![]() We keep updating the output tensor giving it to each layer and getting a new output (if we were interested in creating branches, we would use a different var for each output of interest to keep track of them): mergedOut = Flatten()(mergedOut) This same idea apply to all the following layers. #it will demand that both model1 and model2 have the same output shape #The second parentheses "calls" the layer with the output tensors of the two models #Add() -> creates a merge layer that sums the inputs Models and layers can be called exactly the same way.įor the merge layer, I prefer using other merge layers that are more intuitive, such as Add(), Multiply() and Concatenate() for instance. You define a layer, then you call the layer with an input tensor to get the output tensor. ![]() When using the functional API, you need to keep track of inputs and outputs, instead of just defining layers. Using the functional API brings you all possibilities. How can I merge these 2 Sequential models that use different window sizes and apply functions like 'max', 'sum' etc to them? Received type: class ''.įull input: [ object at 0x2b32d518a780, ValueError: Layer merge_1 was called with an input that isn't a ![]() ' ValueError: Unexpectedly found an instance of Raise ValueError('Unexpectedly found an instance of type ' + str(type(x)) + '. "/nics/d/home/dsawant/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", ![]() Here is the code: model1 = Sequential(layers=[Ĭonv1D(128, kernel_size=12, strides=4, padding='valid', activation='relu', input_shape=input_shape),Ĭonv1D(256, kernel_size=12, strides=4, padding='valid', activation='relu'),Ĭonv1D(128, kernel_size=20, strides=5, padding='valid', activation='relu', input_shape=input_shape),Ĭonv1D(256, kernel_size=20, strides=5, padding='valid', activation='relu'), I a trying to merge 2 sequential models in keras. ![]()
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