2-Dropout_model.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f15229f7-7661-4a51-a3a1-c79c93a54765",
   "metadata": {
    "id": "168YUOMq-SrL"
   },
   "source": [
    "# **2. Dropout model**\n",
    "\n",
    "Our next goal is optimizing the base model so that we can reach higher accuracies and avoid overfitting as much as possible. We will first start by increasing the complexity of the base model (i.e., adding more layers), as well as incorporating regularization techniques to prevent us from overfitting to the training set. \n",
    "\n",
    "In this notebook we will explore the power of dropout for helping us reduce overfitting to the data. We will start by building a similar architecture to the one used by the base model, reusing the weights from the previously trained model. \n",
    "\n",
    "We will start by defining a dropout rate of 0.5 after the last fully connected layer. Since we will be increasing the number of samples by using data augmentation later on, using a higher penalty will probably hinder the training process and ultimately causing the model to meet early stopping criteria before converging."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6019276f",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "762f0512",
   "metadata": {},
   "source": [
    "## Dropout"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "6d79233c",
   "metadata": {},
   "source": [
    "Dropout is a regularization technique used to prevent overfitting during the training process, where some of the neurons in the CNN are randomly selected and their outputs are ignored during training. The percentage of neurons dropped depends on a certain probability, which we can indicate when building the model. This random dropout of neurons will force the model to find more robust by learning multiple independent features, as it cannot always rely on the presence of all neurons to extract features from the image.\n",
    "\n",
    "When applying the CNN to an inference problem, all neurons are used, but their outputs are scaled down by the same factor as the dropout probability used during training to ensure that the expected value of each neuron is the consistent with the training process.\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "**Sources**:\n",
    "\n",
    "+ Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](https://jmlr.org/papers/v15/srivastava14a.html). Journal of Machine Learning Research, 15(56), 1929-1958.\n",
    "+ [Hinton, G.E. et al. (no date) Improving neural networks by preventing co-adaptation of feature detectors, NASA/ADS](https://ui.adsabs.harvard.edu/abs/2012arXiv1207.0580H/abstract) (Accessed: April 1, 2023). \n",
    "+ [Brownlee, J. (2019) A gentle introduction to dropout for Regularizing Deep Neural Networks, MachineLearningMastery.com.](https://machinelearningmastery.com/dropout-for-regularizing-deep-neural-networks/) (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"
   },
   "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": {},
   "cell_type": "markdown",
   "id": "27d1584f-a8b2-4da6-bd86-f0969351a13e",
   "metadata": {
    "id": "ksRFFq2-WgBw"
   },
   "source": [
    "# 2.1. Model structure\n",
    "\n",
    "Let's first create the model structure:"
   ]
  },
  {
   "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",
    "# 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",
    "# 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",
    "# 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",
    "\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(4, activation='softmax'))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b97baed2-fd95-40b0-af7b-64946377cc76",
   "metadata": {
    "id": "R9Juyt3rLCKk"
   },
   "source": [
    "In this case, we have included a single dropout layer in the classifier part of the network, which is the one that typically converges faster and thus needs more regularization efforts."
   ]
  },
  {
   "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",
      " conv2d_1 (Conv2D)           (None, 125, 125, 64)      18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 62, 62, 64)       0         \n",
      " 2D)                                                             \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",
      " conv2d_3 (Conv2D)           (None, 28, 28, 128)       147584    \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 14, 14, 128)      0         \n",
      " 2D)                                                             \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: 13,088,452\n",
      "Trainable params: 13,088,452\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b07be938-fc08-49cd-9bf3-f2856aa8013b",
   "metadata": {
    "id": "DB3cPq2nLVaR"
   },
   "source": [
    "With respect to the optimizer, we will use the same one as before, Adam, with a learning rate of 0.0001."
   ]
  },
  {
   "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": [
    "# 2.2. Data preprocessing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "974494c6",
   "metadata": {},
   "source": [
    "The preprocessing steps are the same than those used in the base model:"
   ]
  },
  {
   "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": [
    "# 2.3. Training\n",
    "\n",
    "We can now train this modified version of our base 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, as well as `ModelCheckpoint` to save the best model weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5c502f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model name and path\n",
    "model_path = os.path.join(\"models\", \"dropout_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",
      "  1/125 [..............................] - ETA: 14:53 - loss: 1.3876 - acc: 0.2422"
     ]
    },
    {
     "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: 1.2702 - acc: 0.4207\n",
      "Epoch 1: val_loss improved from inf to 1.12679, saving model to models\\dropout_model.h5\n",
      "125/125 [==============================] - 644s 5s/step - loss: 1.2702 - acc: 0.4207 - val_loss: 1.1268 - val_acc: 0.5578\n",
      "Epoch 2/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 1.1268 - acc: 0.5178\n",
      "Epoch 2: val_loss improved from 1.12679 to 1.12130, saving model to models\\dropout_model.h5\n",
      "125/125 [==============================] - 549s 4s/step - loss: 1.1268 - acc: 0.5178 - val_loss: 1.1213 - val_acc: 0.5375\n",
      "Epoch 3/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 1.0612 - acc: 0.5519\n",
      "Epoch 3: val_loss improved from 1.12130 to 1.05073, saving model to models\\dropout_model.h5\n",
      "125/125 [==============================] - 540s 4s/step - loss: 1.0612 - acc: 0.5519 - val_loss: 1.0507 - val_acc: 0.5820\n",
      "Epoch 4/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 1.0289 - acc: 0.5646\n",
      "Epoch 4: val_loss improved from 1.05073 to 0.98134, saving model to models\\dropout_model.h5\n",
      "125/125 [==============================] - 542s 4s/step - loss: 1.0289 - acc: 0.5646 - val_loss: 0.9813 - val_acc: 0.6258\n",
      "Epoch 5/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.9942 - acc: 0.5803\n",
      "Epoch 5: val_loss improved from 0.98134 to 0.91010, saving model to models\\dropout_model.h5\n",
      "125/125 [==============================] - 546s 4s/step - loss: 0.9942 - acc: 0.5803 - val_loss: 0.9101 - val_acc: 0.6578\n",
      "Epoch 6/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.9560 - acc: 0.5992\n",
      "Epoch 6: val_loss did not improve from 0.91010\n",
      "125/125 [==============================] - 543s 4s/step - loss: 0.9560 - acc: 0.5992 - val_loss: 0.9162 - val_acc: 0.6469\n",
      "Epoch 7/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.9233 - acc: 0.6175\n",
      "Epoch 7: val_loss did not improve from 0.91010\n",
      "125/125 [==============================] - 543s 4s/step - loss: 0.9233 - acc: 0.6175 - val_loss: 0.9849 - val_acc: 0.6016\n",
      "Epoch 8/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.8930 - acc: 0.6318\n",
      "Epoch 8: val_loss did not improve from 0.91010\n",
      "125/125 [==============================] - 542s 4s/step - loss: 0.8930 - acc: 0.6318 - val_loss: 0.9837 - val_acc: 0.5945\n",
      "Epoch 9/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.8563 - acc: 0.6489\n",
      "Epoch 9: val_loss improved from 0.91010 to 0.86442, saving model to models\\dropout_model.h5\n",
      "125/125 [==============================] - 543s 4s/step - loss: 0.8563 - acc: 0.6489 - val_loss: 0.8644 - val_acc: 0.6680\n",
      "Epoch 10/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.8235 - acc: 0.6651\n",
      "Epoch 10: val_loss did not improve from 0.86442\n",
      "125/125 [==============================] - 544s 4s/step - loss: 0.8235 - acc: 0.6651 - val_loss: 0.9112 - val_acc: 0.6578\n",
      "Epoch 11/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.7727 - acc: 0.6909\n",
      "Epoch 11: val_loss did not improve from 0.86442\n",
      "125/125 [==============================] - 523s 4s/step - loss: 0.7727 - acc: 0.6909 - val_loss: 0.8851 - val_acc: 0.6523\n",
      "Epoch 12/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.7410 - acc: 0.7034\n",
      "Epoch 12: val_loss did not improve from 0.86442\n",
      "125/125 [==============================] - 542s 4s/step - loss: 0.7410 - acc: 0.7034 - val_loss: 0.8930 - val_acc: 0.6625\n",
      "Epoch 13/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.6954 - acc: 0.7254\n",
      "Epoch 13: val_loss did not improve from 0.86442\n",
      "125/125 [==============================] - 541s 4s/step - loss: 0.6954 - acc: 0.7254 - val_loss: 0.9408 - val_acc: 0.6219\n",
      "Epoch 14/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.6492 - acc: 0.7469\n",
      "Epoch 14: val_loss improved from 0.86442 to 0.85343, saving model to models\\dropout_model.h5\n",
      "125/125 [==============================] - 540s 4s/step - loss: 0.6492 - acc: 0.7469 - val_loss: 0.8534 - val_acc: 0.6781\n",
      "Epoch 15/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.6108 - acc: 0.7613\n",
      "Epoch 15: val_loss did not improve from 0.85343\n",
      "125/125 [==============================] - 524s 4s/step - loss: 0.6108 - acc: 0.7613 - val_loss: 1.0015 - val_acc: 0.6227\n",
      "Epoch 16/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.5453 - acc: 0.7963\n",
      "Epoch 16: val_loss did not improve from 0.85343\n",
      "125/125 [==============================] - 524s 4s/step - loss: 0.5453 - acc: 0.7963 - val_loss: 0.9712 - val_acc: 0.6266\n",
      "Epoch 17/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.4982 - acc: 0.8134\n",
      "Epoch 17: val_loss did not improve from 0.85343\n",
      "125/125 [==============================] - 524s 4s/step - loss: 0.4982 - acc: 0.8134 - val_loss: 1.0570 - val_acc: 0.5875\n",
      "Epoch 18/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.4365 - acc: 0.8406\n",
      "Epoch 18: val_loss did not improve from 0.85343\n",
      "125/125 [==============================] - 504s 4s/step - loss: 0.4365 - acc: 0.8406 - val_loss: 1.0077 - val_acc: 0.6336\n",
      "Epoch 19/30\n",
      "125/125 [==============================] - ETA: 0s - loss: 0.4040 - acc: 0.8536\n",
      "Epoch 19: val_loss did not improve from 0.85343\n",
      "125/125 [==============================] - 520s 4s/step - loss: 0.4040 - acc: 0.8536 - val_loss: 1.0595 - val_acc: 0.6211\n",
      "Epoch 19: 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",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0ca0f815-2f2f-45b1-93c3-5c4120ac9a66",
   "metadata": {},
   "source": [
    "Now let's save 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",
    "dropout_model = load_model(model_path)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "44ac2e2c-b0fc-49ed-b23d-f3a2439a71bb",
   "metadata": {
    "id": "JWuATgMUS8bI"
   },
   "source": [
    "### 2.4. 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": "92f2a0db",
   "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, \"navy\", \"deepskyblue\", \"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "328f0fcf",
   "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, \"navy\", \"deepskyblue\", \"Accuracy\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "29bcd14d",
   "metadata": {},
   "source": [
    "Looking at the above graphs we can see that Dropout indeed improved the training process. Compared to our base model, the evolution of the loss function in the validation dataset is much smoother, although peaks and valleys are still present. \n",
    "\n",
    "In this case, the validation accuracy is similar to the one obtained for the base model (over 80%) but we did not manage to significantly reduced overfitting, as the validation accuracy is similar to the one obtained in the base model.\n",
    "\n",
    "Let's now see how well this model performs with unseen data."
   ]
  },
  {
   "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": 23,
   "id": "95a15d3d-33f0-4f65-871d-bb7048c39494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 [==============================] - 49s 979ms/step - loss: 0.9610 - acc: 0.6220\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.9609625339508057, 0.621999979019165]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropout_model.evaluate(test_generator)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f6b59d53",
   "metadata": {},
   "source": [
    "Unfortunately, the level of dropout implemented was not enough to significantly improve the results in our separated test dataset, achieving similar results compared to the base model."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dl",
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}