Question

在通过反卷积馈送该层之前，我在重塑该层时遇到了麻烦。我不知道如何逆转卷积平坦层。感谢您的帮助！

def build_deep_autoencoder(img_shape, code_size):
H,W,C = img_shape
encoder = keras.models.Sequential()
encoder.add(L.InputLayer(img_shape))
encoder.add(L.Conv2D(32, (3,3), padding = 'same', activation = 'elu', name='layer_1'))
encoder.add(L.MaxPooling2D((3,3), padding = 'same',name = 'max_pooling_1'))
encoder.add(L.Conv2D(64, (3,3), padding = 'same', activation = 'elu', name='layer_2'))
encoder.add(L.MaxPooling2D((3,3),padding = 'same',name = 'max_pooling_2'))
encoder.add(L.Conv2D(128, (3,3), padding = 'same', activation = 'elu', name='layer_3'))
encoder.add(L.MaxPooling2D((3,3),padding = 'same',name = 'max_pooling_3'))
encoder.add(L.Conv2D(256, (3,3), padding = 'same', activation = 'elu', name='layer_4'))
encoder.add(L.MaxPooling2D((3,3),padding = 'same',name = 'max_pooling_4'))

encoder.add(L.Flatten())
encoder.add(L.Dense(256))

# decoder
decoder = keras.models.Sequential()
decoder.add(L.InputLayer((code_size,)))
decoder.add(L.Dense(256))
decoder.add(L.Conv2DTranspose(filters=128, kernel_size=(3, 3), strides=2, activation='elu', padding='same'))
decoder.add(L.Conv2DTranspose(filters=64, kernel_size=(3, 3), strides=2, activation='elu', padding='same'))
decoder.add(L.Conv2DTranspose(filters=32, kernel_size=(3, 3), strides=2, activation='elu', padding='same'))
decoder.add(L.Conv2DTranspose(filters=3, kernel_size=(3, 3), strides=2, activation='none', padding='same'))



return encoder, decoder

Answer 1

在编码器中，使用以下命令代替添加密集的256层：

decoder.add(L.Dense(2*2*256))             #actual encoder 
decoder.add(L.Reshape((2,2,256)))         #un-flatten

输入0与层conv2d_transpose_1不兼容：预期ndim = 4，找到的ndim = 2

1 个答案: