My model is a simple fully connected network like this:
inp=Input(shape=(10,))
d=Dense(64, activation='relu')(inp)
d=Dense(128,activation='relu')(d)
d=Dense(256,activation='relu')(d) #want to give input here, layer3
d=Dense(512,activation='relu')(d)
d=Dense(1024,activation='relu')(d)
d=Dense(128,activation='linear')(d)
So, after saving the model I want to give input to layer 3. What I am doing right now is this:
model=load_model('blah.h5') #above described network
print(temp_input.shape) #(16,256), which is equal to what I want to give
index=3
intermediate_layer_model = Model(inputs=temp_input,
outputs=model.output)
End_output = intermediate_layer_model.predict(temp_input)
But it isn't working, i.e. I am getting errors like incompatible input, inputs should be tuple etc. The error message is:
raise TypeError('`inputs` should be a list or tuple.')
TypeError: `inputs` should be a list or tuple.
Is there any way I can pass my own inputs in middle of network and get the output instead of giving an input at the start and getting output from the end? Any help will be highly appreciated.
Keras is a high level neural network library used for fast experimentation, user friendliness and easy extensibility. It is highly recommended library for a beginner in neural networks. In this blog we will learn how to use an intermediate layer of a neural network as input to another network.
layer_output = model.get_layer ('Your-Model-Object').get_layer ('the-layer-contained-in-your-Model-object').output Thanks for contributing an answer to Data Science Stack Exchange!
Now if you want to get its intermediate layer, use following steps: Find index of the input layer to decoder ( in the given autoencoder model it is the 6th layer from last so -6) Use autoencoder.layers to get that layer. Iterate through the following layers in the autoencoder model, till the decoder_output layer.
Visualize what the Output looks like at the intermediate layer, Look at its Weight, Count Params, and Look at the layer Summary. We will actually be visualizing the result after each Activation Layer. In case if you don’t have the following modules, we need to install the following Libraries by typing in the Python virtual environment:
First you must learn that in Keras when you apply a layer on an input, a new node is created inside this layer which connects the input and output tensors. Each layer may have multiple nodes connecting different input tensors to their corresponding output tensors. To build a model, these nodes are traversed and a new graph of the model is created which consists all the nodes needed to reach output tensors from input tensors (i.e. which you specify when creating a model: model = Model(inputs=[...], outputs=[...])
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Now you would like to feed an intermediate layer of a model and get the output of the model. Since this is a new data-flow path, we need to create new nodes for each layer corresponding to this new computational graph. We can do it like this:
idx = 3 # index of desired layer
input_shape = model.layers[idx].get_input_shape_at(0) # get the input shape of desired layer
layer_input = Input(shape=input_shape) # a new input tensor to be able to feed the desired layer
# create the new nodes for each layer in the path
x = layer_input
for layer in model.layers[idx:]:
x = layer(x)
# create the model
new_model = Model(layer_input, x)
Fortunately, your model consists of one-branch and we could simply use a for
loop to construct the new model. However, for more complex models it may not be easy to do so and you may need to write more codes to construct the new model.
Here is another method for achieving the same result. Initially create a new input layer and then connect it to the lower layers(with weights).
For this purpose, first re-initialize these layers(with same name) and reload the corresponding weights from the parent model using
new_model.load_weights("parent_model.hdf5", by_name=True)
This will load the required weights from the parent model.Just make sure you name your layers properly beforehand.
idx = 3
input_shape = model.layers[idx].get_input_shape_at(0) layer
new_input = Input(shape=input_shape)
d=Dense(256,activation='relu', name='layer_3')(new_input)
d=Dense(512,activation='relu', name='layer_4'))(d)
d=Dense(1024,activation='relu', name='layer_5'))(d)
d=Dense(128,activation='linear', name='layer_6'))(d)
new_model = Model(new_input, d)
new_model.load_weights("parent_model.hdf5", by_name=True)
This method will work for complex models with multiple inputs or branches.You just need to copy the same code for required layers, connect the new inputs and finally load the corresponding weights.
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