Keras give input to intermediate layer and get final output

by

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=[...]).

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.

More Related Contents:

  1. How can I use a weight matrix created by a trained neural network to make predictions in another file?
  2. Keras Dense layer’s input is not flattened
  3. How does Keras calculate the accuracy?
  4. Keras Sequential model input layer
  5. How to concatenate two layers in keras?
  6. Negative dimension size caused by subtracting 3 from 1 for ‘conv2d_2/convolution’
  7. What is the role of TimeDistributed layer in Keras?
  8. How to use fit_generator with multiple inputs
  9. keras: how to save the training history attribute of the history object
  10. How to add and remove new layers in keras after loading weights?
  11. How to log Keras loss output to a file
  12. Can I send callbacks to a KerasClassifier?
  13. Make a custom loss function in keras
  14. How to apply gradient clipping in TensorFlow?
  15. Keras + Tensorflow and Multiprocessing in Python
  16. Does Any one got “AttributeError: ‘str’ object has no attribute ‘decode’ ” , while Loading a Keras Saved Model
  17. ValueError at /image/ Tensor Tensor(“activation_5/Softmax:0”, shape=(?, 4), dtype=float32) is not an element of this graph
  18. ValueError: Error when checking target: expected model_2 to have shape (None, 252, 252, 1) but got array with shape (300, 128, 128, 3)
  19. How to get other metrics in Tensorflow 2.0 (not only accuracy)?
  20. How can I use a pre-trained neural network with grayscale images?
  21. Removing then Inserting a New Middle Layer in a Keras Model
  22. memory issues when transforming np.array using to_categorical
  23. Where do I call the BatchNormalization function in Keras?
  24. How do I get the weights of a layer in Keras?
  25. TimeDistributed(Dense) vs Dense in Keras – Same number of parameters
  26. Tensor is not an element of this graph
  27. How to create a keras layer with a custom gradient in TF2.0?
  28. Custom ImageDataGenerator() for half a million images where labels and pixels are in 2 separate DataFrames using Keras (or any other library) [closed]
  29. Tensorflow One Hot Encoder?
  30. How to construct a network with two inputs in PyTorch

Leave a Comment