anton/mlpractical
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Removing final backpropagation of gradients to inputs as unnecessary.

Browse Source
This commit is contained in:
Matt Graham 2016-10-10 09:27:02 +01:00
parent a24ff90f71
commit cfad36c91f
1 changed files with 7 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
mlp/models.py
View File

@ -122,11 +122,16 @@ class MultipleLayerModel(object):
for i, layer in enumerate(self.layers[::-1]):
inputs = activations[-i - 2]
outputs = activations[-i - 1]
grads_wrt_inputs = layer.bprop(inputs, outputs, grads_wrt_outputs)
if isinstance(layer, LayerWithParameters):
# Gradients are appended in reversed order, going backwards
# through layers, so grads_wrt_params can be reversed at end to
# give gradients in consistent order with self.params
grads_wrt_params += layer.grads_wrt_params(
inputs, grads_wrt_outputs)[::-1]
grads_wrt_outputs = grads_wrt_inputs
# If not at first layer back-propagate gradients
if i != len(self.layers) - 1:
grads_wrt_outputs = layer.bprop(
inputs, outputs, grads_wrt_outputs)
return grads_wrt_params[::-1]
def params_cost(self):