Mercurial > pylearn
changeset 759:61a3608d5767
Merged
| author | Olivier Delalleau <delallea@iro> |
|---|---|
| date | Tue, 02 Jun 2009 22:26:29 -0400 |
| parents | c60ad32e1f40 (diff) 8447bc9bb2d4 (current diff) |
| children | 60394c460390 |
| files | |
| diffstat | 1 files changed, 13 insertions(+), 12 deletions(-) [+] |
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--- a/pylearn/algorithms/sandbox/DAA_inputs_groups.py Tue Jun 02 21:15:41 2009 -0400 +++ b/pylearn/algorithms/sandbox/DAA_inputs_groups.py Tue Jun 02 22:26:29 2009 -0400 @@ -195,13 +195,7 @@ container.hidden = self.hid_fn(container.hidden_activation) self.define_propdown(container, idx_list , auxinput) container.rec = self.hid_fn(container.rec_activation) - if (self.ignore_missing is not None and self.input is not None and not - self.reconstruct_missing): - # Apply mask to gradient to ensure we do not backpropagate on the - # cost computed on missing inputs (that were replaced with zeros). - container.rec = mask_gradient(container.rec, - self.input_missing_mask) - + def define_propup(self, container, input, idx_list, auxinput): if self.input is not None: container.hidden_activation = self.filter_up(input, self.wenc, self.benc) @@ -226,7 +220,14 @@ container.rec_activation = rec_activation1 else: container.rec_activation = rec_activation2 - + + if (self.ignore_missing is not None and self.input is not None and not + self.reconstruct_missing): + # Apply mask to gradient to ensure we do not backpropagate on the + # cost computed on missing inputs (that have been imputed). + container.rec_activation = mask_gradient(container.rec_activation, + self.input_missing_mask) + def filter_up(self, vis, w, b=None): out = T.dot(vis, w) return out + b if b else out @@ -315,11 +316,11 @@ mask = self.random.binomial(T.shape(self.input), 1, 1 - self.noise_level) elif self.corruption_pattern == 'by_pair': shape = T.shape(self.input) - scale = numpy.ones(2) - scale[1] = 2 - shape = shape / scale + # Do not ask me why, but just doing "/ 2" does not work (there is + # a bug in the optimizer). + shape = T.stack(shape[0], (shape[1] * 2) / 4) mask = self.random.binomial(shape, 1, 1 - self.noise_level) - mask = T.hstack((mask, mask)) + mask = T.horizontal_stack(mask, mask) else: raise ValueError('Unknown value for corruption_pattern: %s' % self.corruption_pattern)