Mercurial > pylearn
comparison sandbox/simple_autoassociator/graph.py @ 393:36baeb7125a4
Made sandbox directory
| author | Joseph Turian <turian@gmail.com> |
|---|---|
| date | Tue, 08 Jul 2008 18:46:26 -0400 |
| parents | simple_autoassociator/graph.py@e2cb8d489908 |
| children | 8cc11ac97087 |
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| 392:e2cb8d489908 | 393:36baeb7125a4 |
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| 1 """ | |
| 2 Theano graph for a simple autoassociator. | |
| 3 @todo: Make nearly everything private. | |
| 4 """ | |
| 5 | |
| 6 from pylearn.nnet_ops import sigmoid, binary_crossentropy | |
| 7 from theano import tensor as t | |
| 8 from theano.tensor import dot | |
| 9 x = t.dvector() | |
| 10 w1 = t.dmatrix() | |
| 11 b1 = t.dvector() | |
| 12 w2 = t.dmatrix() | |
| 13 b2 = t.dvector() | |
| 14 h = sigmoid(dot(x, w1) + b1) | |
| 15 y = sigmoid(dot(h, w2) + b2) | |
| 16 | |
| 17 loss_unsummed = binary_crossentropy(y, x) | |
| 18 loss = t.sum(loss_unsummed) | |
| 19 | |
| 20 (gw1, gb1, gw2, gb2, gy) = t.grad(loss, [w1, b1, w2, b2, y]) | |
| 21 | |
| 22 import theano.compile | |
| 23 | |
| 24 inputs = [x, w1, b1, w2, b2] | |
| 25 outputs = [y, h, loss, loss_unsummed, gw1, gb1, gw2, gb2, gy] | |
| 26 trainfn = theano.compile.function(inputs, outputs) |