调用多路输入 model 时。 我收到错误消息：无法计算 output KerasTensor

Question

尝试将嵌入合并到我的 GAN 错误时，我遇到了一个错误：

AssertionError: Could not compute output KerasTensor(type_spec=TensorSpec(shape=(None, 1), dtype=tf.float32, name=None), name='dense_270/BiasAdd:0', description="created by layer 'dense_270'")

我通过发送一个直接的 numpy 数组来玩这个代码。 这是没有嵌入的工作版本：

# Build Generative model ...
g_input = Input(shape=[Xtrain.shape[1]])
H1 = Dense(512)(g_input)
H1r = Activation('relu')(H1)
H2 = Dense(256)(H1r)
H2r = Activation('relu')(H2)
H3 = Dense(256)(H2r)
H3r = Activation('relu')(H3)
H4 = Dense(128)(H3r)
H4r = Activation('relu')(H4)
H5 = Dense(128)(H4r)

H5r = Activation('relu')(H5)
H6 = Dense(64)(H5r)
H6r = Activation('relu')(H6)
H7 = Dense(32)(H6r)
Hr = Activation('relu')(H7)
g_V = Dense(1)(Hr)

generator = Model(g_input,g_V)
generator.compile(loss='binary_crossentropy', optimizer=opt)
generator.summary()


# Build Discriminative model ...
d_input = Input(shape=[Xtrain.shape[1]+1])
H = Dense(256)(d_input)
H = Activation('relu')(H)
H = Dense(256)(H)
H = Activation('relu')(H)
H = Dense(64)(H)
H = Activation('relu')(H)
H = Dense(32)(H)
H = Activation('relu')(H)
d_V = Dense(2,activation='softmax')(H)
discriminator = Model(d_input,d_V)
discriminator.compile(loss='categorical_crossentropy', optimizer=dopt)
discriminator.summary()

# Freeze weights in the discriminator for stacked training
make_trainable(discriminator, False)

# Build stacked GAN model
gan_input = Input(shape=Xtrain.shape[1])
H = generator(gan_input)
gd_input=Concatenate()([gan_input,H])
gan_V = discriminator(gd_input)
GAN = Model(gan_input, [gan_V,H])
GAN.compile(loss=['categorical_crossentropy','mse'], optimizer=opt) #Complete GAN have both loss functions
GAN.summary()

然后，我将原始的 numpy 数组添加到每一行中，并添加了 2 个 numpy arrays ，所以它基本上是几个值，然后是一个数组。 像这样 -

[32,53, .43, [1,2,3,4],[5,6,7,8]]

为了在数据中使用这个 model，我做了以下更改：

# Build Generative model ...
g_input = Input(shape=[len(total_x[0])-2]) #have a array that's only a numpy array by excluding last 2 items)
np_input = Input(shape=[len(total_x[0][:-1])]) #include only the last arrray
mergedLayer = layers.concatenate([g_input, np_input]) #merge both 
H1 = Dense(512)(mergedLayer) #everything after is the same

然后我将生成器更改为接受两个输入而不是一个：

generator = Model([g_input,np_input],g_V)

但是后来我在这部分得到了上述错误：

H = generator(gan_input)

我是 ML 的新手，但我认为基于上述内容，也许我可以将它传递给一个连接层，但它不起作用（基本上与上面的逻辑相同）：

gan_dataframe_input = Input(shape=[Xtrain.shape[1]-2])
numpy_input = Input(shape=2) 
gan_input = layers.concatenate([gan_dataframe_input, numpy_input])
H = generator(gan_input)

我希望这是足够的信息来帮助或给我建议。 如果有帮助，这是完整的堆栈跟踪：

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-92-52760dc68480> in <module>()
     54 gan_input = layers.concatenate([gan_dataframe_input, numpy_input])
     55 
---> 56 H = generator(gan_dataframe_input)
     57 gd_input=Concatenate()([gan_input,H])
     58 gan_V = discriminator(gd_input)

5 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
    950     if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
    951       return self._functional_construction_call(inputs, args, kwargs,
--> 952                                                 input_list)
    953 
    954     # Maintains info about the `Layer.call` stack.

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
   1089         # Check input assumptions set after layer building, e.g. input shape.
   1090         outputs = self._keras_tensor_symbolic_call(
-> 1091             inputs, input_masks, args, kwargs)
   1092 
   1093         if outputs is None:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs)
    820       return nest.map_structure(keras_tensor.KerasTensor, output_signature)
    821     else:
--> 822       return self._infer_output_signature(inputs, args, kwargs, input_masks)
    823 
    824   def _infer_output_signature(self, inputs, args, kwargs, input_masks):

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _infer_output_signature(self, inputs, args, kwargs, input_masks)
    861           # TODO(kaftan): do we maybe_build here, or have we already done it?
    862           self._maybe_build(inputs)
--> 863           outputs = call_fn(inputs, *args, **kwargs)
    864 
    865         self._handle_activity_regularization(inputs, outputs)

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py in call(self, inputs, training, mask)
    423     """
    424     return self._run_internal_graph(
--> 425         inputs, training=training, mask=mask)
    426 
    427   def compute_output_shape(self, input_shape):

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py in _run_internal_graph(self, inputs, training, mask)
    567     for x in self.outputs:
    568       x_id = str(id(x))
--> 569       assert x_id in tensor_dict, 'Could not compute output ' + str(x)
    570       output_tensors.append(tensor_dict[x_id].pop())
    571 

AssertionError: Could not compute output KerasTensor(type_spec=TensorSpec(shape=(None, 1), dtype=tf.float32, name=None), name='dense_270/BiasAdd:0', description="created by layer 'dense_270'")

此外，如果它有帮助，这里是上面的代码，我所做的修改会产生这个错误：

# Build Generative model ...
g_input = Input(shape=[len(total_x[0])-2])
np_input = Input(shape=[len(total_x[0][:-1])]) 
mergedLayer = layers.concatenate([g_input, np_input])
H1 = Dense(512)(mergedLayer)
H1r = Activation('relu')(H1)
H2 = Dense(256)(H1r)
H2r = Activation('relu')(H2)
H3 = Dense(256)(H2r)
H3r = Activation('relu')(H3)
H4 = Dense(128)(H3r)
H4r = Activation('relu')(H4)
H5 = Dense(128)(H4r)

H5r = Activation('relu')(H5)
H6 = Dense(64)(H5r)
H6r = Activation('relu')(H6)
H7 = Dense(32)(H6r)
Hr = Activation('relu')(H7)
g_V = Dense(1)(Hr)

generator = Model([g_input,np_input],g_V)
generator.compile(loss='binary_crossentropy', optimizer=opt)
# generator.summary()
# utils.plot_model(generator, show_layer_names=False, show_shapes=True)

# Build Discriminative model ...
d_input = Input(shape=[Xtrain.shape[1]+1])
H = Dense(256)(d_input)
H = Activation('relu')(H)
H = Dense(256)(H)
H = Activation('relu')(H)
H = Dense(64)(H)
H = Activation('relu')(H)
H = Dense(32)(H)
H = Activation('relu')(H)
d_V = Dense(2,activation='softmax')(H)
discriminator = Model(d_input,d_V)
discriminator.compile(loss='categorical_crossentropy', optimizer=dopt)
# discriminator.summary()

# Freeze weights in the discriminator for stacked training
make_trainable(discriminator, False)

# Build stacked GAN model
# gan_input = Input(shape=Xtrain.shape[1]) #old

gan_dataframe_input = Input(shape=[Xtrain.shape[1]-2])
numpy_input = Input(shape=2) 
gan_input = layers.concatenate([gan_dataframe_input, numpy_input])

H = generator(gan_input)
gd_input=Concatenate()([gan_input,H])
gan_V = discriminator(gd_input)
GAN = Model(gan_input, [gan_V,H])
GAN.compile(loss=['categorical_crossentropy','mse'], optimizer=opt) #Complete GAN have both loss functions
GAN.summary()

任何要查看的建议或资源都会有所帮助！

更新 - 我不确定我是越来越近还是越来越远。 我进行了以下更改（尝试使形状匹配）：

gan_dataframe_input = Input(shape=[len(total_x[0])-2])
numpy_input = Input(shape=[len(total_x[0][:-1])]) 
gan_input = layers.concatenate([gan_dataframe_input, numpy_input])

H = generator([gan_dataframe_input,numpy_input])
gd_input=Concatenate()([gan_input,H])
gan_V = discriminator(gd_input)
GAN = Model(gan_input, [gan_V,H])

但现在我在最后一行得到一个错误：

ValueError: Graph disconnected: cannot obtain value for tensor KerasTensor(type_spec=TensorSpec(shape=(None, 659), dtype=tf.float32, name='input_230'), name='input_230', description="created by layer 'input_230'") at layer "concatenate_94". The following previous layers were accessed without issue: []

我不确定我是否越来越近，但奇怪的是我的 xtrain numpy 数组是 (661,) 而我的 numpy 数组是 (77,0) 所以我不知道为什么当我打印(mergedLayer ) 我得到KerasTensor(type_spec=TensorSpec(shape=(None, 1319), dtype=tf.float32, name=None), name='concatenate_93/concat:0', description="created by layer 'concatenate_93'")它似乎它没有看到列，只有行？ 我不确定这是否有意义，我可能会更加困惑自己试图理解多输入。