Question

我有以下两个模型，其中首先训练model_A，然后使用model_A的输出来训练model_C：

import keras
from keras.layers import Input, Dense
from keras.models import Model

inputs = Input(shape=(12,))

# ---------------------------------------
# model_A
x = Dense(64, activation='relu')(inputs)
x = Dense(64, activation='relu')(x)
predictions_A = Dense(3, activation='softmax')(x)
model_A = Model(inputs=inputs, outputs=predictions_A)

model_A.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model_A.fit(my_data_x, axis = 1), pd.get_dummies(my_data['target_cate'],prefix=['cate_']))
#----------------------------------------

input_C_out_A = Input(shape=(3,))
# Concatenating the two input layers
concat = keras.layers.concatenate([inputs, 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=[inputs, input_C_out_A], outputs=predictions_C)
model_C.compile(loss='mean_squared_error', optimizer='adam')
model_C.fit([my_data_x,predictions_A], my_data['target_numeric'])

model_A训练似乎很好，但是在训练model_C时出现以下错误：

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！

Answer 1

将模型的输出（符号张量）放入model.fit是没有意义的，因为那里没有输入数据。您应该首先从模型A获取预测，然后使用它们拟合模型C：

pred_a = model_A.predict(my_data_x)

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

Answer 2

model_C的工作原理：

将输入数据提供给model_A，
获取model_A的输出，并且
将其与原始输入一起馈送到第一Dense层。

因此，按照您所说的去实现（并尝试将模型分开，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'])

如果您不想在训练model_A时训练model_C（即，如果您已经训练了model_A并且不希望改变其权重），只需在编译 model_A.trainable = False之前设置model_C。

Keras：将模型输出用作另一个模型的输入：将符号张量馈送到模型时，我们期望张量具有静态批大小

2 个答案: