Keras Graph 断开连接无法获取张量 KerasTensor 的值

Question

张量流：2.4.0

这是完整的错误信息：

ValueError: Graph disconnected: cannot obtain value for tensor KerasTensor(type_spec=TensorSpec(shape=(None, 64, 64, 3), dtype=tf.float32, name='input_1'), name='input_1', description="created by layer 'input_1'") at layer "flatten". The following previous layers were accessed without issue: []

我一直在尝试制作一个可控的自动编码器，其中我有 10 个功能可以改变以获得图像（64x64 RGB）

而且我一直无法让它正常工作。我想将神经网络分成一个我可以拟合的完整模型和一个解码器，我可以在训练解析值后使用它来生成图像

顺便说一句，我知道这不是做自动编码器的完美方法，它只是我能想到的最简单的方法。

def Create_Generator(Image_Shape):
    Input_Layer = Input(shape=Image_Shape)
    Flatten_Layer1 = Flatten()(Input_Layer)
    Dense_Layer1 = Dense(12288,activation="relu")(Flatten_Layer1)
    Dense_Layer2 = Dense(6144,activation="relu")(Dense_Layer1)
    Dense_Layer3 = Dense(1024, activation="relu")(Dense_Layer2)

    Dense_Layer4 = Dense(10,activation="relu")(Dense_Layer3)

    Dense_Layer5 = Dense(1024, activation="relu")(Dense_Layer4)
    Dense_Layer6 = Dense(6144,activation="relu")(Dense_Layer5)
    Dense_Layer7 = Dense(12288,activation="relu")(Dense_Layer6)
    Reshape_Layer = Reshape(Image_Shape)(Dense_Layer7)

    AutoEncoder = Model(Input_Layer,Reshape_Layer)
    AutoEncoder.compile(optimizer='adam', loss ='binary_crossentropy')

    encoded_input = Input(shape=(10,))
    Decoder = Model([encoded_input,Dense_Layer5,Dense_Layer6,Dense_Layer7],Reshape_Layer)

    return AutoEncoder,Decoder

data = np.load("data.npz")
X_train = data['X']

AutoEncoder,Decoder = Create_Generator((64,64,3))

#Just for testing if it works
print(AutoEncoder.predict([X_train[0]]))
print(Decoder([[1,1,1,1,1,1,1,1,1,1]]))

Answer 1

我认为你这里有一个错误：

Decoder = Model([encoded_input,Dense_Layer5,Dense_Layer6,Dense_Layer7],Reshape_Layer)

Dense_Layer5、Dense_Layer6、Dense_Layer7 不是tf.keras.layers.Input。你不能这样创建解码器。