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from keras.models import Model |
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from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda |
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def simple_unet_model(IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS): |
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inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS)) |
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s = inputs |
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c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(s) |
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c1 = Dropout(0.1)(c1) |
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c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c1) |
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p1 = MaxPooling2D((2, 2))(c1) |
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c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1) |
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c2 = Dropout(0.1)(c2) |
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c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c2) |
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p2 = MaxPooling2D((2, 2))(c2) |
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c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p2) |
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c3 = Dropout(0.2)(c3) |
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c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c3) |
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p3 = MaxPooling2D((2, 2))(c3) |
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c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p3) |
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c4 = Dropout(0.2)(c4) |
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c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c4) |
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p4 = MaxPooling2D(pool_size=(2, 2))(c4) |
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c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p4) |
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c5 = Dropout(0.3)(c5) |
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c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c5) |
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u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5) |
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u6 = concatenate([u6, c4]) |
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c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u6) |
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c6 = Dropout(0.2)(c6) |
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c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6) |
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u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6) |
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u7 = concatenate([u7, c3]) |
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c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u7) |
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c7 = Dropout(0.2)(c7) |
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c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7) |
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u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7) |
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u8 = concatenate([u8, c2]) |
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c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u8) |
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c8 = Dropout(0.1)(c8) |
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c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8) |
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u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8) |
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u9 = concatenate([u9, c1], axis=3) |
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c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9) |
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c9 = Dropout(0.1)(c9) |
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c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9) |
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outputs = Conv2D(1, (1, 1), activation='sigmoid')(c9) |
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model = Model(inputs=[inputs], outputs=[outputs]) |
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model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) |
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model.summary() |
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return model |
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