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- model = Sequential()
- model.add(Conv2D(32, (3, 3), activation='relu', padding='same', name='conv_1',
- input_shape=(150, 150, 3)))
- model.add(MaxPooling2D((2, 2), name='maxpool_1'))
- model.add(Conv2D(64, (3, 3), activation='relu', padding='same', name='conv_2'))
- model.add(MaxPooling2D((2, 2), name='maxpool_2'))
- model.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='conv_3'))
- model.add(MaxPooling2D((2, 2), name='maxpool_3'))
- model.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='conv_4'))
- model.add(MaxPooling2D((2, 2), name='maxpool_4'))
- model.add(Flatten())
- model.add(Dropout(0.5))
- model.add(Dense(512, activation='relu', name='dense_1'))
- model.add(Dense(128, activation='relu', name='dense_2'))
- model.add(Dense(1, activation='sigmoid', name='output'))
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