|
|
|
@ -29,13 +29,13 @@ class NeuralNetworkEncoder:
|
|
|
|
|
self.model = keras.Sequential([
|
|
|
|
|
layers.Reshape((512, 512, 1), input_shape=(262144,)),
|
|
|
|
|
#layers.InputLayer(input_shape=(512 * 512, 1, 1)),
|
|
|
|
|
layers.Conv2D(32, (3, 3), activation=internal_activation_function, padding='same'),
|
|
|
|
|
layers.MaxPooling2D((2, 2)),
|
|
|
|
|
layers.Conv2D(64, (3, 3), activation=internal_activation_function, padding='same'),
|
|
|
|
|
layers.Conv2D(256, (3, 3), activation=internal_activation_function, padding='same'),
|
|
|
|
|
layers.MaxPooling2D((2, 2)),
|
|
|
|
|
layers.Conv2D(128, (3, 3), activation=internal_activation_function, padding='same'),
|
|
|
|
|
layers.MaxPooling2D((2, 2)),
|
|
|
|
|
layers.Conv2D(256, (3, 3), activation=internal_activation_function, padding='same'),
|
|
|
|
|
layers.Conv2D(64, (3, 3), activation=internal_activation_function, padding='same'),
|
|
|
|
|
layers.MaxPooling2D((2, 2)),
|
|
|
|
|
layers.Conv2D(32, (3, 3), activation=internal_activation_function, padding='same'),
|
|
|
|
|
layers.Flatten(),
|
|
|
|
|
layers.Dense(64, activation=external_activation_function)
|
|
|
|
|
])
|
|
|
|
|