Code/CIFAR10-CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HGvDA_xaaGYR"
   },
   "source": [
    "This is the initial part of the notebook where all of the dependencies are imported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "YL5kz3lrxSJs"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras import optimizers\n",
    "from keras.losses import sparse_categorical_crossentropy\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "import keras\n",
    "from tensorboard.backend.event_processing import event_accumulator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E7qyp68ia-5g"
   },
   "source": [
    "Creating the callbacks that will be used for detecting when the model no longer improves and adjusting the learning parameter when a plateau is detected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Z6y6KXaAJ88P"
   },
   "outputs": [],
   "source": [
    "# Main example code is based on https://teksands.ai/blog/callbacks-in-keras#what-are-callbacks-in-keros\n",
    "\n",
    "early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                        min_delta=0,\n",
    "                        patience=5,\n",
    "                        verbose=1,\n",
    "                        restore_best_weights=True)\n",
    "\n",
    "# Main example the callback is based on https://www.kaggle.com/curiousprogrammer/lenet-5-cnn-with-keras-99-48\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_accuracy', \n",
    "                                patience=3, \n",
    "                                verbose=1, \n",
    "                                factor=0.2, \n",
    "                                min_lr=1e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3UpL0qW7fvJ6"
   },
   "source": [
    "Loading the dataset to the environment, we can download each time as it is provided by tensorflow.keras.datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kBjF0V3dxXaG",
    "outputId": "a7a876a8-c9fc-456f-8a3b-7b23ecfed894"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
      "170500096/170498071 [==============================] - 6s 0us/step\n",
      "170508288/170498071 [==============================] - 6s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# Code used from https://www.tensorflow.org/tutorials/images/cnn\n",
    "(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()\n",
    "\n",
    "# Normalize pixel values to be between 0 and 1\n",
    "train_images, test_images = train_images / 255.0, test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "RsU0iiztEw17"
   },
   "outputs": [],
   "source": [
    "# Normalizing and resizing the images to avoid bias in the model\n",
    "# Dataset influenced by https://towardsdatascience.com/implementing-alexnet-cnn-architecture-using-tensorflow-2-0-and-keras-2113e090ad98\n",
    "\n",
    "def process_images_28(image, label):\n",
    "    # Normalize images to have a mean of 0 and standard deviation of 1\n",
    "    image = tf.image.per_image_standardization(image)\n",
    "    # Resize images from 32x32 to 28x28\n",
    "    image = tf.image.resize(image, (28,28))\n",
    "    return image, label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xj9UbtJjhIho"
   },
   "source": [
    "Pre-processing data to avoid bias in the model. This can easily be done using from_tensor_slices as it is a dataloader that allows the procedure to be done more easily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-C0pTJS2EybR",
    "outputId": "3c9d36c8-8f26-4674-e6ea-7c712874ba94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data size: 50000\n",
      "Test data size: 10000\n"
     ]
    }
   ],
   "source": [
    "# Dataset influenced by https://towardsdatascience.com/implementing-alexnet-cnn-architecture-using-tensorflow-2-0-and-keras-2113e090ad98\n",
    "\n",
    "train_ds = tf.data.Dataset.from_tensor_slices((train_images, train_labels))\n",
    "test_ds = tf.data.Dataset.from_tensor_slices((test_images, test_labels))\n",
    "train_ds_size = tf.data.experimental.cardinality(train_ds).numpy()\n",
    "test_ds_size = tf.data.experimental.cardinality(test_ds).numpy()\n",
    "print(\"Training data size:\", train_ds_size)\n",
    "print(\"Test data size:\", test_ds_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xGuuqhGnjNUe"
   },
   "source": [
    "Batch  size used is 32 and shuffle has buffere size equal to the dataset allowing randomization in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "b4gtSorFE6-x"
   },
   "outputs": [],
   "source": [
    "# Dataset influenced by https://towardsdatascience.com/implementing-alexnet-cnn-architecture-using-tensorflow-2-0-and-keras-2113e090ad98\n",
    "\n",
    "train_ds_28 = (train_ds\n",
    "                  .map(process_images_28)\n",
    "                  .shuffle(buffer_size=train_ds_size)\n",
    "                  .batch(batch_size=32, drop_remainder=True))\n",
    "test_ds_28 = (test_ds\n",
    "                  .map(process_images_28)\n",
    "                  .shuffle(buffer_size=train_ds_size)\n",
    "                  .batch(batch_size=32, drop_remainder=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "HulFfKgnxZYt"
   },
   "outputs": [],
   "source": [
    "# Model adapted from https://www.kaggle.com/curiousprogrammer/lenet-5-cnn-with-keras-99-48\n",
    "\n",
    "baseline_model = Sequential()\n",
    "\n",
    "baseline_model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(5,5), padding='same', activation='relu', input_shape=(28, 28, 3)))\n",
    "baseline_model.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "baseline_model.add(tf.keras.layers.BatchNormalization())\n",
    "baseline_model.add(tf.keras.layers.Conv2D(filters=48, kernel_size=(5,5), padding='same', activation='relu'))\n",
    "baseline_model.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "baseline_model.add(tf.keras.layers.BatchNormalization())\n",
    "baseline_model.add(tf.keras.layers.Flatten())\n",
    "baseline_model.add(tf.keras.layers.Dropout(0.2))\n",
    "baseline_model.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "baseline_model.add(tf.keras.layers.Dropout(0.2))\n",
    "baseline_model.add(tf.keras.layers.Dense(84, activation='relu'))\n",
    "baseline_model.add(tf.keras.layers.Dropout(0.2))\n",
    "baseline_model.add(tf.keras.layers.Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CF9QLJqpxm8G",
    "outputId": "e8c4aafa-319d-449d-d155-8f14aeea2c6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 28, 28, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 14, 14, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 14, 14, 32)       128       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 14, 14, 48)        38448     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 7, 7, 48)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 7, 7, 48)         192       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2352)              0         \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 2352)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 256)               602368    \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 84)                21588     \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 84)                0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                850       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 666,006\n",
      "Trainable params: 665,846\n",
      "Non-trainable params: 160\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "baseline_model.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.Adam(), metrics=['accuracy'])\n",
    "baseline_model.summary()\n",
    "model_change = baseline_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n-Z9nKrljvm-"
   },
   "source": [
    "Initial Evaluation of the baseline model using 8 epochs to analyse its performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JOkNDB1UypOW",
    "outputId": "1114db51-58a1-4995-bcf1-1ee95be3c546"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1562/1562 [==============================] - 193s 119ms/step - loss: 1.5025 - accuracy: 0.4687 - val_loss: 1.1470 - val_accuracy: 0.5947\n",
      "Epoch 2/8\n",
      "1562/1562 [==============================] - 187s 118ms/step - loss: 1.1156 - accuracy: 0.6108 - val_loss: 0.9640 - val_accuracy: 0.6617\n",
      "Epoch 3/8\n",
      "1562/1562 [==============================] - 210s 130ms/step - loss: 0.9525 - accuracy: 0.6698 - val_loss: 0.8527 - val_accuracy: 0.7046\n",
      "Epoch 4/8\n",
      "1562/1562 [==============================] - 194s 122ms/step - loss: 0.8369 - accuracy: 0.7117 - val_loss: 0.8149 - val_accuracy: 0.7182\n",
      "Epoch 5/8\n",
      "1562/1562 [==============================] - 207s 130ms/step - loss: 0.7487 - accuracy: 0.7413 - val_loss: 0.7977 - val_accuracy: 0.7203\n",
      "Epoch 6/8\n",
      "1562/1562 [==============================] - 215s 136ms/step - loss: 0.6759 - accuracy: 0.7650 - val_loss: 0.7920 - val_accuracy: 0.7297\n",
      "Epoch 7/8\n",
      "1562/1562 [==============================] - 201s 125ms/step - loss: 0.6088 - accuracy: 0.7886 - val_loss: 0.7921 - val_accuracy: 0.7413\n",
      "Epoch 8/8\n",
      "1562/1562 [==============================] - 192s 121ms/step - loss: 0.5602 - accuracy: 0.8044 - val_loss: 0.7644 - val_accuracy: 0.7485\n"
     ]
    }
   ],
   "source": [
    "history = baseline_model.fit(train_ds_28, epochs=8,verbose=1,\n",
    "                    validation_data=(test_ds_28))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "538EPb2KkU0z"
   },
   "source": [
    "Printing a graph of the accuracy of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "hP_rLCsmLG0v",
    "outputId": "39c42738-c5ae-4dcb-a51c-a4e8ea8e2b6b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qR41xlVfV8umqHczKKWD+6kIqa+rpGhXGmYMSGT8kiVMH9KBThHVHDbaOkyA8MnLkSHbs2MHWrVspLCwkPj6epKQkbr/9dj777DNCQkLIz89n+/btJCUleR2uMZ4pqqhm7ortzMwpYOG6YmrqG+gRE8mFI/swYUgSJx7TnYgw647akjpOgjjEL/1guvjii3nzzTcpKCjg0ksvZerUqRQWFrJkyRLCw8NJS0vzO8y3Me1dfukeZi13uqNmbyqhQSGlWyeuOqkvE4cmMTIl3rqjeqjjJAgPXXrppVx33XUUFRWxYMECXn/9dXr27El4eDjz5s1j8+bNXodoTItZt6OcWTnO3czf5ZcBkJkYw02nZzBhSCKDe3W16tZWwhJECxgyZAjl5eX06dOHXr16ccUVV3DeeecxbNgwsrKyGDhwoNchGhM0qsry/F3MzNnGzOUFrC905lsekRLHXWcNZMKQJNITuhziLMYLliBayHff7WsgT0hIYNGiRX73s3sgTHtQ36As2bzTvXGtgPzSPYSGCKPTu3HVSWmMH5zU4QfCawssQRhjmkVNXQNfbCjmY58hsyNCQ/hhRgK3nZnBmYMSie8S4XWY5jBYgjDGHLE9NfV8traQWcsLmLtyO7t8hsyeOCSJ0wb2bJNTbRpHu//LqWqHaPBqLzMDmtbP9x6FeasK2VNbT2yncMYPSWLikCROzrAhs9uLdp0goqKiKC4upnv37u06SagqxcXFREVZna4JjpLdNcxZUcDM5QX8x+cehR8d34eJQ3ox+phuhNuQ2e1Ou04QycnJ5OXlUVhY6HUoQRcVFUVycrLXYZh2ZFvZHma73VG/3FhMg0JyvN2j0JEENUGIyETgb0Ao8LyqPtxk+6PAae5iZ6Cnqsa52+qBxq4/W1T1/MN9//DwcNLT0480fGM6nE1Fu5mZ45QUluaWApDRM5obT+vPhCFJDOlt9yh0JEFLECISCjwJjAPygMUiMkNVVzTuo6q3++x/MzDS5xR7VHVEsOIzxuw/j8LM5QWsKigHYFifWO6ckMmEIUn07xntcZTGK8EsQYwC1qnqBgARmQ5MAlYcYP/LgHuDGI8xBicpLMsr23uPwsai3YjACX278V/nDmbCkESS420eBRPcBNEHyPVZzgNG+9tRRPoC6cCnPqujRCQbqAMeVtV3/Rx3PXA9QGpqajOFbUz7U9+gfLWxhFk5TlLYVlZFWIjwg37dufaH6YwfnESPmEivwzQH09AAe0qgvAAqCqB8+77nLj3g1Dub/S1bSyP1ZOBNVa33WddXVfNF5BjgUxH5TlXX+x6kqs8BzwFkZWVZP09jfFTX1bNwfTGzlhcwZ8V2infXEBkWwqkDenDnhEzOGJhIbGebR8Fz9XWwe4d74d9+4OeKHdBQ+/3jI2Ig7eSghBbMBJEPpPgsJ7vr/JkM3Oi7QlXz3ecNIjIfp31i/fcPNcY0qqyp47M1hc7kOit3UF5dR3RkGKcP7MnEoUmMzexB54jW8ruwnautci/svhf7bfv/8q8ogN1FgJ/ft526QUwSRCdCj0znuXHZ9zkieONYBfNfymIgQ0TScRLDZODypjuJyEAgHljksy4eqFTVahFJAMYA/xvEWI1ps8r21PLpKqc76oI1hVTVNhDfOZyzh/Vi4tAkTurfncgwu3Gt2VSX+1zkD/Krv6r0+8dKCHTpCTGJ0LU39BkJ0UnOcnTSvgt/dCKEeT8sSdAShKrWichNwCycbq4vqmqOiDwAZKvqDHfXycB03f9W4EHAsyLSAITgtEEcqHHbmA6nvKqWD7/dxsfLC1i4vojaeiWpaxSXZqUwYWgSo9K6EWY3rh2+qjIoWgfF66B86/d/7Zdvh9rd3z8uNGLfhb57f6fKZ78Lv/vcJQFC2k6ylvYyRENWVpZmZ2d7HYYxQaOqfJtXxrQvtzBj2Vb21NbTt3tnJg51hrg4NjnOblwLhCrs2gpFa/Z/FK5xkoCviOgDV+34PneKhzZ6f4iILFHVLH/brDLSmFauvKqW95ZuZdqXW1ixbRedI0KZNKI3k0elcmxyrN24diB1NVCyAYpWu0lgLRSudkoHNT7D6kfGQo8B0P8MSMiAhAHQPcOpAors2PeAWIIwphVqLC28+pVTWqisqWdwr648dMFQJo3oTUyU9T7aa89O5+LvWxIoWgM7N4Fvx8jYFCcBpE5xE0Gmkwyie7bZX//BZgnCmFaksbTw6ldbyNm6i07hoZx/bG8uH53K8I5cWmhogF15+5cEGpPC7h379guNcNoAkobC0IvcJJDhrOvgpYEjYQnCmFbg27zSvW0LlTX1DOrVlQfd0kLXjlRaqK2CkvX7lwSK1jjVQrWV+/aLinO6fg4Yv68kkJAB8WltqhG4tbMEYYxHKqrreG9pPq9+tYXl+ftKC5eN7gBtC5UlbhLwaR8oWg07N7PvngCBuBTn4p/2w33tAz0yoXN3qxZqAZYgjGlh3+WVMe2rzby31CktDEyK4cFJQ5g0sk/7KS2oOkmgdLPz2LnZbTB2E0Fl8b59w6KcKqDex8HwyU4i6JEJ3fpBhI0J5SVLEMa0AH+lhfOO7cVlo1IZkRLXNksLVWVQusW5+Jdu/v5r355C4PzqT8iEgefuKwkkZDiNx1Yt1CpZgjAmiJzSwhZmLM1nd1srLdTshtLcfSUA39JA6Zbv3ykcEQ1xfZ12gPRTIS4V4vs66+JSIaqrJx/DHDlLEMY0s4rqOma4PZG+yy8jKjyE84Y7PZFaVWmhrnpfAvBXAtjdZCbGsCjnQh/XF1JG7XvdmATa8M1ixj9LEMY0k+X5ZUz9cv/SwgOThnCBV6WF+jrYlb//r37f1+Xb2G+QuJBwiE12LviZZ7slgLR9JQC7X6DDsQRhzFGoqK7j/WXOXc6+pYXLRqcyMtilhYYGZ2gIv20Am6Esf/8bxSQEuvZxLvj9TmtSAkiFmF7WFmD2YwnCmEA1NEB9DdRXszKviBlLNjI/J4+62moyu4fzy1O7c8oxXYkOLYTKfFhR7Qz3UF/tVOfU13z/2d+6umr3mJoDPDfuVwXasH+M0UnOBT/lRBjWpA0gNhlCW3m7h2lVLEGYjquh3ul6WfAdbF/uPO/aeuALdEPd3kMHuY/fhgCRQAXwpfsIhIRCWKRz529YJIRGOsM7N32OjHG3hzfZFum0CXTtva8UEJsC4VHN/S2ZDswShOkYqsthe45PMlgOO1bsuztXQp1ul3F93Yvvvot30R7I2VFFzvYqKupCiY2J5ti0ngxP60nnTp33v8iHhgdw4Y+0qhzTJliCMO2LKpTlOomgYDlsd593bty3T1QsJA2H465yxuxJHAo9Bu7363t3Y9vCV1v4Nq+MyLAQznV7Ih2X2op6IhkTRJYgTNtVWwWFK50E0Fgy2L7cuYELAIFu6dBrOIy4Yl8yiE0+YG+cnK3OfAvvLd1KRXUdmYkx3HfeYC4cmWzzN5sOxxKEaRvKt+8rDTS2FxSt3ddLJ7wLJA6GIRdB0jDn0XNwwCN45mwt448frmTh+mIrLRjjsgRhWpf6Oihe61YR+bQX+A7p3DXZKQ0MPHdfMohPh5DDn2JzR3kVf521hteX5BLXKZx7zhnExcenWGnBGCxBGC/tKd1XGmhsL9ixyukxBE5Db4+BkDHOqRpKGgaJQ6Bzt6N+66rael74fCNPzVtHTX0DPxuTzs2nZ1hiMMaHJQgTfA0NTiNxY2mgsWRQlrtvny49nCQw+npIHOaUEBIGNHu/fVXlg2+38fDHq8gv3cO4wYn87uxBpCd0adb3MaY9sARhgmfTf2DBw5D/9b6RPSXEufCnjIYTfuYmg2EQkxj0cJbllvLgByvI3ryTQb268ucfD+ek/glBf19j2qqgJggRmQj8DQgFnlfVh5tsfxQ4zV3sDPRU1Th321XAPe62h1T15WDGappRaS7M+S/IecdpL/DtQdRzEIR3atFwtpXt4X9nruadb/JJiI7k4YuGcXFWCqEh1vhszMEELUGISCjwJDAOyAMWi8gMVV3RuI+q3u6z/83ASPd1N+BeIAtnNLEl7rE7gxWvaQY1lbDwcfj8MWd57O9gzC0tnhAaVdbU8eyCDTz72XoaFG4Y248bTutPdKQVnI0JRDD/p4wC1qnqBgARmQ5MAlYcYP/LcJICwARgjqqWuMfOASYCrwYxXnOkVGHFuzD7v5x2hSEXwbgHnOkiPdDQoLy7NJ//nbmagl1VnDO8F3dNHEhKN5udzJjDEcwE0QfwaYUkDxjtb0cR6QukA58e5Ng+fo67HrgeIDU19egjNoevYDl8/FvY/LnTnnDhM5B2smfhZG8q4YEPVvBtXhnDk2N54vKRnJB29L2ejOmIWktZezLwpqrv2MSHpqrPAc8BZGVl6SF2N81pdzHM+yMseQmi4uCcR+D4qz0bYyi3pJKHZ67iw2+3kdQ1ikcuOZYLRvQhxNoZjDliwUwQ+YBvHUOyu86fycCNTY4d2+TY+c0YmzlS9XWQ/QLM+5MzAN6o62HsXc5sYh4or6rlqfnreeHzjYQI3HpGBj8/9Rg6R7SW3z7GtF3B/F+0GMgQkXScC/5k4PKmO4nIQCAeWOSzehbwJxFpvOqMB+4OYqwmEBvmw8d3OeMfHTMWJj7s9EryQH2D8kZ2Ln+ZvYaiimouGtmHOydm0ivWmwZxY9qjoCUIVa0TkZtwLvahwIuqmiMiDwDZqjrD3XUyMF1V1efYEhF5ECfJADzQ2GBtPLBzE8z6Paz6wBkO+9KpMPAcz6afXLiuiAc/XMnKbbs4vm88z1+VxYiUOE9iMaY9E5/rcpuWlZWl2dnZXofRvtTshn8/AgufcNoWfvhr+MFNnk1Ks7FoN3/6aCVzVmynT1wn7j57IOcM62WD6RlzFERkiapm+dtmFbXm+1Thuzdhzh+gfCsMuwTG3e/MXuaBsspaHv90Lf9ctImI0BDunJDJz05OJyrcJt0xJpgsQZj9bV3qdFvN/QJ6jYCL/wGpfnsnB11dfQPTvtrCo3PWULqnlkuzUvjV+AH0jLFpNY1pCZYgjKOiED59AL7+F3RJgPOfgBFTjmgI7eYwf/UOHvpwJet2VPCDY7pzz7mDGNI71pNYjOmoLEF0dPW18NVzMP9/oHY3/OBGOPU3zrScHli7vZyHPlzJgjWFpHXvzHNXHs+4wYnWzmCMByxBdGTr5sLMu6FoDfQ/Eyb8N/QY4EkoJbtreGzuGqZ+uYXOEaHcc84gfvKDNCLCvCnBGGMsQXRMxeudbqtrPoZux8Blr8GACZ50W62pa+Cfizbxt0/WUllTz+WjUrl93AC6dYlo8ViMMfuzBNGRVJfDZ3+GRU9BWCSceT+c+EvndQtTVWav2M5/f7SSTcWVnDqgB/ecM4iMxJgWj8UY458liI6goQG+nQ5z74OK7XDs5XDmvRCT5Ek4OVvLeOiDlSzaUEz/ntG89NMTOC2zpyexGGMOzBJEe5e3BD7+DeRnQ5/jYfI0SPZ7T0zQ7Siv4q+z1vD6klziOoXzwKQhXDYqlfBQa2cwpjWyBNFelRfA3Pth2TSIToQLnobhkz3ptlpVW88Ln2/kqXnrqK5r4Jox6dxyegaxnZt3vmljTPOyBNHe1FXDF087bQ111TDmVjjlToj0pm7/w2+38aePVpJfuodxgxP53dmDSE/o4kksxpjDE1CCEJG3gReAj1W1IbghmSOiCmtmway7oWQDDJgIE/4E3ft5Ek51XT33zcjh1a9yGZgUw7RrR3NS/wRPYjHGHJlASxBPAT8FHheRN4CXVHV18MIyh6VwjZMY1s2F7hlwxVuQcaZn4WzfVcUvXlnCN1tKuWFsP349PpNQm7jHmDYnoAShqnOBuSISizN39FwRyQX+DryiqrVBjNEcSFUZLPhf+PIZCO8M4//oTOAT5t09BEs2l/CLV75md3UdT11xHGcP6+VZLMaYoxNwG4SIdAemAFcC3wBTgZOBq9h/9jcTbA0NsPQV+OQB2F0EI6fAGX+AaG+7ik79cjP3zcihd1wnXvnZaDKT7J4GY9qyQNsg3gEygX8B56nqNnfTayJikzC0pG3fwoybYdtSSBkNV7wBvUd6GpJve8OpA3rw+OSR1kPJmHYg0BLE46o6z9+GAyrRlcMAABhvSURBVE00YYJg/acwfYrTI+miv8Owiz2b1a2Rb3vDjaf141fjrL3BmPYi0AQxWES+UdVSAHeu6MtU9anghWb2892b8M4voEcmTHnLs7ugffm2Nzx9xXGcZe0NxrQrgd41dV1jcgBQ1Z3AdcEJyXzPF0/DWz9zqpR++lGrSA5Tv9zM5Oe+oEtEKO/eOMaSgzHtUKAliFAREXUnsBaRUMCG2ww2Vach+vNHYNB5cNHzns0H3ci3vWFsZg/+dqm1NxjTXgVagpiJ0yB9hoicAbzqrjsoEZkoIqtFZJ2I3HWAfS4RkRUikiMi03zW14vIUvcxI8A424/6Ophxk5Mcjr8aLn7Z8+SwfVcVk5/7gle/yuXG0/rxwlUnWHIwph0LtATxW+DnwC/d5TnA8wc7wC1lPAmMA/KAxSIyQ1VX+OyTAdwNjFHVnSLi209zj6qOCDC+9qWmEt68xpmv4dTfwti7PW+MtvYGYzqeQG+UawCedh+BGgWsU9UNACIyHZgErPDZ5zrgSbdNA1XdcRjnb58qS+DVyyD3Szj7LzDK+6aexvsb+sR1Yuq1oxlgczYY0yEEeh9EBvDfwGBgbz2Hqh5zkMP6ALk+y3nA6Cb7DHDP/x8gFLhPVRurrqLceyzqgIdV9V0/cV0PXA+QmpoayEdp3cry4ZUfQcl6uPgfMOQCT8Ox9gZjOrZAq5heAu4FHgVOwxmXqTnGjQ4DMnDuxE4GPhORYW6Pqb6qmi8ixwCfish3qrre92BVfQ54DiArK0ubIR7vFK6Gf13kDJ9xxZtwzKmehmP3NxhjAr3Id1LVTwBR1c2qeh9wziGOyQdSfJaT3XW+8oAZqlqrqhuBNTgJA1XNd583APMBb28XDqa8bHhxAtTXwE8/9Dw5LNlcwrlPfM7qgnKevuI47pww0JKDMR1QoAmiWkRCgLUicpOIXAhEH+KYxUCGiKSLSAQwGWjaG+ld3HGcRCQBp8ppg4jEi0ikz/ox7N920X6snQMvnwdRcfCzWdDrWM9CUVW7v8EYs1egVUy3Ap2BW4AHcaqZrjrYAapaJyI3AbNw2hdeVNUcEXkAyFbVGe628SKyAqgH7lTVYhE5CXhWRBpwktjDvr2f2o1l0+G9G6HnYOfuaA8H26uuq+fe93KYvtjaG4wxDnHvfTvwDk531f9R1TtaJqQjk5WVpdnZbWjcwIVPwOx7IP0UuHQqRHX1LJSCMqe9YWmutTcY09GIyJIDjal3yBKEqtaLyMnNH1YH1dAAc//gJIjBF8BFz0FYpGfhZG8q4ZdT7f4GY8z3BVrF9I17N/MbwO7Glar6dlCiaq/qa52hupe9CidcB2f9D4SEehKK096whfvft/sbjDH+BZogooBi4HSfdQpYgghUzW5442pYOxtOuwdOucOzu6OtvcEYE4hA76T+abADadcqS2DqxbD1azjvb87YSh6x9gZjTKACvZP6JZwSw35U9Zpmj6i9Kc2FVy6CnZvhkn86o7J6xNobjDGHI9Aqpg98XkcBFwJbmz+cdmbHSufu6JrdcOU7kDbGkzCsvcEYcyQCrWJ6y3dZRF4FPg9KRO3Fli9g2iUQ1smZ5CdpqCdh+LY3nJbZg8cmjyS2k7U3GGMOLdASRFMZgHd3dbV2qz92GqRjk2HK2xDf15MwfNsbbjqtP7ePG2DtDcaYgAXaBlHO/m0QBThzRJimvnkFZtwCvYY7g+51SfAkDN/2hmemHMfEodbeYIw5PIFWMVmF9aGowuePwif3Q7/T4ZJ/QeShhqsKRhjW3mCMaR6BliAuBD5V1TJ3OQ4Y62+Ohg6poQFm/x6+eAqG/hgueBrCWn7KbmtvMMY0p0DbIO5V1XcaF1S1VETuxRmNtWOrq4H3boDv3oDRv4QJf4KQ5pgq4/BYe4MxprkFmiD8XfGOtIG7/aiugNevhPWfwhn3wsm3e3J3tLU3GGOCIdCLfLaIPAI86S7fCCwJTkhtxO4i5+7obctg0pMwckqLh6CqvPLlFu6fkUNyvLU3GGOaV6AJ4mbgv4DXcHozzcFJEh3Tzs3wrwthVz5MngqZZ7V4CNV19fzh3Rxey7b2BmNMcATai2k3cFeQY2kbCpbDKz+Cuir4yQxIHd3yIVh7gzGmBQTUmioic9yeS43L8SIyK3hhtVKb/gMvnQ0SAtfM9CQ5fJtXyrlPfM6a7eU8M+U47phgg+0ZY4Ij0CqmBFUtbVxQ1Z0i0rHupF75Abx5jXNX9JS3IS6lxUOob1DueGMZEaHCtBvHWHuDMSaoAu2P2SAiqY0LIpKGn9Fd260l/3B6KyUNg2tmeZIcAN5ftpU12yu4++xBlhyMMUEXaAni98DnIrIAEOCHwPVBi6q1UIXP/gLzHoL+4+CSlyGiiyeh1NY38OjcNQzq1ZVzbJhuY0wLCKgEoaozgSxgNfAq8Gtgz6GOE5GJIrJaRNaJiN9GbhG5RERWiEiOiEzzWX+ViKx1H1cF9GmaU0M9fHSnkxyGT4bLXvUsOQC8uSSPzcWV/HrcAEKszcEY0wICHWrjWuBWIBlYCpwILGL/KUibHhOKc9/EOCAPWCwiM1R1hc8+GcDdwBjfdg0R6Qbci5OUFFjiHrvz8D/iEairhnd+DjnvwEk3w5kPeHJ3dKOq2noe/2QtI1LiOGNQx2r6McZ4J9Cr3q3ACcBmVT0NGAmUHvwQRgHrVHWDqtYA04FJTfa5Dniy8cKvqjvc9ROAOapa4m6bA0wMMNajU7ULpv7YSQ7jHoTxD3maHACmfbmFbWVV/GZCJuLRPNbGmI4n0CtflapWAYhIpKquAjIPcUwfINdnOc9d52sAMEBE/iMiX4jIxMM4tvlV7IB/nAObF8KFz8KYW4L+lodSWVPHU/PXcVK/7pzU35uhw40xHVOgjdR57n0Q7wJzRGQnsLmZ3j8DGItTffWZiAwL9GARuR63sTw1NfUQex9CyQZnetCK7XDZdMgYd3TnayYv/WcTRRU1PPeTQ+VjY4xpXoHeSX2h+/I+EZkHxAIzD3FYPuDbHzTZXecrD/hSVWuBjSKyBidh5OMkDd9j5/uJ6zngOYCsrKwj73a7bRm88mNoqIOr3ofkrCM+VXMq21PLswvWc8bAnhyXGu91OMaYDuawK9dVdYGqznDbFQ5mMZAhIukiEgFMBmY02edd3EQgIgk4VU4bgFnAePeO7XhgvLuu+RWthZfOgdAI5x6HVpIcAJ7/9wZ2VdXxq/EDvA7FGNMBBW3IblWtE5GbcC7socCLqpojIg8A2ao6g32JYAVQD9ypqsUAIvIgTpIBeEBVS4ISaLd+MPrnkHUNxAa/mSNQxRXVvPj5Rs4Z3oshvWO9DscY0wGJavu4ITorK0uzs7O9DqPZPPTBCl78z0Zm334q/Xu2/NSlxpiOQUSWqKrfqhNv+28avwrKqvjnF5u56LhkSw7GGM9YgmiFnvh0LarKrWdkeB2KMaYDswTRymwpruS1xblMPiGVlG6dvQ7HGNOBWYJoZR77ZA2hIcJNp/f3OhRjTAdnCaIVWbu9nHe/yeeqk9JI7BrldTjGmA7OEkQr8sicNXSOCOMXp/bzOhRjjLEE0Voszy/j4+UFXHNyOt26RHgdjjHGWIJoLf4yezWxncK59ofpXodijDGAJYhWIXtTCfNXF/KLU/vRNSrc63CMMQawBOE5VeXPs1aTEB3JVSf19TocY4zZyxKExz5fV8SXG0u46bR+dI4I2tBYxhhz2CxBeEhV+cus1fSJ68Rlo49yPgtjjGlmliA8NGfFdpbllXHLGf2JDAv1OhxjjNmPJQiPNDQoj8xZQ3pCF350XLLX4RhjzPdYgvDI+99uZVVBObedmUFYqP0ZjDGtj12ZPFBX38Bjc9cyMCmG84b39jocY4zxyxKEB976Oo+NRbv51bgBhISI1+EYY4xfliBaWHVdPY9/so5jU+IYNzjR63CMMeaALEG0sFe/3EJ+6R7uGD8AESs9GGNaL0sQLaiypo7/m7ee0endOLl/gtfhGGPMQVmCaEEvL9xMUUU1d07ItNKDMabVC2qCEJGJIrJaRNaJyF1+tl8tIoUistR9XOuzrd5n/YxgxtkSdlXV8syC9YzN7EFWWjevwzHGmEMK2uA/IhIKPAmMA/KAxSIyQ1VXNNn1NVW9yc8p9qjqiGDF19Ke//dGyvbUcsf4TK9DMcaYgASzBDEKWKeqG1S1BpgOTAri+7VaJbtreOHfGzhraBJD+8R6HY4xxgQkmAmiD5Drs5znrmvqRyLyrYi8KSIpPuujRCRbRL4QkQv8vYGIXO/uk11YWNiMoTevZxasp7K2nl+NG+B1KMYYEzCvG6nfB9JUdTgwB3jZZ1tfVc0CLgceE5HvTdSsqs+papaqZvXo0aNlIj5M23dV8fLCTVw4og8ZiTFeh2OMMQELZoLIB3xLBMnuur1UtVhVq93F54Hjfbblu88bgPnAyCDGGjT/9+k66huU28600oMxpm0JZoJYDGSISLqIRACTgf16I4lIL5/F84GV7vp4EYl0XycAY4CmjdutXm5JJdMXb+GSE1JI7d7Z63CMMeawBK0Xk6rWichNwCwgFHhRVXNE5AEgW1VnALeIyPlAHVACXO0ePgh4VkQacJLYw356P7V6f/tkLSLCzaf39zoUY4w5bEGd41JVPwI+arLuDz6v7wbu9nPcQmBYMGMLtnU7Knj76zx+OiadXrGdvA7HGGMOm9eN1O3Wo3PXEBUeyi/Hfq9t3Rhj2gRLEEGQs7WMD7/dxjVj0kmIjvQ6HGOMOSKWIILgkdlr6BoVxnWnHON1KMYYc8QsQTSzr7fs5JNVO/j5qf2I7RTudTjGGHPELEE0s7/MWk1CdARXn5TmdSjGGHNULEE0o4Xrili4vpgbxvanS2RQO4gZY0zQWYJoJqrKn2evpldsFJePTvU6HGOMOWqWIJrJp6t28M2WUm45I4Oo8FCvwzHGmKNmCaIZNDQof5m9hr7dO/Pj45O9DscYY5qFJYhm8NHybazctovbzxxAeKh9pcaY9sGuZkeprr6BR+asYUBiNOcd29vrcIwxptlYgjhK73yTz4bC3fxqXCahIeJ1OMYY02wsQRyF6rp6Hpu7luHJsUwYkuh1OMYY06wsQRyF1xbnkl+6h1+Pz0TESg/GmPbFEsQR2lNTzxOfrmNUWjdOyUjwOhxjjGl2liCO0D8XbaKwvJo7JljpwRjTPlmCOALlVbU8vWA9pwzowaj0bl6HY4wxQWEJ4gi88PlGSitruWP8AK9DMcaYoLEEcZh27q7h+X9vZMKQRIYnx3kdjjHGBI0liMP0zGfr2V1Tx6/HZ3odijHGBFVQE4SITBSR1SKyTkTu8rP9ahEpFJGl7uNan21Xicha93FVMOMM1I5dVby8cBOTju3NgMQYr8MxxpigCtqkBSISCjwJjAPygMUiMkNVVzTZ9TVVvanJsd2Ae4EsQIEl7rE7gxVvIJ6ct47aeuW2M63twRjT/gWzBDEKWKeqG1S1BpgOTArw2AnAHFUtcZPCHGBikOIMSN7OSqZ9tYVLspJJS+jiZSjGGNMigpkg+gC5Pst57rqmfiQi34rImyKScjjHisj1IpItItmFhYXNFbdfj3+yFkG4+fSMoL6PMca0Fl43Ur8PpKnqcJxSwsuHc7CqPqeqWaqa1aNHj6AECLChsIK3vs7nihNT6R3XKWjvY4wxrUkwE0Q+kOKznOyu20tVi1W12l18Hjg+0GNb0qNz1xIRGsINY/t7FYIxxrS4YCaIxUCGiKSLSAQwGZjhu4OI9PJZPB9Y6b6eBYwXkXgRiQfGu+ta3Mptu3h/2VZ+OiaNHjGRXoRgjDGeCFovJlWtE5GbcC7socCLqpojIg8A2ao6A7hFRM4H6oAS4Gr32BIReRAnyQA8oKolwYr1YP46ew0xUWH8/JR+Xry9McZ4RlTV6xiaRVZWlmZnZzfrOb/ZspMLn1rIr8cN4OYzrHHaGNP+iMgSVc3yt83rRupW7a+z19CtSwQ/PTnd61CMMabFWYI4gEXri/l8XRE3jO1HdGTQauKMMabVsgThh6ryl9mrSewayZQT+3odjjHGeMIShB/zVxeyZPNObj49g6jwUK/DMcYYT1iCaKKhwSk9pHTrxCVZKYc+wBhj2ilLEE3MzCkgZ+subjtjABFh9vUYYzouuwL6qG9QHpmzhv49o7lgpL9ho4wxpuOwBOHj3W/yWbejgl+NG0BoiHgdjjHGeMoShKumroHHPlnDkN5dmTgkyetwjDHGc5YgXK9n55Jbsoc7xmcSYqUHY4yxBAFQVVvPE5+u5fi+8YzNDN6w4cYY05ZYggBe+WIz23dVc8f4TESs9GCMMWAJgorqOp6av56T+yfwg37dvQ7HGGNajQ4/yFBldR2j07tx/SnHeB2KMca0Kh0+QfTsGsXTU44/9I7GGNPBdPgqJmOMMf5ZgjDGGOOXJQhjjDF+WYIwxhjjlyUIY4wxflmCMMYY45clCGOMMX5ZgjDGGOOXqKrXMTQLESkENh/FKRKAomYKJ9jaUqzQtuJtS7FC24q3LcUKbSveo4m1r6r6HaW03SSIoyUi2aqa5XUcgWhLsULbirctxQptK962FCu0rXiDFatVMRljjPHLEoQxxhi/LEHs85zXARyGthQrtK1421Ks0LbibUuxQtuKNyixWhuEMcYYv6wEYYwxxi9LEMYYY/zq8AlCRCaKyGoRWScid3kdz8GIyIsiskNElnsdy6GISIqIzBORFSKSIyK3eh3TwYhIlIh8JSLL3Hjv9zqmQxGRUBH5RkQ+8DqWQxGRTSLynYgsFZFsr+M5GBGJE5E3RWSViKwUkR94HdOBiEim+502PnaJyG3Ndv6O3AYhIqHAGmAckAcsBi5T1RWeBnYAInIKUAH8U1WHeh3PwYhIL6CXqn4tIjHAEuCCVvzdCtBFVStEJBz4HLhVVb/wOLQDEpFfAVlAV1U91+t4DkZENgFZqtrqbzwTkZeBf6vq8yISAXRW1VKv4zoU93qWD4xW1aO5aXivjl6CGAWsU9UNqloDTAcmeRzTAanqZ0CJ13EEQlW3qerX7utyYCXQx9uoDkwdFe5iuPtotb+eRCQZOAd43utY2hMRiQVOAV4AUNWatpAcXGcA65srOYAliD5Ars9yHq34ItZWiUgaMBL40ttIDs6tslkK7ADmqGprjvcx4DdAg9eBBEiB2SKyRESu9zqYg0gHCoGX3Oq750Wki9dBBWgy8GpznrCjJwgTZCISDbwF3Kaqu7yO52BUtV5VRwDJwCgRaZXVeCJyLrBDVZd4HcthOFlVjwPOAm50q0tbozDgOOBpVR0J7AZaddskgFsVdj7wRnOet6MniHwgxWc52V1nmoFbl/8WMFVV3/Y6nkC5VQrzgIlex3IAY4Dz3Xr96cDpIvKKtyEdnKrmu887gHdwqndbozwgz6f0+CZOwmjtzgK+VtXtzXnSjp4gFgMZIpLuZuDJwAyPY2oX3EbfF4CVqvqI1/Ecioj0EJE493UnnI4Lq7yNyj9VvVtVk1U1Deff7KeqOsXjsA5IRLq4HRVwq2vGA62yJ56qFgC5IpLprjoDaJUdK5q4jGauXgKnONVhqWqdiNwEzAJCgRdVNcfjsA5IRF4FxgIJIpIH3KuqL3gb1QGNAa4EvnPr9QF+p6ofeRjTwfQCXnZ7goQAr6tqq+8+2kYkAu84vxkIA6ap6kxvQzqom4Gp7o/GDcBPPY7noNykOw74ebOfuyN3czXGGHNgHb2KyRhjzAFYgjDGGOOXJQhjjDF+WYIwxhjjlyUIY4wxflmCMKYVEJGxbWFUVtOxWIIwxhjjlyUIYw6DiExx541YKiLPugP8VYjIo+48Ep+ISA933xEi8oWIfCsi74hIvLu+v4jMdeee+FpE+rmnj/aZh2Cqeze6MZ6xBGFMgERkEHApMMYd1K8euALoAmSr6hBgAXCve8g/gd+q6nDgO5/1U4EnVfVY4CRgm7t+JHAbMBg4BududGM806GH2jDmMJ0BHA8sdn/cd8IZGrwBeM3d5xXgbXdegThVXeCufxl4wx2TqI+qvgOgqlUA7vm+UtU8d3kpkIYzcZExnrAEYUzgBHhZVe/eb6XIfzXZ70jHr6n2eV2P/f80HrMqJmMC9wnwYxHpCSAi3USkL87/ox+7+1wOfK6qZcBOEfmhu/5KYIE7u16eiFzgniNSRDq36KcwJkD2C8WYAKnqChG5B2dmtBCgFrgRZ1KZUe62HTjtFABXAc+4CcB3VNArgWdF5AH3HBe34McwJmA2mqsxR0lEKlQ12us4jGluVsVkjDHGLytBGGOM8ctKEMYYY/yyBGGMMcYvSxDGGGP8sgRhjDHGL0sQxhhj/Pp/7MKCkim4BRIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Baseline model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nxyrQ8RGksiG"
   },
   "source": [
    "Modification on the initial model to test how changing the optimiser to SGD affects the training performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "s3o2t2tX-ER7",
    "outputId": "cf2ceda2-1bac-4025-ba22-6d286a3b50d9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_8 (Conv2D)           (None, 28, 28, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_8 (MaxPooling  (None, 14, 14, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_8 (Batc  (None, 14, 14, 32)       128       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_9 (Conv2D)           (None, 14, 14, 48)        38448     \n",
      "                                                                 \n",
      " max_pooling2d_9 (MaxPooling  (None, 7, 7, 48)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_9 (Batc  (None, 7, 7, 48)         192       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " flatten_4 (Flatten)         (None, 2352)              0         \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 2352)              0         \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 256)               602368    \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 256)               0         \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 84)                21588     \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 84)                0         \n",
      "                                                                 \n",
      " dense_14 (Dense)            (None, 10)                850       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 666,006\n",
      "Trainable params: 665,846\n",
      "Non-trainable params: 160\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_SGD = Sequential()\n",
    "\n",
    "model_SGD.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(5,5), padding='same', activation='relu', input_shape=(28, 28, 3)))\n",
    "model_SGD.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "model_SGD.add(tf.keras.layers.BatchNormalization())\n",
    "model_SGD.add(tf.keras.layers.Conv2D(filters=48, kernel_size=(5,5), padding='same', activation='relu'))\n",
    "model_SGD.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "model_SGD.add(tf.keras.layers.BatchNormalization())\n",
    "model_SGD.add(tf.keras.layers.Flatten())\n",
    "model_SGD.add(tf.keras.layers.Dropout(0.2))\n",
    "model_SGD.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "model_SGD.add(tf.keras.layers.Dropout(0.2))\n",
    "model_SGD.add(tf.keras.layers.Dense(84, activation='relu'))\n",
    "model_SGD.add(tf.keras.layers.Dropout(0.2))\n",
    "model_SGD.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "model_SGD.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.SGD(), metrics=['accuracy'])\n",
    "model_SGD.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BsF2H3uvlNnQ"
   },
   "source": [
    "Testing the training of the SGD variant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NoVyq5dK-WSu",
    "outputId": "124876e4-ab5f-4a61-a120-888efaa2cece"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1562/1562 [==============================] - 182s 114ms/step - loss: 1.6683 - accuracy: 0.4042 - val_loss: 1.2832 - val_accuracy: 0.5429\n",
      "Epoch 2/8\n",
      "1562/1562 [==============================] - 186s 117ms/step - loss: 1.3110 - accuracy: 0.5293 - val_loss: 1.1193 - val_accuracy: 0.6005\n",
      "Epoch 3/8\n",
      "1562/1562 [==============================] - 189s 119ms/step - loss: 1.1643 - accuracy: 0.5871 - val_loss: 1.0250 - val_accuracy: 0.6364\n",
      "Epoch 4/8\n",
      "1562/1562 [==============================] - 195s 123ms/step - loss: 1.0521 - accuracy: 0.6288 - val_loss: 0.9510 - val_accuracy: 0.6662\n",
      "Epoch 5/8\n",
      "1562/1562 [==============================] - 206s 129ms/step - loss: 0.9816 - accuracy: 0.6546 - val_loss: 0.9271 - val_accuracy: 0.6746\n",
      "Epoch 6/8\n",
      "1562/1562 [==============================] - 186s 117ms/step - loss: 0.9233 - accuracy: 0.6757 - val_loss: 0.8735 - val_accuracy: 0.6907\n",
      "Epoch 7/8\n",
      "1562/1562 [==============================] - 187s 118ms/step - loss: 0.8638 - accuracy: 0.6958 - val_loss: 0.8197 - val_accuracy: 0.7108\n",
      "Epoch 8/8\n",
      "1562/1562 [==============================] - 188s 118ms/step - loss: 0.8234 - accuracy: 0.7106 - val_loss: 0.7922 - val_accuracy: 0.7171\n"
     ]
    }
   ],
   "source": [
    "history_SGD = model_SGD.fit(train_ds_28, epochs=8,verbose=1,\n",
    "                    validation_data=(test_ds_28))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A3F1_K6GlQob"
   },
   "source": [
    "Plotting the performance of both models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "l_cfRYNYK4Zd",
    "outputId": "82e9ade3-dd50-46db-b618-936b6498514e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.plot(history_SGD.history['val_accuracy'])\n",
    "plt.title('Model Accuracy SGD vs Adam')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['Adam', 'SGD'], loc='upper left')\n",
    "plt.savefig('SGDAdam')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s7YMW8_GlUdj"
   },
   "source": [
    "Modified version of the model with input of 32x32x3. Similar to baseline architecture but the main difference if the input size in the first layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UPQXr_JLkRny"
   },
   "outputs": [],
   "source": [
    "model_v1 = Sequential()\n",
    "\n",
    "model_v1.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(5,5), padding='same', activation='relu', input_shape=(32, 32, 3)))\n",
    "model_v1.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "model_v1.add(tf.keras.layers.BatchNormalization())\n",
    "model_v1.add(tf.keras.layers.Conv2D(filters=48, kernel_size=(5,5), padding='same', activation='relu'))\n",
    "model_v1.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "model_v1.add(tf.keras.layers.BatchNormalization())\n",
    "model_v1.add(tf.keras.layers.Flatten())\n",
    "model_v1.add(tf.keras.layers.Dropout(0.2))\n",
    "model_v1.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "model_v1.add(tf.keras.layers.Dropout(0.2))\n",
    "model_v1.add(tf.keras.layers.Dense(84, activation='relu'))\n",
    "model_v1.add(tf.keras.layers.Dropout(0.2))\n",
    "model_v1.add(tf.keras.layers.Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IgDPNAGlkZf5",
    "outputId": "21260ffa-fe54-4d34-a87c-74124d504319"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_9 (Conv2D)           (None, 32, 32, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_9 (MaxPooling  (None, 16, 16, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_9 (Batc  (None, 16, 16, 32)       128       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_10 (Conv2D)          (None, 16, 16, 48)        38448     \n",
      "                                                                 \n",
      " max_pooling2d_10 (MaxPoolin  (None, 8, 8, 48)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_10 (Bat  (None, 8, 8, 48)         192       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " flatten_5 (Flatten)         (None, 3072)              0         \n",
      "                                                                 \n",
      " dropout_13 (Dropout)        (None, 3072)              0         \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 256)               786688    \n",
      "                                                                 \n",
      " dropout_14 (Dropout)        (None, 256)               0         \n",
      "                                                                 \n",
      " dense_14 (Dense)            (None, 84)                21588     \n",
      "                                                                 \n",
      " dropout_15 (Dropout)        (None, 84)                0         \n",
      "                                                                 \n",
      " dense_15 (Dense)            (None, 10)                850       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 850,326\n",
      "Trainable params: 850,166\n",
      "Non-trainable params: 160\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_v1.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.Adam(), metrics=['accuracy'])\n",
    "model_v1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0G9y7MfVmIZa"
   },
   "source": [
    "Testing the training the new version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PyAc8XwYkjgC",
    "outputId": "eda57928-f888-423b-9788-bce646def296"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1562/1562 [==============================] - 248s 154ms/step - loss: 1.4568 - accuracy: 0.4851 - val_loss: 1.0884 - val_accuracy: 0.6193\n",
      "Epoch 2/8\n",
      "1562/1562 [==============================] - 237s 150ms/step - loss: 1.0596 - accuracy: 0.6327 - val_loss: 1.0505 - val_accuracy: 0.6452\n",
      "Epoch 3/8\n",
      "1562/1562 [==============================] - 233s 147ms/step - loss: 0.8993 - accuracy: 0.6898 - val_loss: 0.8409 - val_accuracy: 0.7082\n",
      "Epoch 4/8\n",
      "1562/1562 [==============================] - 238s 150ms/step - loss: 0.7889 - accuracy: 0.7262 - val_loss: 0.7919 - val_accuracy: 0.7304\n",
      "Epoch 5/8\n",
      "1562/1562 [==============================] - 236s 149ms/step - loss: 0.6986 - accuracy: 0.7573 - val_loss: 0.7926 - val_accuracy: 0.7382\n",
      "Epoch 6/8\n",
      "1562/1562 [==============================] - 235s 148ms/step - loss: 0.6233 - accuracy: 0.7830 - val_loss: 0.7262 - val_accuracy: 0.7546\n",
      "Epoch 7/8\n",
      "1562/1562 [==============================] - 228s 144ms/step - loss: 0.5546 - accuracy: 0.8063 - val_loss: 0.7321 - val_accuracy: 0.7547\n",
      "Epoch 8/8\n",
      "1562/1562 [==============================] - 234s 148ms/step - loss: 0.4994 - accuracy: 0.8256 - val_loss: 0.7383 - val_accuracy: 0.7572\n"
     ]
    }
   ],
   "source": [
    "history_v1 = model_v1.fit(train_ds, epochs=8,verbose=1,\n",
    "                    validation_data=(test_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5COAq9ComQAW"
   },
   "source": [
    "Plotting the performance of the modified input model with the baseline model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "kgvgeyOgmdFd",
    "outputId": "aca12110-9991-4cd2-ef58-4d651f80aa23"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.plot(history_v1.history['val_accuracy'])\n",
    "plt.title('Model Accuracy 28x28 vs 32x32')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['28x28', '32x32'], loc='upper left')\n",
    "plt.savefig('DifferentInputs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E_CrGx8MmXV2"
   },
   "source": [
    "Plotting the accuracy of training and testing of the modified model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "sptTKjsekrSq",
    "outputId": "35729458-1c55-4fe1-e218-f5e1b0685910"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_v1.history['accuracy'])\n",
    "plt.plot(history_v1.history['val_accuracy'])\n",
    "plt.title('Model Accuracy 32x32')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PGNdPk_Ymfj_"
   },
   "source": [
    "Modified baseline model with different filter sizes. This is to test how they affect the performance of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HXNk3b1axBiq",
    "outputId": "b1dc87f8-15f5-457c-8175-9af00bbab655"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_10\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_20 (Conv2D)          (None, 28, 28, 48)        3648      \n",
      "                                                                 \n",
      " max_pooling2d_20 (MaxPoolin  (None, 14, 14, 48)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_20 (Bat  (None, 14, 14, 48)       192       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv2d_21 (Conv2D)          (None, 14, 14, 64)        76864     \n",
      "                                                                 \n",
      " max_pooling2d_21 (MaxPoolin  (None, 7, 7, 64)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_21 (Bat  (None, 7, 7, 64)         256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " flatten_10 (Flatten)        (None, 3136)              0         \n",
      "                                                                 \n",
      " dropout_19 (Dropout)        (None, 3136)              0         \n",
      "                                                                 \n",
      " dense_30 (Dense)            (None, 256)               803072    \n",
      "                                                                 \n",
      " dropout_20 (Dropout)        (None, 256)               0         \n",
      "                                                                 \n",
      " dense_31 (Dense)            (None, 84)                21588     \n",
      "                                                                 \n",
      " dropout_21 (Dropout)        (None, 84)                0         \n",
      "                                                                 \n",
      " dense_32 (Dense)            (None, 10)                850       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 906,470\n",
      "Trainable params: 906,246\n",
      "Non-trainable params: 224\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_dif_filter_48 = Sequential()\n",
    "\n",
    "model_dif_filter_48.add(tf.keras.layers.Conv2D(filters=48, kernel_size=(5,5), padding='same', activation='relu', input_shape=(28, 28, 3)))\n",
    "model_dif_filter_48.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "model_dif_filter_48.add(tf.keras.layers.BatchNormalization())\n",
    "model_dif_filter_48.add(tf.keras.layers.Conv2D(filters=64, kernel_size=(5,5), padding='same', activation='relu'))\n",
    "model_dif_filter_48.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "model_dif_filter_48.add(tf.keras.layers.BatchNormalization())\n",
    "model_dif_filter_48.add(tf.keras.layers.Flatten())\n",
    "model_dif_filter_48.add(tf.keras.layers.Dropout(0.2))\n",
    "model_dif_filter_48.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "model_dif_filter_48.add(tf.keras.layers.Dropout(0.2))\n",
    "model_dif_filter_48.add(tf.keras.layers.Dense(84, activation='relu'))\n",
    "model_dif_filter_48.add(tf.keras.layers.Dropout(0.2))\n",
    "model_dif_filter_48.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "model_dif_filter_48.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.Adam(), metrics=['accuracy'])\n",
    "model_dif_filter_48.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ajYhqp42m-fp"
   },
   "source": [
    "Training the new version of the model with the new filters and collecting the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iu5-iiGXxTkE",
    "outputId": "f09a4d7a-e22d-4a22-d3f8-a56bd7fd391c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1562/1562 [==============================] - 233s 145ms/step - loss: 1.5060 - accuracy: 0.4674 - val_loss: 1.1221 - val_accuracy: 0.6068\n",
      "Epoch 2/8\n",
      "1562/1562 [==============================] - 261s 165ms/step - loss: 1.1048 - accuracy: 0.6185 - val_loss: 0.9164 - val_accuracy: 0.6794\n",
      "Epoch 3/8\n",
      "1562/1562 [==============================] - 265s 168ms/step - loss: 0.9333 - accuracy: 0.6799 - val_loss: 0.8740 - val_accuracy: 0.6968\n",
      "Epoch 4/8\n",
      "1562/1562 [==============================] - 269s 170ms/step - loss: 0.8233 - accuracy: 0.7180 - val_loss: 0.8155 - val_accuracy: 0.7139\n",
      "Epoch 5/8\n",
      "1562/1562 [==============================] - 270s 170ms/step - loss: 0.7270 - accuracy: 0.7504 - val_loss: 0.7930 - val_accuracy: 0.7323\n",
      "Epoch 6/8\n",
      "1562/1562 [==============================] - 260s 165ms/step - loss: 0.6489 - accuracy: 0.7738 - val_loss: 0.7590 - val_accuracy: 0.7453\n",
      "Epoch 7/8\n",
      "1562/1562 [==============================] - 274s 173ms/step - loss: 0.5849 - accuracy: 0.7969 - val_loss: 0.7878 - val_accuracy: 0.7385\n",
      "Epoch 8/8\n",
      "1562/1562 [==============================] - 260s 164ms/step - loss: 0.5191 - accuracy: 0.8201 - val_loss: 0.7525 - val_accuracy: 0.7572\n"
     ]
    }
   ],
   "source": [
    "history_dif_filter_48 = model_dif_filter_48.fit(train_ds_28, epochs=8,verbose=1,\n",
    "                    validation_data=(test_ds_28))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CHv4N6NEnLR4"
   },
   "source": [
    "Plotting the results of the testing dataset of the baseline model and of the neew filter model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "Q_RRAzigAuGm",
    "outputId": "73ab2a81-2c1f-48fb-84f5-d50a7014c6c2"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.plot(history_dif_filter_48.history['val_accuracy'])\n",
    "plt.title('Model Accuracy using different filters')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['Orig', 'New'], loc='upper left')\n",
    "plt.savefig('DifferentFilters')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3isR66Ztn-Gt"
   },
   "source": [
    "Plotting the training results of the different filter version of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "LFWs60BOxcfv",
    "outputId": "2c346019-c1e4-40d3-8291-bf60a62b3c3c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_dif_filter_48.history['accuracy'])\n",
    "plt.plot(history_dif_filter_48.history['val_accuracy'])\n",
    "plt.title('Model Accuracy new filters')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aaMwdVf_oaz9"
   },
   "source": [
    "Preparing the data for the 32x32x3 input. The code is the same code as used for the input of size 28x28x3 used in the baseline model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nz4GkiLz7rwO"
   },
   "outputs": [],
   "source": [
    "train_ds = tf.data.Dataset.from_tensor_slices((train_images, train_labels))\n",
    "test_ds = tf.data.Dataset.from_tensor_slices((test_images, test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SbFeAv9O8LMp"
   },
   "outputs": [],
   "source": [
    "# Normalizing the images to avoid bias in the model\n",
    "# Dataset influenced by https://towardsdatascience.com/implementing-alexnet-cnn-architecture-using-tensorflow-2-0-and-keras-2113e090ad98\n",
    "\n",
    "def process_images(image, label):\n",
    "    # Normalize images to have a mean of 0 and standard deviation of 1\n",
    "    image = tf.image.per_image_standardization(image)\n",
    "    return image, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HvypKJOv8NuW",
    "outputId": "014bfd5d-a17f-49c4-a86c-2524bed75d7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data size: 50000\n",
      "Test data size: 10000\n"
     ]
    }
   ],
   "source": [
    "# Dataset influenced by https://towardsdatascience.com/implementing-alexnet-cnn-architecture-using-tensorflow-2-0-and-keras-2113e090ad98\n",
    "\n",
    "train_ds_size = tf.data.experimental.cardinality(train_ds).numpy()\n",
    "test_ds_size = tf.data.experimental.cardinality(test_ds).numpy()\n",
    "print(\"Training data size:\", train_ds_size)\n",
    "print(\"Test data size:\", test_ds_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5OGwmdQC8SVW"
   },
   "outputs": [],
   "source": [
    "# Dataset influenced by https://towardsdatascience.com/implementing-alexnet-cnn-architecture-using-tensorflow-2-0-and-keras-2113e090ad98\n",
    "\n",
    "train_ds = (train_ds\n",
    "                  .map(process_images)\n",
    "                  .shuffle(buffer_size=train_ds_size)\n",
    "                  .batch(batch_size=32, drop_remainder=True))\n",
    "test_ds = (test_ds\n",
    "                  .map(process_images)\n",
    "                  .shuffle(buffer_size=train_ds_size)\n",
    "                  .batch(batch_size=32, drop_remainder=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eZU6kzuno1vw"
   },
   "source": [
    "Modifying the baseline model to accomodate the strides modification in the input model. This is to see the effect that the stride has on the output of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5Mojf8HtA0rw",
    "outputId": "2786ce8c-91e2-426c-a677-50d043a99ef6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_14\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_31 (Conv2D)          (None, 14, 14, 48)        3648      \n",
      "                                                                 \n",
      " max_pooling2d_31 (MaxPoolin  (None, 7, 7, 48)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_31 (Bat  (None, 7, 7, 48)         192       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv2d_32 (Conv2D)          (None, 7, 7, 64)          76864     \n",
      "                                                                 \n",
      " max_pooling2d_32 (MaxPoolin  (None, 3, 3, 64)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_32 (Bat  (None, 3, 3, 64)         256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv2d_33 (Conv2D)          (None, 3, 3, 128)         204928    \n",
      "                                                                 \n",
      " max_pooling2d_33 (MaxPoolin  (None, 1, 1, 128)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_33 (Bat  (None, 1, 1, 128)        512       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " flatten_14 (Flatten)        (None, 128)               0         \n",
      "                                                                 \n",
      " dropout_30 (Dropout)        (None, 128)               0         \n",
      "                                                                 \n",
      " dense_42 (Dense)            (None, 256)               33024     \n",
      "                                                                 \n",
      " dropout_31 (Dropout)        (None, 256)               0         \n",
      "                                                                 \n",
      " dense_43 (Dense)            (None, 84)                21588     \n",
      "                                                                 \n",
      " dropout_32 (Dropout)        (None, 84)                0         \n",
      "                                                                 \n",
      " dense_44 (Dense)            (None, 10)                850       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 341,862\n",
      "Trainable params: 341,382\n",
      "Non-trainable params: 480\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_stride = Sequential()\n",
    "\n",
    "model_stride.add(tf.keras.layers.Conv2D(filters=48, kernel_size=(5,5), padding='same', activation='relu', input_shape=(28, 28, 3), strides=2))\n",
    "model_stride.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "model_stride.add(tf.keras.layers.BatchNormalization())\n",
    "model_stride.add(tf.keras.layers.Conv2D(filters=64, kernel_size=(5,5), padding='same', activation='relu'))\n",
    "model_stride.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "model_stride.add(tf.keras.layers.BatchNormalization())\n",
    "model_stride.add(tf.keras.layers.Conv2D(filters=128, kernel_size=(5,5), padding='same', activation='relu'))\n",
    "model_stride.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "model_stride.add(tf.keras.layers.BatchNormalization())\n",
    "model_stride.add(tf.keras.layers.Flatten())\n",
    "model_stride.add(tf.keras.layers.Dropout(0.2))\n",
    "model_stride.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "model_stride.add(tf.keras.layers.Dropout(0.2))\n",
    "model_stride.add(tf.keras.layers.Dense(84, activation='relu'))\n",
    "model_stride.add(tf.keras.layers.Dropout(0.2))\n",
    "model_stride.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "model_stride.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.Adam(), metrics=['accuracy'])\n",
    "model_stride.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UZCfsaQypbcM"
   },
   "source": [
    "Training the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-w9d63CLEIDN",
    "outputId": "9a0fa1c1-0e65-4862-f830-d1331d6210b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1562/1562 [==============================] - 132s 81ms/step - loss: 1.5017 - accuracy: 0.4635 - val_loss: 1.1791 - val_accuracy: 0.5757\n",
      "Epoch 2/8\n",
      "1562/1562 [==============================] - 124s 77ms/step - loss: 1.1537 - accuracy: 0.5973 - val_loss: 1.0094 - val_accuracy: 0.6462\n",
      "Epoch 3/8\n",
      "1562/1562 [==============================] - 125s 78ms/step - loss: 0.9890 - accuracy: 0.6559 - val_loss: 0.9428 - val_accuracy: 0.6717\n",
      "Epoch 4/8\n",
      "1562/1562 [==============================] - 125s 78ms/step - loss: 0.8793 - accuracy: 0.6983 - val_loss: 0.8992 - val_accuracy: 0.6864\n",
      "Epoch 5/8\n",
      "1562/1562 [==============================] - 126s 78ms/step - loss: 0.7949 - accuracy: 0.7298 - val_loss: 0.8462 - val_accuracy: 0.7103\n",
      "Epoch 6/8\n",
      "1562/1562 [==============================] - 125s 78ms/step - loss: 0.7276 - accuracy: 0.7503 - val_loss: 0.8514 - val_accuracy: 0.7075\n",
      "Epoch 7/8\n",
      "1562/1562 [==============================] - 124s 77ms/step - loss: 0.6577 - accuracy: 0.7738 - val_loss: 0.8716 - val_accuracy: 0.7065\n",
      "Epoch 8/8\n",
      "1562/1562 [==============================] - 124s 77ms/step - loss: 0.6073 - accuracy: 0.7922 - val_loss: 0.8683 - val_accuracy: 0.7143\n"
     ]
    }
   ],
   "source": [
    "history_stride = model_stride.fit(train_ds_28, epochs=8,verbose=1, validation_data=test_ds_28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3v-EWPuspfRu"
   },
   "source": [
    "Poltting the training and testing accuracy of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "5r04ULc7HRNu",
    "outputId": "cdd1b12d-0ad3-4ca1-c18d-3ea9f2470cca"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_stride.history['accuracy'])\n",
    "plt.plot(history_stride.history['val_accuracy'])\n",
    "plt.title('Model Accuracy with stride')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SLXKA1Xvpk_2"
   },
   "source": [
    "Plotting the difference in accuracy of the baseline model and of the model with different stride."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "5R_nys_cL0P4",
    "outputId": "2ed46e65-ec61-42cd-cd7a-a28a662969a9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.plot(history_stride.history['val_accuracy'])\n",
    "plt.title('Model Accuracy with stride vs without')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['Without', 'Stride'], loc='upper left')\n",
    "plt.savefig('Stride')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gLICdJoipshj"
   },
   "source": [
    "Dropout is considered an important component in creating a model that learns from robust features and is resilient in the data noise. Since the model cannot rely on other neurons and it avoids overfitting.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IfBjwJi3Riid",
    "outputId": "4b98ae9e-89c5-4c0b-eb74-d9ecea2d82a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_15\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_34 (Conv2D)          (None, 28, 28, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_34 (MaxPoolin  (None, 14, 14, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_34 (Bat  (None, 14, 14, 32)       128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv2d_35 (Conv2D)          (None, 14, 14, 48)        38448     \n",
      "                                                                 \n",
      " max_pooling2d_35 (MaxPoolin  (None, 7, 7, 48)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_35 (Bat  (None, 7, 7, 48)         192       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " flatten_15 (Flatten)        (None, 2352)              0         \n",
      "                                                                 \n",
      " dense_45 (Dense)            (None, 256)               602368    \n",
      "                                                                 \n",
      " dense_46 (Dense)            (None, 84)                21588     \n",
      "                                                                 \n",
      " dense_47 (Dense)            (None, 10)                850       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 666,006\n",
      "Trainable params: 665,846\n",
      "Non-trainable params: 160\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_no_dropout = Sequential()\n",
    "\n",
    "model_no_dropout.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(5,5), padding='same', activation='relu', input_shape=(28, 28, 3)))\n",
    "model_no_dropout.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "model_no_dropout.add(tf.keras.layers.BatchNormalization())\n",
    "model_no_dropout.add(tf.keras.layers.Conv2D(filters=48, kernel_size=(5,5), padding='same', activation='relu'))\n",
    "model_no_dropout.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "model_no_dropout.add(tf.keras.layers.BatchNormalization())\n",
    "model_no_dropout.add(tf.keras.layers.Flatten())\n",
    "model_no_dropout.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "model_no_dropout.add(tf.keras.layers.Dense(84, activation='relu'))\n",
    "model_no_dropout.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "model_no_dropout.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.Adam(), metrics=['accuracy'])\n",
    "model_no_dropout.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zzJKDIJqR95t",
    "outputId": "c0ffcd5d-a0d6-4756-a283-70684beac167"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1562/1562 [==============================] - 199s 123ms/step - loss: 1.2880 - accuracy: 0.5451 - val_loss: 1.0252 - val_accuracy: 0.6377\n",
      "Epoch 2/8\n",
      "1562/1562 [==============================] - 197s 124ms/step - loss: 0.8763 - accuracy: 0.6902 - val_loss: 0.9033 - val_accuracy: 0.6859\n",
      "Epoch 3/8\n",
      "1562/1562 [==============================] - 202s 126ms/step - loss: 0.6798 - accuracy: 0.7630 - val_loss: 0.8338 - val_accuracy: 0.7120\n",
      "Epoch 4/8\n",
      "1562/1562 [==============================] - 195s 123ms/step - loss: 0.5252 - accuracy: 0.8135 - val_loss: 0.8506 - val_accuracy: 0.7236\n",
      "Epoch 5/8\n",
      "1562/1562 [==============================] - 204s 129ms/step - loss: 0.3870 - accuracy: 0.8654 - val_loss: 0.9909 - val_accuracy: 0.7114\n",
      "Epoch 6/8\n",
      "1562/1562 [==============================] - 203s 128ms/step - loss: 0.2859 - accuracy: 0.8979 - val_loss: 1.0723 - val_accuracy: 0.7197\n",
      "Epoch 7/8\n",
      "1562/1562 [==============================] - 201s 127ms/step - loss: 0.2214 - accuracy: 0.9229 - val_loss: 1.1924 - val_accuracy: 0.7153\n",
      "Epoch 8/8\n",
      "1562/1562 [==============================] - 198s 125ms/step - loss: 0.1718 - accuracy: 0.9395 - val_loss: 1.3045 - val_accuracy: 0.7175\n"
     ]
    }
   ],
   "source": [
    "history_no_dropout = model_no_dropout.fit(train_ds_28, epochs=8,verbose=1, validation_data=test_ds_28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jQ8qIXiAuVia"
   },
   "source": [
    "Plotting the difference of using dropout and not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "rH1JHjejSEJG",
    "outputId": "c20526ed-e60b-46d9-b95d-6890e267de48"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.plot(history_no_dropout.history['val_accuracy'])\n",
    "plt.title('Model Accuracy with dropout vs without')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['With', 'Without'], loc='upper left')\n",
    "plt.savefig('NoDropout')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T-LpeNdnut8A"
   },
   "source": [
    "Implementing a simple architecture to evaluate different modfications in the activation function.\n",
    "\n",
    "The simple model used ReLu in the input layer and Softmax in the output layer.\n",
    "\n",
    "\n",
    "Naming works like the following, simple_model_[activation] indicates the activation function in the convolutional layer that is being tested. All tests are conducted with the output layer having a softmax activation function.\n",
    "\n",
    "\n",
    "simple_model_[activation]_end indicates the activation function used in the output layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Y0ZgoAwJD1BO",
    "outputId": "96dbf1dd-7619-41d6-8ff0-147f76dc4fe9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_11 (Conv2D)          (None, 28, 28, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_11 (MaxPoolin  (None, 14, 14, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_11 (Bat  (None, 14, 14, 32)       128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " flatten_6 (Flatten)         (None, 6272)              0         \n",
      "                                                                 \n",
      " dropout_16 (Dropout)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dense_16 (Dense)            (None, 10)                62730     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 65,290\n",
      "Trainable params: 65,226\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "simple_model = Sequential()\n",
    "\n",
    "simple_model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(5,5), padding='same', activation='relu', input_shape=(28, 28, 3)))\n",
    "simple_model.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "simple_model.add(tf.keras.layers.BatchNormalization())\n",
    "simple_model.add(tf.keras.layers.Flatten())\n",
    "simple_model.add(tf.keras.layers.Dropout(0.2))\n",
    "simple_model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "simple_model.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.Adam(), metrics=['accuracy'])\n",
    "simple_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t4LWweXfEAvj",
    "outputId": "4a4ffc75-1393-4b2b-cbe1-8c9c823c7ae4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1562/1562 [==============================] - 100s 58ms/step - loss: 1.6308 - accuracy: 0.4805 - val_loss: 1.2488 - val_accuracy: 0.5681\n",
      "Epoch 2/8\n",
      "1562/1562 [==============================] - 84s 52ms/step - loss: 1.1667 - accuracy: 0.5960 - val_loss: 1.1286 - val_accuracy: 0.6065\n",
      "Epoch 3/8\n",
      "1562/1562 [==============================] - 82s 50ms/step - loss: 1.0706 - accuracy: 0.6273 - val_loss: 1.0846 - val_accuracy: 0.6240\n",
      "Epoch 4/8\n",
      "1562/1562 [==============================] - 81s 50ms/step - loss: 1.0202 - accuracy: 0.6471 - val_loss: 1.0737 - val_accuracy: 0.6307\n",
      "Epoch 5/8\n",
      "1562/1562 [==============================] - 82s 50ms/step - loss: 0.9817 - accuracy: 0.6627 - val_loss: 1.0734 - val_accuracy: 0.6292\n",
      "Epoch 6/8\n",
      "1562/1562 [==============================] - 82s 50ms/step - loss: 0.9524 - accuracy: 0.6695 - val_loss: 1.0515 - val_accuracy: 0.6390\n",
      "Epoch 7/8\n",
      "1562/1562 [==============================] - 81s 50ms/step - loss: 0.9236 - accuracy: 0.6798 - val_loss: 1.0521 - val_accuracy: 0.6386\n",
      "Epoch 8/8\n",
      "1562/1562 [==============================] - 83s 51ms/step - loss: 0.9012 - accuracy: 0.6879 - val_loss: 1.0381 - val_accuracy: 0.6461\n"
     ]
    }
   ],
   "source": [
    "history_simple = simple_model.fit(train_ds_28, epochs=8,verbose=1, validation_data=test_ds_28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SDjt1UitOQg1",
    "outputId": "df2ece0a-9361-432f-87c2-0694855bbb31"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_28\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_48 (Conv2D)          (None, 28, 28, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_48 (MaxPoolin  (None, 14, 14, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_48 (Bat  (None, 14, 14, 32)       128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " flatten_28 (Flatten)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dropout_44 (Dropout)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dense_59 (Dense)            (None, 10)                62730     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 65,290\n",
      "Trainable params: 65,226\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "simple_model_softmax = Sequential()\n",
    "\n",
    "simple_model_softmax.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(5,5), padding='same', activation='softmax', input_shape=(28, 28, 3)))\n",
    "simple_model_softmax.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "simple_model_softmax.add(tf.keras.layers.BatchNormalization())\n",
    "simple_model_softmax.add(tf.keras.layers.Flatten())\n",
    "simple_model_softmax.add(tf.keras.layers.Dropout(0.2))\n",
    "simple_model_softmax.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "simple_model_softmax.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.Adam(), metrics=['accuracy'])\n",
    "simple_model_softmax.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gvaHSWFOOZFC",
    "outputId": "9c558d29-ee50-432e-fbd6-d00e67bf6086"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1562/1562 [==============================] - 97s 57ms/step - loss: 1.4131 - accuracy: 0.5069 - val_loss: 1.2291 - val_accuracy: 0.5797\n",
      "Epoch 2/8\n",
      "1562/1562 [==============================] - 93s 58ms/step - loss: 1.1856 - accuracy: 0.5941 - val_loss: 1.2195 - val_accuracy: 0.5854\n",
      "Epoch 3/8\n",
      "1562/1562 [==============================] - 93s 57ms/step - loss: 1.1034 - accuracy: 0.6185 - val_loss: 1.1568 - val_accuracy: 0.6092\n",
      "Epoch 4/8\n",
      "1562/1562 [==============================] - 92s 57ms/step - loss: 1.0485 - accuracy: 0.6381 - val_loss: 1.1292 - val_accuracy: 0.6213\n",
      "Epoch 5/8\n",
      "1562/1562 [==============================] - 93s 58ms/step - loss: 1.0086 - accuracy: 0.6528 - val_loss: 1.1095 - val_accuracy: 0.6227\n",
      "Epoch 6/8\n",
      "1562/1562 [==============================] - 92s 57ms/step - loss: 0.9728 - accuracy: 0.6618 - val_loss: 1.0964 - val_accuracy: 0.6289\n",
      "Epoch 7/8\n",
      "1562/1562 [==============================] - 92s 57ms/step - loss: 0.9474 - accuracy: 0.6722 - val_loss: 1.1138 - val_accuracy: 0.6286\n",
      "Epoch 8/8\n",
      "1562/1562 [==============================] - 91s 56ms/step - loss: 0.9186 - accuracy: 0.6820 - val_loss: 1.0859 - val_accuracy: 0.6385\n"
     ]
    }
   ],
   "source": [
    "history_simple_softmax = simple_model_softmax.fit(train_ds_28, epochs=8,verbose=1, validation_data=test_ds_28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8M6bHUcNbIhS",
    "outputId": "345bc6dc-bc65-4be7-b9a1-d3c599616919"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_18\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_38 (Conv2D)          (None, 28, 28, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_38 (MaxPoolin  (None, 14, 14, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_38 (Bat  (None, 14, 14, 32)       128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " flatten_18 (Flatten)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dropout_34 (Dropout)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dense_49 (Dense)            (None, 10)                62730     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 65,290\n",
      "Trainable params: 65,226\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "simple_model_relu = Sequential()\n",
    "\n",
    "simple_model_relu.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(5,5), padding='same', activation='relu', input_shape=(28, 28, 3)))\n",
    "simple_model_relu.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "simple_model_relu.add(tf.keras.layers.BatchNormalization())\n",
    "simple_model_relu.add(tf.keras.layers.Flatten())\n",
    "simple_model_relu.add(tf.keras.layers.Dropout(0.2))\n",
    "simple_model_relu.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "simple_model_relu.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.Adam(), metrics=['accuracy'])\n",
    "simple_model_relu.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_SymvYhCcxG_",
    "outputId": "d2e0a14c-0c8c-4a69-e460-fe008b11520b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1562/1562 [==============================] - 113s 70ms/step - loss: 1.6073 - accuracy: 0.4886 - val_loss: 1.2022 - val_accuracy: 0.5891\n",
      "Epoch 2/8\n",
      "1562/1562 [==============================] - 99s 61ms/step - loss: 1.1352 - accuracy: 0.6095 - val_loss: 1.1232 - val_accuracy: 0.6149\n",
      "Epoch 3/8\n",
      "1562/1562 [==============================] - 105s 65ms/step - loss: 1.0543 - accuracy: 0.6314 - val_loss: 1.0737 - val_accuracy: 0.6309\n",
      "Epoch 4/8\n",
      "1562/1562 [==============================] - 114s 71ms/step - loss: 1.0098 - accuracy: 0.6513 - val_loss: 1.0751 - val_accuracy: 0.6323\n",
      "Epoch 5/8\n",
      "1562/1562 [==============================] - 115s 72ms/step - loss: 0.9796 - accuracy: 0.6622 - val_loss: 1.0551 - val_accuracy: 0.6344\n",
      "Epoch 6/8\n",
      "1562/1562 [==============================] - 115s 72ms/step - loss: 0.9518 - accuracy: 0.6710 - val_loss: 1.0317 - val_accuracy: 0.6418\n",
      "Epoch 7/8\n",
      "1562/1562 [==============================] - 111s 69ms/step - loss: 0.9265 - accuracy: 0.6789 - val_loss: 1.0579 - val_accuracy: 0.6406\n",
      "Epoch 8/8\n",
      "1562/1562 [==============================] - 113s 70ms/step - loss: 0.9072 - accuracy: 0.6849 - val_loss: 1.0232 - val_accuracy: 0.6455\n"
     ]
    }
   ],
   "source": [
    "history_relu = simple_model_relu.fit(train_ds_28, epochs=8,verbose=1, validation_data=test_ds_28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eenDDQC9cDV0",
    "outputId": "7870fc57-ca0b-4ae2-99dd-d63a06cbc05c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_19\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_39 (Conv2D)          (None, 28, 28, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_39 (MaxPoolin  (None, 14, 14, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_39 (Bat  (None, 14, 14, 32)       128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " flatten_19 (Flatten)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dropout_35 (Dropout)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dense_50 (Dense)            (None, 10)                62730     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 65,290\n",
      "Trainable params: 65,226\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "simple_model_sigmoid = Sequential()\n",
    "\n",
    "simple_model_sigmoid.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(5,5), padding='same', activation='sigmoid', input_shape=(28, 28, 3)))\n",
    "simple_model_sigmoid.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "simple_model_sigmoid.add(tf.keras.layers.BatchNormalization())\n",
    "simple_model_sigmoid.add(tf.keras.layers.Flatten())\n",
    "simple_model_sigmoid.add(tf.keras.layers.Dropout(0.2))\n",
    "simple_model_sigmoid.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "simple_model_sigmoid.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.Adam(), metrics=['accuracy'])\n",
    "simple_model_sigmoid.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YbGvhiB1c3Ma",
    "outputId": "37d1ef64-eb02-4e20-e42e-82f8e6e61329"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1562/1562 [==============================] - 127s 78ms/step - loss: 1.6794 - accuracy: 0.4537 - val_loss: 1.3437 - val_accuracy: 0.5402\n",
      "Epoch 2/8\n",
      "1562/1562 [==============================] - 125s 78ms/step - loss: 1.2512 - accuracy: 0.5674 - val_loss: 1.1994 - val_accuracy: 0.5847\n",
      "Epoch 3/8\n",
      "1562/1562 [==============================] - 125s 78ms/step - loss: 1.1540 - accuracy: 0.6002 - val_loss: 1.1810 - val_accuracy: 0.5884\n",
      "Epoch 4/8\n",
      "1562/1562 [==============================] - 116s 72ms/step - loss: 1.0937 - accuracy: 0.6208 - val_loss: 1.1168 - val_accuracy: 0.6107\n",
      "Epoch 5/8\n",
      "1562/1562 [==============================] - 119s 74ms/step - loss: 1.0492 - accuracy: 0.6369 - val_loss: 1.1021 - val_accuracy: 0.6182\n",
      "Epoch 6/8\n",
      "1562/1562 [==============================] - 119s 74ms/step - loss: 1.0182 - accuracy: 0.6454 - val_loss: 1.0710 - val_accuracy: 0.6300\n",
      "Epoch 7/8\n",
      "1562/1562 [==============================] - 128s 79ms/step - loss: 0.9874 - accuracy: 0.6553 - val_loss: 1.0750 - val_accuracy: 0.6268\n",
      "Epoch 8/8\n",
      "1562/1562 [==============================] - 129s 81ms/step - loss: 0.9632 - accuracy: 0.6661 - val_loss: 1.0568 - val_accuracy: 0.6356\n"
     ]
    }
   ],
   "source": [
    "history_sigmoid = simple_model_sigmoid.fit(train_ds_28, epochs=8,verbose=1, validation_data=test_ds_28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q9GoFNBTcM14",
    "outputId": "8e6d4583-d1e8-4f49-d53f-21be8b1a569c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_24\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_44 (Conv2D)          (None, 28, 28, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_44 (MaxPoolin  (None, 14, 14, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_44 (Bat  (None, 14, 14, 32)       128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " flatten_24 (Flatten)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dropout_40 (Dropout)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dense_55 (Dense)            (None, 10)                62730     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 65,290\n",
      "Trainable params: 65,226\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "simple_model_tanh = Sequential()\n",
    "\n",
    "simple_model_tanh.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(5,5), padding='same', activation='tanh', input_shape=(28, 28, 3)))\n",
    "simple_model_tanh.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "simple_model_tanh.add(tf.keras.layers.BatchNormalization())\n",
    "simple_model_tanh.add(tf.keras.layers.Flatten())\n",
    "simple_model_tanh.add(tf.keras.layers.Dropout(0.2))\n",
    "simple_model_tanh.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "simple_model_tanh.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.Adam(), metrics=['accuracy'])\n",
    "simple_model_tanh.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nuv8C1Uwc86J",
    "outputId": "f1e20c4b-395f-46b7-e61a-5aa55805958e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1562/1562 [==============================] - 124s 76ms/step - loss: 1.7406 - accuracy: 0.4478 - val_loss: 1.2493 - val_accuracy: 0.5616\n",
      "Epoch 2/8\n",
      "1562/1562 [==============================] - 109s 67ms/step - loss: 1.2072 - accuracy: 0.5787 - val_loss: 1.1400 - val_accuracy: 0.6029\n",
      "Epoch 3/8\n",
      "1562/1562 [==============================] - 113s 70ms/step - loss: 1.1078 - accuracy: 0.6139 - val_loss: 1.0967 - val_accuracy: 0.6204\n",
      "Epoch 4/8\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 1.0478 - accuracy: 0.6337 - val_loss: 1.0847 - val_accuracy: 0.6232\n",
      "Epoch 5/8\n",
      "1562/1562 [==============================] - 106s 66ms/step - loss: 1.0080 - accuracy: 0.6475 - val_loss: 1.0744 - val_accuracy: 0.6274\n",
      "Epoch 6/8\n",
      "1562/1562 [==============================] - 106s 66ms/step - loss: 0.9700 - accuracy: 0.6627 - val_loss: 1.0192 - val_accuracy: 0.6466\n",
      "Epoch 7/8\n",
      "1562/1562 [==============================] - 104s 64ms/step - loss: 0.9396 - accuracy: 0.6725 - val_loss: 1.0181 - val_accuracy: 0.6486\n",
      "Epoch 8/8\n",
      "1562/1562 [==============================] - 104s 64ms/step - loss: 0.9138 - accuracy: 0.6803 - val_loss: 1.0217 - val_accuracy: 0.6435\n"
     ]
    }
   ],
   "source": [
    "history_tanh = simple_model_tanh.fit(train_ds_28, epochs=8,verbose=1, validation_data=test_ds_28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4yILU1RJcTaS",
    "outputId": "cd313cc1-e647-4c7d-ab66-33c2d07d88da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_21\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_41 (Conv2D)          (None, 28, 28, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_41 (MaxPoolin  (None, 14, 14, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_41 (Bat  (None, 14, 14, 32)       128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " flatten_21 (Flatten)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dropout_37 (Dropout)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dense_52 (Dense)            (None, 10)                62730     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 65,290\n",
      "Trainable params: 65,226\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "simple_model_sigmoid_end = Sequential()\n",
    "\n",
    "simple_model_sigmoid_end.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(5,5), padding='same', activation='relu', input_shape=(28, 28, 3)))\n",
    "simple_model_sigmoid_end.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "simple_model_sigmoid_end.add(tf.keras.layers.BatchNormalization())\n",
    "simple_model_sigmoid_end.add(tf.keras.layers.Flatten())\n",
    "simple_model_sigmoid_end.add(tf.keras.layers.Dropout(0.2))\n",
    "simple_model_sigmoid_end.add(tf.keras.layers.Dense(10, activation='sigmoid'))\n",
    "\n",
    "simple_model_sigmoid_end.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.Adam(), metrics=['accuracy'])\n",
    "simple_model_sigmoid_end.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SwIl8fuUdA0A",
    "outputId": "5dcee724-fb06-4d49-819a-c1d23b70f4a1"
   },
   "outputs": [
    {
     "metadata": {
      "tags": null
     },
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1562/1562 [==============================] - 99s 61ms/step - loss: 1.6199 - accuracy: 0.4846 - val_loss: 1.2367 - val_accuracy: 0.5710\n",
      "Epoch 2/8\n",
      "1562/1562 [==============================] - 90s 55ms/step - loss: 1.1482 - accuracy: 0.6041 - val_loss: 1.1442 - val_accuracy: 0.6032\n",
      "Epoch 3/8\n",
      "1562/1562 [==============================] - 97s 60ms/step - loss: 1.0545 - accuracy: 0.6339 - val_loss: 1.1023 - val_accuracy: 0.6237\n",
      "Epoch 4/8\n",
      "1562/1562 [==============================] - 96s 59ms/step - loss: 1.0121 - accuracy: 0.6481 - val_loss: 1.0663 - val_accuracy: 0.6311\n",
      "Epoch 5/8\n",
      "1562/1562 [==============================] - 97s 60ms/step - loss: 0.9727 - accuracy: 0.6637 - val_loss: 1.0734 - val_accuracy: 0.6320\n",
      "Epoch 6/8\n",
      "1562/1562 [==============================] - 97s 60ms/step - loss: 0.9455 - accuracy: 0.6732 - val_loss: 1.0186 - val_accuracy: 0.6495\n",
      "Epoch 7/8\n",
      "1562/1562 [==============================] - 95s 59ms/step - loss: 0.9205 - accuracy: 0.6786 - val_loss: 1.0386 - val_accuracy: 0.6407\n",
      "Epoch 8/8\n",
      "1562/1562 [==============================] - 95s 59ms/step - loss: 0.8986 - accuracy: 0.6887 - val_loss: 1.0344 - val_accuracy: 0.6442\n"
     ]
    }
   ],
   "source": [
    "history_sigmoid_end = simple_model_sigmoid_end.fit(train_ds_28, epochs=8,verbose=1, validation_data=test_ds_28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-Lk3YxakcbJk",
    "outputId": "7e65fc23-c206-486e-edb8-f3dc25775af4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_22\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_42 (Conv2D)          (None, 28, 28, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_42 (MaxPoolin  (None, 14, 14, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_42 (Bat  (None, 14, 14, 32)       128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " flatten_22 (Flatten)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dropout_38 (Dropout)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dense_53 (Dense)            (None, 10)                62730     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 65,290\n",
      "Trainable params: 65,226\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "simple_model_tanh_end = Sequential()\n",
    "\n",
    "simple_model_tanh_end.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(5,5), padding='same', activation='relu', input_shape=(28, 28, 3)))\n",
    "simple_model_tanh_end.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "simple_model_tanh_end.add(tf.keras.layers.BatchNormalization())\n",
    "simple_model_tanh_end.add(tf.keras.layers.Flatten())\n",
    "simple_model_tanh_end.add(tf.keras.layers.Dropout(0.2))\n",
    "simple_model_tanh_end.add(tf.keras.layers.Dense(10, activation='tanh'))\n",
    "\n",
    "simple_model_tanh_end.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.Adam(), metrics=['accuracy'])\n",
    "simple_model_tanh_end.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pzt51DvkdF71",
    "outputId": "d3ecef5b-f016-4d94-cac5-99141e8e73b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1562/1562 [==============================] - 102s 62ms/step - loss: 4.2072 - accuracy: 0.1441 - val_loss: 3.5547 - val_accuracy: 0.1413\n",
      "Epoch 2/8\n",
      "1562/1562 [==============================] - 104s 64ms/step - loss: 3.8400 - accuracy: 0.1229 - val_loss: 4.0968 - val_accuracy: 0.1201\n",
      "Epoch 3/8\n",
      "1562/1562 [==============================] - 105s 65ms/step - loss: 3.5346 - accuracy: 0.1266 - val_loss: 3.4095 - val_accuracy: 0.1307\n",
      "Epoch 4/8\n",
      "1562/1562 [==============================] - 99s 61ms/step - loss: 3.5241 - accuracy: 0.1302 - val_loss: 3.5330 - val_accuracy: 0.1333\n",
      "Epoch 5/8\n",
      "1562/1562 [==============================] - 98s 61ms/step - loss: 3.2869 - accuracy: 0.1318 - val_loss: 3.1193 - val_accuracy: 0.1187\n",
      "Epoch 6/8\n",
      "1562/1562 [==============================] - 98s 61ms/step - loss: 3.3019 - accuracy: 0.1227 - val_loss: 3.2558 - val_accuracy: 0.1262\n",
      "Epoch 7/8\n",
      "1562/1562 [==============================] - 99s 61ms/step - loss: 3.1542 - accuracy: 0.1233 - val_loss: 3.2194 - val_accuracy: 0.1165\n",
      "Epoch 8/8\n",
      "1562/1562 [==============================] - 98s 60ms/step - loss: 2.9452 - accuracy: 0.1152 - val_loss: 2.6595 - val_accuracy: 0.1078\n"
     ]
    }
   ],
   "source": [
    "history_tanh_end = simple_model_tanh_end.fit(train_ds_28, epochs=8,verbose=1, validation_data=test_ds_28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zBjiW4jiciq0",
    "outputId": "85a48f57-b7e5-4cc0-e246-138ad6cea4a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_23\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_43 (Conv2D)          (None, 28, 28, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_43 (MaxPoolin  (None, 14, 14, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_43 (Bat  (None, 14, 14, 32)       128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " flatten_23 (Flatten)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dropout_39 (Dropout)        (None, 6272)              0         \n",
      "                                                                 \n",
      " dense_54 (Dense)            (None, 10)                62730     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 65,290\n",
      "Trainable params: 65,226\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "simple_model_relu_end = Sequential()\n",
    "\n",
    "simple_model_relu_end.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(5,5), padding='same', activation='relu', input_shape=(28, 28, 3)))\n",
    "simple_model_relu_end.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "simple_model_relu_end.add(tf.keras.layers.BatchNormalization())\n",
    "simple_model_relu_end.add(tf.keras.layers.Flatten())\n",
    "simple_model_relu_end.add(tf.keras.layers.Dropout(0.2))\n",
    "simple_model_relu_end.add(tf.keras.layers.Dense(10, activation='relu'))\n",
    "\n",
    "simple_model_relu_end.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.Adam(), metrics=['accuracy'])\n",
    "simple_model_relu_end.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zdRBZjqPdKVk",
    "outputId": "04361282-41e2-4ab3-a9d7-89ab8d6885dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1562/1562 [==============================] - 91s 56ms/step - loss: 4.9571 - accuracy: 0.1630 - val_loss: 4.3424 - val_accuracy: 0.1592\n",
      "Epoch 2/8\n",
      "1562/1562 [==============================] - 105s 65ms/step - loss: 4.2008 - accuracy: 0.1257 - val_loss: 3.8680 - val_accuracy: 0.1136\n",
      "Epoch 3/8\n",
      "1562/1562 [==============================] - 105s 65ms/step - loss: 3.6100 - accuracy: 0.1122 - val_loss: 3.3112 - val_accuracy: 0.1078\n",
      "Epoch 4/8\n",
      "1562/1562 [==============================] - 94s 58ms/step - loss: 3.0162 - accuracy: 0.1132 - val_loss: 2.8322 - val_accuracy: 0.1110\n",
      "Epoch 5/8\n",
      "1562/1562 [==============================] - 94s 58ms/step - loss: 3.1178 - accuracy: 0.1111 - val_loss: 3.0510 - val_accuracy: 0.0976\n",
      "Epoch 6/8\n",
      "1562/1562 [==============================] - 103s 64ms/step - loss: 2.9512 - accuracy: 0.1081 - val_loss: 2.7538 - val_accuracy: 0.1059\n",
      "Epoch 7/8\n",
      "1562/1562 [==============================] - 103s 64ms/step - loss: 2.6864 - accuracy: 0.0966 - val_loss: 2.5649 - val_accuracy: 0.0940\n",
      "Epoch 8/8\n",
      "1562/1562 [==============================] - 105s 65ms/step - loss: 2.3790 - accuracy: 0.0990 - val_loss: 2.3092 - val_accuracy: 0.1000\n"
     ]
    }
   ],
   "source": [
    "history_reku_end = simple_model_relu_end.fit(train_ds_28, epochs=8,verbose=1, validation_data=test_ds_28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hiWv4eC0wtBZ"
   },
   "source": [
    "Plotting the Model accuracy of different activation function in the convolutional layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "owPnqksrzJmQ",
    "outputId": "f1f1b61d-24a9-49c4-84b7-2bd009b8b2de"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_simple_softmax.history['val_accuracy'])\n",
    "plt.plot(history_relu.history['val_accuracy'])\n",
    "plt.plot(history_sigmoid.history['val_accuracy'])\n",
    "plt.plot(history_tanh.history['val_accuracy'])\n",
    "plt.title('Model Accuracy of different activation functions on Convolutional Layer')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['SoftMax','ReLu', 'Sigmoid', 'Tanh'], loc='upper left')\n",
    "plt.savefig('ActivationConv')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mW6Xx_faw_up"
   },
   "source": [
    "Plotting the Model accuracy of different activation function in the output layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "lRma0M5Nz0Rq",
    "outputId": "9275c817-bdea-4abb-b952-0e5afb425e00"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_simple.history['val_accuracy'])\n",
    "plt.plot(history_reku_end.history['val_accuracy'])\n",
    "plt.plot(history_sigmoid_end.history['val_accuracy'])\n",
    "plt.plot(history_tanh_end.history['val_accuracy'])\n",
    "plt.title('Model Accuracy Dense Activation Function')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['Softmax','ReLu', 'Sigmoid', 'Tanh'], loc='upper left')\n",
    "plt.savefig('ActivationDense')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jlTxPluPxDsL"
   },
   "source": [
    "In this stage we conduct the final evaluation of all the models. The use of callbacks is done in order to avoid overfitting but also to change the learning rate if needed to make the model learn better and increase accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rrYoDRGfgvNZ",
    "outputId": "03a170cb-f0a3-4b40-9756-b09ae26f93ed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "1562/1562 [==============================] - 170s 105ms/step - loss: 1.5004 - accuracy: 0.4692 - val_loss: 1.1178 - val_accuracy: 0.6053 - lr: 0.0010\n",
      "Epoch 2/25\n",
      "1562/1562 [==============================] - 189s 119ms/step - loss: 1.1300 - accuracy: 0.6074 - val_loss: 0.9947 - val_accuracy: 0.6545 - lr: 0.0010\n",
      "Epoch 3/25\n",
      "1562/1562 [==============================] - 177s 111ms/step - loss: 0.9691 - accuracy: 0.6650 - val_loss: 0.9009 - val_accuracy: 0.6860 - lr: 0.0010\n",
      "Epoch 4/25\n",
      "1562/1562 [==============================] - 182s 115ms/step - loss: 0.8573 - accuracy: 0.7029 - val_loss: 0.8836 - val_accuracy: 0.6909 - lr: 0.0010\n",
      "Epoch 5/25\n",
      "1562/1562 [==============================] - 193s 122ms/step - loss: 0.7668 - accuracy: 0.7342 - val_loss: 0.8132 - val_accuracy: 0.7226 - lr: 0.0010\n",
      "Epoch 6/25\n",
      "1562/1562 [==============================] - 194s 122ms/step - loss: 0.6958 - accuracy: 0.7561 - val_loss: 0.8092 - val_accuracy: 0.7241 - lr: 0.0010\n",
      "Epoch 7/25\n",
      "1562/1562 [==============================] - 188s 119ms/step - loss: 0.6255 - accuracy: 0.7828 - val_loss: 0.7882 - val_accuracy: 0.7296 - lr: 0.0010\n",
      "Epoch 8/25\n",
      "1562/1562 [==============================] - 193s 122ms/step - loss: 0.5708 - accuracy: 0.8016 - val_loss: 0.7870 - val_accuracy: 0.7405 - lr: 0.0010\n",
      "Epoch 9/25\n",
      "1562/1562 [==============================] - 194s 122ms/step - loss: 0.5245 - accuracy: 0.8161 - val_loss: 0.7939 - val_accuracy: 0.7398 - lr: 0.0010\n",
      "Epoch 10/25\n",
      "1562/1562 [==============================] - 195s 123ms/step - loss: 0.4839 - accuracy: 0.8310 - val_loss: 0.7988 - val_accuracy: 0.7496 - lr: 0.0010\n",
      "Epoch 11/25\n",
      "1562/1562 [==============================] - 196s 124ms/step - loss: 0.4506 - accuracy: 0.8441 - val_loss: 0.8010 - val_accuracy: 0.7533 - lr: 0.0010\n",
      "Epoch 12/25\n",
      "1562/1562 [==============================] - 193s 122ms/step - loss: 0.4129 - accuracy: 0.8558 - val_loss: 0.8078 - val_accuracy: 0.7580 - lr: 0.0010\n",
      "Epoch 13/25\n",
      "1562/1562 [==============================] - ETA: 0s - loss: 0.3866 - accuracy: 0.8651Restoring model weights from the end of the best epoch: 8.\n",
      "1562/1562 [==============================] - 198s 125ms/step - loss: 0.3866 - accuracy: 0.8651 - val_loss: 0.8201 - val_accuracy: 0.7523 - lr: 0.0010\n",
      "Epoch 13: early stopping\n"
     ]
    }
   ],
   "source": [
    "# 28x28x3 input\n",
    "\n",
    "history_25_28 = baseline_model.fit(train_ds_28, epochs=25,verbose=1,\n",
    "                    validation_data=test_ds_28, callbacks=[reduce_lr, early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ejqgRxfhg4vp",
    "outputId": "02081e9f-e73e-4486-ab52-22af8bf17344"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "1562/1562 [==============================] - 244s 154ms/step - loss: 1.4647 - accuracy: 0.4823 - val_loss: 1.0857 - val_accuracy: 0.6179 - lr: 0.0010\n",
      "Epoch 2/25\n",
      "1562/1562 [==============================] - 283s 179ms/step - loss: 1.0657 - accuracy: 0.6292 - val_loss: 0.9250 - val_accuracy: 0.6738 - lr: 0.0010\n",
      "Epoch 3/25\n",
      "1562/1562 [==============================] - 271s 172ms/step - loss: 0.8950 - accuracy: 0.6913 - val_loss: 0.8499 - val_accuracy: 0.7064 - lr: 0.0010\n",
      "Epoch 4/25\n",
      "1562/1562 [==============================] - 268s 170ms/step - loss: 0.7811 - accuracy: 0.7311 - val_loss: 0.8021 - val_accuracy: 0.7308 - lr: 0.0010\n",
      "Epoch 5/25\n",
      "1562/1562 [==============================] - 265s 168ms/step - loss: 0.6999 - accuracy: 0.7583 - val_loss: 0.7641 - val_accuracy: 0.7372 - lr: 0.0010\n",
      "Epoch 6/25\n",
      "1562/1562 [==============================] - 264s 167ms/step - loss: 0.6174 - accuracy: 0.7887 - val_loss: 0.7947 - val_accuracy: 0.7336 - lr: 0.0010\n",
      "Epoch 7/25\n",
      "1562/1562 [==============================] - 261s 166ms/step - loss: 0.5559 - accuracy: 0.8081 - val_loss: 0.7415 - val_accuracy: 0.7559 - lr: 0.0010\n",
      "Epoch 8/25\n",
      "1562/1562 [==============================] - 274s 174ms/step - loss: 0.4997 - accuracy: 0.8259 - val_loss: 0.7573 - val_accuracy: 0.7532 - lr: 0.0010\n",
      "Epoch 9/25\n",
      "1562/1562 [==============================] - 280s 177ms/step - loss: 0.4567 - accuracy: 0.8420 - val_loss: 0.7671 - val_accuracy: 0.7608 - lr: 0.0010\n",
      "Epoch 10/25\n",
      "1562/1562 [==============================] - 270s 171ms/step - loss: 0.4126 - accuracy: 0.8595 - val_loss: 0.7680 - val_accuracy: 0.7573 - lr: 0.0010\n",
      "Epoch 11/25\n",
      "1562/1562 [==============================] - 257s 163ms/step - loss: 0.3818 - accuracy: 0.8681 - val_loss: 0.7776 - val_accuracy: 0.7653 - lr: 0.0010\n",
      "Epoch 12/25\n",
      "1562/1562 [==============================] - ETA: 0s - loss: 0.3487 - accuracy: 0.8794Restoring model weights from the end of the best epoch: 7.\n",
      "1562/1562 [==============================] - 261s 166ms/step - loss: 0.3487 - accuracy: 0.8794 - val_loss: 0.7957 - val_accuracy: 0.7607 - lr: 0.0010\n",
      "Epoch 12: early stopping\n"
     ]
    }
   ],
   "source": [
    "# 32x32x3 input\n",
    "\n",
    "history_25_32 = model_v1.fit(train_ds, epochs=25,verbose=1,\n",
    "                    validation_data=test_ds, callbacks=[reduce_lr, early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uDM04Fp7hHSF",
    "outputId": "87b46100-67a8-48c9-d446-02bd449242cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "1562/1562 [==============================] - 234s 109ms/step - loss: 1.6068 - accuracy: 0.4898 - val_loss: 1.2260 - val_accuracy: 0.5778 - lr: 0.0010\n",
      "Epoch 2/25\n",
      "1562/1562 [==============================] - 52s 32ms/step - loss: 1.1520 - accuracy: 0.6016 - val_loss: 1.1210 - val_accuracy: 0.6135 - lr: 0.0010\n",
      "Epoch 3/25\n",
      "1562/1562 [==============================] - 52s 32ms/step - loss: 1.0687 - accuracy: 0.6270 - val_loss: 1.0713 - val_accuracy: 0.6300 - lr: 0.0010\n",
      "Epoch 4/25\n",
      "1562/1562 [==============================] - 52s 31ms/step - loss: 1.0219 - accuracy: 0.6448 - val_loss: 1.0771 - val_accuracy: 0.6284 - lr: 0.0010\n",
      "Epoch 5/25\n",
      "1562/1562 [==============================] - 52s 31ms/step - loss: 0.9864 - accuracy: 0.6577 - val_loss: 1.0790 - val_accuracy: 0.6228 - lr: 0.0010\n",
      "Epoch 6/25\n",
      "1562/1562 [==============================] - 52s 31ms/step - loss: 0.9532 - accuracy: 0.6702 - val_loss: 1.0230 - val_accuracy: 0.6454 - lr: 0.0010\n",
      "Epoch 7/25\n",
      "1562/1562 [==============================] - 52s 32ms/step - loss: 0.9266 - accuracy: 0.6797 - val_loss: 1.0256 - val_accuracy: 0.6472 - lr: 0.0010\n",
      "Epoch 8/25\n",
      "1562/1562 [==============================] - 52s 31ms/step - loss: 0.9052 - accuracy: 0.6871 - val_loss: 1.0267 - val_accuracy: 0.6457 - lr: 0.0010\n",
      "Epoch 9/25\n",
      "1562/1562 [==============================] - 52s 31ms/step - loss: 0.8866 - accuracy: 0.6934 - val_loss: 1.0375 - val_accuracy: 0.6444 - lr: 0.0010\n",
      "Epoch 10/25\n",
      "1562/1562 [==============================] - 68s 42ms/step - loss: 0.8694 - accuracy: 0.6982 - val_loss: 1.0136 - val_accuracy: 0.6560 - lr: 0.0010\n",
      "Epoch 11/25\n",
      "1562/1562 [==============================] - 52s 31ms/step - loss: 0.8559 - accuracy: 0.7050 - val_loss: 0.9938 - val_accuracy: 0.6625 - lr: 0.0010\n",
      "Epoch 12/25\n",
      "1562/1562 [==============================] - 54s 33ms/step - loss: 0.8389 - accuracy: 0.7086 - val_loss: 1.0024 - val_accuracy: 0.6591 - lr: 0.0010\n",
      "Epoch 13/25\n",
      "1562/1562 [==============================] - 52s 32ms/step - loss: 0.8312 - accuracy: 0.7107 - val_loss: 0.9963 - val_accuracy: 0.6643 - lr: 0.0010\n",
      "Epoch 14/25\n",
      "1562/1562 [==============================] - 52s 32ms/step - loss: 0.8173 - accuracy: 0.7168 - val_loss: 1.0343 - val_accuracy: 0.6475 - lr: 0.0010\n",
      "Epoch 15/25\n",
      "1562/1562 [==============================] - 52s 32ms/step - loss: 0.8068 - accuracy: 0.7171 - val_loss: 0.9912 - val_accuracy: 0.6607 - lr: 0.0010\n",
      "Epoch 16/25\n",
      "1561/1562 [============================>.] - ETA: 0s - loss: 0.8018 - accuracy: 0.7205\n",
      "Epoch 16: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "1562/1562 [==============================] - 52s 32ms/step - loss: 0.8017 - accuracy: 0.7205 - val_loss: 1.0190 - val_accuracy: 0.6566 - lr: 0.0010\n",
      "Epoch 17/25\n",
      "1562/1562 [==============================] - 52s 32ms/step - loss: 0.7111 - accuracy: 0.7548 - val_loss: 0.9723 - val_accuracy: 0.6753 - lr: 2.0000e-04\n",
      "Epoch 18/25\n",
      "1562/1562 [==============================] - 52s 32ms/step - loss: 0.6986 - accuracy: 0.7592 - val_loss: 0.9718 - val_accuracy: 0.6761 - lr: 2.0000e-04\n",
      "Epoch 19/25\n",
      "1562/1562 [==============================] - 53s 32ms/step - loss: 0.6880 - accuracy: 0.7632 - val_loss: 0.9764 - val_accuracy: 0.6722 - lr: 2.0000e-04\n",
      "Epoch 20/25\n",
      "1562/1562 [==============================] - 52s 32ms/step - loss: 0.6877 - accuracy: 0.7614 - val_loss: 0.9772 - val_accuracy: 0.6722 - lr: 2.0000e-04\n",
      "Epoch 21/25\n",
      "1561/1562 [============================>.] - ETA: 0s - loss: 0.6839 - accuracy: 0.7609\n",
      "Epoch 21: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
      "1562/1562 [==============================] - 52s 32ms/step - loss: 0.6841 - accuracy: 0.7608 - val_loss: 0.9768 - val_accuracy: 0.6753 - lr: 2.0000e-04\n",
      "Epoch 22/25\n",
      "1562/1562 [==============================] - 52s 32ms/step - loss: 0.6625 - accuracy: 0.7713 - val_loss: 0.9740 - val_accuracy: 0.6776 - lr: 4.0000e-05\n",
      "Epoch 23/25\n",
      "1562/1562 [==============================] - ETA: 0s - loss: 0.6616 - accuracy: 0.7719Restoring model weights from the end of the best epoch: 18.\n",
      "1562/1562 [==============================] - 52s 32ms/step - loss: 0.6616 - accuracy: 0.7719 - val_loss: 0.9745 - val_accuracy: 0.6774 - lr: 4.0000e-05\n",
      "Epoch 23: early stopping\n"
     ]
    }
   ],
   "source": [
    "# simple model\n",
    "\n",
    "history_simple_25 = simple_model.fit(train_ds_28, epochs=25,verbose=1, validation_data=test_ds_28, callbacks=[reduce_lr, early_stop])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_UfaCV1P0BOk"
   },
   "source": [
    "Plotting the accuracy of the test data while learning was conducted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "-ERrr2qOO4wN",
    "outputId": "42f31911-fc59-49b5-880c-96fd8a7b8110"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_25_28.history['val_accuracy'])\n",
    "plt.plot(history_25_32.history['val_accuracy'])\n",
    "plt.plot(history_simple_25.history['val_accuracy'])\n",
    "plt.title('Model Accuracy of different models')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['28x28','32x32', 'Simplified'], loc='upper left')\n",
    "plt.savefig('FinalEvaluation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uOWsppQ60Gj8"
   },
   "source": [
    "Plotting the loss of the test data while learning was conducted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "Ofb6RE0ebycx",
    "outputId": "5affb5ea-e9f5-4e23-b043-aaf2f3b04f46"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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srJoEegaSnZdNbn5ulevKzs6mb9++dO/enZiYGJ544gkArr76as466yy6du3KTTfdhNPpLLee9957r2gswtlnn82WLVsAmD17Nr179yY2NpbevXszd+7cKseslFKANSK2Pj169+5tStqyZcspyyoiJy/HbEreZJJPJFdq++IKCgpMenq6McaY3Nxc07dvX7N06VLz888/m4KCAlNQUGCuuuoq8+6775Zbz/Hjx4ue//jjj+b88883xhizZs0ak5iYaIwxZuPGjSY8PLzKMZ9OZT9XpVTdA6wyZRxXa72zuDZ52j3x9vB2S/OQiBTNA+R0OnE6nYgIY8aMQUQQEfr27Vs0rXRZ01AHBgYW1ZmZmVk04K1nz55FU1jHxMSQlZVFTk5OleNWSqmGN8XErw/BwY0VLh5ZkIsz30mBwwdbWXmxRSyMfuG0deXn59O7d2/i4+O588476devX9E6p9PJ9OnTiyagK28a6nfeeYdXX32V3NzcUpuAvvvuO3r16lU0IZ1SSlVFoz4jAPAQKxe6Y2pqu93OunXrSEhIYMWKFWzatKlo3R133MGQIUOKZgstbxrqO++8k507d/Liiy/yzDPPnLSPzZs3M2XKFN5///0qx6uUUtAQzwgq8M29OBtw4Fg8HjYPooKi3BJCcHAww4cP53//+x9du3blqaeeIjk5+ZSDd1nTUBe66qqrmDRpUtHrhIQELr30Uj7//PNT7l+glFKV1ejPCAACvALIdGaSV5BX6TqSk5NJTU0FICsri9mzZ9OpUyc++ugjZs2axVdffXXSHcjKmoZ6x44dRWV+/vnnommrU1NTueCCC3jhhRcYNGhQpeNUSqmSGt4ZQSUEegZy5MQR0nPTaeLdpFJ1HDhwgOuvv578/HwKCgq44oorGDt2LB4eHrRp04YBAwYAcNlll/GPf/yjzGmo3377bebMmYPD4aBJkyZ89tlnALz99tvEx8czderUok7m33777aR7FiilVGVU2zTU1cUd01CXZIxhR+oOvOxetAlsU9UQGwydhlqphqO2pqGuN0SEQM9AMp2Z1Xo/Y6WUqos0EbgEegZijNG5h5RSjY4mAhcfDx88bB4695BSqtHRROBS2DyU4czQ5iGlVKOiiaCYQC+reSjDmVHboSilVI3RRFCMr4cvdptd+wmUUo2KJoJiCpuH0nPTKTAFZ7z9s88+S0xMDN26daNHjx4sX76cm2++uWgq6aoqnNQuKSmJ8ePHFy2fMGEC3bp147XXXuPxxx9nzpw5Fa5zz549dO3a1S3xKaXqJx1QVkKAZwDHso+R6cwsusl9RSxdupSZM2eyZs0avLy8OHLkCLm5uXz00UdujzE8PJxvv/0WgIMHD7Jy5Uri4+Pdvh+lVOOgZwQl+Dn8sImNtJwzu3rowIEDhIaGFs0IGhoaSnh4OMOGDaNwAJy/vz8PPvggMTExjBw5khUrVjBs2DDatWvHjBkzAJg2bRoXX3wxw4YNIzo6mqeeeuqUfRX/Fn/eeeeRmJhIjx49WLhwITfccENRkli9ejVDhw6ld+/enH/++Rw4cKBoeffu3enevTvvvPNO5T4opVSD0eDOCF5c8SJ/Hv2zSnXk5OeQb/Lx9fAFoFPTTkzpO6Xcbc477zymTp1Kx44dGTlyJFdeeSVDhw49qUxmZibnnHMOL730EpdeeimPPfYYs2fPZsuWLVx//fVcdNFFAEUzl/r6+tKnTx8uuOAC4uJKHRDIjBkzGDt2LOvWrQPg448/Bqxpr++++25+/PFHwsLC+Prrr3n00Uf55JNPuPHGG3n77bcZMmQIDz74YJU+K6VU/dfgEoE7eIgHeQV55Jt87GKv0Db+/v6sXr2ahQsXMm/ePK688kpeeOHkmVA9PT0ZNWoUALGxsXh5eeFwOIiNjWXPnj1F5c4991xCQkIAa26iRYsWlZkIyrJt2zY2bdrEueeeC1j3SmjZsiWpqamkpqYyZMgQAK699lp+/fXXM6pbKdWwNLhEcLpv7hVRYArYdnQbQV5BhPuHV3g7u93OsGHDGDZsGLGxsUUTxhVyOBxFdxyz2WxFzUg2m428vL9mPi0sU9brijDGEBMTw9KlS09aXjhDqlJKFdI+glLYxIa/pz/puelUdFK+bdu2nTSF9Lp162jTpnIT2M2ePZujR4+SlZXFDz/8UKlpp8866yySk5OLEoHT6WTz5s0EBwcTHBzMokWLAPjyyy8rFaNSquHQRFCGQM9A8gryOJF3okLlMzIyuP766+nSpQvdunVjy5YtPPnkk5Xad9++fRk3bhzdunVj3LhxZ9wsBFYz1LfffsuUKVPo3r07PXr0YMmSJQB8+umn3HnnnfTo0aPCiU4p1XDpNNRlyC/IZ9uxbTTxbkJLv5Zurbs806ZNY9WqVbz99ts1ts+y6DTUSjUcOg11Jdhtdvwd/qTnVLx5SCml6iNNBOUI8AzAWeAkOy+7xvZ5ww031ImzAaVU49FgEkF1fGsvHFncGKem1rMgpRqPBpEIvL29SUlJcfvBy8PmgZ/Dj7TctEZ1YDTGkJKSgre3d22HopSqAQ1iHEGrVq1ISEggOTm5zDL5BYZsZz6+nvYzui4/Ky+LY9nHSPVKxd/h745w6wVvb29atWpV22EopWpAg0gEDoeDtm3blltm5oYk7vr3Wr6bNJDebZpUuG5jDHfPvZtlB5bxzYXf0C6oXVXDVUqpOqVBNA1VxNkdQhGBBdvLPmsojYjwxIAn8Pbw5tGFj5JXkHf6jZRSqh5pNIkg2NeTbq2CWbjjzBIBQJhvGI/1f4xNKZv4eOPH1RCdUkrVnkaTCACGRoeybn8qx084z3jbUVGjGB01mvfWv8fWlK3VEJ1SStWOaksEIvKJiBwWkU1lrO8kIktFJEdEHqiuOIob3DGMAgNLdh6p1PaP9n+UJt5NeGTRI+Tm57o5OqWUqh3VeUYwDRhVzvqjwD3Ay9UYw0l6RAYT4OXBgko0DwEEeQXx5MAniU+N5+11OuhLKdUwVFsiMMYswDrYl7X+sDFmJXDm7TSV5LDbGNghhAXbj1R6XMCQVkMYFz2OaZumsfbwWjdHqJRSNa9R9READI4OIzE1i11HMitdx4N9HiTcP5xHFz3KCWfFZidVSqm6ql4kAhG5VURWiciq8gaNVcTQjmEALDzDy0iL83P48fSgp0lIT+DV1a9WKR6llKpt9SIRGGM+MMbEGWPiwsLCqlRXZFNfokJ8WbCjch3Ghfq06MM1Xa7h621fsyRpSZXqUkqp2lQvEoG7DekYxtKdKeTk5Vepnnt63kO7oHb8Y/E/GuXEdEqphqE6Lx/9ClgKnCUiCSIyUURuF5HbXetbiEgCcD/wmKtMYHXFU9zg6DCynPms3nusSvV4e3jz3NnPkZKVwgvLXzj9BkopVQdV21xDxpgJp1l/EKi5Wc1OHIW9i6HjKAa0D8HDJizYfoSB7UOrVG1MaAy3dLuF99a/x4jWIxjRZoSbAlZKqZrReJqGds6Fr6+Bw1vx9/KgV5smlZpuojS3druVzk07M3XZVFKyUtxSp1JK1ZTGkwgiels/E1cD1tVDm5PSSE7PqXLVDpuDZ89+lvTcdKYundqo7l2glKr/GsQ01BXSJAp8mlqJIO5GBkeH8tKsbSyOP8IlPSOqXH10k2ju7nk3r65+lZm7ZnJh+wurHrNLXkEemc5MMp2ZpOemk+nMJMOZQUZuhvXTmcEJ5wnGthtLVFCU2/arlGocGk8iELHOChLXANA1PIimfp4s2J7slkQAcF2X6/hj/x88v/x5+rToQwu/FqfdJjc/lz1pe4g/Fk98qvU4knXkpAN9Vl5Whfa/MHEh/xrzL+w2e1XfilKqEWk8iQCsRLDzd8jJwOblz9kdQlmww5pu4kzuWlYWu83OM4OeYdxP43h88eO8f+77RfXmFeSxL20f8anx7EzdyY7UHexM3cnetL3kG+syVrvYaRPYhhZ+LWjp1xJ/T3/8Ha6H518//Rx+BDgC8PN0/XT4MXf/XB5e+DDfbv+WKztdWeX3opRqPBpfIjAFcGA9RA1icHQoM9YnsfVAOl3C3XPlamRgJA/EPcDTy57mscWP4SxwsjN1J7uP78ZZYE2rJAiRAZF0CO7AiNYjiG4STfvg9kQFRuFp96zUfi9oewHf7/ieN9a+wcg2IwnxCXHL+1FKNXyNLBH0sn4mroaoQQwpnG5iR7LbEgHA5R0v54/9fzBj5wzC/cLp0KQDgyIGER1sHfDbBrXFx8PHbfsD605qj/R7hPEzxvPa6td45uxn3Fq/UqrhalyJwC8UgtsUXTnUPNCbs5oHsGBHMrcNbe+23YgIb57zJrn5ufg6fN1W7+m0D27PtTHX8ummTxnXcRw9m/WssX0rpeqvxnP5aKFiHcYAQzqGsnL3MbJyqzbdREkeNo8aTQKFbu92O819m/PMsmf0/spKqQppfImgVRwc3wcZhwFruonc/AKW7W4YA8F8Hb5M6TuF7ce289WfX9V2OEqpeqDxJYKigWXWWUHftk3x8rCxoArTUtc1I1uPZFD4IN5Z9w6HTxyu7XCUUnVc40sELbqB2Iv6Cbwddvq2bcrCKk5LXZcUdhw78528vKrG7gSqlKqnGl8i8PSF5l2KEgFY003EH84gKbViA7fqg9aBrbkp9iZ+3f0ryw8sr+1wlFJ1WONLBODqMF4NrjmBBkf/dRlpQzKx60Qi/CN4dvmzOPNr7NbQSql6pvEmguxUOLoLgI7N/WkR6M2C7Q2neQis+yU80u8Rdh/fzedbPq/tcJRSdVTjTQRQ1GEsIgyODmVR/BHyCxrWzKFDWg1heORw3t/wPgcyDtR2OEqpOqhxJoKwTuDwO6mfYHDHMI5nOdmQkFqLgVWPh/o+hDGG/7fy/9V2KEqpOqhxJgKbHcJ7nJQIzu4QiggN6uqhQuH+4dza7Vbm7JvDosRFtR2OUqqOaZyJAKx5hw6sB1cnalM/T2IjghrUeILiro+5nqjAKJ5b/hw5+VW/GY9SquFoxImgN+TnwKHNRYuGRIexdn8qadkN7wobT7snj/R7hP3p+/lk4ye1HU6F7U/fz2ebP+Obbd+QX+DeaUCUUpbGNelcccVvXRneA4DB0aG8PS+eJfEpjOp6+pvK1DcDwgdwftT5fLTxI8a2G0tkYGRth3QKYwzbj23n932/8/u+39l+bHvRupm7ZvLc2c/RKqBVLUZYN5xwniAhI4GOTTrWdiiqAWi8ZwRBkeAXdtIEdL3aNMHP086CBjaeoLgH4x7Ew+bB8yuerzP3Vs4vyGfNoTW8tPIlxvx3DON/Gs9769/D3+HPA3EP8Mtlv/D84OfZcWwH438az4ydM+pM7LUhOy+b2+fczvgZ41mYsLC2w1ENQOM9Iyi6deVfHcYOu40B7UNZsD3ZbXctq2ua+zXnjh538PKql5m7fy4jWo+olThy83NZfmA5v+/7nXn753E0+ygOm4N+LfsxMXYiwyKHEeoTWlQ+MiCSXs168ciiR3h00aPM3z+fxwc8TpBXUK3EX1vyC/J5aOFDrDu8jpZ+LZmycApfj/2ayIC6d3an6o/GmwjASgTbZ0FOOngFADC0Yyhzth5iT8oJ2ob61XKA1eNvnf/GD/E/8OKKFxnQcoDbp8s2xpBXkEd2fjY5+TnWIy+H7Pxs9qXvY+7euSxMXEiGMwNfD18GtxrMiNYjGBwxGH9P/zLrDfcP5+PzPubTzZ/yztp3WJe8jmfPfpb+Lfu7Nf66yhjDCyte4Pd9v/NQ34cY0moIV828iv+b939MHzPd7Tc7Uo1HI08EvQADSeug7WDg5OkmGmoicNgcPNrvUW6cdSMfbvyQe3vdW7TOGEO6M53U7FRSc4o9SrxOy0kjKz+LnDzrQJ+dn110sM/Jz6HAFJS5/6beTTkv6jxGtB5Bv5b98LJ7VTh2u83OzbE3MzB8IFMWTOGW327hui7XcU+ve86onvrok02f8O9t/+bGmBu5uvPVALw45EXumHMHU5dO5bmzn2uQZ7Gq+jXuRBBe7NaVrkQQFepH66a+LNiezHUDomovtmoW1yKOC9tdyLTN01hzaE3RAf54znHyTelX59jFTpBXkPXwDMLXw5emXk3x8vDCy+6Ft90bLw/XTytmXPAAACAASURBVLsX3h7Wz+LPm3o3JTY0FrvNXqX4u4R04ZsLv+GVVa/w+ZbPWXpgKS8MfqHBdp7+tPMnXl/zOmPajuG+3vcVLT874mzu6HEH76x7h9jQWP7W+W+1GKWqrxp3IvBtCk3bndRPANZdy75fk0huXgGeHg23P/3+uPs5dOIQBkP74PYEewUT7BVMkFcQTbybFL0uXBbgGYBN6s7n4ePhw2P9H2NIqyH8Y/E/mDBzAvf1vo+rO19dp+KsqiVJS3h88eP0a9mPZwY9c8p7u7XbrWw+spmXVr5E55DO1XKL0sIzvIb0uaq/SH27+iIuLs6sWrXKfRV+dzPsXQr3/zWeYNbmg9w2fTX/vrU//duFuG9fqtqkZKXw5JIn+SPhD/q37M8zg56huV/z2g6ryrambOWG/91AZEAk00ZNK7MPJS03jQkzJ5CVl8XXY78mzDfMbTHsOLaD++bdR5vANrw94m1NBvWUiKw2xsSVtk5/oxG9IS0B0g8WLRrYPgS7TRrctNQNWYhPCG+e8yaPD3ic9cnrGffTOGbvnV3bYVVJQnoCd/x+B0FeQbw78t1yO9IDPQN5ffjrZDgzeGD+AzgL3DMoct6+eVzzyzWkZKewMHEhn2761C31qrpFE0HxgWUuAd4OerUObnDTUjd0IsLlHS/nm7HfEOkfyf1/3M/kBZM5lHmotkM7Y8eyjzFpziRy83N5b+R7NPNtdtptoptE89TAp1hzeA2vrHqlSvs3xvDhhg+5d969tA1qyw8X/8B5bc7j7bVvszF5Y5XqVnWPJoIWsWDzOLWfIDqMTUnHScnQeXnqm6igKD4f8zmTuk/i972/c+EPF/LRxo/qzRxLWXlZ3D33bpIyknjrnLdoF9yuwtuObjuaa7tcy5dbv+TnXT9Xev+TF0zmzbVvMrrtaKaNmkYLvxY8MfAJwnzDmLxgMhm5GZWqW9VNmggcPtA85pREMLhjGMbAong9K6iPHDYHd/S4gx8u+YGB4QN5Y80bXPrjpczbN69Oj0rOK8hj8oLJbEjewItDXqRX815nXMf/9f4/ejfvzZNLnmTb0W1ntO3BzINc/+v1zNozi/t63ccLg1/A28MbsJqfXhzyIkmZSTy97Ok6/TmqM1NtiUBEPhGRwyKyqYz1IiJviki8iGwQkTP/i3eXiN6QuBYK/rr2PTYiiGBfhzYP1XORAZG8Pvx13j/3fRw2B/fMu4dJcyax6/iu2g7tFMYYnlv+HH/s/4OH+j7EyDYjK1WPw+bg5aEvE+gZyH3z7uN4zvEKbbfu8DqumnkV+9L38dY5bzExduIp4xJ6NuvJpO6T+GX3L/y066dKxafqnuo8I5gGjCpn/Wgg2vW4FfhnNcZSvojekHMcju4sWmS3CYM6hLJwR7J+82kABoYP5NuLvmVKnylsSN7AuB/H8dLKl0jPTXdL/Rm5Gaw8uJK9aXvLHUxXng83fsh/tv+HiV0nVnk8QKhPKK8Me4WDJw7y8MKHTxvT9zu+56ZZN+Hr8OXLMV8yNHJomWVvib2F3s1788yyZ9ibtrdKcaq6odrGERhjFohIVDlFLgY+N9ZRdpmIBItIS2NMzd9PsXiHcWh00eKh0WH8vOEA2w6l06lFYI2HpdzLYXNwTZdrGN12NG+tfYvpW6Yzc9dM7ut1Hxd3uPiMLos84TzB2sNrWXFwBSsPrmRzyuaig62Phw/RwdFEN4nmrKZn0bFJRzo26UiAZ0CZ9f0Q/wNvrX2LC9tdeNJI76ro0awHD/V5iGeWP8P7699nUo9Jp5TJK8jj1dWvMn3LdPq17McrQ1857fxNdpudFwa/wPifxjN5wWS+GP0FDrvDLTGr2lGbA8oigP3FXie4lp2SCETkVqyzBlq3bu3+SEI7gqe/lQi6X1W0eHBHa9KzhduPaCJoQEJ8Qnhy4JNcftblPL/8eR5f8jhfb/uah/o+RI9mPUrdJjsvm/XJ61lxcAUrDqxg05FN5Jk8PGwedAvtxi2xt9A9rDtHso6w/dh2th3bxuy9s/lux3dFdYT7hdOxqZUUzmpiJYjIgEiWJC3hySVPMqDlAJ4a+JRbp4m44qwr2HBkA++uf5eY0BiGtBpStO54znEmL5jMkqQlXN35ah6IewAPW8UOCS38WvDUwKe4b959vLn2Tf4e93e3xaxqXoUGlInIvcCnQDrwEdATeMgY89tptosCZhpjupaybibwgjFmkev178AUY0y5o8XcPqCs0LSx4DwBt8w9afG5r86nRZA30yf2c/8+Va0zxvDz7p95ddWrJGclc1H7i7iv130EeQWxIXkDKw+uZMXBFaxPXo+zwIld7MSExNCnRR/6tuhLj2Y9ypy0zxjDoROH2H5su/U4aiWIPWl7Tjp7yC/Ip31wez4d9Sl+DvfPb5Wdl811v15HQkYCX1/wNZGBkew6vot75t5DYkYij/V7jHEdx1Wq7meWPcPX277mvZHvMShikJsjV+5U3oCyiiaC9caY7iJyPnAb8A9gujGm3A7e0ySC94E/jDFfuV5vA4adrmmo2hLB7Mdh2T/h4QTw+GvysqdnbmH6sr2sfmwkAd56+ttQZToz+XDDh3y+5XM8bB4YY8jOz0YQOjXtRN8Wfenbsi+9mvUqd2BXRWTnZbPz+E62H7USRIYzg3t63uPW0cAlJWYkcuXMK2nm24xJ3Sfx+OLH8bR78tqw1yp1ZVKh7LxsJvw8gaPZR/nuou9Omjpc1S3uSAQbjDHdROQNrIP39yKy1hhT7qQmp0kEFwB3AWOAfsCbxpi+p4ul2hLBlh/hm+usM4LCPgNg9d5jjPvnEl6+vDvje+udsRq6fWn7+GjjR/g6fOnTog9xzeMazD0PliQu4fY5t2MwdGraiTeHv0lL/5ZVrnfHsR1M+HkCcc3jeHfkuzoFRR1VXiKoaB/BahH5DWgLPCwiAUC5lyGIyFfAMCBURBKAJwAHgDHmPeAXrCQQD5wAbqxgLNWjqMN4zUmJoFfrYNqE+PL92gRNBI1A68DWTB00tbbDqBYDIwbyjwH/YNvRbdzf+3633Yciukk0k/tM5ullTzN9y3Suj7neLfWqmlPRRDAR6AHsMsacEJGmnObAbYyZcJr1BrizgvuvfoER4N/cNbDslqLFIsIlPSJ4c+4ODhzPomWQ3vxD1V+Xd7y82updnLiY19e8TlyLOGJCYqplP6p6VPQcbgCwzRiTKiLXAI8BFRulUl+UcuvKQpf0jMAYmLEuqRYCU6ruExGmDppKiHcIk+dPJtOZWdshqTNQ0UTwT+CEiHQH/g7sBD6vtqhqS0QvOLIdsk/OcW1D/ejZOpjv1ybWUmBK1X1BXkE8P/h5EjISeG75c7UdjjoDFU0Eea6mnIuBt40x7wBlj46prwr7BpLWnrLq0p4R/Hkwna0H0mo4KKXqjz4t+nBrt1uZsXNGpSe9q2kFpgBngbPSI8Ibgor2EaSLyMPAtcBgEbHh6vhtUMJdF0ElroZ2w05aNbZbOFN/2sL3axPp3FIHlylVltu63cbyA8t5etnTdAvrRmRAZLXsJzsvm4T0BPan7y96HMw8SE5+Ds4C58mPfCd5BXknLcsryLOWm7yiOgXBbrNjl2IPmx2b2PAQD2w220nLve3eNPFuYj28mtDUu2nR66beTWniZT2va3f3K6miieBK4G/ATcaYgyLSGnip+sKqJT5NIKSDdeVQCU39PBl2Vhg/rktkyqhO2G16k3ClSuNh87CmoJgxnikLpvDZqM8qPQXF8ZzjJx3o96XtY3/6fhLSEzicdfiksgGOAFr6t8TbwxuHzYGPhw+BtkAcNgcOu8P6Wfgo8domNgpMAXkmjwJTQH5BPvnG9XA9LzAF5BXknbQ8Ky+LY9nH2Ju2l2PZxziRd6LU92EXO8FewUUJwtPuWVRvUZ2Fr01emesmdJrA7d1vr9RnWZ4KJQLXwf9LoI+IjAVWGGMaXh8BWM1DuxeUuurSnq2Ys/UwS3emcHa0DpxRqizh/uE8MfAJHpj/AL2+6IVNrG/SHjaPom/TdrGf9C27+DqwpsQuOSlgmE8YkQGR9A/vT2RAJJEBkbQOaE1kQCRBXkFunZ6jMrLzsknNSeVo9lGOZR/jaPZRUnNSi54fyz7GsZxjnHCe+OszsHngJV7YbdbnUXRGUuK1h82Djk06VkvcFUoEInIF1hnAH4AAb4nIg8aYb6slqtoU0Rs2fA1pSRAYftKqEZ2bEeDlwfdrEzURKHUa50edjyDsTN1Z7rfckt+yC5/3ataLyIBIWgW0sn76t3Lb2Ifq4u3hTQuPFrTwa1HboZyRijYNPQr0McYcBhCRMGAO0DATAVj9BCUSgbfDzpjYlszckMQzl3TFx9NeCwEqVX+cF3VebYegKqCivRe2wiTgknIG29YvzbuCzVHqeAKAS3tFkJmbz29bDpa6Ximl6puKHsz/JyKzROQGEbkB+BlrioiGx+ENLbqWmQj6RjUlIthHxxQopRqMCiUCY8yDwAdAN9fjA2PMlOoMrFaVcuvKQjabcHGPcBbuOEJyev24GbpSSpWnws07xpjvjDH3ux7fV2dQtS6iN+SmQ8qOUldf2jOC/ALDT+t1ygmlVP1XbiIQkXQRSSvlkS4iDXeIbfEO41JENw+ga0QgP6zT5iGlVP1XbiIwxgQYYwJLeQQYYxru8NqQaPAMKDMRAFzSI4INCceJP5xRg4EppZT7Ncwrf6rKZoOInuUmgot6hGMT+EE7jZVS9ZwmgrJE9IaDm8CZXerqZgHenB0dxvdrEykoOP1d3pRSqq7SRFCWiN5Q4IRDm8osclnPCBJTs1i191gNBqaUUu6liaAsp+kwBjgvpjm+nna+X5tQQ0EppZT7aSIoS2A4BLQsNxH4enowKqYFMzccINuZX4PBKaWU+2giKE8Zt64s7pKeEaRn5zHvz8PlllNKqbpKE0F5InpBSjxkld0HMKhDKGEBXvxXrx5SStVTmgjKU86tKwvZbcLF3cP5Y9thjmXm1lBgSinlPpoIylP81pXluLRXBM58w8yNB2ogKKWUci9NBOXxDoLQjqXeurK4Li0D6djcXweXKaXqJU0EpxPRGxJWgSl70JiIcGnPVqzee4y9KZk1GJxSSlWdJoLTaX8OZB6G+N/LLXZxj3BE4Ie1OiOpUqp+0URwOl0ugYBwWPJmucXCg33o3zaE79cmYMo5e1BKqbpGE8HpeHhCv9tg93w4sKHcopf2imBPygnW7U+toeCUUqrqNBFURO8bwNMflr5dbrHRXVvg5WHT21gqpeoVTQQV4RMMva6DTd/B8bIP8gHeDs7t0pyf1ifhzD/1NpdKKVUXaSKoqH63W1cOLX+v3GKX9ozg2Akn87cl11BgSilVNZoIKqpJG4i5BFZPg+yy79I5pGMYTf08+V5vY6mUqieqNRGIyCgR2SYi8SLyUCnr24jI7yKyQUT+EJFW1RlPlQ24C3LSYM3nZRZx2G1c2K0ls7ccIi3bWYPBKaVU5VRbIhARO/AOMBroAkwQkS4lir0MfG6M6QZMBZ6vrnjcIqIXtDnbah7KL/sgf2mvVuTmFfC/jQdrMDillKqc6jwj6AvEG2N2GWNygX8DF5co0wWY63o+r5T1dc/Au+H4ftjyY5lFurcKom2oH+/N38nyXSk1GJxSSp256kwEEcD+Yq8TXMuKWw9c5np+KRAgIiElKxKRW0VklYisSk6u5U7Y6PMgJNoaYFbGwDER4fELu5Cek8eVHyzjmo+Ws1pvZ6mUqqNqu7P4AWCoiKwFhgKJwCm3+jLGfGCMiTPGxIWFhdV0jCez2WDgXXBgPexZVGax4Wc1Y+Hk4Tx2QWe2Hkhj3D+XcP0nK1ivg82UUnVMdSaCRCCy2OtWrmVFjDFJxpjLjDE9gUddy+r+kbLbVeAbCkveKreYt8POzYPbsXDKcKaM6sT6hFQufmcxN3+2kk2Jx2soWKWUKl91JoKVQLSItBURT+AqYEbxAiISKiKFMTwMfFKN8biPwxv63go7ZkHyttMW9/X0YNKw9iycPJwHzuvIit1HGfvWIm6fvpo/D5Z9KapSStWEaksExpg84C5gFrAV+MYYs1lEporIRa5iw4BtIrIdaA48W13xuF2fm8HD+7TTThQX4O3grnOiWTjlHO4dEc3i+COMfmMhd/1rDfGH06sxWKWUKpvUt5ky4+LizKpVq2o7DMvM/4O1X8B9myCg+Rlvnnoilw8X7uLTxXvIduZzcY8I7hkRTdtQv2oIVinVmInIamNMXGnraruzuH7rf6c1nmDlh5XaPNjXkwfP78TCycO5ZXA7ft10gJGvzuejhbvcHKhSSpVNE0FVhHaAThfAyo8g90Slqwnx9+LhMZ1ZMHk4A9uH8Ors7RzNzHVjoEopVTZNBFU14C7IOgbrvqxyVc0CvPnH2C6cyM3n08W73RCcUkqdniaCqmrdHyLiYOk7UHDKEIgz1rF5AKNiWjBtyR6dq0gpVSM0EVSViDXA7Nhu2PaLW6q8c3gH0rPzmL50r1vqU0qp8mgicIdOF0Jwm9MOMKuo2FZBDO0YxseLdnMiN88tdSqlVFk0EbiD3QMG3An7l8P+FW6p8u5zOnA0M5evVuw/fWGllKoCTQTu0uNq8A5y21lBXFRT+rVtygcLdpKTV/W+B6WUKosmAnfx8oe4ibD1JzjqnnEAd58TzaG0HL5dneCW+pRSqjSaCNyp321g84Cl77qlukEdQugeGcx783eSl1/gljqVUqokTQTuFNACul1hjSk4cbTK1YkIdw3vwP6jWcxYn+SGAJVS6lSaCNxtwF3gPAGrPnZLdSM6NaNTiwDemRdPQUH9mhdKKVU/aCJwt+ZdoP0IWP4BOLOrXJ3NJtw5vAM7kzP532a9B7JSyv00EVSHgXdD5mHY+B+3VDcmtiXtQv14e2489W22WKVU3aeJoDq0GwbNY2Hhy5CbWeXq7DZh0rD2bDmQxrxth6tcn1JKFaeJoDqIwKjn4dhe+N/Dbqnykp4RRAT76FmBUsrtNBFUl7aDYdC9sOYz2DqzytU57DZuH9qONftSWborxQ0BKqWURRNBdRr+KLTsDjPuhrQDVa7u8rhIwgK8eHtuvBuCU0opiyaC6uThCZd9BM4s+GESFFRtUJi3w86tg9uxZGcKa/Ydc1OQSqnGThNBdQvrCKOeg13zYPl7Va7ub/1aE+zr4B09K1BKuYkmgprQ+0Y4awzMeQIObqpSVX5eHkwc1Jbf/zzM5qTjbgpQKdWYaSKoCSJw0Vvg0wS+u9lqKqqC6wZGEeDlwbvzdropQKVUY6aJoKb4hcIl70LyVpjzZJWqCvJxcO2ANvyy6QDxhzPcE59SqtHSRFCTOoyEfpOsvoIds6tU1cSz2+LlYePdP7SvQClVNZoIatrIJ6FZF/jhDshIrnQ1If5e/K1vG35cl8T+oyfcFp5SqvHRRFDTHN4w7iPIPm6NL6jCKOFbh7TDLsJ787WvoD7LzdN7TajapYmgNjSPgXOfgu2/wqpPKl1NiyBvxse14j+rEjh4vOoznaqak19gWBx/hL9/s57eT88mMbVqFxAoVRWaCGpL39us6apnPQrJ2ytdzaSh7ck3hg8Xuuf2mKp6/Xkwjed/2cqgF+Zy9UfL+W3zQUbHttB7Taha5VHbATRaNpt1FdE/B8J3E+Hm362RyGcosqkvF/cI51/L93Hb0HY0C/CuhmBVVRxKy+bHdYl8vzaJrQfS8LAJQzuG8djYzozs3Bxvh722Q1SNnNS3mSzj4uLMqlWrajsM9/nzZ/j336wJ6s6dWqkq4g9ncO5r8zEGQv29iAj2JjzYh4hgH8JdD+u5N039PBERN78JVVJmTh6zNh/k+7WJLI4/QoGB7pHBXNYzgrHdWhLi71XbIapGRkRWG2PiSlunZwS1rdMF1sjjxW9aTUXthp5xFR2a+fPVLf1ZsfsoSalZJKZmsf1QOvO2HSbbeXJHpLfD9ldiCPIhsqkPl8dF0jxQzySqKi+/gMU7U/h+TQKzNh8iy5lPZFMf7hregYt7RtA+zL+2Q1SqVHpGUBfkZsL7QyD3BExaDL5N3VKtMYZjJ5xFySEpNYvEY1kkHc8iMTWbpNQsktNz8Pfy4IHzOnLtgCjsNj1bqKjk9Bw2JqayMSGNjYmprN2XSkpmLoHeHlzQLZzLekUQ16aJnoGpOqG8M4JqTQQiMgp4A7ADHxljXiixvjXwGRDsKvOQMeaX8upskIkAIGktfDTSOkO4/DNrWooasOdIJv/4cRMLdxwhNiKI5y6NJbZVUI3suz5JychhY+JxNiYcZ0PicTYlHueA60otEWgf5k+3iCDOi2nO8E7N8PLQdn9Vt9RKIhARO7AdOBdIAFYCE4wxW4qV+QBYa4z5p4h0AX4xxkSVV2+DTQQAi16zpp+46C3odV2N7dYYw8wNB5g6cwspGTlcNyCKv5/XkQBvR43FUFdkO/M5nuVk28H0ogP/xsTjJ13e2S7Mj9iIIGIjgujWKpgu4YH4e2krq6rbaquPoC8Qb4zZ5Qri38DFwJZiZQwQ6HoeBCRVYzx138B7YOc8mHk/BLepVH9BZYgIF3YPZ0jHMF6etY3Plu7hl40HeOLCGMbEtqiXTRvZznz2HT3BkfQcjmc5Sct2Wj+z8opep2W5lmW7lmU5ySkxuCsqxJdebZpw/cA2xEYEExMRSGAjTJCqYavOM4LxwChjzM2u19cC/YwxdxUr0xL4DWgC+AEjjTGrS6nrVuBWgNatW/feu3dvtcRcJ2Qdg09Gw/EEuOlXaBFb4yGs25/KI//dyJYDaQw7K4ypF3WldYhvjcdxOjl5+ew/eoLdR06w50gme1JcjyMnSDqeVeqgbbtNCPT2INDHQaC3gyAfB4E+HtZPb4e13MdB+1A/YiKCCPI5g4N+Vip4B9VYs55SZ6K2moYqkgjud8XwiogMAD4Guhpjyhxz36CbhgodT4SPz4WCfLh5NgS3rp795GbCms+h25WndFDn5Rfw2dK9vPrbNvIKDPeMiOaWwe3w9KjZMYjGGBJTs9h2MJ09KX8d8HcfySQpNYvi47CCfR1EhfjRNtSPqBA/okJ9aR7o7TrYWwd9P0+7+89wcjOtiQQXvQHjPoSO57u3fqXcoLaahhKByGKvW7mWFTcRGAVgjFkqIt5AKHC4GuOq+4Ii4Opv4dNR8MU4uGmW264kKpKZAv+6HBJXw76lcMXnJ632sNuYeHZbxsS24KkZW3hp1jZ+WJvIs5fG0retm2Mp5lBaNhsSjrMhIZUNrvb5o5m5ResDvT1oG+pH7zZNGNerFVGhvkUH/2DfMx+QVyX5TljzGcz/f5BxyLr5UJOomo1BKTeozjMCD6zO4hFYCWAl8DdjzOZiZX4FvjbGTBORzsDvQIQpJ6hGcUZQaM9imH4JhPeE634Eh4976j22F764zGp+ij4Xtv4EV30FncaUucnvWw/x+I+bSUzN4vLerXh4TGea+lXtwJuSkcOGwitxXAf+w+k5gNWEE93Mn26trA7Zzi0DaRvqRxNfR+33WRQUwOb/wtxn4NhuaD3AmlW2df/ajUupctTm5aNjgNexLg39xBjzrIhMBVYZY2a4rhT6EPDH6jiebIz5rbw6G1UiANj8PfznRuuy0is+B1sVL0s8uNE6y8jLgb99DRG94f2hVt/EncvBO7DMTU/k5vHm7/F8tHAXDruNpn6e+Hja8XbY8Paw4+Npx8vDeu3jsOPtsJZ5e9jwctjxcdjJcuafciVO8csvY10H/i4tA/HxrGOXYBoDO3+HOU/BwQ3QvCuMeMJKprWdnJQ6jVpLBNWh0SUCgGXvwf+mQNxEuOCVyh90di+0prPwCoBrvoNmna3lCausMQx9XPWfxp8H0/hq+T7Sc/LIduaT7Swg25lPVrHnhY/CZcVFhfgS2yq46MDfNSKo7l9+mbDKurR3z0Lriq5zHoOu4605o5SqB3SKifqu/+2QngSL34DAcBjywJnXsfkH+O8t0LSdlQSCWv21rlUc9LsNlr8PsVdA637lVtWpRSBPXdy1wrs2xpCTZyUIu03q1/iE5G3w+1T4cyb4hcHol6D3DZWaIFCpukoTQX0x4klIOwBzn4aAltDz6opvu/wD+HWy1YZ91b9K73g+5zHYOhN+ugduWwAe7psUTUTwdjUV1RvHE+CP52Hdv8DhB8Mfhf53gJfOF6QaHk0E9YXNBhe/A5mHrTub+TeH6JHlb2OMlTgWvgJnXQDjPy67w9krAMa+Cv+6Aha9DsOmuP891HV5udYVVH/OhNWfAca6x/Tgv4NfSG1Hp1S10URQn3h4whXTYdoY+OY6uGEmRPQqvWx+Hvx0L6z7wmrKGPMK2E/z6+54PsRcBgtfhphLIOwst7+FOid1H+yYDfFzYNd8cGaCzQHdroBhD0Nw5OnrUKqe087i+ij9EHw8EpxZMPE3q92/uNwT8J8bYMcsGPoQDHuo4h3MGYfh7T5WR/INvzS8ztC8HNi7xDrw75gNR7ZZy4NaW2dYHc6FtkO0CUg1ONpZ3NAENIdr/gsfn+cacPYb+IdZ604ctZp3ElfD2Ncg7qYzq9u/GZz/LPx4J6z+1LqSqL47tsf1rf932L3A+tZv94Q2g6D39dBhJIR21EtAVaOlZwT12f6V8NmFrm/vM+FECky/zGruGP8JdB5buXqNgc8vgqR11tiCwHD3xn2msST/CSnx1rf5fCfk5578yMs9dVl+rlU+aR2k7LDqCm5jXfPf4VxoOxg8/WrvfSlVw3QcQUP25y/w9dXWt9uUeHCegAlfQ5sBVas3Zad1P+UOI+GqL90Ta0XlnrC+ue/4zfomf3zf6bcRG9i9rG/6dsdfP0PaWwf+6HMhpIN+61eNljYNNWSdxsAFr8LM+yAgHG78HzTvUvV6Q9rD0Cnw+1PWFBSdL6x6neU5utt14P/NGviWn2NdttluGAy+3+oU9/B2HeA9/zrQ16FaUgAABptJREFUe7gO/lUdca1UI6aJoCGIu9Ga7KxZZwho4b56B94Nm/4LPz9gdaB6u/HOZXm5sG8JbHcd/Aubb0I6WP0S0edaZzluHM+glCqdJoKGov1w99dpd8BFb1jTT8x50up8roqsVNjyo3Xg3/UH5GZY3+ajzoY+N7uab9q7I3Kl1BnQRKDKF9Eb+t0Oy96F2MuhzcAzryMtCZa+A6unWQf/wFZWXR3Pt840tNNWqVqliUCd3vBHXdNP3Au3L6p4c03ydljyBqz/Gkw+dB0HA+6Elj2001apOkQTgTo9L3+rWejLcdZ0FcMfKb98wipY9Br8+bOVNHrfAAPv0pu2KFVHaSJQFRM90mrOWfgqxFz61xTWhYyxBmwteg32LgLvYGuW1L63/TXYTSlVJ2kiUBV3/vPW1Aw/3WtdpmqzWXMabfnBmqju0EbrEtbznrVG7HoF1HbESqkK0ESgKs4/DM5/Dn6YBMvesa7rX/IWpO61pmi42NWhrHP1K1WvaCJQZ6b7BNjwNfz2mPW6VR8Y9Tx0HN3wJqhTqpHQRKDOjAhc9LbVF9B1nHU5qV4BpFS9polAnbn/3969hUpVxXEc//46VpRGFpWEmab5kEGdLkikhRGE9aKB3RPppR4MEnrIoiiCHstepCwST2T3tCR6qA5xzIdSE0vTIhEjxc4pisqgm/572OvEdPY5Il5mzcz6fV5m7zUzm//8WTP/2WvvvfbYCdVNbMysI3hf3syscC4EZmaFcyEwMyucC4GZWeFcCMzMCudCYGZWOBcCM7PCuRCYmRWu7W5eL+kH4NvDfPsZwI9HMZxO4bzUOSd1zkldO+VkYkQMOxVw2xWCIyFpY0RcnjuOVuO81Dkndc5JXafkxENDZmaFcyEwMytcaYXgudwBtCjnpc45qXNO6joiJ0UdIzAzs7rS9gjMzGwIFwIzs8IVUwgkzZb0taQdkhbnjqcVSNolaYukzZI25o4nF0nLJQ1I2trQdrqkDyR9kx5Pyxljs42Qk8ck7Un9ZbOkG3LG2GySJkj6SNI2SV9Kui+1t31fKaIQSOoClgLXA9OA2yRNyxtVy7gmIro74VzoI7ACmD2kbTHQGxFTgd60XpIV1HMCsCT1l+6IeK/JMeX2D3B/REwDrgAWpt+Rtu8rRRQCYDqwIyJ2RsRfwKvAnMwxWYuIiLXAT0Oa5wA9abkHmNvUoDIbISdFi4i9EbEpLf8GbAfG0wF9pZRCMB74rmF9d2orXQDvS/pM0t25g2kx4yJib1r+HhiXM5gWcq+kL9LQUdsNgRwtkiYBlwCf0gF9pZRCYMObGRGXUg2ZLZR0de6AWlFU51j7PGt4BpgCdAN7gSfzhpOHpDHAW8CiiPi18bl27SulFII9wISG9XNSW9EiYk96HABWUw2hWaVf0tkA6XEgczzZRUR/ROyPiAPA8xTYXyQdT1UEVkbEqtTc9n2llEKwAZgq6TxJJwC3Amsyx5SVpNGSThlcBq4Dth78XUVZAyxIywuAdzLG0hIGf+ySGymsv0gS8AKwPSKeaniq7ftKMVcWp1Pdnga6gOUR8UTmkLKSNJlqLwBgFPByqTmR9Aowi2pK4X7gUeBt4HXgXKppz2+OiGIOno6Qk1lUw0IB7ALuaRgb73iSZgIfA1uAA6n5IarjBG3dV4opBGZmNrxShobMzGwELgRmZoVzITAzK5wLgZlZ4VwIzMwK50Jg1kSSZkl6N3ccZo1cCMzMCudCYDYMSXdKWp/m3V8mqUvSPklL0lz0vZLOTK/tlvRJmoxt9eBkbJLOl/ShpM8lbZI0JW1+jKQ3JX0laWW6YtUsGxcCsyEkXQDcAsyIiG5gP3AHMBrYGBEXAn1UV9sCvAg8EBEXUV11Oti+ElgaERcDV1JN1AbVrJWLqO6NMRmYccw/lNlBjModgFkLuha4DNiQ/qyfRDWR2AHgtfSal4BVkk4FxkZEX2rvAd5I8ziNj4jVABHxB0Da3vqI2J3WNwOTgHXH/mOZDc+FwKxOQE9EPPi/RumRIa873PlZ/mxY3o+/h5aZh4bM6nqBeZLOgv/uSTuR6vsyL73mdmBdRPwC/CzpqtQ+H+hLd7DaLWlu2saJkk5u6qcwO0T+J2I2RERsk/Qw1d3bjgP+BhYCvwPT03MDVMcRoJp6+Nn0Q78TuCu1zweWSXo8beOmJn4Ms0Pm2UfNDpGkfRExJnccZkebh4bMzArnPQIzs8J5j8DMrHAuBGZmhXMhMDMrnAuBmVnhXAjMzAr3LzW6W2GDmIhVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_25_28.history['val_loss'])\n",
    "plt.plot(history_25_32.history['val_loss'])\n",
    "plt.plot(history_simple_25.history['val_loss'])\n",
    "plt.title('Model val_loss of different models')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['28x28','32x32', 'Simplified'], loc='upper left')\n",
    "plt.savefig('FinalEvaluationLoss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gwvP533U0JDo"
   },
   "source": [
    "In this step we evaluate the models without using EarlyStop to see how the loss function changes but also how their accuracy increases when running the whole of the 25 epochs.\n",
    "\n",
    "Also, we are using a callback to change the learning parameter when a plateau is detected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m0Y22lY-aI4s",
    "outputId": "49e955a4-baae-410e-996d-32ff22a08a47"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "1562/1562 [==============================] - 215s 135ms/step - loss: 1.4814 - accuracy: 0.4763 - val_loss: 1.1306 - val_accuracy: 0.5917 - lr: 0.0010\n",
      "Epoch 2/25\n",
      "1562/1562 [==============================] - 228s 144ms/step - loss: 1.0964 - accuracy: 0.6186 - val_loss: 0.9862 - val_accuracy: 0.6592 - lr: 0.0010\n",
      "Epoch 3/25\n",
      "1562/1562 [==============================] - 203s 128ms/step - loss: 0.9436 - accuracy: 0.6739 - val_loss: 0.8711 - val_accuracy: 0.6963 - lr: 0.0010\n",
      "Epoch 4/25\n",
      "1562/1562 [==============================] - 208s 131ms/step - loss: 0.8350 - accuracy: 0.7106 - val_loss: 0.7991 - val_accuracy: 0.7225 - lr: 0.0010\n",
      "Epoch 5/25\n",
      "1562/1562 [==============================] - 224s 142ms/step - loss: 0.7565 - accuracy: 0.7397 - val_loss: 0.8200 - val_accuracy: 0.7175 - lr: 0.0010\n",
      "Epoch 6/25\n",
      "1562/1562 [==============================] - 204s 129ms/step - loss: 0.6816 - accuracy: 0.7640 - val_loss: 0.8013 - val_accuracy: 0.7258 - lr: 0.0010\n",
      "Epoch 7/25\n",
      "1562/1562 [==============================] - 228s 144ms/step - loss: 0.6164 - accuracy: 0.7885 - val_loss: 0.7595 - val_accuracy: 0.7401 - lr: 0.0010\n",
      "Epoch 8/25\n",
      "1562/1562 [==============================] - 227s 143ms/step - loss: 0.5630 - accuracy: 0.8058 - val_loss: 0.7498 - val_accuracy: 0.7445 - lr: 0.0010\n",
      "Epoch 9/25\n",
      "1562/1562 [==============================] - 219s 139ms/step - loss: 0.5175 - accuracy: 0.8210 - val_loss: 0.7813 - val_accuracy: 0.7460 - lr: 0.0010\n",
      "Epoch 10/25\n",
      "1562/1562 [==============================] - 234s 148ms/step - loss: 0.4759 - accuracy: 0.8349 - val_loss: 0.7868 - val_accuracy: 0.7448 - lr: 0.0010\n",
      "Epoch 11/25\n",
      "1562/1562 [==============================] - 242s 153ms/step - loss: 0.4463 - accuracy: 0.8443 - val_loss: 0.7615 - val_accuracy: 0.7534 - lr: 0.0010\n",
      "Epoch 12/25\n",
      "1562/1562 [==============================] - 251s 159ms/step - loss: 0.4096 - accuracy: 0.8587 - val_loss: 0.7755 - val_accuracy: 0.7545 - lr: 0.0010\n",
      "Epoch 13/25\n",
      "1562/1562 [==============================] - 209s 132ms/step - loss: 0.3852 - accuracy: 0.8672 - val_loss: 0.8208 - val_accuracy: 0.7522 - lr: 0.0010\n",
      "Epoch 14/25\n",
      "1562/1562 [==============================] - 273s 173ms/step - loss: 0.3613 - accuracy: 0.8754 - val_loss: 0.8208 - val_accuracy: 0.7493 - lr: 0.0010\n",
      "Epoch 15/25\n",
      "1562/1562 [==============================] - ETA: 0s - loss: 0.3373 - accuracy: 0.8828\n",
      "Epoch 15: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "1562/1562 [==============================] - 276s 175ms/step - loss: 0.3373 - accuracy: 0.8828 - val_loss: 0.8208 - val_accuracy: 0.7537 - lr: 0.0010\n",
      "Epoch 16/25\n",
      "1562/1562 [==============================] - 284s 180ms/step - loss: 0.2364 - accuracy: 0.9170 - val_loss: 0.8288 - val_accuracy: 0.7706 - lr: 2.0000e-04\n",
      "Epoch 17/25\n",
      "1562/1562 [==============================] - 287s 182ms/step - loss: 0.1994 - accuracy: 0.9303 - val_loss: 0.8624 - val_accuracy: 0.7736 - lr: 2.0000e-04\n",
      "Epoch 18/25\n",
      "1562/1562 [==============================] - 308s 195ms/step - loss: 0.1855 - accuracy: 0.9355 - val_loss: 0.8686 - val_accuracy: 0.7718 - lr: 2.0000e-04\n",
      "Epoch 19/25\n",
      "1562/1562 [==============================] - 297s 188ms/step - loss: 0.1711 - accuracy: 0.9392 - val_loss: 0.8967 - val_accuracy: 0.7712 - lr: 2.0000e-04\n",
      "Epoch 20/25\n",
      "1562/1562 [==============================] - ETA: 0s - loss: 0.1630 - accuracy: 0.9430\n",
      "Epoch 20: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
      "1562/1562 [==============================] - 307s 195ms/step - loss: 0.1630 - accuracy: 0.9430 - val_loss: 0.9208 - val_accuracy: 0.7731 - lr: 2.0000e-04\n",
      "Epoch 21/25\n",
      "1562/1562 [==============================] - 313s 198ms/step - loss: 0.1483 - accuracy: 0.9491 - val_loss: 0.9107 - val_accuracy: 0.7771 - lr: 4.0000e-05\n",
      "Epoch 22/25\n",
      "1562/1562 [==============================] - 295s 187ms/step - loss: 0.1423 - accuracy: 0.9511 - val_loss: 0.9129 - val_accuracy: 0.7754 - lr: 4.0000e-05\n",
      "Epoch 23/25\n",
      "1562/1562 [==============================] - 317s 201ms/step - loss: 0.1391 - accuracy: 0.9517 - val_loss: 0.9164 - val_accuracy: 0.7745 - lr: 4.0000e-05\n",
      "Epoch 24/25\n",
      "1562/1562 [==============================] - ETA: 0s - loss: 0.1305 - accuracy: 0.9554\n",
      "Epoch 24: ReduceLROnPlateau reducing learning rate to 8.000000525498762e-06.\n",
      "1562/1562 [==============================] - 290s 184ms/step - loss: 0.1305 - accuracy: 0.9554 - val_loss: 0.9315 - val_accuracy: 0.7730 - lr: 4.0000e-05\n",
      "Epoch 25/25\n",
      "1562/1562 [==============================] - 296s 188ms/step - loss: 0.1335 - accuracy: 0.9540 - val_loss: 0.9300 - val_accuracy: 0.7744 - lr: 8.0000e-06\n"
     ]
    }
   ],
   "source": [
    "history_25_28_1 = baseline_model.fit(train_ds_28, epochs=25,verbose=1,\n",
    "                    validation_data=test_ds_28, callbacks=[reduce_lr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wvhkMx0naV9w",
    "outputId": "9b79bc66-96a2-4156-fb0b-8607893849a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "1562/1562 [==============================] - 293s 185ms/step - loss: 1.4879 - accuracy: 0.4763 - val_loss: 1.1219 - val_accuracy: 0.6056 - lr: 0.0010\n",
      "Epoch 2/25\n",
      "1562/1562 [==============================] - 282s 179ms/step - loss: 1.0661 - accuracy: 0.6304 - val_loss: 0.9164 - val_accuracy: 0.6809 - lr: 0.0010\n",
      "Epoch 3/25\n",
      "1562/1562 [==============================] - 284s 180ms/step - loss: 0.8999 - accuracy: 0.6894 - val_loss: 0.8253 - val_accuracy: 0.7163 - lr: 0.0010\n",
      "Epoch 4/25\n",
      "1562/1562 [==============================] - 305s 193ms/step - loss: 0.7793 - accuracy: 0.7313 - val_loss: 0.7860 - val_accuracy: 0.7309 - lr: 0.0010\n",
      "Epoch 5/25\n",
      "1562/1562 [==============================] - 312s 198ms/step - loss: 0.6914 - accuracy: 0.7649 - val_loss: 0.7904 - val_accuracy: 0.7327 - lr: 0.0010\n",
      "Epoch 6/25\n",
      "1562/1562 [==============================] - 311s 198ms/step - loss: 0.6140 - accuracy: 0.7862 - val_loss: 0.7690 - val_accuracy: 0.7405 - lr: 0.0010\n",
      "Epoch 7/25\n",
      "1562/1562 [==============================] - 302s 192ms/step - loss: 0.5472 - accuracy: 0.8096 - val_loss: 0.7736 - val_accuracy: 0.7433 - lr: 0.0010\n",
      "Epoch 8/25\n",
      "1562/1562 [==============================] - 294s 187ms/step - loss: 0.4897 - accuracy: 0.8308 - val_loss: 0.7354 - val_accuracy: 0.7598 - lr: 0.0010\n",
      "Epoch 9/25\n",
      "1562/1562 [==============================] - 310s 197ms/step - loss: 0.4462 - accuracy: 0.8464 - val_loss: 0.7471 - val_accuracy: 0.7596 - lr: 0.0010\n",
      "Epoch 10/25\n",
      "1562/1562 [==============================] - 314s 199ms/step - loss: 0.4047 - accuracy: 0.8608 - val_loss: 0.7457 - val_accuracy: 0.7643 - lr: 0.0010\n",
      "Epoch 11/25\n",
      "1562/1562 [==============================] - 324s 206ms/step - loss: 0.3659 - accuracy: 0.8724 - val_loss: 0.7959 - val_accuracy: 0.7657 - lr: 0.0010\n",
      "Epoch 12/25\n",
      "1562/1562 [==============================] - 297s 189ms/step - loss: 0.3462 - accuracy: 0.8813 - val_loss: 0.7981 - val_accuracy: 0.7650 - lr: 0.0010\n",
      "Epoch 13/25\n",
      "1562/1562 [==============================] - 327s 207ms/step - loss: 0.3169 - accuracy: 0.8928 - val_loss: 0.8267 - val_accuracy: 0.7649 - lr: 0.0010\n",
      "Epoch 14/25\n",
      "1562/1562 [==============================] - 315s 200ms/step - loss: 0.2969 - accuracy: 0.8983 - val_loss: 0.8298 - val_accuracy: 0.7661 - lr: 0.0010\n",
      "Epoch 15/25\n",
      "1562/1562 [==============================] - 299s 190ms/step - loss: 0.2748 - accuracy: 0.9073 - val_loss: 0.8594 - val_accuracy: 0.7628 - lr: 0.0010\n",
      "Epoch 16/25\n",
      "1562/1562 [==============================] - 297s 188ms/step - loss: 0.2619 - accuracy: 0.9117 - val_loss: 0.8652 - val_accuracy: 0.7621 - lr: 0.0010\n",
      "Epoch 17/25\n",
      "1562/1562 [==============================] - ETA: 0s - loss: 0.2436 - accuracy: 0.9173\n",
      "Epoch 17: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "1562/1562 [==============================] - 300s 190ms/step - loss: 0.2436 - accuracy: 0.9173 - val_loss: 0.8763 - val_accuracy: 0.7620 - lr: 0.0010\n",
      "Epoch 18/25\n",
      "1562/1562 [==============================] - 302s 192ms/step - loss: 0.1681 - accuracy: 0.9429 - val_loss: 0.8663 - val_accuracy: 0.7840 - lr: 2.0000e-04\n",
      "Epoch 19/25\n",
      "1562/1562 [==============================] - 302s 192ms/step - loss: 0.1347 - accuracy: 0.9545 - val_loss: 0.8985 - val_accuracy: 0.7841 - lr: 2.0000e-04\n",
      "Epoch 20/25\n",
      "1562/1562 [==============================] - 302s 192ms/step - loss: 0.1219 - accuracy: 0.9597 - val_loss: 0.9279 - val_accuracy: 0.7836 - lr: 2.0000e-04\n",
      "Epoch 21/25\n",
      "1562/1562 [==============================] - 298s 189ms/step - loss: 0.1107 - accuracy: 0.9623 - val_loss: 0.9420 - val_accuracy: 0.7867 - lr: 2.0000e-04\n",
      "Epoch 22/25\n",
      "1562/1562 [==============================] - 304s 193ms/step - loss: 0.1069 - accuracy: 0.9637 - val_loss: 0.9570 - val_accuracy: 0.7838 - lr: 2.0000e-04\n",
      "Epoch 23/25\n",
      "1562/1562 [==============================] - 309s 196ms/step - loss: 0.1018 - accuracy: 0.9662 - val_loss: 0.9674 - val_accuracy: 0.7851 - lr: 2.0000e-04\n",
      "Epoch 24/25\n",
      "1562/1562 [==============================] - ETA: 0s - loss: 0.0961 - accuracy: 0.9686\n",
      "Epoch 24: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
      "1562/1562 [==============================] - 303s 193ms/step - loss: 0.0961 - accuracy: 0.9686 - val_loss: 0.9829 - val_accuracy: 0.7852 - lr: 2.0000e-04\n",
      "Epoch 25/25\n",
      "1562/1562 [==============================] - 300s 190ms/step - loss: 0.0838 - accuracy: 0.9722 - val_loss: 0.9847 - val_accuracy: 0.7874 - lr: 4.0000e-05\n"
     ]
    }
   ],
   "source": [
    "history_25_32_1 = model_v1.fit(train_ds, epochs=25,verbose=1,\n",
    "                    validation_data=test_ds, callbacks=[reduce_lr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Rn0llI9MaZ5e",
    "outputId": "67742bd3-75ce-4ba9-9c37-e349eb4d504d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "1562/1562 [==============================] - 109s 68ms/step - loss: 1.6103 - accuracy: 0.4895 - val_loss: 1.2054 - val_accuracy: 0.5860 - lr: 0.0010\n",
      "Epoch 2/25\n",
      "1562/1562 [==============================] - 107s 67ms/step - loss: 1.1332 - accuracy: 0.6086 - val_loss: 1.1225 - val_accuracy: 0.6087 - lr: 0.0010\n",
      "Epoch 3/25\n",
      "1562/1562 [==============================] - 108s 68ms/step - loss: 1.0514 - accuracy: 0.6332 - val_loss: 1.0677 - val_accuracy: 0.6320 - lr: 0.0010\n",
      "Epoch 4/25\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 0.9998 - accuracy: 0.6547 - val_loss: 1.0640 - val_accuracy: 0.6306 - lr: 0.0010\n",
      "Epoch 5/25\n",
      "1562/1562 [==============================] - 107s 67ms/step - loss: 0.9675 - accuracy: 0.6656 - val_loss: 1.0504 - val_accuracy: 0.6403 - lr: 0.0010\n",
      "Epoch 6/25\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 0.9384 - accuracy: 0.6752 - val_loss: 1.0297 - val_accuracy: 0.6483 - lr: 0.0010\n",
      "Epoch 7/25\n",
      "1562/1562 [==============================] - 110s 69ms/step - loss: 0.9115 - accuracy: 0.6832 - val_loss: 1.0411 - val_accuracy: 0.6439 - lr: 0.0010\n",
      "Epoch 8/25\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 0.8920 - accuracy: 0.6905 - val_loss: 1.0311 - val_accuracy: 0.6468 - lr: 0.0010\n",
      "Epoch 9/25\n",
      "1562/1562 [==============================] - 107s 67ms/step - loss: 0.8755 - accuracy: 0.6990 - val_loss: 1.0175 - val_accuracy: 0.6537 - lr: 0.0010\n",
      "Epoch 10/25\n",
      "1562/1562 [==============================] - 107s 67ms/step - loss: 0.8590 - accuracy: 0.7010 - val_loss: 1.0139 - val_accuracy: 0.6570 - lr: 0.0010\n",
      "Epoch 11/25\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 0.8418 - accuracy: 0.7074 - val_loss: 0.9882 - val_accuracy: 0.6638 - lr: 0.0010\n",
      "Epoch 12/25\n",
      "1562/1562 [==============================] - 107s 67ms/step - loss: 0.8312 - accuracy: 0.7096 - val_loss: 1.0145 - val_accuracy: 0.6593 - lr: 0.0010\n",
      "Epoch 13/25\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 0.8230 - accuracy: 0.7147 - val_loss: 1.0027 - val_accuracy: 0.6595 - lr: 0.0010\n",
      "Epoch 14/25\n",
      "1562/1562 [==============================] - ETA: 0s - loss: 0.8103 - accuracy: 0.7186\n",
      "Epoch 14: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 0.8103 - accuracy: 0.7186 - val_loss: 1.0118 - val_accuracy: 0.6612 - lr: 0.0010\n",
      "Epoch 15/25\n",
      "1562/1562 [==============================] - 107s 67ms/step - loss: 0.7186 - accuracy: 0.7540 - val_loss: 0.9642 - val_accuracy: 0.6781 - lr: 2.0000e-04\n",
      "Epoch 16/25\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 0.7059 - accuracy: 0.7552 - val_loss: 0.9641 - val_accuracy: 0.6791 - lr: 2.0000e-04\n",
      "Epoch 17/25\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 0.6978 - accuracy: 0.7578 - val_loss: 0.9762 - val_accuracy: 0.6751 - lr: 2.0000e-04\n",
      "Epoch 18/25\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 0.6949 - accuracy: 0.7572 - val_loss: 0.9764 - val_accuracy: 0.6773 - lr: 2.0000e-04\n",
      "Epoch 19/25\n",
      "1562/1562 [==============================] - ETA: 0s - loss: 0.6916 - accuracy: 0.7591\n",
      "Epoch 19: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 0.6916 - accuracy: 0.7591 - val_loss: 0.9725 - val_accuracy: 0.6781 - lr: 2.0000e-04\n",
      "Epoch 20/25\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 0.6708 - accuracy: 0.7689 - val_loss: 0.9659 - val_accuracy: 0.6799 - lr: 4.0000e-05\n",
      "Epoch 21/25\n",
      "1562/1562 [==============================] - 109s 68ms/step - loss: 0.6636 - accuracy: 0.7713 - val_loss: 0.9679 - val_accuracy: 0.6806 - lr: 4.0000e-05\n",
      "Epoch 22/25\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 0.6590 - accuracy: 0.7715 - val_loss: 0.9676 - val_accuracy: 0.6821 - lr: 4.0000e-05\n",
      "Epoch 23/25\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 0.6629 - accuracy: 0.7712 - val_loss: 0.9689 - val_accuracy: 0.6807 - lr: 4.0000e-05\n",
      "Epoch 24/25\n",
      "1562/1562 [==============================] - 108s 67ms/step - loss: 0.6628 - accuracy: 0.7709 - val_loss: 0.9696 - val_accuracy: 0.6813 - lr: 4.0000e-05\n",
      "Epoch 25/25\n",
      "1562/1562 [==============================] - ETA: 0s - loss: 0.6589 - accuracy: 0.7713\n",
      "Epoch 25: ReduceLROnPlateau reducing learning rate to 8.000000525498762e-06.\n",
      "1562/1562 [==============================] - 107s 67ms/step - loss: 0.6589 - accuracy: 0.7713 - val_loss: 0.9702 - val_accuracy: 0.6806 - lr: 4.0000e-05\n"
     ]
    }
   ],
   "source": [
    "history_simple_25_1 = simple_model.fit(train_ds_28, epochs=25,verbose=1, validation_data=test_ds_28, callbacks=[reduce_lr])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "osnUEH481sj7"
   },
   "source": [
    "Plotting the accuracy of the models.\n",
    "\n",
    "32x32x2 input on the baseline model results in a better accuracy although it takes way longer to train the model. The simplified model is the worse performing model as expected.\n",
    "\n",
    "All models seem to plateau after the 18 epoch mark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "anRF1mp4aj2w",
    "outputId": "e3e28258-358f-4e82-c2d7-9fe7d80158ce"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_25_28_1.history['val_accuracy'])\n",
    "plt.plot(history_25_32_1.history['val_accuracy'])\n",
    "plt.plot(history_simple_25_1.history['val_accuracy'])\n",
    "plt.title('Model Accuracy of different models')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['28x28','32x32', 'Simplified'], loc='upper left')\n",
    "plt.savefig('FinalEvaluationNoEarlyStop')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yXieqa4f2PKz"
   },
   "source": [
    "Plotting the loss function of all models being tested.\n",
    "\n",
    "The simplified model can be seen performing the worst although suprisingly it seems to be slowly improving. The model with 32x32x3 input seems to be performing initially better but then it starts overfitting significantly and faster than the model with 28x28x3 input.\n",
    "\n",
    "Both models follow the same trajectory when it comes to overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "VSJsh7r3p1dA",
    "outputId": "39c00665-e4e0-4469-9f29-f28966f90594"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_25_28_1.history['val_loss'])\n",
    "plt.plot(history_25_32_1.history['val_loss'])\n",
    "plt.plot(history_simple_25_1.history['val_loss'])\n",
    "plt.title('Model val_loss of different models')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['28x28','32x32', 'Simplified'], loc='upper left')\n",
    "plt.savefig('FinalEvaluationLossNoEarlyStop')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZQu_Qe1y2O1L"
   },
   "source": [
    "Here we are testing a modification on the baseline network. The modification is the addition of extra convolutional and Dense filters. The idea behind this modification is to observe whether this model results in extracting more features from the images.\n",
    "\n",
    "We can see that the new model has less parameters than the original architecture meaning that the increase depth may not result in a better performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OF9GLaiLTsC0",
    "outputId": "16c47f23-e314-4a87-9c21-af96520408e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_12 (Conv2D)          (None, 28, 28, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_12 (MaxPoolin  (None, 14, 14, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_12 (Bat  (None, 14, 14, 32)       128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv2d_13 (Conv2D)          (None, 14, 14, 48)        38448     \n",
      "                                                                 \n",
      " max_pooling2d_13 (MaxPoolin  (None, 7, 7, 48)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_13 (Bat  (None, 7, 7, 48)         192       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv2d_14 (Conv2D)          (None, 7, 7, 64)          76864     \n",
      "                                                                 \n",
      " max_pooling2d_14 (MaxPoolin  (None, 3, 3, 64)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_14 (Bat  (None, 3, 3, 64)         256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " flatten_7 (Flatten)         (None, 576)               0         \n",
      "                                                                 \n",
      " dropout_17 (Dropout)        (None, 576)               0         \n",
      "                                                                 \n",
      " dense_17 (Dense)            (None, 256)               147712    \n",
      "                                                                 \n",
      " dropout_18 (Dropout)        (None, 256)               0         \n",
      "                                                                 \n",
      " dense_18 (Dense)            (None, 128)               32896     \n",
      "                                                                 \n",
      " dropout_19 (Dropout)        (None, 128)               0         \n",
      "                                                                 \n",
      " dense_19 (Dense)            (None, 84)                10836     \n",
      "                                                                 \n",
      " dropout_20 (Dropout)        (None, 84)                0         \n",
      "                                                                 \n",
      " dense_20 (Dense)            (None, 10)                850       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 310,614\n",
      "Trainable params: 310,326\n",
      "Non-trainable params: 288\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "deep_model = Sequential()\n",
    "\n",
    "deep_model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(5,5), padding='same', activation='relu', input_shape=(28, 28, 3)))\n",
    "deep_model.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "deep_model.add(tf.keras.layers.BatchNormalization())\n",
    "deep_model.add(tf.keras.layers.Conv2D(filters=48, kernel_size=(5,5), padding='same', activation='relu'))\n",
    "deep_model.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "deep_model.add(tf.keras.layers.BatchNormalization())\n",
    "deep_model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=(5,5), padding='same', activation='relu'))\n",
    "deep_model.add(tf.keras.layers.MaxPool2D(strides=2))\n",
    "deep_model.add(tf.keras.layers.BatchNormalization())\n",
    "deep_model.add(tf.keras.layers.Flatten())\n",
    "deep_model.add(tf.keras.layers.Dropout(0.2))\n",
    "deep_model.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "deep_model.add(tf.keras.layers.Dropout(0.2))\n",
    "deep_model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
    "deep_model.add(tf.keras.layers.Dropout(0.2))\n",
    "deep_model.add(tf.keras.layers.Dense(84, activation='relu'))\n",
    "deep_model.add(tf.keras.layers.Dropout(0.2))\n",
    "deep_model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "deep_model.compile(loss=sparse_categorical_crossentropy, optimizer=tf.optimizers.Adam(), metrics=['accuracy'])\n",
    "deep_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ullqNRZ63xbq"
   },
   "source": [
    "Training the network and keeping track of the accuracy and loss when using training and test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OD3bvC3B5pDk",
    "outputId": "b52c707d-5c99-43a3-ebf4-bddc92613011"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "1562/1562 [==============================] - 295s 187ms/step - loss: 1.5536 - accuracy: 0.4440 - val_loss: 1.2390 - val_accuracy: 0.5634 - lr: 0.0010\n",
      "Epoch 2/25\n",
      "1562/1562 [==============================] - 294s 186ms/step - loss: 1.1698 - accuracy: 0.5931 - val_loss: 0.9824 - val_accuracy: 0.6585 - lr: 0.0010\n",
      "Epoch 3/25\n",
      "1562/1562 [==============================] - 293s 186ms/step - loss: 0.9999 - accuracy: 0.6586 - val_loss: 0.8993 - val_accuracy: 0.6901 - lr: 0.0010\n",
      "Epoch 4/25\n",
      "1562/1562 [==============================] - 292s 185ms/step - loss: 0.8808 - accuracy: 0.7012 - val_loss: 0.8340 - val_accuracy: 0.7142 - lr: 0.0010\n",
      "Epoch 5/25\n",
      "1562/1562 [==============================] - 294s 186ms/step - loss: 0.8044 - accuracy: 0.7283 - val_loss: 0.8072 - val_accuracy: 0.7271 - lr: 0.0010\n",
      "Epoch 6/25\n",
      "1562/1562 [==============================] - 293s 186ms/step - loss: 0.7459 - accuracy: 0.7480 - val_loss: 0.7770 - val_accuracy: 0.7346 - lr: 0.0010\n",
      "Epoch 7/25\n",
      "1562/1562 [==============================] - 293s 186ms/step - loss: 0.6918 - accuracy: 0.7688 - val_loss: 0.7562 - val_accuracy: 0.7454 - lr: 0.0010\n",
      "Epoch 8/25\n",
      "1562/1562 [==============================] - 294s 186ms/step - loss: 0.6477 - accuracy: 0.7823 - val_loss: 0.7286 - val_accuracy: 0.7532 - lr: 0.0010\n",
      "Epoch 9/25\n",
      "1562/1562 [==============================] - 294s 186ms/step - loss: 0.6047 - accuracy: 0.7954 - val_loss: 0.7509 - val_accuracy: 0.7477 - lr: 0.0010\n",
      "Epoch 10/25\n",
      "1562/1562 [==============================] - 294s 186ms/step - loss: 0.5726 - accuracy: 0.8074 - val_loss: 0.7179 - val_accuracy: 0.7597 - lr: 0.0010\n",
      "Epoch 11/25\n",
      "1562/1562 [==============================] - 299s 189ms/step - loss: 0.5352 - accuracy: 0.8184 - val_loss: 0.7196 - val_accuracy: 0.7647 - lr: 0.0010\n",
      "Epoch 12/25\n",
      "1562/1562 [==============================] - 294s 186ms/step - loss: 0.5153 - accuracy: 0.8265 - val_loss: 0.7203 - val_accuracy: 0.7650 - lr: 0.0010\n",
      "Epoch 13/25\n",
      "1562/1562 [==============================] - 293s 186ms/step - loss: 0.4893 - accuracy: 0.8359 - val_loss: 0.7217 - val_accuracy: 0.7623 - lr: 0.0010\n",
      "Epoch 14/25\n",
      "1562/1562 [==============================] - 294s 186ms/step - loss: 0.4592 - accuracy: 0.8453 - val_loss: 0.7314 - val_accuracy: 0.7694 - lr: 0.0010\n",
      "Epoch 15/25\n",
      "1562/1562 [==============================] - 294s 186ms/step - loss: 0.4404 - accuracy: 0.8513 - val_loss: 0.7302 - val_accuracy: 0.7759 - lr: 0.0010\n",
      "Epoch 16/25\n",
      "1562/1562 [==============================] - 294s 187ms/step - loss: 0.4254 - accuracy: 0.8567 - val_loss: 0.7685 - val_accuracy: 0.7659 - lr: 0.0010\n",
      "Epoch 17/25\n",
      "1562/1562 [==============================] - 296s 187ms/step - loss: 0.4134 - accuracy: 0.8614 - val_loss: 0.7279 - val_accuracy: 0.7717 - lr: 0.0010\n",
      "Epoch 18/25\n",
      "1562/1562 [==============================] - ETA: 0s - loss: 0.3864 - accuracy: 0.8711\n",
      "Epoch 18: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "1562/1562 [==============================] - 294s 186ms/step - loss: 0.3864 - accuracy: 0.8711 - val_loss: 0.7501 - val_accuracy: 0.7739 - lr: 0.0010\n",
      "Epoch 19/25\n",
      "1562/1562 [==============================] - 294s 186ms/step - loss: 0.2931 - accuracy: 0.9013 - val_loss: 0.7484 - val_accuracy: 0.7932 - lr: 2.0000e-04\n",
      "Epoch 20/25\n",
      "1562/1562 [==============================] - 293s 186ms/step - loss: 0.2527 - accuracy: 0.9133 - val_loss: 0.7722 - val_accuracy: 0.7927 - lr: 2.0000e-04\n",
      "Epoch 21/25\n",
      "1562/1562 [==============================] - 305s 193ms/step - loss: 0.2362 - accuracy: 0.9188 - val_loss: 0.7725 - val_accuracy: 0.7939 - lr: 2.0000e-04\n",
      "Epoch 22/25\n",
      "1562/1562 [==============================] - 297s 188ms/step - loss: 0.2175 - accuracy: 0.9250 - val_loss: 0.8070 - val_accuracy: 0.7931 - lr: 2.0000e-04\n",
      "Epoch 23/25\n",
      "1562/1562 [==============================] - 293s 186ms/step - loss: 0.2100 - accuracy: 0.9292 - val_loss: 0.8181 - val_accuracy: 0.7942 - lr: 2.0000e-04\n",
      "Epoch 24/25\n",
      "1562/1562 [==============================] - 293s 185ms/step - loss: 0.2036 - accuracy: 0.9309 - val_loss: 0.8424 - val_accuracy: 0.7893 - lr: 2.0000e-04\n",
      "Epoch 25/25\n",
      "1562/1562 [==============================] - 294s 186ms/step - loss: 0.1908 - accuracy: 0.9351 - val_loss: 0.8488 - val_accuracy: 0.7928 - lr: 2.0000e-04\n"
     ]
    }
   ],
   "source": [
    "history_deep = deep_model.fit(train_ds_28, epochs=25,verbose=1, validation_data=test_ds_28, callbacks=[reduce_lr])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2dPZYH-N365i"
   },
   "source": [
    "We can see a significant improvement when it comes to the accuracy using testing data. This is important as it indicates that the extra depth result in better accuracy somethingh that we were concerned about due to the less number of parameters that the model has."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "hKjbd38tMdtM",
    "outputId": "23477991-2e15-435f-ec02-6a593980588e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_deep.history['accuracy'])\n",
    "plt.plot(history_deep.history['val_accuracy'])\n",
    "plt.title('Deep Model Accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XVRHk9mm6-_-"
   },
   "source": [
    "The model seems to be not performing as well as the other models when it comes to the loss function. The loss is actually higher than the other 2 models we have previously seen, baseline 32x32x3 and baseline 28x28x3. This may be due to the extra layers of the network that do not allow for the loss to propagate correctly through the network. Thus meaning that the model is not learning and performing well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "KMGwR3mjLphZ",
    "outputId": "d3f2d24a-2c4b-4a0a-8be8-ea591329aa98"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_deep.history['loss'])\n",
    "plt.plot(history_deep.history['val_loss'])\n",
    "plt.title('Deep Model Loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['loss', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "21COC102_B915247.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}