TypeError: 'Tensor' object is not callable

Question

I'm trying to display the output of each layer of the convolutions neural network. The backend I'm using is TensorFlow. Here is the code:

import ....
from keras import backend as K

model = Sequential()

model.add(Convolution2D(32, 3, 3, input_shape = (1,28,28))) 
convout1 = Activation('relu')
model.add(convout1)

(X_train, y_train), (X_test, y_test) = mnist_dataset = mnist.load_data("mnist.pkl")
reshaped = X_train.reshape(X_train.shape[0], 1, X_train.shape[1], X_train.shape[2])


from random import randint
img_to_visualize = randint(0, len(X_train) - 1)


# Generate function to visualize first layer
# ERROR HERE
convout1_f = K.function([model.input(train=False)], convout1.get_output(train=False))   #ERROR HERE
convolutions = convout1_f(reshaped[img_to_visualize: img_to_visualize+1])

The full Error is:

convout1_f = K.function([model.input(train=False)], convout1.get_output(train=False)) TypeError: 'Tensor' object is not callable

Any comment or suggestion is highly appreciated. Thank you.

Answer 1

Both get_output and get_input methods return either Theano or TensorFlow tensor. It's not callable because of the nature of this objects.

In order to compile a function you should provide only layer tensors and a special Keras tensor called learning_phase which sets in which option your model should be called.

Following this answer your function should look like this:

convout1_f = K.function([model.input, K.learning_phase()], convout1.get_output)

Remember that you need to pass either True or False when calling your function in order to make your model computations in either learning or training phase mode.