Keras：使用模型输出作为另一个的输入：当向模型输入符号张量时，我们希望传感器具有静态批量大小

我有以下两个模型，首先训练模型A，然后用模型A的输出训练模型C：

Epoch 1/1
374667/374667 [==============================] - 11s 30us/step - loss: 0.3157 - acc: 0.9119
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-78-8df7b1dec93f> in <module>
     28 model_C = Model(inputs=[inputs, input_C_out_A], outputs=predictions_C)
     29 model_C.compile(loss='mean_squared_error', optimizer='adam')
---> 30 model_C.fit([my_data_x,predictions_A], my_data['target_numeric'])

~/workspace/git/tensorplay/venv/lib/python3.7/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
    950             sample_weight=sample_weight,
    951             class_weight=class_weight,
--> 952             batch_size=batch_size)
    953         # Prepare validation data.
    954         do_validation = False

~/workspace/git/tensorplay/venv/lib/python3.7/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    749             feed_input_shapes,
    750             check_batch_axis=False,  # Don't enforce the batch size.
--> 751             exception_prefix='input')
    752 
    753         if y is not None:

~/workspace/git/tensorplay/venv/lib/python3.7/site-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
     90         data = data.values if data.__class__.__name__ == 'DataFrame' else data
     91         data = [data]
---> 92     data = [standardize_single_array(x) for x in data]
     93 
     94     if len(data) != len(names):

~/workspace/git/tensorplay/venv/lib/python3.7/site-packages/keras/engine/training_utils.py in <listcomp>(.0)
     90         data = data.values if data.__class__.__name__ == 'DataFrame' else data
     91         data = [data]
---> 92     data = [standardize_single_array(x) for x in data]
     93 
     94     if len(data) != len(names):

~/workspace/git/tensorplay/venv/lib/python3.7/site-packages/keras/engine/training_utils.py in standardize_single_array(x)
     23                 'When feeding symbolic tensors to a model, we expect the'
     24                 'tensors to have a static batch size. '
---> 25                 'Got tensor with shape: %s' % str(shape))
     26         return x
     27     elif x.ndim == 1:

ValueError: When feeding symbolic tensors to a model, we expect thetensors to have a static batch size. Got tensor with shape: (None, 3)

model_A培训似乎不错，但在培训model_C时，我出现了以下错误：

Epoch 1/1
374667/374667 [==============================] - 11s 30us/step - loss: 0.3157 - acc: 0.9119
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-78-8df7b1dec93f> in <module>
     28 model_C = Model(inputs=[inputs, input_C_out_A], outputs=predictions_C)
     29 model_C.compile(loss='mean_squared_error', optimizer='adam')
---> 30 model_C.fit([my_data_x,predictions_A], my_data['target_numeric'])

~/workspace/git/tensorplay/venv/lib/python3.7/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
    950             sample_weight=sample_weight,
    951             class_weight=class_weight,
--> 952             batch_size=batch_size)
    953         # Prepare validation data.
    954         do_validation = False

~/workspace/git/tensorplay/venv/lib/python3.7/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    749             feed_input_shapes,
    750             check_batch_axis=False,  # Don't enforce the batch size.
--> 751             exception_prefix='input')
    752 
    753         if y is not None:

~/workspace/git/tensorplay/venv/lib/python3.7/site-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
     90         data = data.values if data.__class__.__name__ == 'DataFrame' else data
     91         data = [data]
---> 92     data = [standardize_single_array(x) for x in data]
     93 
     94     if len(data) != len(names):

~/workspace/git/tensorplay/venv/lib/python3.7/site-packages/keras/engine/training_utils.py in <listcomp>(.0)
     90         data = data.values if data.__class__.__name__ == 'DataFrame' else data
     91         data = [data]
---> 92     data = [standardize_single_array(x) for x in data]
     93 
     94     if len(data) != len(names):

~/workspace/git/tensorplay/venv/lib/python3.7/site-packages/keras/engine/training_utils.py in standardize_single_array(x)
     23                 'When feeding symbolic tensors to a model, we expect the'
     24                 'tensors to have a static batch size. '
---> 25                 'Got tensor with shape: %s' % str(shape))
     26         return x
     27     elif x.ndim == 1:

ValueError: When feeding symbolic tensors to a model, we expect thetensors to have a static batch size. Got tensor with shape: (None, 3)

---

知道我错过了什么吗？谢谢

将模型的输出作为符号张量放入model.fit没有意义，因为那里没有输入数据。您应该首先从模型A获得预测，然后使用它们来拟合模型C：

pred_a = model_A.predict(my_data_x)

model_C.fit([my_data_x, pred_a], my_data['target_numeric'])

模型C的工作原理是：

将输入数据提供给模型_A， 获取模型_A的输出，以及 将其与原始输入一起送入第一个致密层。 因此，实现您所说的，并尝试保持模型独立，即每个模型都有自己的输入/输出层：

input_C = Input(shape=(12,))
out_A = model_A(input_C) # get the output of model_A
concat = keras.layers.concatenate([input_C, out_A])
x1 = Dense(64, activation='relu')(concat)
x1 = Dense(64, activation='relu')(x1)
predictions_C= Dense(1, activation='sigmoid')(x1)

model_C = Model(inputs=input_C, outputs=predictions_C)
model_C.compile(loss='mean_squared_error', optimizer='adam')
model_C.fit(my_data_x, my_data['target_numeric'])

如果您不希望在训练模型_C时训练模型_A，即如果您已经训练了模型_A，并且不希望更改其权重，只需在编译模型_C之前将模型_A.trainable=False