我正在尝试构建一个看起来像这样的模型：
input
/
convlayers/flatten
/ \
first_output \
\ /
second_output
但是在第一个转换层失败，并显示以下错误：
ValueError: Layer conv2d_4 was called with an input that isn't a symbolic tensor.
Received type: <class 'keras.layers.convolutional.Conv2D'>.
Full input: [<keras.layers.convolutional.Conv2D object at 0x7f450d7b8630>].
All inputs to the layer should be tensors.
，并且错误指向具有inputshape调用的第一个转换之后的层。
我们将不胜感激。
代码如下：
conv1 = Conv2D(8, 4, padding = "same", strides = 2)(inputs)
conv2 = Conv2D(16 ,4, padding = "same", strides = 2)(conv1)
flat = Flatten()(conv2)
dense1 = Dense(32)(flat)
dense2 = Dense(32)(dense1)
first_output = Dense(64)(dense2)
merged = concatenate([flat,first_output])
second_output_dense1 = Dense(32)(merged)
second_output_dense2 = Dense(32)(second_output_dense1)
second_output = Dense(64)(second_output_dense2)
model = Model(inputs=conv1, outputs=[first_output,second_output])
model.compile(loss = "mse", optimizer = "adam" )
答案：
我的印象是，您可以在没有输入层的情况下调用模型，而只需在第一层中定义输入：conv1 = Conv2D（8，4，padding =“ same”，步幅= 2， < em> input_shape =（6,8,8，） ）
但这没用，因此您必须删除输入形状的东西并创建输入层，这里是固定代码
inputs = Input(shape=(6,8,8,))
conv1 = Conv2D(8, 4, padding = "same", strides = 2, input_shape = (6,8,8,))
conv2 = Conv2D(16 ,4, padding = "same", strides = 2)(conv1)
flat = Flatten()(conv2)
dense1 = Dense(32)(flat)
dense2 = Dense(32)(dense1)
first_output = Dense(64)(dense2)
merged = concatenate([flat,first_output])
second_output_dense1 = Dense(32)(merged)
second_output_dense2 = Dense(32)(second_output_dense1)
second_output = Dense(64)(second_output_dense2)
model = Model(inputs=inputs, outputs=[first_output,second_output])
model.compile(loss = "mse", optimizer = "adam" )