CODEWITHSUNDEEP
tensorflowneural-networkdeep-learningcaffe
cerebrou
cerebrou

Reputation: 5540

Getting and editing gradient parameters in Caffe

Is it possible to get the gradients with respect to each layer in Caffe in CNNs, edit them and again apply the new gradients in the training process? If possible, using pycaffe interface.

For example in TensorFlow, it could be done by means of functions:

given_optimizer.compute_gradients(total_loss)
given_optimizer.apply_gradients(grads)

Upvotes: 1

Views: 237

Answers (1)

Shai
Shai

Reputation: 114816

I'm not sure what you mean by "apply the new gradients in the training process", but you can access the gradients in the pycaffe interface:

import caffe
net = caffe.Net('/path/to/net.prototxt', '/path/to/weights.caffemodel', caffe.TEST)
# provide inputs to the net, do a pass so that meaningful data/gradients propagate to all the layers
net.forward_backward_all()

# once data/gradients are updated, you can access them
net.blobs['blob_name'].diff  # access the gradient of blob 'blob_name'
net.layers[5].blobs[0].diff  # access the gradient of the first parameter blob of the 6th layer

To map between layer names and layer indices, you can use this code:

list(net._layer_names).index('layer_name')

This will return the index of layer 'layer_name'.

Upvotes: 1

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