52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
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import numpy as np
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import tensorflow as tf
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from tensorflow import keras
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from keras import layers
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import matplotlib.pyplot as plt
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def normalize(image, label):
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return image / 255, label
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train_data_dir = "Training/"
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train_ds = tf.keras.utils.image_dataset_from_directory(train_data_dir,
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validation_split=0.2, subset="training", seed=123, batch_size=32,
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image_size=(100, 100), color_mode='grayscale')
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val_ds = tf.keras.utils.image_dataset_from_directory(train_data_dir,
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validation_split=0.2, subset="validation", seed=123, batch_size=32,
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image_size=(100, 100), color_mode='grayscale')
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class_names = train_ds.class_names
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print(class_names)
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train_ds = train_ds.map(normalize)
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val_ds = val_ds.map(normalize)
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model = keras.Sequential([
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layers.Conv2D(64, (3, 3), activation='relu', input_shape=(100, 100, 1)),
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layers.MaxPool2D((2, 2)),
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layers.Conv2D(128, (3, 3), activation='relu'),
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layers.MaxPool2D((2, 2)),
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layers.Conv2D(256, (3, 3), activation='relu'),
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layers.MaxPool2D((2, 2)),
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layers.Flatten(),
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layers.Dense(1024, activation='relu'),
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layers.Dense(1, activation='sigmoid')
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])
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print(model.summary())
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model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
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trainHistory = model.fit(train_ds, epochs=10, validation_data=val_ds)
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(loss, accuracy) = model.evaluate(val_ds)
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print(loss)
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print(accuracy)
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model.save("mode2.h5")
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