1.5524 - val_accuracy: 0.5257\n",
            "Epoch 61/400000\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.0091 - accuracy: 0.6568 - val_loss: 1.5648 - val_accuracy: 0.5258\n",
            "Epoch 62/400000\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 1.0167 - accuracy: 0.6548 - val_loss: 1.5608 - val_accuracy: 0.5260\n",
            "Epoch 63/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.0062 - accuracy: 0.6578 - val_loss: 1.5846 - val_accuracy: 0.5214\n",
            "Epoch 64/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.9854 - accuracy: 0.6654 - val_loss: 1.5538 - val_accuracy: 0.5314\n",
            "Epoch 65/400000\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.9944 - accuracy: 0.6646 - val_loss: 1.5978 - val_accuracy: 0.5165\n",
            "Epoch 66/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.9732 - accuracy: 0.6700 - val_loss: 1.5637 - val_accuracy: 0.5310\n",
            "Epoch 67/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.9670 - accuracy: 0.6702 - val_loss: 1.5773 - val_accuracy: 0.5307\n",
            "Epoch 68/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.9692 - accuracy: 0.6723 - val_loss: 1.5671 - val_accuracy: 0.5297\n",
            "Epoch 69/400000\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.9516 - accuracy: 0.6784 - val_loss: 1.5906 - val_accuracy: 0.5230\n",
            "Epoch 70/400000\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 0.9567 - accuracy: 0.6732 - val_loss: 1.6174 - val_accuracy: 0.5174\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Evaluation on test data and inference\n",
        "At this point, we will perform a basic evaluation and inference. With the model.evaluate function, the same metrics are calculated, that were used during training:"
      ],
      "metadata": {
        "id": "2FtKp-a2-A9m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.evaluate(X_test, Y_test))"
      ],
      "metadata": {
        "id": "I3_9wgDREUna",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82378b60-2043-4aab-91a4-287e1bf9216e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 1.4710 - accuracy: 0.5248\n",
            "[1.4709663391113281, 0.5248000025749207]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we would like to see similar values, as in the validation set. If those are close to each other, then the generalization ability of the model is good (in case of an independent test-set)."
      ],
      "metadata": {
        "id": "ECHguBFtGL7f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1. Exercise\n",
        "Predict the class of the first 10 elements in the test set, and compare the predicted values with the actual, target values in the test set. Hints:\n",
        "* you can use [model.predict](https://www.activestate.com/resources/quick-reads/how-to-use-a-model-to-do-predictions-with-keras/) for prediction\n",
        "* from the output you can select the largest value with the [argmax() function of Numpy](https://stackoverflow.com/questions/62358642/convert-one-hot-encoding-back-to-number-label). As there are multiple values, you have to call it with axis=1 parameter.\n",
        "* it is enought to compare the predictions and the targets by printing the values out and inspecting them."
      ],
      "metadata": {
        "id": "0Io4zYJeHjz6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "preds = model.predict(X_test[:10])\n",
        "preds_dense =  np.argmax(preds, axis=1)"
      ],
      "metadata": {
        "id": "XEEyU7exILqF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05aa297d-7501-49ec-fbeb-22611938c379"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 45ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before comparision, you have to convert back the one-hot encoded target values the same way, as you converted the output of the neural network to class values with argmax() function."
      ],
      "metadata": {
        "id": "seZTEFI-I5cZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Target labels:\", np.argmax(Y_test[:10],axis=1))\n",
        "print(\"Predicted labels:\", preds_dense)"
      ],
      "metadata": {
        "id": "PHLvoMuIIkJ6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a485960-deb5-4e9c-ee3b-5c6fe5c9ed10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Target labels: [3 8 8 0 6 6 1 6 3 1]\n",
            "Predicted labels: [3 1 0 0 4 6 1 6 5 1]\n"
          ]
        }
      ]
    }
  ]
}