diff --git a/1a_model_training.ipynb b/1a_model_training.ipynb new file mode 100644 index 00000000..d1ecbee2 --- /dev/null +++ b/1a_model_training.ipynb @@ -0,0 +1,846 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 0. Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "from tensorflow.keras import Model\n", + "from tensorflow.keras.utils import to_categorical\n", + "from keras.layers import Dense\n", + "from keras.layers import GlobalAveragePooling2D\n", + "from keras.optimizers import Adam\n", + "from keras.optimizers import SGD\n", + "from tensorflow.keras.callbacks import ModelCheckpoint\n", + "\n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "from tensorflow.keras.applications import InceptionV3\n", + "from tensorflow.image import resize" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Data Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Data loading\n", + "def unpickle(file):\n", + " with open(file, 'rb') as fo:\n", + " dict = pickle.load(fo, encoding='bytes')\n", + " return dict\n", + "\n", + "batch_1 = unpickle(\"data/data_batch_1\")\n", + "batch_2 = unpickle(\"data/data_batch_2\")\n", + "batch_3 = unpickle(\"data/data_batch_3\")\n", + "batch_4 = unpickle(\"data/data_batch_4\")\n", + "batch_5 = unpickle(\"data/data_batch_5\")\n", + "test_batch = unpickle(\"data/test_batch\")\n", + "label_names = unpickle(\"data/batches.meta\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Turn the labels lists into np.arrays()\n", + "batch_1[b'labels'] = np.asarray(batch_1[b'labels'])\n", + "batch_2[b'labels'] = np.asarray(batch_2[b'labels'])\n", + "batch_3[b'labels'] = np.asarray(batch_3[b'labels'])\n", + "batch_4[b'labels'] = np.asarray(batch_4[b'labels'])\n", + "batch_5[b'labels'] = np.asarray(batch_5[b'labels'])\n", + "test_batch[b'labels'] = np.asarray(test_batch[b'labels'])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Reshape every image from (3073,) to (32,32,3)so we can see it with plt.imshow()\n", + "def reshape_transpose(batch):\n", + " images = batch[b\"data\"].reshape(10000, 3, 32, 32) # Because of how np.reshape works, this returns an array with np.shape=(10000,3,32,32)\n", + " images = images.transpose(0,2,3,1) # We transpose it so it has the correct np.shape=(10000,32,32,3) for plt.imshow()\n", + " return images\n", + "\n", + "images1 = reshape_transpose(batch_1)\n", + "images2 = reshape_transpose(batch_2)\n", + "images3 = reshape_transpose(batch_3)\n", + "images4 = reshape_transpose(batch_4)\n", + "images5 = reshape_transpose(batch_5)\n", + "test_images = reshape_transpose(test_batch)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize one image to check if it worked\n", + "n = 100\n", + "image = images1[n] # This returns the n-th image from images1\n", + "image_label_int = batch_1[b'labels'][n] # This returns the label of the n-th image from images1 as an int (eg: if n=0, a 6)\n", + "image_label_str = label_names[b'label_names'][image_label_int] # This takes the label of the n-th image from images1 and returns the corresponding str-label (eg: if n=0, 6 --> \"frog\")\n", + "\n", + "plt.imshow(image)\n", + "plt.title(f\"{image_label_str}\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Model Architecture" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare the data\n", + "num_classes = len(label_names[b'label_names']) # Length of the label_names list --> 10\n", + "sample_size = 10000\n", + "\n", + "# We don't need to do a train_test_split because the data is already split\n", + "X_train = images1[:sample_size]\n", + "y_train = batch_1[b'labels'][:sample_size]\n", + "X_test = test_images[:sample_size]\n", + "y_test = test_batch[b'labels'][:sample_size]\n", + "\n", + "# Convert labels to categorical. This way, every int [0:10] is a class and it won't be treated as continous\n", + "y_train = to_categorical(y_train, num_classes=num_classes)\n", + "y_test = to_categorical(y_test, num_classes=num_classes)\n", + "\n", + "# Scale images to the [0, 1] range\n", + "X_train = X_train.astype(\"float32\") / 255.0\n", + "X_test = X_test.astype(\"float32\") / 255.0" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv2d (Conv2D)                 │ (None, 32, 32, 32)     │           896 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization             │ (None, 32, 32, 32)     │           128 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_1 (Conv2D)               │ (None, 32, 32, 64)     │        18,496 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_1           │ (None, 32, 32, 64)     │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d (MaxPooling2D)    │ (None, 16, 16, 64)     │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_2 (Conv2D)               │ (None, 16, 16, 64)     │        36,928 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_2           │ (None, 16, 16, 64)     │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_3 (Conv2D)               │ (None, 16, 16, 128)    │        73,856 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_3           │ (None, 16, 16, 128)    │           512 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_1 (MaxPooling2D)  │ (None, 8, 8, 128)      │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout (Dropout)               │ (None, 8, 8, 128)      │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_4 (Conv2D)               │ (None, 8, 8, 128)      │       147,584 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_4           │ (None, 8, 8, 128)      │           512 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_5 (Conv2D)               │ (None, 8, 8, 256)      │       295,168 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_5           │ (None, 8, 8, 256)      │         1,024 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_2 (MaxPooling2D)  │ (None, 4, 4, 256)      │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_6 (Conv2D)               │ (None, 4, 4, 256)      │       590,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_6           │ (None, 4, 4, 256)      │         1,024 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (Dense)                   │ (None, 4, 4, 128)      │        32,896 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_1 (Dropout)             │ (None, 4, 4, 128)      │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (Dense)                 │ (None, 4, 4, 128)      │        16,512 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten (Flatten)               │ (None, 2048)           │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (Dense)                 │ (None, 10)             │        20,490 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m295,168\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m32,896\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m16,512\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m20,490\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 1,236,618 (4.72 MB)\n",
+       "
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 Trainable params: 1,234,762 (4.71 MB)\n",
+       "
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Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 756ms/step - accuracy: 0.2810 - loss: 2.0590 - precision: 0.4018 - recall: 0.0889 - val_accuracy: 0.1080 - val_loss: 2.5025 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n", + "Epoch 2/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 734ms/step - accuracy: 0.4603 - loss: 1.4773 - precision: 0.6139 - recall: 0.2786 - val_accuracy: 0.1150 - val_loss: 2.7252 - val_precision: 0.3333 - val_recall: 0.0010\n", + "Epoch 3/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 740ms/step - accuracy: 0.5463 - loss: 1.2512 - precision: 0.6887 - recall: 0.3836 - val_accuracy: 0.1430 - val_loss: 3.3741 - val_precision: 1.0000 - val_recall: 0.0020\n", + "Epoch 4/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 740ms/step - accuracy: 0.6045 - loss: 1.0983 - precision: 0.7313 - recall: 0.4583 - val_accuracy: 0.1510 - val_loss: 3.4151 - val_precision: 0.1786 - val_recall: 0.1120\n", + "Epoch 5/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 735ms/step - accuracy: 0.6521 - loss: 0.9519 - precision: 0.7696 - recall: 0.5411 - val_accuracy: 0.2580 - val_loss: 2.8401 - val_precision: 0.4356 - val_recall: 0.0710\n", + "Epoch 6/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 757ms/step - accuracy: 0.7001 - loss: 0.8288 - precision: 0.8005 - recall: 0.6058 - val_accuracy: 0.2740 - val_loss: 2.4089 - val_precision: 0.3744 - val_recall: 0.1520\n", + "Epoch 7/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 749ms/step - accuracy: 0.7251 - loss: 0.7574 - precision: 0.8130 - recall: 0.6404 - val_accuracy: 0.4990 - val_loss: 1.4410 - val_precision: 0.6188 - val_recall: 0.3360\n", + "Epoch 8/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 732ms/step - accuracy: 0.7624 - loss: 0.6693 - precision: 0.8404 - recall: 0.6866 - val_accuracy: 0.5460 - val_loss: 1.3411 - val_precision: 0.6262 - val_recall: 0.4690\n", + "Epoch 9/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 736ms/step - accuracy: 0.7907 - loss: 0.5848 - precision: 0.8557 - recall: 0.7331 - val_accuracy: 0.6610 - val_loss: 0.9995 - val_precision: 0.7256 - val_recall: 0.5950\n", + "Epoch 10/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 733ms/step - accuracy: 0.8230 - loss: 0.5049 - precision: 0.8769 - recall: 0.7744 - val_accuracy: 0.6850 - val_loss: 0.9444 - val_precision: 0.7575 - val_recall: 0.6060\n", + "Epoch 11/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 741ms/step - accuracy: 0.8462 - loss: 0.4491 - precision: 0.8879 - recall: 0.7997 - val_accuracy: 0.6800 - val_loss: 1.0902 - val_precision: 0.7389 - val_recall: 0.6340\n", + "Epoch 12/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 734ms/step - accuracy: 0.8763 - loss: 0.3568 - precision: 0.9115 - recall: 0.8459 - val_accuracy: 0.6850 - val_loss: 1.0762 - val_precision: 0.7349 - val_recall: 0.6460\n", + "Epoch 13/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 684ms/step - accuracy: 0.8974 - loss: 0.3012 - precision: 0.9235 - recall: 0.8707 - val_accuracy: 0.7150 - val_loss: 0.9568 - val_precision: 0.7550 - val_recall: 0.6840\n", + "Epoch 14/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 686ms/step - accuracy: 0.9134 - loss: 0.2464 - precision: 0.9315 - recall: 0.8920 - val_accuracy: 0.7000 - val_loss: 1.0705 - val_precision: 0.7355 - val_recall: 0.6730\n", + "Epoch 15/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 755ms/step - accuracy: 0.9109 - loss: 0.2599 - precision: 0.9263 - recall: 0.8942 - val_accuracy: 0.6740 - val_loss: 1.3079 - val_precision: 0.7039 - val_recall: 0.6610\n", + "Epoch 16/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 911ms/step - accuracy: 0.9201 - loss: 0.2208 - precision: 0.9358 - recall: 0.9063 - val_accuracy: 0.7030 - val_loss: 1.1156 - val_precision: 0.7266 - val_recall: 0.6830\n", + "Epoch 17/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 903ms/step - accuracy: 0.9351 - loss: 0.1763 - precision: 0.9472 - recall: 0.9242 - val_accuracy: 0.7030 - val_loss: 1.1314 - val_precision: 0.7372 - val_recall: 0.6900\n", + "Epoch 18/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 857ms/step - accuracy: 0.9535 - loss: 0.1446 - precision: 0.9597 - recall: 0.9467 - val_accuracy: 0.6790 - val_loss: 1.5365 - val_precision: 0.7030 - val_recall: 0.6580\n", + "Epoch 19/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 796ms/step - accuracy: 0.9567 - loss: 0.1278 - precision: 0.9614 - recall: 0.9521 - val_accuracy: 0.6910 - val_loss: 1.3849 - val_precision: 0.7064 - val_recall: 0.6690\n", + "Epoch 20/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 795ms/step - accuracy: 0.9538 - loss: 0.1363 - precision: 0.9596 - recall: 0.9482 - val_accuracy: 0.6880 - val_loss: 1.5019 - val_precision: 0.7086 - val_recall: 0.6810\n", + "Epoch 21/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 793ms/step - accuracy: 0.9582 - loss: 0.1217 - precision: 0.9622 - recall: 0.9532 - val_accuracy: 0.7240 - val_loss: 1.2496 - val_precision: 0.7484 - val_recall: 0.7170\n", + "Epoch 22/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 794ms/step - accuracy: 0.9625 - loss: 0.1181 - precision: 0.9669 - recall: 0.9564 - val_accuracy: 0.7190 - val_loss: 1.3571 - val_precision: 0.7360 - val_recall: 0.7080\n", + "Epoch 23/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 789ms/step - accuracy: 0.9673 - loss: 0.0959 - precision: 0.9710 - recall: 0.9641 - val_accuracy: 0.7050 - val_loss: 1.3307 - val_precision: 0.7196 - val_recall: 0.6800\n", + "Epoch 24/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 788ms/step - accuracy: 0.9633 - loss: 0.1008 - precision: 0.9672 - recall: 0.9612 - val_accuracy: 0.7020 - val_loss: 1.5014 - val_precision: 0.7140 - val_recall: 0.6890\n", + "Epoch 25/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 787ms/step - accuracy: 0.9709 - loss: 0.0825 - precision: 0.9727 - recall: 0.9698 - val_accuracy: 0.7160 - val_loss: 1.4416 - val_precision: 0.7253 - val_recall: 0.7050\n", + "Epoch 26/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 788ms/step - accuracy: 0.9641 - loss: 0.1067 - precision: 0.9672 - recall: 0.9609 - val_accuracy: 0.7100 - val_loss: 1.3118 - val_precision: 0.7274 - val_recall: 0.6990\n", + "Epoch 27/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 786ms/step - accuracy: 0.9748 - loss: 0.0772 - precision: 0.9784 - recall: 0.9722 - val_accuracy: 0.7170 - val_loss: 1.4018 - val_precision: 0.7265 - val_recall: 0.7120\n", + "Epoch 28/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 787ms/step - accuracy: 0.9770 - loss: 0.0694 - precision: 0.9783 - recall: 0.9739 - val_accuracy: 0.7050 - val_loss: 1.5717 - val_precision: 0.7160 - val_recall: 0.7010\n", + "Epoch 29/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 799ms/step - accuracy: 0.9719 - loss: 0.0802 - precision: 0.9738 - recall: 0.9710 - val_accuracy: 0.7240 - val_loss: 1.4672 - val_precision: 0.7344 - val_recall: 0.7080\n", + "Epoch 30/30\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 788ms/step - accuracy: 0.9761 - loss: 0.0667 - precision: 0.9786 - recall: 0.9744 - val_accuracy: 0.7410 - val_loss: 1.3495 - val_precision: 0.7551 - val_recall: 0.7340\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.compile(loss=\"categorical_crossentropy\", optimizer=Adam(learning_rate=0.001), metrics=[\"accuracy\", \"precision\", \"recall\"])\n", + "\n", + "batch_size = 128\n", + "epochs = 30\n", + "model_history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Model Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 37ms/step\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 48ms/step\n" + ] + } + ], + "source": [ + "y_train_pred = np.argmax(model.predict(X_train), axis=1) # Turn the predictions from a float to an int so they match the labels\n", + "y_test_pred = np.argmax(model.predict(X_test), axis=1) # Turn the predictions from a float to an int so they match the labels" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test loss: 1.4801626205444336\n", + "Test accuracy: 0.722100019454956\n", + "Test precision: 0.7344714403152466\n", + "Test recall: 0.7142000198364258\n" + ] + } + ], + "source": [ + "score = model.evaluate(X_test, y_test, verbose=0)\n", + "print(\"Test loss:\", score[0])\n", + "print(\"Test accuracy:\", score[1])\n", + "print(\"Test precision:\", score[2])\n", + "print(\"Test recall:\", score[3])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "conf_matrix_train = confusion_matrix(np.argmax(y_train, axis=1), y_train_pred)\n", + "conf_matrix_test = confusion_matrix(np.argmax(y_test, axis=1), y_test_pred)\n", + "\n", + "# Confusion matrix for train data\n", + "plt.figure(figsize=(10,8))\n", + "sns.heatmap(conf_matrix_train,\n", + " annot=True, fmt=\"d\",\n", + " cmap=\"Blues\",\n", + " xticklabels=label_names[b'label_names'],\n", + " yticklabels=label_names[b'label_names']\n", + " )\n", + "plt.xlabel(\"Predicted Labels\")\n", + "plt.ylabel(\"True Labels\")\n", + "plt.title(\"Confusion Matrix for TRAIN split\")\n", + "plt.show()\n", + "\n", + "# Confusion matrix for test data\n", + "plt.figure(figsize=(10,8))\n", + "sns.heatmap(conf_matrix_test,\n", + " annot=True,\n", + " fmt=\"d\",\n", + " cmap=\"Greens\",\n", + " xticklabels=label_names[b'label_names'],\n", + " yticklabels=label_names[b'label_names'],\n", + " )\n", + "plt.xlabel(\"Predicted Labels\")\n", + "plt.ylabel(\"True Labels\")\n", + "plt.title(\"Confusion Matrix for TEST split\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Save the trained model\n", + "pickle.dump(model, open(f\"trained_model_acc_{score[1]:.4f}.pkl\", \"wb\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Transfer Learning" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Preprocess the data to adapt it to the Inception model\n", + "X_train_inception= resize(X_train, (75,75))\n", + "X_test_inception = resize(X_test, (75,75))\n", + "\n", + "# Model parameters\n", + "input_shape_inception = X_train_inception[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract the Inception pre-trained model\n", + "base_model = InceptionV3(weights=\"imagenet\", include_top=False, input_shape=input_shape_inception) # weights='imagenet' loads the model trained on the ImageNet dataset. include_top=False stops the model from loading the top layer" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "base_model.trainable = False # Freeze the layers of the base model\n", + "\n", + "avg = GlobalAveragePooling2D()(base_model.output) # This layer transforms 2D into 1D, or from a Convolution layer to a Dense layer\n", + "output = Dense(num_classes, activation=\"softmax\")(avg) # This layer classifies the input into one of n_classes categories\n", + "\n", + "combined_model = Model(inputs=base_model.input, outputs=output) # Combining these layers to create the combined model of Inception with a new top layer" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a checkpoint to save the model while it's training\n", + "checkpoint = ModelCheckpoint(\n", + " filepath='model_epoch_{epoch:02d}_val_acc_{val_accuracy:.4f}.keras', # Path to save the model file\n", + " monitor='val_accuracy', # Metric to monitor\n", + " save_best_only=True, # Save only the best model (based on monitored metric)\n", + " mode='max', # Minimize the monitored metric (for 'val_loss', use 'min'; for accuracy, use 'max')\n", + " save_weights_only=False, # Whether to save the whole model or just the weights\n", + " verbose=0\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 702ms/step - accuracy: 0.3405 - loss: 24.9689 - precision: 0.3568 - recall: 0.3357 - val_accuracy: 0.4910 - val_loss: 11.6513 - val_precision: 0.4900 - val_recall: 0.4890\n", + "Epoch 2/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 625ms/step - accuracy: 0.5791 - loss: 8.1029 - precision: 0.5805 - recall: 0.5778 - val_accuracy: 0.4340 - val_loss: 16.5199 - val_precision: 0.4346 - val_recall: 0.4320\n", + "Epoch 3/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 574ms/step - accuracy: 0.5986 - loss: 8.5365 - precision: 0.6010 - recall: 0.5972 - val_accuracy: 0.4800 - val_loss: 14.2902 - val_precision: 0.4805 - val_recall: 0.4800\n", + "Epoch 4/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 577ms/step - accuracy: 0.6213 - loss: 7.6500 - precision: 0.6229 - recall: 0.6210 - val_accuracy: 0.4990 - val_loss: 14.0808 - val_precision: 0.4990 - val_recall: 0.4970\n", + "Epoch 5/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 556ms/step - accuracy: 0.6495 - loss: 7.1959 - precision: 0.6503 - recall: 0.6489 - val_accuracy: 0.4920 - val_loss: 15.7479 - val_precision: 0.4920 - val_recall: 0.4920\n", + "Epoch 6/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 576ms/step - accuracy: 0.6522 - loss: 7.5714 - precision: 0.6532 - recall: 0.6508 - val_accuracy: 0.5020 - val_loss: 17.4261 - val_precision: 0.5035 - val_recall: 0.5020\n", + "Epoch 7/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 578ms/step - accuracy: 0.6793 - loss: 6.2678 - precision: 0.6798 - recall: 0.6782 - val_accuracy: 0.5190 - val_loss: 17.0908 - val_precision: 0.5195 - val_recall: 0.5190\n", + "Epoch 8/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 553ms/step - accuracy: 0.6935 - loss: 5.9522 - precision: 0.6945 - recall: 0.6934 - val_accuracy: 0.4890 - val_loss: 19.4853 - val_precision: 0.4895 - val_recall: 0.4880\n", + "Epoch 9/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 549ms/step - accuracy: 0.6997 - loss: 6.2658 - precision: 0.7010 - recall: 0.6996 - val_accuracy: 0.4990 - val_loss: 19.1222 - val_precision: 0.4995 - val_recall: 0.4990\n", + "Epoch 10/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 556ms/step - accuracy: 0.7150 - loss: 5.6828 - precision: 0.7155 - recall: 0.7145 - val_accuracy: 0.4950 - val_loss: 19.6313 - val_precision: 0.4960 - val_recall: 0.4950\n" + ] + } + ], + "source": [ + "# Train the new top layer with a high learning rate\n", + "combined_model.compile(loss=\"categorical_crossentropy\", optimizer=Adam(learning_rate=0.2), metrics=[\"accuracy\", \"precision\", \"recall\"])\n", + "\n", + "batch_size_inception = 128\n", + "epochs_inception = 10\n", + "history = combined_model.fit(X_train_inception,\n", + " y_train,\n", + " batch_size=batch_size_inception,\n", + " epochs=epochs_inception,\n", + " validation_split=0.1,\n", + " callbacks=[checkpoint]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m158s\u001b[0m 2s/step - accuracy: 0.2631 - loss: 34.2797 - precision: 0.2648 - recall: 0.2602 - val_accuracy: 0.2560 - val_loss: 42.2205 - val_precision: 0.2568 - val_recall: 0.2550\n", + "Epoch 2/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m132s\u001b[0m 2s/step - accuracy: 0.4055 - loss: 5.8973 - precision: 0.4317 - recall: 0.3853 - val_accuracy: 0.2520 - val_loss: 16.7927 - val_precision: 0.2535 - val_recall: 0.2510\n", + "Epoch 3/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 2s/step - accuracy: 0.4891 - loss: 3.2371 - precision: 0.5478 - recall: 0.4376 - val_accuracy: 0.3450 - val_loss: 8.2355 - val_precision: 0.3557 - val_recall: 0.3390\n", + "Epoch 4/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 2s/step - accuracy: 0.5406 - loss: 2.3791 - precision: 0.6107 - recall: 0.4874 - val_accuracy: 0.4770 - val_loss: 4.7262 - val_precision: 0.5213 - val_recall: 0.4290\n", + "Epoch 5/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 2s/step - accuracy: 0.5928 - loss: 1.8556 - precision: 0.6766 - recall: 0.5174 - val_accuracy: 0.5040 - val_loss: 3.4504 - val_precision: 0.5695 - val_recall: 0.4300\n", + "Epoch 6/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 1s/step - accuracy: 0.6294 - loss: 1.4605 - precision: 0.6987 - recall: 0.5719 - val_accuracy: 0.5140 - val_loss: 2.8194 - val_precision: 0.5849 - val_recall: 0.4410\n", + "Epoch 7/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 1s/step - accuracy: 0.6900 - loss: 1.2480 - precision: 0.7706 - recall: 0.6094 - val_accuracy: 0.5120 - val_loss: 2.8009 - val_precision: 0.5821 - val_recall: 0.4500\n", + "Epoch 8/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 1s/step - accuracy: 0.7224 - loss: 1.0274 - precision: 0.7943 - recall: 0.6499 - val_accuracy: 0.5190 - val_loss: 3.6611 - val_precision: 0.5949 - val_recall: 0.4700\n", + "Epoch 9/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m126s\u001b[0m 2s/step - accuracy: 0.7483 - loss: 1.0686 - precision: 0.8104 - recall: 0.6952 - val_accuracy: 0.5310 - val_loss: 3.8063 - val_precision: 0.5955 - val_recall: 0.4740\n", + "Epoch 10/10\n", + "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m126s\u001b[0m 2s/step - accuracy: 0.7824 - loss: 0.8687 - precision: 0.8323 - recall: 0.7376 - val_accuracy: 0.5490 - val_loss: 4.2634 - val_precision: 0.6002 - val_recall: 0.4940\n" + ] + } + ], + "source": [ + "# Fine-tune the whole model\n", + "base_model.trainable = True # Unfreeze the layers of the base model\n", + "\n", + "for layer in base_model.layers[:150]: # Freeze only some layers of the base model to avoid overfitting\n", + " layer.trainable = False\n", + "\n", + "combined_model.compile(loss=\"categorical_crossentropy\", optimizer=Adam(learning_rate=0.0001), metrics=[\"accuracy\", \"precision\", \"recall\"]) # Use a low learning rate so the pre-trained weights don't change much\n", + "\n", + "history = combined_model.fit(X_train_inception,\n", + " y_train,\n", + " batch_size=batch_size_inception,\n", + " epochs=epochs_inception,\n", + " validation_split=0.1,\n", + " callbacks=[checkpoint]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "# score_inception = model.evaluate(X_test_inception, y_test, verbose=0)\n", + "# print(\"Test loss:\", score_inception[0])\n", + "# print(\"Test accuracy:\", score_inception[1])\n", + "# print(\"Test precision:\", score_inception[2])\n", + "# print(\"Test recall:\", score_inception[3])" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "# y_pred_inception = np.argmax(model.predict(X_test_inception), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# conf_matrix_inception = confusion_matrix(np.argmax(y_test, axis=1), y_test_pred)\n", + "\n", + "# plt.figure(figsize=(10,8))\n", + "# sns.heatmap(conf_matrix_inception,\n", + "# annot=True,\n", + "# fmt=\"d\",\n", + "# cmap=\"Greens\",\n", + "# xticklabels=label_names[b'label_names'],\n", + "# yticklabels=label_names[b'label_names'],\n", + "# )\n", + "# plt.xlabel(\"Predicted Labels\")\n", + "# plt.ylabel(\"True Labels\")\n", + "# plt.title(\"Confusion Matrix for TEST split\")\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Save the combined model\n", + "pickle.dump(combined_model, open(\"combined_model.pkl\", \"wb\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/1b_model_training_whole_dataset.py b/1b_model_training_whole_dataset.py new file mode 100644 index 00000000..f316aa40 --- /dev/null +++ b/1b_model_training_whole_dataset.py @@ -0,0 +1,211 @@ +# %% [markdown] +# # 0. Libraries + +# %% +import pickle +import numpy as np + +from tensorflow import keras +from tensorflow.keras import layers +from tensorflow.keras import Model +from tensorflow.keras.utils import to_categorical +from keras.layers import Dense +from keras.layers import GlobalAveragePooling2D +from keras.optimizers import Adam +from tensorflow.keras.callbacks import ModelCheckpoint + +from tensorflow.keras.applications import InceptionV3 +from tensorflow.image import resize + + +# %% [markdown] +# # 1. Data Preprocessing + +# %% +# Data loading +def unpickle(file): + with open(file, 'rb') as fo: + dict = pickle.load(fo, encoding='bytes') + return dict + +batch_1 = unpickle("data/data_batch_1") +batch_2 = unpickle("data/data_batch_2") +batch_3 = unpickle("data/data_batch_3") +batch_4 = unpickle("data/data_batch_4") +batch_5 = unpickle("data/data_batch_5") +test_batch = unpickle("data/test_batch") +label_names = unpickle("data/batches.meta") + +# %% +# Turn the labels lists into np.arrays() +batch_1[b'labels'] = np.asarray(batch_1[b'labels']) +batch_2[b'labels'] = np.asarray(batch_2[b'labels']) +batch_3[b'labels'] = np.asarray(batch_3[b'labels']) +batch_4[b'labels'] = np.asarray(batch_4[b'labels']) +batch_5[b'labels'] = np.asarray(batch_5[b'labels']) +test_batch[b'labels'] = np.asarray(test_batch[b'labels']) + +# %% +# Reshape every image from (3073,) to (32,32,3)so we can see it with plt.imshow() +def reshape_transpose(batch): + images = batch[b"data"].reshape(10000, 3, 32, 32) # Because of how np.reshape works, this returns an array with np.shape=(10000,3,32,32) + images = images.transpose(0,2,3,1) # We transpose it so it has the correct np.shape=(10000,32,32,3) for plt.imshow() + return images + +images1 = reshape_transpose(batch_1) +images2 = reshape_transpose(batch_2) +images3 = reshape_transpose(batch_3) +images4 = reshape_transpose(batch_4) +images5 = reshape_transpose(batch_5) +test_images = reshape_transpose(test_batch) + +# %% [markdown] +# # 2. Model Architecture + +# %% +# Prepare the data +num_classes = len(label_names[b'label_names']) # Length of the label_names list --> 10 + +# We don't need to do a train_test_split because the data is already split +X_train = np.concatenate((images1, images2, images3, images4, images5)) +y_train = np.concatenate((batch_1[b'labels'], batch_2[b'labels'], batch_3[b'labels'], batch_4[b'labels'], batch_5[b'labels'])) +X_test = test_images +y_test = test_batch[b'labels'] + +# Convert labels to categorical. This way, every int [0:10] is a class and it won't be treated as continous +y_train = to_categorical(y_train, num_classes=num_classes) +y_test = to_categorical(y_test, num_classes=num_classes) + +# Scale images to the [0, 1] range +X_train = X_train.astype("float32") / 255.0 +X_test = X_test.astype("float32") / 255.0 + + +# Model parameters +input_shape = X_train[0].shape # Shape of any image from any of the batches --> (32, 32, 3) + +# Model Architecture +model = keras.Sequential( + [ + keras.Input(shape=input_shape), + + layers.Conv2D(32, kernel_size=(3, 3), activation="relu", padding="same"), + layers.BatchNormalization(), + # layers.Dropout(0.4), + + layers.Conv2D(64, kernel_size=(3, 3), activation="relu", padding="same"), + layers.BatchNormalization(), + layers.MaxPooling2D(pool_size=(2, 2)), + # layers.Dropout(0.4), + + layers.Conv2D(64, kernel_size=(3, 3), activation="relu", padding="same"), + layers.BatchNormalization(), + # layers.Dropout(0.4), + + layers.Conv2D(128, kernel_size=(3, 3), activation="relu", padding="same"), + layers.BatchNormalization(), + layers.MaxPooling2D(pool_size=(2, 2)), + layers.Dropout(0.4), + + + layers.Conv2D(128, kernel_size=(3, 3), activation="relu", padding="same"), + layers.BatchNormalization(), + # layers.MaxPooling2D(pool_size=(2, 2)), + # layers.Dropout(0.4), + + + layers.Conv2D(256, kernel_size=(3, 3), activation="relu", padding="same"), + layers.BatchNormalization(), + layers.MaxPooling2D(pool_size=(2, 2)), + # layers.Dropout(0.4), + + + layers.Conv2D(256, kernel_size=(3, 3), activation="relu", padding="same"), + layers.BatchNormalization(), + # layers.MaxPooling2D(pool_size=(2, 2)), + # layers.Dropout(0.4), + + + layers.Dense(128, activation="relu"), + layers.Dropout(0.4), + + layers.Dense(128, activation="relu"), + # layers.Dropout(0.4), + + + layers.Flatten(), + + layers.Dense(num_classes, activation="softmax"), + ] +) + +model.compile(loss="categorical_crossentropy", optimizer=Adam(learning_rate=0.001), metrics=["accuracy", "precision", "recall"]) +# model.compile(loss="categorical_crossentropy", optimizer=SGD(learning_rate=0.001, momentum=0.9, nesterov=True), metrics=["accuracy", "precision", "recall"]) + +# %% +# Create a checkpoint to save the model while it's training +checkpoint = ModelCheckpoint( + filepath='model_epoch_{epoch:02d}_val_acc_{val_accuracy:.4f}.keras', # Path to save the model file + monitor='val_accuracy', # Metric to monitor (you can also use 'val_accuracy' or other metrics) + save_best_only=True, # Save only the best model (based on monitored metric) + mode='max', # Minimize the monitored metric (for 'val_loss', use 'min'; for accuracy, use 'max') + save_weights_only=False, # Whether to save the whole model or just the weights + verbose=0 # Display info when saving +) + +# %% +batch_size = 128 +epochs = 30 +model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1, callbacks=[checkpoint]) + +#%% +pickle.dump(model, open(f"trained_model_whole_dataset.pkl", "wb")) +# %% [markdown] +# # 5. Transfer Learning + +# %% +# Preprocess the data to adapt it to the Inception model +X_train_inception= resize(X_train, (75,75)) +X_test_inception = resize(X_test, (75,75)) + +# Model parameters +input_shape_inception = X_train_inception[0].shape + +# %% +# Extract the Inception pre-trained model +base_model = InceptionV3(weights="imagenet", include_top=False, input_shape=input_shape_inception) # weights='imagenet' loads the model trained on the ImageNet dataset. include_top=False stops the model from loading the first layer + +# %% +base_model.trainable = False # Freeze the layers of the base model + +avg = GlobalAveragePooling2D()(base_model.output) # This layer transforms 2D into 1D, or from a Convolution layer to a Dense layer +output = Dense(num_classes, activation="softmax")(avg) # This layer classifies the input into one of n_classes categories + +combined_model = Model(inputs=base_model.input, outputs=output) # Combining these layers to create the combined model of Inception with a new top layer + + + +# %% +# Train the new top layer with a high learning rate +combined_model.compile(loss="categorical_crossentropy", optimizer=Adam(learning_rate=0.2), metrics=["accuracy", "precision", "recall"]) + +batch_size_inception = 128 +epochs_inception = 30 +history = combined_model.fit(X_train_inception, y_train, batch_size=batch_size_inception, epochs=epochs_inception, validation_split=0.1, callbacks=[checkpoint]) + +# %% +# Fine-tune the whole model +base_model.trainable = True # Unfreeze the layers of the base model + +for layer in base_model.layers[:150]: # Freeze only some layers of the base model to avoid overfitting + layer.trainable = False + +combined_model.compile(loss="categorical_crossentropy", optimizer=Adam(learning_rate=0.0001), metrics=["accuracy", "precision", "recall"]) + +history = combined_model.fit(X_train_inception, y_train, batch_size=batch_size_inception, epochs=epochs_inception, validation_split=0.1) + +# %% +# Save the combined model +pickle.dump(combined_model, open("combined_model_whole_dataset.pkl", "wb")) + + diff --git a/2_streamlit_interface.py b/2_streamlit_interface.py new file mode 100644 index 00000000..a09b4178 --- /dev/null +++ b/2_streamlit_interface.py @@ -0,0 +1,47 @@ +# %% +import streamlit as st +from PIL import Image +import numpy as np +import pickle +from tensorflow import keras + +# %% +def unpickle(file): + with open(file, 'rb') as fo: + dict = pickle.load(fo, encoding='bytes') + return dict + +label_names = unpickle("data/batches.meta") + +# %% +st.set_page_config(layout="wide", page_title="Image Classification") + +st.write("## Classify your image in one of 10 classes") +st.sidebar.write("## Upload and download") + +MAX_FILE_SIZE = 5 * 1024 * 1024 # 5MB + +def classify_image(upload): + image = Image.open(upload) + model = pickle.load(open("trained_model_whole_dataset.pkl", "rb")) + # image = np.pad(image, ((0, 0), (0, 0), (0, 3072 - (image.size[0]*image.size[1]) % 3072)), mode='constant') + # image = np.asarray(image).reshape(None, 3, 32, 32).transpose(0,2,3,1) + label = model.predict(np.expand_dims(np.asarray(image),axis=0)) + return np.argmax(label) + +col1, col2 = st.columns(2) +col1.write("Original Image") +col2.write("Label") +my_upload = st.sidebar.file_uploader("Upload an image", type=["png", "jpg", "jpeg"]) + +if my_upload is not None: + if my_upload.size > MAX_FILE_SIZE: + st.error("The uploaded file is too large. Please upload an image smaller than 5MB.") + else: + col1.image(my_upload, width=500) + label = classify_image(upload=my_upload) + col2.write(label_names[b'label_names'][label]) +else: + pass + + diff --git a/4_best_model_test.ipynb b/4_best_model_test.ipynb new file mode 100644 index 00000000..dd0dfaff --- /dev/null +++ b/4_best_model_test.ipynb @@ -0,0 +1,178 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use the best performing model to predict the labels of the test data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "from tensorflow.keras.utils import to_categorical\n", + "import pickle\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.metrics import confusion_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the CIFAR-10 dataset\n", + "def unpickle(file):\n", + " with open(file, 'rb') as fo:\n", + " dict = pickle.load(fo, encoding='bytes')\n", + " return dict\n", + "test_batch = unpickle(\"data/test_batch\")\n", + "label_names = unpickle(\"data/batches.meta\")\n", + "\n", + "# Reshape every image from (3073,) to (32,32,3)so we can see it with plt.imshow()\n", + "def reshape_transpose(batch):\n", + " images = batch[b\"data\"].reshape(10000, 3, 32, 32) # Because of how np.reshape works, this returns an array with np.shape=(10000,3,32,32)\n", + " images = images.transpose(0,2,3,1) # We transpose it so it has the correct np.shape=(10000,32,32,3) for plt.imshow()\n", + " return images\n", + "test_images = reshape_transpose(test_batch)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "X_test = test_images\n", + "y_test = test_batch[b'labels']\n", + "\n", + "# Convert labels to categorical. This way, every int [0:10] is a class and it won't be treated as continous\n", + "y_test = to_categorical(y_test, num_classes=10)\n", + "\n", + "# Scale images to the [0, 1] range\n", + "X_test = X_test.astype(\"float32\") / 255.0" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the best model\n", + "best_model = pickle.load(open(\"trained_model_whole_dataset.pkl\", \"rb\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 43ms/step\n" + ] + } + ], + "source": [ + "# Use the best model to predict the labels\n", + "y_pred = np.argmax(best_model.predict(X_test), axis=1) # Turn the predictions from a float to an int so they match the labels" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test loss: 0.7766907811164856\n", + "Test accuracy: 0.8285999894142151\n", + "Test precision: 0.8396689295768738\n", + "Test recall: 0.8216999769210815\n" + ] + } + ], + "source": [ + "score = best_model.evaluate(X_test, y_test, verbose=0)\n", + "print(\"Test loss:\", score[0])\n", + "print(\"Test accuracy:\", score[1])\n", + "print(\"Test precision:\", score[2])\n", + "print(\"Test recall:\", score[3])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "conf_matrix_test = confusion_matrix(np.argmax(y_test, axis=1), y_pred)\n", + "\n", + "# Confusion matrix for test data\n", + "plt.figure(figsize=(10,8))\n", + "sns.heatmap(conf_matrix_test,\n", + " annot=True,\n", + " fmt=\"d\",\n", + " cmap=\"Greens\",\n", + " xticklabels=label_names[b'label_names'],\n", + " yticklabels=label_names[b'label_names'],\n", + " )\n", + "plt.xlabel(\"Predicted Labels\")\n", + "plt.ylabel(\"True Labels\")\n", + "plt.title(\"Confusion Matrix for TEST split\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/trained_model_whole_dataset.pkl b/trained_model_whole_dataset.pkl new file mode 100644 index 00000000..cc8155b2 Binary files /dev/null and b/trained_model_whole_dataset.pkl differ