3-Batch_normalization_model.ipynb

{
 "cells": [
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   "metadata": {
    "id": "168YUOMq-SrL"
   },
   "source": [
    "# **3. Model + Dropout and Batch Normalization**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6dd4764b",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "560d8267",
   "metadata": {},
   "source": [
    "Batch normalization is a regularization technique that is used to speed up training and reduce overfitting by scaling the inputs of each layer so that they have zero mean and unit variance. This allows the inputs to preserve the same distribution accross layers, thus making the training process more stable. In order to do this, the following steps are implemented:\n",
    "\n",
    "1. Calculate the mean and variance of the inputs over the current batch.\n",
    "2. Normalize the inputs (i.e.:¡, substract the mean and divide by the standard deviation).\n",
    "3. Scale and shift the normalizes inputs using two learnable parameters ($\\beta$ and $\\gamma$).\n",
    "\n",
    "<p>\n",
    "    <img src=\"docs/batch-norm.png\" width=\"600\" height=\"450\" />\n",
    "</p>\n",
    "\n",
    "Ketan Doshi. *[Batch Norm Explained Visually — How it works, and why neural networks need it, Towards Data Science](https://towardsdatascience.com/batch-norm-explained-visually-how-it-works-and-why-neural-networks-need-it-b18919692739)*\n",
    "\n",
    "**Sources**:\n",
    "\n",
    "+ Ioffe, S., & Szegedy, C. (2015). Batch Normalization: [Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167). arXiv preprint arXiv:1502.03167.\n",
    "+ Brownlee, J. (2019) [A gentle introduction to batch normalization for Deep Neural Networks, MachineLearningMastery.com](https://machinelearningmastery.com/batch-normalization-for-training-of-deep-neural-networks/) (Accessed: April 1, 2023).\n",
    "+ Riva, W.by: M. (2022) [Batch normalization in Convolutional Neural Networks, Baeldung on Computer Science.](https://www.baeldung.com/cs/batch-normalization-cnn) (Accessed: April 1, 2023)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ebd7e6-de01-4dd2-a211-099e327856c2",
   "metadata": {
    "id": "3w6rBQM0_o7T"
   },
   "source": [
    "First of all, let's load the required libraries in order to run the code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "56148681-04e5-4f5c-8865-a7cfaaea8bca",
   "metadata": {
    "id": "Rh87ovI1-TCv"
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   "outputs": [],
   "source": [
    "# Base libraries\n",
    "import os\n",
    "\n",
    "# Visualization libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Tensorflow\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras import models, layers\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "\n",
    "# Own modules\n",
    "from src.utils import drop_checkpoints, dataset_stats, plot_metric_curves"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee57621d-c6e3-489f-88ba-d5543ed70490",
   "metadata": {
    "id": "D8x-hAiaAblq"
   },
   "source": [
    "Now let's see how our data is structured:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7558b466-0276-47fa-820e-9b723fa381ea",
   "metadata": {
    "id": "pIfrW7zx-TEX"
   },
   "outputs": [],
   "source": [
    "# Root folder\n",
    "base_dir = \"./data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7622e546-7d82-4a94-a6dd-545a29235884",
   "metadata": {
    "id": "4A14qVrGMomZ"
   },
   "outputs": [],
   "source": [
    "# Train folder\n",
    "train_dir = os.path.join(base_dir, \"train\")\n",
    "\n",
    "# Validation folder\n",
    "validation_dir = os.path.join(base_dir, \"validation\")\n",
    "\n",
    "# Test folder\n",
    "test_dir = os.path.join(base_dir, \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ebe7c45c-b715-4217-b48b-97343c0f53a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_checkpoints(base_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8258082b-1c56-445c-a355-13a882a48c6a",
   "metadata": {
    "id": "FWvgTz7ejmF0"
   },
   "source": [
    "Let's also see how many images there are for each class in the training, validation and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "906904f6-fe43-4c22-92b9-e68621589e67",
   "metadata": {
    "id": "oGDiubz2jmOT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of classes: 4\n",
      "Existing classes: ['Baroque', 'Realism', 'Renaissance', 'Romanticism']\n",
      "\n",
      "----------------------------------------\n",
      "Number of images per class and dataset:\n",
      "----------------------------------------\n",
      "             Train  Validation  Test\n",
      "Style                               \n",
      "Baroque       4000         500   500\n",
      "Realism       4000         500   500\n",
      "Renaissance   4000         500   500\n",
      "Romanticism   4000         500   500\n"
     ]
    }
   ],
   "source": [
    "dataset_stats(train_dir, validation_dir, test_dir)"
   ]
  },
  {
   "attachments": {},
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   "id": "27d1584f-a8b2-4da6-bd86-f0969351a13e",
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   },
   "source": [
    "# 3.1. Model structure\n",
    "\n",
    "Let's first create the model structure. In this case, we will be implementing batch-normalization after each convolutional layer. Also, we will not be using dropout and batch normalization together in the convolutional part of the network, as the statistics used to normalize the inputs can potentially become noisy when both techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ae2dba80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Steps per epoch: 125\n"
     ]
    }
   ],
   "source": [
    "# Define some hyperparameters' values\n",
    "\n",
    "# Input shape\n",
    "input_shape = (256, 256,  3)\n",
    "\n",
    "# Batch_size and steps per epoch\n",
    "training_size = sum([len(file) for path, folder, file in os.walk(train_dir)])\n",
    "batch_size = 128\n",
    "steps_per_epoch = training_size // batch_size\n",
    "print(f\"Steps per epoch: {steps_per_epoch}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cbdf3f8c-271d-4fea-8a4c-e2ad9137b2a5",
   "metadata": {
    "id": "35S_bt4t-TIX"
   },
   "outputs": [],
   "source": [
    "model = models.Sequential()\n",
    "# 1st Convolution Layer\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu',\n",
    "                        input_shape=input_shape\n",
    "                        )\n",
    ")\n",
    "# 1st Pooling Layer\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.BatchNormalization())\n",
    "\n",
    "# 2nd Convolution Layer\n",
    "model.add(layers.Conv2D(64, \n",
    "                        (3, 3), \n",
    "                        activation='relu'\n",
    "                       )\n",
    "         )\n",
    "# 2nd Pooling Layer\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.BatchNormalization())\n",
    "\n",
    "# 3rd Convolution Layer\n",
    "model.add(layers.Conv2D(128, \n",
    "                        (3, 3), \n",
    "                        activation='relu'\n",
    "                        )\n",
    ")\n",
    "# 3rd Pooling Layer\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.BatchNormalization())\n",
    "\n",
    "# 4th Convolution Layer\n",
    "model.add(layers.Conv2D(128, \n",
    "                        (3, 3), \n",
    "                        activation='relu'\n",
    "                        )\n",
    ")\n",
    "# 4th Pooling Layer\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.BatchNormalization())\n",
    "\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation='relu'))\n",
    "model.add(layers.Dropout(0.5))\n",
    "\n",
    "#model.add(layers.Dense(128, activation='relu'))\n",
    "model.add(layers.Dense(4, activation='softmax'))"
   ]
  },
  {
   "attachments": {},
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   "id": "497274d2",
   "metadata": {},
   "source": [
    "As we can see, in this case we implemented batch normalization after each max-pooling layer of the CNN part of the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7886fd9f-20a3-4e37-9f37-dd0f70b39707",
   "metadata": {
    "id": "Zqn2mE-n-TKl"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 254, 254, 32)      896       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 127, 127, 32)     0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 127, 127, 32)     128       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 125, 125, 64)      18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 62, 62, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 62, 62, 64)       256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 60, 60, 128)       73856     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 30, 30, 128)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 30, 30, 128)      512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 28, 28, 128)       147584    \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 14, 14, 128)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 14, 14, 128)      512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 25088)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 512)               12845568  \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 512)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 4)                 2052      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 13,089,860\n",
      "Trainable params: 13,089,156\n",
      "Non-trainable params: 704\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b07be938-fc08-49cd-9bf3-f2856aa8013b",
   "metadata": {
    "id": "DB3cPq2nLVaR"
   },
   "source": [
    "Once again, we'll use Adam as our optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6c73ae3a-6296-4aa5-bc43-a9988a33b39c",
   "metadata": {
    "id": "-PG_KXks-TMm"
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss='categorical_crossentropy',\n",
    "    optimizer=optimizers.Adam(learning_rate=1e-4),\n",
    "    metrics=['acc']\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ead2b842-77e7-40d5-93c9-40eadf68d664",
   "metadata": {
    "id": "q3I-ZQfNMUej"
   },
   "source": [
    "### 1.2. Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "998fdbe7-8ccf-4408-a334-9f6607d80997",
   "metadata": {
    "id": "rF9cfUPS-TO1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 16000 images belonging to 4 classes.\n",
      "Found 2000 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "# All images will be rescaled by 1./255\n",
    "train_datagen = ImageDataGenerator(rescale=1./255)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        train_dir,\n",
    "        # All images will be resized to the dimensions specified\n",
    "        target_size=input_shape[:2],\n",
    "        batch_size=batch_size,\n",
    "        class_mode='categorical',\n",
    "        shuffle=True\n",
    "        )\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "        validation_dir,\n",
    "        # All images will be resized to the dimensions specified\n",
    "        target_size=input_shape[:2],\n",
    "        batch_size=batch_size,\n",
    "        class_mode='categorical',\n",
    "        shuffle=False\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f8ca1dc2-110f-4297-b605-2008fda45781",
   "metadata": {
    "id": "1Jf4AvO_-TRW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data batch shape: (128, 256, 256, 3)\n",
      "labels batch shape: (128, 4)\n"
     ]
    }
   ],
   "source": [
    "for data_batch, labels_batch in train_generator:\n",
    "    print('data batch shape:', data_batch.shape)\n",
    "    print('labels batch shape:', labels_batch.shape)\n",
    "    break"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c1b0b6e3",
   "metadata": {},
   "source": [
    "# 3.3. Training\n",
    "\n",
    "We can now train this improved version of our model and see if it improves performance upon the first version. We will still implement EarlyStopping to make sure we avoid *overfitting* as much as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5c502f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model name and path\n",
    "model_path = os.path.join(\"models\", \"batch_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "92816f76-414e-421e-9065-9248b20cd79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5)\n",
    "mc = ModelCheckpoint(model_path, monitor='val_loss', \n",
    "                     mode='min', verbose=1, save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7a895e76-e3be-4158-b61d-7f6af5b99901",
   "metadata": {
    "id": "wrYxrr1OQ6qH",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      " 81/125 [==================>...........] - ETA: 3:18 - loss: 1.9075 - acc: 0.4475"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\carlo\\anaconda3\\envs\\venv_art\\lib\\site-packages\\PIL\\TiffImagePlugin.py:858: UserWarning: Corrupt EXIF data.  Expecting to read 2 bytes but only got 0. \n",
      "  warnings.warn(str(msg))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125/125 [==============================] - ETA: 0s - loss: 1.7947 - acc: 0.4579\n",
      "Epoch 1: val_loss improved from inf to 1.40587, saving model to models\\dropout_model.h5\n",
      "125/125 [==============================] - 602s 5s/step - loss: 1.7947 - acc: 0.4579 - val_loss: 1.4059 - val_acc: 0.3906\n",
      "Epoch 2/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 1.0543 - acc: 0.6121\n",
      "Epoch 2: val_loss did not improve from 1.40587\n",
      "125/125 [==============================] - 541s 4s/step - loss: 1.0543 - acc: 0.6121 - val_loss: 1.5572 - val_acc: 0.3820\n",
      "Epoch 3/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.7342 - acc: 0.7201\n",
      "Epoch 3: val_loss did not improve from 1.40587\n",
      "125/125 [==============================] - 539s 4s/step - loss: 0.7342 - acc: 0.7201 - val_loss: 1.6743 - val_acc: 0.3773\n",
      "Epoch 4/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.5285 - acc: 0.8009\n",
      "Epoch 4: val_loss improved from 1.40587 to 1.34503, saving model to models\\dropout_model.h5\n",
      "125/125 [==============================] - 544s 4s/step - loss: 0.5285 - acc: 0.8009 - val_loss: 1.3450 - val_acc: 0.4617\n",
      "Epoch 5/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.3933 - acc: 0.8601\n",
      "Epoch 5: val_loss improved from 1.34503 to 1.03445, saving model to models\\dropout_model.h5\n",
      "125/125 [==============================] - 542s 4s/step - loss: 0.3933 - acc: 0.8601 - val_loss: 1.0345 - val_acc: 0.6234\n",
      "Epoch 6/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.2904 - acc: 0.8988\n",
      "Epoch 6: val_loss improved from 1.03445 to 1.00751, saving model to models\\dropout_model.h5\n",
      "125/125 [==============================] - 544s 4s/step - loss: 0.2904 - acc: 0.8988 - val_loss: 1.0075 - val_acc: 0.6578\n",
      "Epoch 7/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.2158 - acc: 0.9308\n",
      "Epoch 7: val_loss did not improve from 1.00751\n",
      "125/125 [==============================] - 541s 4s/step - loss: 0.2158 - acc: 0.9308 - val_loss: 1.0675 - val_acc: 0.6547\n",
      "Epoch 8/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.1643 - acc: 0.9517\n",
      "Epoch 8: val_loss did not improve from 1.00751\n",
      "125/125 [==============================] - 540s 4s/step - loss: 0.1643 - acc: 0.9517 - val_loss: 1.0614 - val_acc: 0.6570\n",
      "Epoch 9/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.1289 - acc: 0.9656\n",
      "Epoch 9: val_loss did not improve from 1.00751\n",
      "125/125 [==============================] - 540s 4s/step - loss: 0.1289 - acc: 0.9656 - val_loss: 1.0656 - val_acc: 0.6648\n",
      "Epoch 10/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.1055 - acc: 0.9722\n",
      "Epoch 10: val_loss did not improve from 1.00751\n",
      "125/125 [==============================] - 541s 4s/step - loss: 0.1055 - acc: 0.9722 - val_loss: 1.1035 - val_acc: 0.6641\n",
      "Epoch 11/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.0853 - acc: 0.9785\n",
      "Epoch 11: val_loss did not improve from 1.00751\n",
      "125/125 [==============================] - 526s 4s/step - loss: 0.0853 - acc: 0.9785 - val_loss: 1.0649 - val_acc: 0.6766\n",
      "Epoch 11: early stopping\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=steps_per_epoch,\n",
    "    epochs=30,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=10,\n",
    "    callbacks = [es, mc]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca0f815-2f2f-45b1-93c3-5c4120ac9a66",
   "metadata": {},
   "source": [
    "Now let's load the best model found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eb2f7580-54fa-441f-8a5a-041e3ba84462",
   "metadata": {
    "id": "R6ScrxYY-TTQ"
   },
   "outputs": [],
   "source": [
    "# load the saved model\n",
    "batch_model = load_model(model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ac2e2c-b0fc-49ed-b23d-f3a2439a71bb",
   "metadata": {
    "id": "JWuATgMUS8bI"
   },
   "source": [
    "### 1.4. Validation\n",
    "\n",
    "Let's plot how the loss and the accuracy from both training and validations sets have evolved during the training process. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3bb50f3d-f9db-4fa8-b395-b9c5337dbb2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a8f5fb83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot loss curves\n",
    "plot_metric_curves(epochs, loss, val_loss, \"darkred\", \"salmon\", \"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ba49185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot accuracy curves\n",
    "plot_metric_curves(epochs, acc, val_acc, \"darkred\", \"salmon\", \"Accuracy\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a9cf298b",
   "metadata": {},
   "source": [
    "It can be noticed that the model seems to overfit clearly, since there is a huge gap between the training and validation loss. The same happens with the accuracy (i.e, the training accuracy is clearly higher than the validation accuracy at every epoch). Therefore, we can conclude that in this case batch normalization does not seem to be helping to reduce overfitting. In fact, it seems to be making the model overfit even more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "91d735f4-0de1-4d11-9761-bb812d5cbcb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "test_generator = test_datagen.flow_from_directory(\n",
    "        test_dir,\n",
    "        target_size=input_shape[:2],\n",
    "        batch_size=40,\n",
    "        class_mode='categorical',\n",
    "        shuffle=False\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "95a15d3d-33f0-4f65-871d-bb7048c39494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 [==============================] - 47s 935ms/step - loss: 1.1297 - acc: 0.5905\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.129737377166748, 0.590499997138977]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_model.evaluate(test_generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6b59d53",
   "metadata": {},
   "source": [
    "As we can see, the level of dropout implemented was not enough to prevent overfitting from the model and only helped us marginally increase performance in the validation dataset."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "noest",
   "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": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]"
  },
  "vscode": {
   "interpreter": {
    "hash": "68f99e127082767cb10a28281b36d5e014efd97e4f53940f875fecf538dc5a7a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}