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import os |
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from keras.models import Model |
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from tensorflow.keras.optimizers import Adam |
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from keras.applications.vgg16 import VGG16, preprocess_input |
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from keras.preprocessing.image import ImageDataGenerator |
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from keras.callbacks import ModelCheckpoint, EarlyStopping |
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from keras.layers import Dense, Dropout, Flatten |
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from pathlib import Path |
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import numpy as np |
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BATCH_SIZE = 64 |
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train_generator = ImageDataGenerator(rotation_range=90, |
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brightness_range=[0.1, 0.7], |
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width_shift_range=0.5, |
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height_shift_range=0.5, |
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horizontal_flip=True, |
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vertical_flip=True, |
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validation_split=0.15, |
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preprocessing_function=preprocess_input) |
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test_generator = ImageDataGenerator(preprocessing_function=preprocess_input) |
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train_data_dir = '/kaggle/input/pic-a-plant2/DBa4/DBa/train' |
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test_data_dir = '/kaggle/input/pic-a-plant2/DBa4/DBa/test' |
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class_subset = sorted(os.listdir(train_data_dir))[:] |
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traingen = train_generator.flow_from_directory(train_data_dir, |
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target_size=(150, 150), |
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class_mode='categorical', |
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classes=class_subset, |
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subset='training', |
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batch_size=BATCH_SIZE, |
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shuffle=True, |
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seed=42) |
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validgen = train_generator.flow_from_directory(train_data_dir, |
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target_size=(150, 150), |
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class_mode='categorical', |
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classes=class_subset, |
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subset='validation', |
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batch_size=BATCH_SIZE, |
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shuffle=True, |
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seed=42) |
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testgen = test_generator.flow_from_directory(test_data_dir, |
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target_size=(150, 150), |
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class_mode=None, |
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classes=class_subset, |
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batch_size=1, |
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shuffle=False, |
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seed=42) |
