Gradient of the output wrt to its weights (agnostic - backend)

[afcruzs]

I need to compute the gradient of the output w.r.t to its weights, the keras backend has a method called gradients which seems to do the work. Here is a question on this matter:

http://stackoverflow.com/questions/39561560/getting-gradient-of-model-output-w-r-t-weights-using-keras

This seems to work pretty well on tensorflow, but when I run the code (with theano as a backend) of the accepted answer (which makes sense to me) it shows the following exception "TypeError: cost must be a scalar." when calling the tensor.grad method.

To actually run the gradients they use tensorflow, I wonder if there is a way of computing the gradients with keras without use an specific backend.

This is the code which fails when using theano as backend:

from keras.models import Sequential
from keras.layers import Dense, Activation
from keras import backend as k

model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
outputTensor = model.output
listOfVariableTensors = model.trainable_weights
gradients = k.gradients(outputTensor, listOfVariableTensors)

[patrickmesana]

Anyone has an answer on this?

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[AmirAlavi]

I'm running into this as well. I'm trying to run an "improved" WGAN from here: https://github.com/eriklindernoren/Keras-GAN/tree/master/wgan_gp I only have access to the Theano backend on my cluster.