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"!pip install tensorflow datasets tqdm typing-extensions==4.11.0 --quiet\n",
"\n",
"# 2. IMPORT LIBRARIES\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization, LeakyReLU\n",
"from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation\n",
"from tensorflow.keras.regularizers import l2\n",
"from tensorflow.keras.optimizers import Adam\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator,img_to_array\n",
"from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n",
"from sklearn.utils.class_weight import compute_class_weight\n",
"from datasets import load_dataset\n",
"from tqdm.auto import tqdm\n",
"from sklearn.preprocessing import LabelEncoder\n",
"import matplotlib.pyplot as plt\n"
],
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"base_uri": "https://localhost:8080/"
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.2/69.2 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m644.9/644.9 MB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m68.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.5/57.5 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.5/24.5 MB\u001b[0m \u001b[31m70.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.5/143.5 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m106.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.1/5.1 MB\u001b[0m \u001b[31m101.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m274.9/274.9 kB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m232.5/232.5 kB\u001b[0m \u001b[31m16.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.6/6.6 MB\u001b[0m \u001b[31m111.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.5/72.5 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m344.1/344.1 kB\u001b[0m \u001b[31m25.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"pydantic 2.10.6 requires typing-extensions>=4.12.2, but you have typing-extensions 4.11.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0m"
]
}
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},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "k8fTZRWiSyX1"
},
"outputs": [],
"source": [
"CONFIG = {\n",
" \"image_size\": (128, 128),\n",
" \"batch_size\": 64,\n",
" \"epochs\": 30,\n",
" \"num_train_samples\": 5000,\n",
" \"num_test_samples\": 1000,\n",
" \"learning_rate\": 3e-4,\n",
" \"weight_decay\": 1e-4,\n",
" \"early_stop_patience\": 10,\n",
" \"lr_patience\": 5,\n",
"\n",
"}"
]
},
{
"cell_type": "code",
"source": [
"def preprocess_image(sample):\n",
" \"\"\"Preprocess images for TensorFlow Dataset\"\"\"\n",
" img = sample['image'].convert(\"RGB\")\n",
" img = img.resize(CONFIG[\"image_size\"])\n",
"\n",
" img_array = np.array(img, dtype=np.float32) / 255.0\n",
"\n",
" return img_array, sample['label']\n",
"\n",
"def load_data_generator(split, le, batch_size=CONFIG[\"batch_size\"]):\n",
" \"\"\"Loads data as a generator using streaming\"\"\"\n",
" dataset = load_dataset(\"GVJahnavi/PlantVillage_dataset\", split=split, streaming=True)\n",
"\n",
" images, labels = [], []\n",
" count = 0 # Track the number of samples processed\n",
"\n",
" for sample in tqdm(dataset, desc=f\"Loading {split}\"):\n",
" try:\n",
" img_array, label = preprocess_image(sample)\n",
"\n",
" # Ensure image has correct shape (128, 128, 3)\n",
" if img_array.shape != (CONFIG[\"image_size\"][0], CONFIG[\"image_size\"][1], 3):\n",
" print(f\"Skipping image with wrong shape: {img_array.shape}\")\n",
" continue\n",
"\n",
" images.append(img_array)\n",
" labels.append(label)\n",
" count += 1\n",
"\n",
" # Yield batch\n",
" if len(images) == batch_size:\n",
" yield np.array(images), le.transform(np.array(labels))\n",
" images, labels = [], [] # Reset batch\n",
"\n",
" except Exception as e:\n",
" print(f\"Skipping image due to error: {e}\")\n",
" continue\n",
"\n",
" # Yield last batch if exists\n",
" if images:\n",
" yield np.array(images), le.transform(np.array(labels)) # Encode labels"
],
"metadata": {
"id": "ZgMPkjYzr94R"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# ==============================================\n",
"# Data Loading\n",
"# ==============================================\n",
"print(\"Loading data...\")\n",
"\n",
"# Initialize LabelEncoder\n",
"le = LabelEncoder()\n",
"\n",
"# Collect all labels to encode them\n",
"all_labels = []\n",
"dataset = load_dataset(\"GVJahnavi/PlantVillage_dataset\", split=\"train\", streaming=True)\n",
"for sample in tqdm(dataset, desc=\"Collecting labels\"):\n",
" try:\n",
" _, label = preprocess_image(sample)\n",
" all_labels.append(label)\n",
" except Exception as e:\n",
" print(f\"Skipping label due to error: {e}\")\n",
" continue\n",
"\n",
"le.fit(all_labels) # Fit on all labels\n",
"\n",
"# Split labels for train and test\n",
"train_labels = all_labels[:len(all_labels)//2]\n",
"test_labels = all_labels[len(all_labels)//2:]\n",
"\n",
"# Compute class weights\n",
"y_train = le.transform(train_labels)\n",
"class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)\n",
"class_weights = dict(enumerate(class_weights))\n",
"\n",
"# Initialize generators and consume them to load data\n",
"train_generator = list(load_data_generator(\"train\", le))\n",
"test_generator = list(load_data_generator(\"test\", le))\n",
"\n",
"\n",
"print(\"Data loading complete.\")\n"
],
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"1e90bbfe08054cd395cf330f5ebdb57f",
"e183b07566c44060a98d699dec31f19f",
"0d56c5aedfda4190b4b71d66dfe3be2c",
"b385e38a2d3b492f81535a19da9c10e7",
"8053de86bdbb4f53985fb9c9310f9301",
"2a7e45b8dd534e67a23577404d725b58",
"85ac73f124394664a17c9dfe940605f3",
"d84b85a8a85a4ad09cd2a76d878b51f6",
"5d0b465783eb4d979570e7780276a421",
"d00caeab260b476f8d36a206d116526d",
"da2dcd5cb02e4412b8536a1839d8adf5",
"e240cfa86ed74ea0ad3b7226530ff41f",
"69771c4b6a914764a2f367bcf59bea49",
"44fc02ca931641608193a34922977d08",
"834144da422b4a9495d0e5655714cbf9",
"5521f8014e854f1cbe3c93ecb2780af5"
]
},
"id": "bU4XWv-MTAUG",
"outputId": "cccc9488-3848-4a51-8ea8-033b47b1af42"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Loading data...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Collecting labels: 0it [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "69a62d46747e46ddad5261495a1c3790"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Loading train: 0it [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "2046fdcbaee744edaa528dd60fa31d21"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Loading test: 0it [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "2a7e45b8dd534e67a23577404d725b58"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Data loading complete.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# ==============================================\n",
"# Data Augmentation\n",
"# ==============================================\n",
"train_datagen = ImageDataGenerator(\n",
" rotation_range=40,\n",
" width_shift_range=0.2,\n",
" height_shift_range=0.2,\n",
" shear_range=0.2,\n",
" zoom_range=0.2,\n",
" horizontal_flip=True,\n",
" vertical_flip=True, # Added vertical flip\n",
" fill_mode='nearest'\n",
")\n",
"\n",
"val_datagen = ImageDataGenerator()\n"
],
"metadata": {
"id": "jxzXz9NoTCCT"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# ==============================================\n",
"# Enhanced Model Architecture\n",
"# ==============================================\n",
"def build_model():\n",
" model = Sequential()\n",
" input_shape = (*CONFIG[\"image_size\"], 3)\n",
"\n",
" # Conv Blocks\n",
" filters = [32, 64, 128, 256]\n",
" dropouts = [0.25, 0.3, 0.4, 0.5]\n",
"\n",
" for i, (f, dr) in enumerate(zip(filters, dropouts)):\n",
" # First block has input_shape\n",
" if i == 0:\n",
" model.add(Conv2D(f, (3,3), padding=\"same\", input_shape=input_shape,\n",
" kernel_regularizer=l2(CONFIG[\"weight_decay\"])) )\n",
" else:\n",
" model.add(Conv2D(f, (3,3), padding=\"same\", kernel_regularizer=l2(CONFIG[\"weight_decay\"])))\n",
"\n",
" model.add(LeakyReLU(alpha=0.1))\n",
" model.add(BatchNormalization())\n",
"\n",
" # Add second conv layer for deeper blocks\n",
" if i >= 1:\n",
" model.add(Conv2D(f, (3,3), padding=\"same\"))\n",
" model.add(LeakyReLU(alpha=0.1))\n",
" model.add(BatchNormalization())\n",
"\n",
" model.add(MaxPooling2D((2,2)))\n",
" model.add(Dropout(dr))\n",
"\n",
" # Classifier\n",
" model.add(Flatten())\n",
" model.add(Dense(1024, kernel_regularizer=l2(CONFIG[\"weight_decay\"])) )\n",
" model.add(LeakyReLU(alpha=0.1))\n",
" model.add(BatchNormalization())\n",
" model.add(Dropout(0.6))\n",
"\n",
" model.add(Dense(len(le.classes_), activation='softmax'))\n",
"\n",
" return model\n",
"\n",
"model = build_model()\n",
"model.summary()"
],
"metadata": {
"id": "_vJw4cBKXDT1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"collapsed": true,
"outputId": "1ee5d619-7e01-49af-91ad-4c96e2b0d0cb"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.11/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
"/usr/local/lib/python3.11/dist-packages/keras/src/layers/activations/leaky_relu.py:41: UserWarning: Argument `alpha` is deprecated. Use `negative_slope` instead.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"\u001b[1mModel: \"sequential\"\u001b[0m\n"
],
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
"</pre>\n"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"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;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\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;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\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;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu_1 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu_2 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\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_1 (\u001b[38;5;33mMaxPooling2D\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;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout_1 (\u001b[38;5;33mDropout\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;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ conv2d_3 (\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;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu_3 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ conv2d_4 (\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;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu_4 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\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_2 (\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;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout_2 (\u001b[38;5;33mDropout\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;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ conv2d_5 (\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;34m256\u001b[0m) │ \u001b[38;5;34m295,168\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu_5 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ conv2d_6 (\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;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu_6 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\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_3 (\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;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout_3 (\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;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m16,778,240\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu_7 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\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;34m38\u001b[0m) │ \u001b[38;5;34m38,950\u001b[0m │\n",
"└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
],
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
"│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_1 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_2 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_3 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_4 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_5 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_6 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16384</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16,778,240</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ leaky_re_lu_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_7 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">38</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">38,950</span> │\n",
"└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
"</pre>\n"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m17,988,006\u001b[0m (68.62 MB)\n"
],
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">17,988,006</span> (68.62 MB)\n",
"</pre>\n"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m17,984,102\u001b[0m (68.60 MB)\n"
],
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">17,984,102</span> (68.60 MB)\n",
"</pre>\n"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m3,904\u001b[0m (15.25 KB)\n"
],
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,904</span> (15.25 KB)\n",
"</pre>\n"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# ==============================================\n",
"# Training Setup\n",
"# ==============================================\n",
"optimizer = Adam(learning_rate=CONFIG[\"learning_rate\"],\n",
" weight_decay=CONFIG[\"weight_decay\"])\n",
"\n",
"model.compile(\n",
" loss=\"sparse_categorical_crossentropy\",\n",
" optimizer=optimizer,\n",
" metrics=[\"accuracy\", \"sparse_top_k_categorical_accuracy\"]\n",
")\n",
"\n",
"\n",
"callbacks = [\n",
" EarlyStopping(patience=CONFIG[\"early_stop_patience\"],\n",
" restore_best_weights=True,\n",
" monitor='val_accuracy'),\n",
" ReduceLROnPlateau(factor=0.5,\n",
" patience=CONFIG[\"lr_patience\"],\n",
" verbose=1),\n",
"]\n"
],
"metadata": {
"collapsed": true,
"id": "fXlZmUeOXPiQ"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# ==============================================\n",
"# Training Execution\n",
"# ==============================================\n",
"print(\"\\nTraining model...\")\n",
"\n",
"train_dataset = tf.data.Dataset.from_generator(\n",
" #lambda: load_data_generator(\"train\", le),\n",
" lambda: load_data_generator(\"train\", le),\n",
"\n",
" output_signature=(\n",
" tf.TensorSpec(shape=(None, CONFIG[\"image_size\"][0], CONFIG[\"image_size\"][1], 3), dtype=tf.float32),\n",
" tf.TensorSpec(shape=(None,), dtype=tf.int32)\n",
" )\n",
").prefetch(tf.data.AUTOTUNE)\n",
"\n",
"test_dataset = tf.data.Dataset.from_generator(\n",
" #lambda: load_data_generator(\"test\", le),\n",
" lambda: load_data_generator(\"test\", le),\n",
" output_signature=(\n",
" tf.TensorSpec(shape=(None, CONFIG[\"image_size\"][0], CONFIG[\"image_size\"][1], 3), dtype=tf.float32),\n",
" tf.TensorSpec(shape=(None,), dtype=tf.int32)\n",
" )\n",
").prefetch(tf.data.AUTOTUNE)\n",
"\n",
"steps_per_epoch = len(train_labels) // CONFIG[\"batch_size\"]\n",
"validation_steps = len(test_labels) // CONFIG[\"batch_size\"]\n",
"\n",
"history = model.fit(\n",
" train_dataset,\n",
" validation_data=test_dataset,\n",
" epochs=CONFIG[\"epochs\"],\n",
" steps_per_epoch=steps_per_epoch,\n",
" validation_steps=validation_steps,\n",
" callbacks=callbacks,\n",
" class_weight=class_weights,\n",
" verbose=1\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 118,
"referenced_widgets": [
"681d23e34e334f1b83e6afa9903fe72c",
"12434f0d1a33473997d752900043cfbc",
"8ba7ea8bfcc84228b7a8b00d27aaec62",
"ef28e650217649a6a09b18a870ccf9f3",
"029419d39b774759a3672b810e5f283d",
"c0c317b2311149bd96a5ed0f5d737411",
"a3837d4e34fa44199c15eee100991c51",
"8be0aef085f14db98c01055ac6377419",
"eac5b5ff64d14810928db28d72f66398",
"8528453a25df4b33acba942a4a74d6ed",
"a69e2de4b71349b0ba962751fe63ee1b"
]
},
"collapsed": true,
"id": "HLnYP2aqXRpn",
"outputId": "1968fa2d-922e-46a3-f492-3934d1272c98"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Training model...\n",
"Epoch 1/30\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Loading train: 0it [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "681d23e34e334f1b83e6afa9903fe72c"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m 22/339\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m52:12\u001b[0m 10s/step - accuracy: 0.0668 - loss: 11.2268 - sparse_top_k_categorical_accuracy: 0.2142"
]
}
]
},
{
"cell_type": "code",
"source": [
"# ==============================================\n",
"# Evaluation & Visualization\n",
"# ==============================================\n",
"plt.figure(figsize=(14, 5))\n",
"plt.subplot(1, 2, 1)\n",
"plt.plot(history.history['accuracy'], label='Train')\n",
"plt.plot(history.history['val_accuracy'], label='Validation')\n",
"plt.title('Model Accuracy')\n",
"plt.ylabel('Accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.legend()\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.plot(history.history['loss'], label='Train')\n",
"plt.plot(history.history['val_loss'], label='Validation')\n",
"plt.title('Model Loss')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.legend()\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 645
},
"id": "9AS1u6snYYHy",
"outputId": "cf27c0a9-a78f-4d70-896d-a07ae233a7e7"
},
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "KeyError",
"evalue": "'accuracy'",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-9566f84ef7d9>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Train'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Validation'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Model Accuracy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'accuracy'"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1400x500 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Final evaluation\n",
"results = model.evaluate(test_dataset, verbose=0)\n",
"print(f\"\\nFinal Metrics:\")\n",
"print(f\"Test Accuracy: {results[1]*100:.2f}%\")\n",
"print(f\"Top-3 Accuracy: {results[2]*100:.2f}%\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QGp4IlF0XU1f",
"outputId": "66bfcaa4-0b2e-4690-dc75-71ed57757ef6"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Final Metrics:\n",
"Test Accuracy: 86.50%\n",
"Top-3 Accuracy: 99.60%\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"model.save('PDD_completemodel.h5') # Save as HDF5 format for easy loading\n",
"\n",
"print(\"Model training and saving complete.\")"
],
"metadata": {
"id": "dy_M7QHwXW4Z"
},
"execution_count": null,
"outputs": []
}
]
} |