keras combine pretrained model

Question

I trained a single model and want to combine it with another keras model using the functional api (backend is tensorflow version 1.4)

My first model looks like this:

import tensorflow.contrib.keras.api.keras as keras

model = keras.models.Sequential()
input = Input(shape=(200,))
dnn = Dense(400, activation="relu")(input)
dnn = Dense(400, activation="relu")(dnn)
output = Dense(5, activation="softmax")(dnn)
model = keras.models.Model(inputs=input, outputs=output)

after I trained this model I save it using the keras model.save() method. I can also load the model and retrain it without problems.

Now I want to use the output of this model as additional input for a second model:

# load first model
old_model = keras.models.load_model(path_to_old_model)

input_1 = Input(shape=(200,))
input_2 = Input(shape=(200,))
output_old_model = old_model(input_2)

merge_layer = concatenate([input_1, output_old_model])
dnn_layer = Dense(200, activation="relu")(merge_layer)
dnn_layer = Dense(200, activation="relu")(dnn_layer)
output = Dense(10, activation="sigmoid")(dnn_layer)
new_model = keras.models.Model(inputs=[input_1, input_2], outputs=output)
new_model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"]
new_model.fit(inputs=[x1,x2], labels=labels, epochs=50, batch_size=32)

when I try this I get the following error message:

FailedPreconditionError (see above for traceback): Attempting to use uninitialized value dense_1/kernel
 [[Node: dense_1/kernel/read = Identity[T=DT_FLOAT, _class=["loc:@dense_1/kernel"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](dense_1/kernel)]]
 [[Node: model_1_1/dense_3/BiasAdd/_79 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_68_model_1_1/dense_3/BiasAdd", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Answer 1

I would do this in following steps:

1. Define function for building a clean model with the same architecture:

   def build_base():
       input = Input(shape=(200,))
       dnn = Dense(400, activation="relu")(input)
       dnn = Dense(400, activation="relu")(dnn)
       output = Dense(5, activation="softmax")(dnn)
       model = keras.models.Model(inputs=input, outputs=output)
       return input, output, model
   

2. Build two copies of the same model:

   input_1, output_1, model_1 = build_base()
   input_2, output_2, model_2 = build_base()
   

3. Set weights in both models:

   model_1.set_weights(old_model.get_weights())
   model_2.set_weights(old_model.get_weights())
   

4. Now do the rest:

   merge_layer = concatenate([input_1, output_2])
   dnn_layer = Dense(200, activation="relu")(merge_layer)
   dnn_layer = Dense(200, activation="relu")(dnn_layer)
   output = Dense(10, activation="sigmoid")(dnn_layer)
   new_model = keras.models.Model(inputs=[input_1, input_2], outputs=output)