How to obtain the gradients in keras?

Question

I am attempting to debug a keras model that I have built. It seems that my gradients are exploding, or there is a division by 0 or some such. It would be convenient to be able to inspect the various gradients as they back-propagate through the network. Something like the following would be ideal:

model.evaluate(np.array([[1,2]]), np.array([[1]])) #gives the loss
model.evaluate_gradient(np.array([[1,2]]), np.array([[1]]), layer=2) #gives the doutput/dloss at layer 2 for the given input
model.evaluate_weight_gradient(np.array([[1,2]]), np.array([[1]]), layer=2) #gives the dweight/dloss at layer 2 for the given input

Answer 1

You need to create a symbolic Keras function, taking the input/output as inputs and returning the gradients. Here is a working example :

import numpy as np import keras from keras import backend as K  model = keras.Sequential() model.add(keras.layers.Dense(20, input_shape = (10, ))) model.add(keras.layers.Dense(5)) model.compile('adam', 'mse')  dummy_in = np.ones((4, 10)) dummy_out = np.ones((4, 5)) dummy_loss = model.train_on_batch(dummy_in, dummy_out)  def get_weight_grad(model, inputs, outputs):     """ Gets gradient of model for given inputs and outputs for all weights"""     grads = model.optimizer.get_gradients(model.total_loss, model.trainable_weights)     symb_inputs = (model._feed_inputs + model._feed_targets + model._feed_sample_weights)     f = K.function(symb_inputs, grads)     x, y, sample_weight = model._standardize_user_data(inputs, outputs)     output_grad = f(x + y + sample_weight)     return output_grad   def get_layer_output_grad(model, inputs, outputs, layer=-1):     """ Gets gradient a layer output for given inputs and outputs"""     grads = model.optimizer.get_gradients(model.total_loss, model.layers[layer].output)     symb_inputs = (model._feed_inputs + model._feed_targets + model._feed_sample_weights)     f = K.function(symb_inputs, grads)     x, y, sample_weight = model._standardize_user_data(inputs, outputs)     output_grad = f(x + y + sample_weight)     return output_grad   weight_grads = get_weight_grad(model, dummy_in, dummy_out) output_grad = get_layer_output_grad(model, dummy_in, dummy_out) 

The first function I wrote returns all the gradients in the model but it wouldn't be difficult to extend it so it supports layer indexing. However, it's probably dangerous because any layer without weights in the model will be ignored by this indexing and you would end up with different layer indexing in the model and the gradients.
The second function I wrote returns the gradient at a given layer's output and there, the indexing is the same as in the model, so it's safe to use it.

Note : This works with Keras 2.2.0, not under, as this release included a major refactoring of keras.engine