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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Retrieving speedtest.net configuration...\n",
"Testing from VNPT (14.177.252.75)...\n",
"Retrieving speedtest.net server list...\n",
"Selecting best server based on ping...\n",
"Hosted by Viettel IDC (Vinh) [261.88 km]: 10.311 ms\n",
"Testing download speed................................................................................\n",
"Download: 184.35 Mbit/s\n",
"Testing upload speed......................................................................................................\n",
"Upload: 202.71 Mbit/s\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"import h5py\n",
"\n",
"tf.get_logger().setLevel('ERROR')\n",
"!curl -s https://raw.githubusercontent.com/sivel/speedtest-cli/master/speedtest.py | python -"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"TRAIN_PATH = 'D:/UETCodeCamp/dataset/dataset/Images/Train'\n",
"VALIDATE_PATH = 'D:/UETCodeCamp/dataset/dataset/Images/Validate'\n",
"TEST_PATH = 'D:/UETCodeCamp/dataset/dataset/Images/Test'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"PATH = 'Models/ResNet152V2'\n",
"\n",
"BASE_MODEL_BEST = os.path.join(PATH, 'base_model_best.hdf5')\n",
"BASE_MODEL_TRAINED = os.path.join(PATH, 'base_model_trained.hdf5')\n",
"BASE_MODEL_FIG = os.path.join(PATH, 'base_model_fig.jpg')\n",
"\n",
"FINE_TUNE_MODEL_BEST = os.path.join(PATH, 'fine_tune_model_best.hdf5')\n",
"FINE_TUNE_MODEL_TRAINED = os.path.join(PATH, 'fine_tune_model_trained.hdf5')\n",
"FINE_TUNE_MODE_FIG = os.path.join(PATH, 'fine_tune_model_fig.jpg')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"IMAGE_SIZE = (300, 300)\n",
"BATCH_SIZE = 128"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"train_generator = ImageDataGenerator(\n",
" rescale = 1./255,\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",
")\n",
"validate_generator = ImageDataGenerator(rescale=1./255)\n",
"test_generator = ImageDataGenerator(rescale=1./255)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"d:\\UETCodeCamp\\Model\n"
]
}
],
"source": [
"print(os.getcwd())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 18751 images belonging to 38 classes.\n",
"Found 2757 images belonging to 38 classes.\n",
"Found 5169 images belonging to 38 classes.\n"
]
}
],
"source": [
"generated_train_data = train_generator.flow_from_directory(TRAIN_PATH, target_size=IMAGE_SIZE, batch_size=BATCH_SIZE)\n",
"generated_validate_data = validate_generator.flow_from_directory(VALIDATE_PATH, target_size=IMAGE_SIZE, batch_size=BATCH_SIZE)\n",
"generated_test_data = test_generator.flow_from_directory(TEST_PATH, target_size=IMAGE_SIZE)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"CLASSES = 38\n",
"INITIAL_EPOCHS = 15\n",
"FINE_TUNE_EPOCHS = 15\n",
"TOTAL_EPOCHS = INITIAL_EPOCHS + FINE_TUNE_EPOCHS\n",
"FINE_TUNE_AT = 516"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.applications.resnet_v2 import ResNet152V2\n",
"from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout\n",
"from tensorflow.keras.models import Model"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"pretrained_model = ResNet152V2(weights='imagenet', include_top=False)\n",
"last_output = pretrained_model.output\n",
"x = GlobalAveragePooling2D()(last_output)\n",
"x = Dense(512, activation='relu')(x)\n",
"x = Dropout(0.2)(x)\n",
"outputs = Dense(CLASSES, activation='softmax')(x)\n",
"model = Model(inputs=pretrained_model.input, outputs=outputs)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
"base_checkpointer = ModelCheckpoint(\n",
" filepath=BASE_MODEL_BEST.replace('.hdf5', '.keras'), \n",
" save_best_only=True, \n",
" verbose=1\n",
")\n",
"\n",
"fine_tune_checkpointer = ModelCheckpoint(\n",
" filepath=FINE_TUNE_MODEL_BEST.replace('.hdf5', '.keras'), \n",
" save_best_only=True,\n",
" verbose=1, \n",
")\n",
"\n",
"\n",
"# Stop if no improvement after 3 epochs\n",
"early_stopping = EarlyStopping(monitor='val_loss', patience=3, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.makedirs('Models/ResNet152V2', exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"for layer in pretrained_model.layers: layer.trainable = False\n",
"model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/15\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\VuongQuan14\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
" self._warn_if_super_not_called()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m 55/146\u001b[0m \u001b[32mβββββββ\u001b[0m\u001b[37mβββββββββββββ\u001b[0m \u001b[1m33:44\u001b[0m 22s/step - accuracy: 0.2474 - loss: 2.9693"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\VuongQuan14\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\PIL\\Image.py:1056: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24s/step - accuracy: 0.3447 - loss: 2.4622 \n",
"Epoch 1: val_loss improved from inf to 1.41906, saving model to Models/ResNet152V2\\base_model_best.keras\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m3788s\u001b[0m 26s/step - accuracy: 0.3453 - loss: 2.4589 - val_accuracy: 0.5796 - val_loss: 1.4191\n",
"Epoch 2/15\n",
"\u001b[1m 1/146\u001b[0m \u001b[37mββββββββββββββββββββ\u001b[0m \u001b[1m38:06\u001b[0m 16s/step - accuracy: 0.5469 - loss: 1.6239"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\VuongQuan14\\AppData\\Local\\Programs\\Python\\Python310\\lib\\contextlib.py:153: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
" self.gen.throw(typ, value, traceback)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Epoch 2: val_loss did not improve from 1.41906\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 59ms/step - accuracy: 0.5469 - loss: 1.6239 - val_accuracy: 0.5217 - val_loss: 1.6660\n",
"Epoch 3/15\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21s/step - accuracy: 0.5845 - loss: 1.3994 \n",
"Epoch 3: val_loss improved from 1.41906 to 1.29527, saving model to Models/ResNet152V2\\base_model_best.keras\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m3664s\u001b[0m 25s/step - accuracy: 0.5846 - loss: 1.3993 - val_accuracy: 0.6131 - val_loss: 1.2953\n",
"Epoch 4/15\n",
"\u001b[1m 1/146\u001b[0m \u001b[37mββββββββββββββββββββ\u001b[0m \u001b[1m1:11:06\u001b[0m 29s/step - accuracy: 0.5938 - loss: 1.2649\n",
"Epoch 4: val_loss improved from 1.29527 to 1.22025, saving model to Models/ResNet152V2\\base_model_best.keras\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 143ms/step - accuracy: 0.5938 - loss: 1.2649 - val_accuracy: 0.6667 - val_loss: 1.2203\n",
"Epoch 5/15\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21s/step - accuracy: 0.6200 - loss: 1.2513 \n",
"Epoch 5: val_loss improved from 1.22025 to 1.17931, saving model to Models/ResNet152V2\\base_model_best.keras\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m3524s\u001b[0m 24s/step - accuracy: 0.6201 - loss: 1.2513 - val_accuracy: 0.6481 - val_loss: 1.1793\n",
"Epoch 6/15\n",
"\u001b[1m 1/146\u001b[0m \u001b[37mββββββββββββββββββββ\u001b[0m \u001b[1m38:32\u001b[0m 16s/step - accuracy: 0.6484 - loss: 1.1963\n",
"Epoch 6: val_loss improved from 1.17931 to 1.05118, saving model to Models/ResNet152V2\\base_model_best.keras\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 95ms/step - accuracy: 0.6484 - loss: 1.1963 - val_accuracy: 0.6232 - val_loss: 1.0512\n",
"Epoch 7/15\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44s/step - accuracy: 0.6569 - loss: 1.1265 \n",
"Epoch 7: val_loss did not improve from 1.05118\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m8147s\u001b[0m 56s/step - accuracy: 0.6569 - loss: 1.1265 - val_accuracy: 0.6715 - val_loss: 1.1207\n",
"Epoch 8/15\n",
"\u001b[1m 1/146\u001b[0m \u001b[37mββββββββββββββββββββ\u001b[0m \u001b[1m4:56:50\u001b[0m 123s/step - accuracy: 0.7188 - loss: 0.9636\n",
"Epoch 8: val_loss did not improve from 1.05118\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m192s\u001b[0m 475ms/step - accuracy: 0.7188 - loss: 0.9636 - val_accuracy: 0.6667 - val_loss: 1.1370\n",
"Epoch 9/15\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20s/step - accuracy: 0.6758 - loss: 1.0515 \n",
"Epoch 9: val_loss did not improve from 1.05118\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m3502s\u001b[0m 23s/step - accuracy: 0.6758 - loss: 1.0515 - val_accuracy: 0.6864 - val_loss: 1.0649\n",
"Epoch 9: early stopping\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
}
],
"source": [
"history = model.fit(\n",
" generated_train_data,\n",
" validation_data = generated_validate_data,\n",
" validation_steps = generated_validate_data.n // BATCH_SIZE,\n",
" steps_per_epoch = generated_train_data.n // BATCH_SIZE,\n",
" callbacks = [base_checkpointer, early_stopping],\n",
" epochs = INITIAL_EPOCHS,\n",
" verbose = 1,\n",
")\n",
"model.save(BASE_MODEL_TRAINED)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(300, 300, 3)\n",
"{'Banh beo': 0, 'Banh bot loc': 1, 'Banh can': 2, 'Banh canh': 3, 'Banh chung': 4, 'Banh cuon': 5, 'Banh duc': 6, 'Banh gio': 7, 'Banh khot': 8, 'Banh mi': 9, 'Banh pia': 10, 'Banh tet': 11, 'Banh trang nuong': 12, 'Banh xeo': 13, 'Bun bo Hue': 14, 'Bun dau mam tom': 15, 'Bun mam': 16, 'Bun rieu': 17, 'Bun thit nuong': 18, 'BΓ‘nh cu ΔΖ‘': 19, 'BΓ‘nh mΓ¬ cay': 20, 'BΓ‘nh Δa cua': 21, 'BΓ‘nh ΔαΊu xanh': 22, 'BΓ² bΓa': 23, 'BΓΊn cΓ‘': 24, 'Ca kho to': 25, 'Canh chua': 26, 'Cao lau': 27, 'Chao long': 28, 'Com tam': 29, 'CΖ‘m chΓ‘y': 30, 'Goi cuon': 31, 'Hu tieu': 32, 'Mi quang': 33, 'Nem chua': 34, 'Nem nΖ°α»ng': 35, 'Pho': 36, 'Xoi xeo': 37}\n"
]
}
],
"source": [
"print(generated_train_data.image_shape)\n",
"print(generated_train_data.class_indices)\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"\n",
"for layer in pretrained_model.layers[:FINE_TUNE_AT]: layer.trainable = False\n",
"for layer in pretrained_model.layers[FINE_TUNE_AT:]: layer.trainable = True"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.optimizers import SGD\n",
"model.compile(\n",
" optimizer = SGD(learning_rate=1e-4, momentum=0.9), \n",
" loss = 'categorical_crossentropy', \n",
" metrics = ['accuracy']\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 9/30\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25s/step - accuracy: 0.5182 - loss: 1.7415 \n",
"Epoch 9: val_loss improved from inf to 1.11962, saving model to Models/ResNet152V2\\fine_tune_model_best.keras\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m4345s\u001b[0m 30s/step - accuracy: 0.5186 - loss: 1.7399 - val_accuracy: 0.6819 - val_loss: 1.1196\n",
"Epoch 10/30\n",
"\u001b[1m 1/146\u001b[0m \u001b[37mββββββββββββββββββββ\u001b[0m \u001b[1m1:22:53\u001b[0m 34s/step - accuracy: 0.7031 - loss: 1.0933\n",
"Epoch 10: val_loss did not improve from 1.11962\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 112ms/step - accuracy: 0.7031 - loss: 1.0933 - val_accuracy: 0.6667 - val_loss: 1.5291\n",
"Epoch 11/30\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33s/step - accuracy: 0.6629 - loss: 1.1745 \n",
"Epoch 11: val_loss improved from 1.11962 to 1.06462, saving model to Models/ResNet152V2\\fine_tune_model_best.keras\n",
"\u001b[1m146/146\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m5206s\u001b[0m 35s/step - accuracy: 0.6630 - loss: 1.1742 - val_accuracy: 0.6886 - val_loss: 1.0646\n",
"Epoch 11: early stopping\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
}
],
"source": [
"history_fine = model.fit(\n",
" generated_train_data,\n",
" validation_data = generated_validate_data,\n",
" validation_steps = generated_validate_data.n // BATCH_SIZE,\n",
" steps_per_epoch = generated_train_data.n // BATCH_SIZE,\n",
" epochs = TOTAL_EPOCHS,\n",
" initial_epoch = history.epoch[-1],\n",
" callbacks = [fine_tune_checkpointer, early_stopping],\n",
" verbose = 1,\n",
")\n",
"model.save(FINE_TUNE_MODEL_TRAINED)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m162/162\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m1063s\u001b[0m 7s/step - accuracy: 0.6897 - loss: 1.0454\n",
"Test accuracy: 0.6889146566390991\n"
]
}
],
"source": [
"loss, accuracy = model.evaluate(generated_test_data)\n",
"print('Test accuracy:', accuracy)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: scipy in c:\\users\\vuongquan14\\appdata\\roaming\\python\\python310\\site-packages (1.10.1)\n",
"Collecting scipy\n",
" Downloading scipy-1.14.0-cp310-cp310-win_amd64.whl.metadata (60 kB)\n",
"Requirement already satisfied: numpy<2.3,>=1.23.5 in c:\\users\\vuongquan14\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from scipy) (1.23.5)\n",
"Downloading scipy-1.14.0-cp310-cp310-win_amd64.whl (44.8 MB)\n",
" ---------------------------------------- 0.0/44.8 MB ? eta -:--:--\n",
" --------------------------------------- 1.0/44.8 MB 7.1 MB/s eta 0:00:07\n",
" ---- ----------------------------------- 5.0/44.8 MB 13.7 MB/s eta 0:00:03\n",
" ----------- ---------------------------- 12.6/44.8 MB 22.5 MB/s eta 0:00:02\n",
" ------------------ --------------------- 20.2/44.8 MB 26.0 MB/s eta 0:00:01\n",
" ------------------------ --------------- 27.0/44.8 MB 27.1 MB/s eta 0:00:01\n",
" ------------------------------ --------- 34.1/44.8 MB 28.1 MB/s eta 0:00:01\n",
" ------------------------------------- -- 41.7/44.8 MB 29.5 MB/s eta 0:00:01\n",
" ---------------------------------------- 44.8/44.8 MB 27.9 MB/s eta 0:00:00\n",
"Installing collected packages: scipy\n",
" Attempting uninstall: scipy\n",
" Found existing installation: scipy 1.10.1\n",
" Uninstalling scipy-1.10.1:\n",
" Successfully uninstalled scipy-1.10.1\n",
" Rolling back uninstall of scipy\n",
" Moving to c:\\users\\vuongquan14\\appdata\\roaming\\python\\python310\\site-packages\\scipy-1.10.1-cp310-cp310-win_amd64.whl\n",
" from C:\\Users\\VuongQuan14\\AppData\\Local\\Temp\\pip-uninstall-bxl8tsg7\\scipy-1.10.1-cp310-cp310-win_amd64.whl\n",
" Moving to c:\\users\\vuongquan14\\appdata\\roaming\\python\\python310\\site-packages\\scipy-1.10.1.dist-info\\\n",
" from C:\\Users\\VuongQuan14\\AppData\\Roaming\\Python\\Python310\\site-packages\\~cipy-1.10.1.dist-info\n",
" Moving to c:\\users\\vuongquan14\\appdata\\roaming\\python\\python310\\site-packages\\scipy.libs\\\n",
" from C:\\Users\\VuongQuan14\\AppData\\Roaming\\Python\\Python310\\site-packages\\~cipy.libs\n",
" Moving to c:\\users\\vuongquan14\\appdata\\roaming\\python\\python310\\site-packages\\scipy\\\n",
" from C:\\Users\\VuongQuan14\\AppData\\Roaming\\Python\\Python310\\site-packages\\~cipy\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: Ignoring invalid distribution -cipy (c:\\users\\vuongquan14\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n",
"WARNING: Ignoring invalid distribution -cipy (c:\\users\\vuongquan14\\appdata\\local\\programs\\python\\python310\\lib\\site-packages)\n",
"ERROR: Could not install packages due to an OSError: [WinError 5] Access is denied: 'c:\\\\Users\\\\VuongQuan14\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\Lib\\\\site-packages\\\\scipy\\\\linalg\\\\cython_blas.cp310-win_amd64.pyd'\n",
"Consider using the `--user` option or check the permissions.\n",
"\n"
]
}
],
"source": [
"pip install --upgrade scipy\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 5169 images belonging to 38 classes.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\VuongQuan14\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
" self._warn_if_super_not_called()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m41/41\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m883s\u001b[0m 22s/step\n",
" precision recall f1-score support\n",
"\n",
" Banh beo 0.84 0.71 0.77 129\n",
" Banh bot loc 0.60 0.61 0.60 144\n",
" Banh can 0.83 0.69 0.75 149\n",
" Banh canh 0.44 0.37 0.40 193\n",
" Banh chung 0.80 0.77 0.79 102\n",
" Banh cuon 0.70 0.68 0.69 228\n",
" Banh duc 0.41 0.20 0.27 133\n",
" Banh gio 0.75 0.81 0.78 129\n",
" Banh khot 0.69 0.83 0.76 167\n",
" Banh mi 0.92 0.91 0.91 268\n",
" Banh pia 0.86 0.84 0.85 89\n",
" Banh tet 0.83 0.73 0.78 138\n",
"Banh trang nuong 0.90 0.75 0.82 159\n",
" Banh xeo 0.81 0.83 0.82 235\n",
" Bun bo Hue 0.54 0.68 0.60 306\n",
" Bun dau mam tom 0.90 0.90 0.90 184\n",
" Bun mam 0.62 0.61 0.62 155\n",
" Bun rieu 0.54 0.68 0.60 231\n",
" Bun thit nuong 0.57 0.65 0.61 150\n",
" BΓ‘nh cu ΔΖ‘ 0.68 0.72 0.70 18\n",
" BÑnh mì cay 0.82 0.64 0.72 14\n",
" BΓ‘nh Δa cua 0.50 0.21 0.30 14\n",
" BΓ‘nh ΔαΊu xanh 0.91 0.59 0.71 17\n",
" BΓ² bΓa 0.57 0.42 0.48 19\n",
" BΓΊn cΓ‘ 0.00 0.00 0.00 13\n",
" Ca kho to 0.86 0.86 0.86 136\n",
" Canh chua 0.62 0.63 0.62 165\n",
" Cao lau 0.60 0.67 0.63 124\n",
" Chao long 0.71 0.73 0.72 215\n",
" Com tam 0.77 0.81 0.79 189\n",
" CΖ‘m chΓ‘y 0.81 0.72 0.76 18\n",
" Goi cuon 0.78 0.78 0.78 172\n",
" Hu tieu 0.46 0.38 0.42 197\n",
" Mi quang 0.55 0.75 0.63 177\n",
" Nem chua 0.67 0.55 0.61 109\n",
" Nem nΖ°α»ng 0.56 0.56 0.56 16\n",
" Pho 0.55 0.48 0.51 162\n",
" Xoi xeo 0.89 0.77 0.83 105\n",
"\n",
" accuracy 0.69 5169\n",
" macro avg 0.68 0.65 0.66 5169\n",
" weighted avg 0.69 0.69 0.69 5169\n",
"\n"
]
}
],
"source": [
"from sklearn.metrics import classification_report\n",
"import numpy as np\n",
"\n",
"\n",
"# Sα» dα»₯ng generator Δα» dα»± ΔoΓ‘n nhΓ£n cho dα»― liα»u kiα»m tra\n",
"generated_test_data = test_generator.flow_from_directory(TEST_PATH, target_size=IMAGE_SIZE, batch_size=BATCH_SIZE, shuffle=False)\n",
"\n",
"# Dα»± ΔoΓ‘n nhΓ£n\n",
"predictions = model.predict(generated_test_data)\n",
"y_pred = np.argmax(predictions, axis=1)\n",
"y_true = generated_test_data.classes\n",
"\n",
"# TΓnh toΓ‘n vΓ in ra cΓ‘c chα» sα»\n",
"class_labels = list(generated_test_data.class_indices.keys())\n",
"report = classification_report(y_true, y_pred, target_names=class_labels)\n",
"print(report)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
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"nbformat_minor": 2
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|