5.3-VGG19.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cb118c07",
   "metadata": {},
   "source": [
    "# **5. Transfer learning with VGG-19**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f2008ba0",
   "metadata": {},
   "source": [
    "In this notebook, we will train a VGG-19 model."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0a54bab9",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2ddb4e91",
   "metadata": {},
   "source": [
    "The main difference between VGG-16 and VGG-19 is the depth of the network. VGG-16 has 16 layers, while VGG-19 has 19 layers. The additional layers in VGG-19 are mostly additional convolutional layers, which increase the depth of the network and allow it to learn more complex features.\n",
    "\n",
    "However, the increased depth of VGG-19 also makes it more prone to overfitting and harder to train. The additional layers also increase the number of parameters in the network, which can lead to slower training and require more memory. While this model may perform better for some tasks that require more complex features or have a larger dataset, VGG-16 is the preferred version for many tasks because it strikes a good balance between depth and complexity. It has fewer layers and parameters than VGG-19, which makes it easier to train and less prone to overfitting. At the same time, it still has enough depth to learn complex features and perform well on a variety of tasks.\n",
    "\n",
    "**Source:** Simonyan, K., & Zisserman, A. (2014). [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556). arXiv preprint arXiv:1409.1556."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "56148681-04e5-4f5c-8865-a7cfaaea8bca",
   "metadata": {
    "id": "Rh87ovI1-TCv"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-02 11:50:01.327626: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
      "2023-04-02 11:50:01.327708: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
      "2023-04-02 11:50:01.327716: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "# Base libraries\n",
    "import os\n",
    "\n",
    "# Visualization libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# Tensorflow\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow import debugging as tfdbg\n",
    "from tensorflow import device\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",
    "# VGG-19\n",
    "from tensorflow.keras.applications import VGG19\n",
    "\n",
    "# Own modules\n",
    "from src.utils import drop_checkpoints, dataset_stats, plot_metric_curves, extract_features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2496f4b5",
   "metadata": {},
   "source": [
    "Now let's see how our data is structured:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "161c96b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Root folder\n",
    "base_dir = \"./data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f0323057",
   "metadata": {},
   "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": "7676c41b",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_checkpoints(base_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39825529",
   "metadata": {},
   "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": "c96280e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of classes: 4\n",
      "Existing classes: ['Renaissance', 'Realism', 'Baroque', 'Romanticism']\n",
      "\n",
      "----------------------------------------\n",
      "Number of images per class and dataset:\n",
      "----------------------------------------\n",
      "             Train  Validation  Test\n",
      "Style                               \n",
      "Renaissance   4000         500   500\n",
      "Realism       4000         500   500\n",
      "Baroque       4000         500   500\n",
      "Romanticism   4000         500   500\n"
     ]
    }
   ],
   "source": [
    "dataset_stats(train_dir, validation_dir, test_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d922941",
   "metadata": {},
   "source": [
    "We'll also create the directory, if not created yet, where the models will be saved:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "08160eba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create directory where to save the models created\n",
    "models_dir = \"./models\"\n",
    "os.makedirs(models_dir, exist_ok=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "76fbba10",
   "metadata": {},
   "source": [
    "# 5.1 Model structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "94fd0f60",
   "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 = (224, 224,  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}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3699316c",
   "metadata": {},
   "source": [
    "The first thing we will do is load the `VGG19` model, which is already included in the `keras` library. We will set `include_top=False` so that we can load just the convolutional part of the model, as we want to train our own DNN to perform the classification task. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cbdf3f8c-271d-4fea-8a4c-e2ad9137b2a5",
   "metadata": {
    "id": "35S_bt4t-TIX"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg19\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv4 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv4 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv4 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 20,024,384\n",
      "Trainable params: 20,024,384\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = models.Sequential()\n",
    "\n",
    "# initialize convolutional base for the model - based on VGG19\n",
    "conv_base = VGG19(weights='imagenet',\n",
    "                  include_top=False,\n",
    "                  pooling=None,\n",
    "                  input_shape=input_shape)\n",
    "\n",
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512b42e6",
   "metadata": {},
   "source": [
    "We can now create our DNN classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca883490",
   "metadata": {},
   "outputs": [],
   "source": [
    "last_layer_shape = (7, 7, 512)\n",
    "last_layer_size = last_layer_shape[0]*last_layer_shape[1]*last_layer_shape[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "95db56d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the model\n",
    "model = models.Sequential()\n",
    "\n",
    "model.add(layers.Dense(256, activation='relu', input_dim=last_layer_size))\n",
    "model.add(layers.Dropout(0.2))\n",
    "model.add(layers.Dense(128, activation='relu'))\n",
    "\n",
    "# Output layer\n",
    "model.add(layers.Dense(4, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bb432117",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 256)               6422784   \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               32896     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 4)                 516       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 6,456,196\n",
      "Trainable params: 6,456,196\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b07be938-fc08-49cd-9bf3-f2856aa8013b",
   "metadata": {
    "id": "DB3cPq2nLVaR"
   },
   "source": [
    "We'll use Adam as our optimizer since it is the most popular optimizer right now, as well as versatile (i.e., it can be used in multiple contexts)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6c73ae3a-6296-4aa5-bc43-a9988a33b39c",
   "metadata": {
    "id": "-PG_KXks-TMm"
   },
   "outputs": [],
   "source": [
    "optimizer = optimizers.Adam(learning_rate=1e-4)\n",
    "\n",
    "model.compile(\n",
    "    loss='categorical_crossentropy',\n",
    "    optimizer=optimizer,\n",
    "    metrics=['acc']\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bdbb1db3",
   "metadata": {},
   "source": [
    "# 5.2. Data preprocessing\n",
    "\n",
    "In this case, we will include the Data Augmentation step to the model preprocessing step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "69134f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply data augmentation to the training set\n",
    "# https://towardsdatascience.com/exploring-image-data-augmentation-with-keras-and-tensorflow-a8162d89b844\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    rotation_range=15,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    brightness_range=(0.8, 1),\n",
    "    zoom_range=[0.9, 1.1],\n",
    "    horizontal_flip=True,\n",
    "    fill_mode='nearest'\n",
    "    )\n",
    "\n",
    "# The data augmentation must not be used for the test set!\n",
    "# All images will be rescaled by 1./255\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "48c043b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 16000 images belonging to 4 classes.\n",
      "Found 2000 images belonging to 4 classes.\n",
      "Found 2000 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "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",
    "        )\n",
    "\n",
    "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",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c52c2c3f",
   "metadata": {},
   "source": [
    "We can now use `VGG19` to extract features from the images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "51ba3b9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 8s 97ms/step\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/studio-lab-user/.conda/envs/default/lib/python3.9/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",
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      "2/2 [==============================] - 0s 125ms/step\n",
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      "2/2 [==============================] - 0s 124ms/step\n",
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      "2/2 [==============================] - 0s 124ms/step\n",
      "2/2 [==============================] - 0s 123ms/step\n",
      "2/2 [==============================] - 0s 124ms/step\n",
      "2/2 [==============================] - 0s 125ms/step\n",
      "2/2 [==============================] - 0s 124ms/step\n",
      "2/2 [==============================] - 0s 125ms/step\n",
      "2/2 [==============================] - 0s 125ms/step\n",
      "2/2 [==============================] - 0s 123ms/step\n",
      "2/2 [==============================] - 0s 124ms/step\n",
      "2/2 [==============================] - 0s 124ms/step\n",
      "2/2 [==============================] - 0s 124ms/step\n",
      "2/2 [==============================] - 0s 125ms/step\n"
     ]
    }
   ],
   "source": [
    "train_features, train_labels = extract_features(16000, last_layer_shape, conv_base, train_generator, batch_size)\n",
    "validation_features, validation_labels = extract_features(2000, last_layer_shape, conv_base, validation_generator, batch_size)\n",
    "test_features, test_labels = extract_features(2000, last_layer_shape, conv_base, test_generator, 40)\n",
    "\n",
    "# Flatten features prior to feeding them to the classifier\n",
    "train_features = np.reshape(train_features, (16000, last_layer_size))\n",
    "validation_features = np.reshape(validation_features, (2000, last_layer_size))\n",
    "test_features = np.reshape(test_features, (2000, last_layer_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5e735994",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data batch shape: (128, 224, 224, 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": "74724be9",
   "metadata": {},
   "source": [
    "# 5.3. Training\n",
    "\n",
    "Let's train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4053f1b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model name and path\n",
    "model_path = os.path.join(\"models\", \"vgg19_frozen_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c75165a7",
   "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": 19,
   "id": "2e8cc890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "121/125 [============================>.] - ETA: 0s - loss: 1.2352 - acc: 0.4288\n",
      "Epoch 1: val_loss improved from inf to 1.11412, saving model to models/vgg19_frozen_model.h5\n",
      "125/125 [==============================] - 3s 12ms/step - loss: 1.2310 - acc: 0.4322 - val_loss: 1.1141 - val_acc: 0.5120\n",
      "Epoch 2/30\n",
      "119/125 [===========================>..] - ETA: 0s - loss: 1.0895 - acc: 0.5272\n",
      "Epoch 2: val_loss improved from 1.11412 to 1.07009, saving model to models/vgg19_frozen_model.h5\n",
      "125/125 [==============================] - 1s 9ms/step - loss: 1.0871 - acc: 0.5279 - val_loss: 1.0701 - val_acc: 0.5480\n",
      "Epoch 3/30\n",
      "121/125 [============================>.] - ETA: 0s - loss: 1.0263 - acc: 0.5605\n",
      "Epoch 3: val_loss improved from 1.07009 to 1.05192, saving model to models/vgg19_frozen_model.h5\n",
      "125/125 [==============================] - 1s 9ms/step - loss: 1.0255 - acc: 0.5613 - val_loss: 1.0519 - val_acc: 0.5550\n",
      "Epoch 4/30\n",
      "124/125 [============================>.] - ETA: 0s - loss: 0.9639 - acc: 0.5956\n",
      "Epoch 4: val_loss improved from 1.05192 to 0.98738, saving model to models/vgg19_frozen_model.h5\n",
      "125/125 [==============================] - 1s 9ms/step - loss: 0.9641 - acc: 0.5956 - val_loss: 0.9874 - val_acc: 0.5850\n",
      "Epoch 5/30\n",
      "120/125 [===========================>..] - ETA: 0s - loss: 0.9202 - acc: 0.6158\n",
      "Epoch 5: val_loss did not improve from 0.98738\n",
      "125/125 [==============================] - 1s 8ms/step - loss: 0.9224 - acc: 0.6149 - val_loss: 1.0032 - val_acc: 0.5730\n",
      "Epoch 6/30\n",
      "118/125 [===========================>..] - ETA: 0s - loss: 0.8707 - acc: 0.6440\n",
      "Epoch 6: val_loss did not improve from 0.98738\n",
      "125/125 [==============================] - 1s 8ms/step - loss: 0.8702 - acc: 0.6445 - val_loss: 0.9899 - val_acc: 0.5945\n",
      "Epoch 7/30\n",
      "120/125 [===========================>..] - ETA: 0s - loss: 0.8171 - acc: 0.6656\n",
      "Epoch 7: val_loss improved from 0.98738 to 0.96699, saving model to models/vgg19_frozen_model.h5\n",
      "125/125 [==============================] - 1s 9ms/step - loss: 0.8163 - acc: 0.6671 - val_loss: 0.9670 - val_acc: 0.6010\n",
      "Epoch 8/30\n",
      "121/125 [============================>.] - ETA: 0s - loss: 0.7619 - acc: 0.6952\n",
      "Epoch 8: val_loss did not improve from 0.96699\n",
      "125/125 [==============================] - 1s 8ms/step - loss: 0.7633 - acc: 0.6949 - val_loss: 0.9833 - val_acc: 0.5965\n",
      "Epoch 9/30\n",
      "121/125 [============================>.] - ETA: 0s - loss: 0.7162 - acc: 0.7176\n",
      "Epoch 9: val_loss did not improve from 0.96699\n",
      "125/125 [==============================] - 1s 8ms/step - loss: 0.7169 - acc: 0.7177 - val_loss: 0.9737 - val_acc: 0.6085\n",
      "Epoch 10/30\n",
      "122/125 [============================>.] - ETA: 0s - loss: 0.6758 - acc: 0.7379\n",
      "Epoch 10: val_loss did not improve from 0.96699\n",
      "125/125 [==============================] - 1s 8ms/step - loss: 0.6759 - acc: 0.7381 - val_loss: 0.9929 - val_acc: 0.6075\n",
      "Epoch 11/30\n",
      "120/125 [===========================>..] - ETA: 0s - loss: 0.6320 - acc: 0.7587\n",
      "Epoch 11: val_loss did not improve from 0.96699\n",
      "125/125 [==============================] - 1s 8ms/step - loss: 0.6293 - acc: 0.7594 - val_loss: 1.0176 - val_acc: 0.5980\n",
      "Epoch 12/30\n",
      "123/125 [============================>.] - ETA: 0s - loss: 0.5925 - acc: 0.7762\n",
      "Epoch 12: val_loss did not improve from 0.96699\n",
      "125/125 [==============================] - 1s 7ms/step - loss: 0.5920 - acc: 0.7769 - val_loss: 1.0738 - val_acc: 0.6040\n",
      "Epoch 12: early stopping\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_features, train_labels,\n",
    "    steps_per_epoch=steps_per_epoch,\n",
    "    epochs=30,\n",
    "    validation_data=(validation_features, validation_labels),\n",
    "    validation_steps=20,\n",
    "    callbacks = [es, mc]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0bdfab1",
   "metadata": {},
   "source": [
    "Now let's load the best model found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9948a2d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the saved model\n",
    "vgg19_model = load_model(model_path)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bba77e4f",
   "metadata": {},
   "source": [
    "# 5.4. 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": 21,
   "id": "963577df",
   "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": 22,
   "id": "a6bc513e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot loss curves\n",
    "plot_metric_curves(epochs, loss, val_loss, \"black\", \"darkgray\", \"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bf2b49d6",
   "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, \"black\", \"darkgray\", \"Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ec1ba70a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 3ms/step - loss: 0.9208 - acc: 0.6105\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.9208182692527771, 0.6104999780654907]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgg19_model.evaluate(test_features, test_labels)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "972bf181",
   "metadata": {},
   "source": [
    "The VGG-19 model exhibits less overfitting than the VGG-16 model, achieving approximately 75% accuracy on the training set at the expense of a slightly lower accuracy of 61% on the test set. These results are consistent with the performance of our models trained from scratch, and they underscore a key point we made in the model's introduction: selecting between VGG-16 and VGG-19 should be based on the specific task and dataset being used. In this case, since our dataset is relatively small, VGG-16 appears to be a better choice."
   ]
  }
 ],
 "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
}