I have the following two models, where model_A is trained first, then the output of model_A is used to train the 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 training seems to be fine, but then I got the following errors when training the model_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)
Any idea what I missed? Thanks!
It makes no sense to put the output of a model (a symbolic tensor) into model.fit, since there is no input data there. You should first obtain predictions from model A and then use them to fit model C:
pred_a = model_A.predict(my_data_x)
model_C.fit([my_data_x, pred_a], my_data['target_numeric'])