Mercurial > pylearn
comparison pylearn/shared/layers/tests/test_kouh2008.py @ 1447:fbe470217937
Use .get_value() and .set_value() of shared instead of the .value property
| author | Pascal Lamblin <lamblinp@iro.umontreal.ca> |
|---|---|
| date | Wed, 16 Mar 2011 20:20:02 -0400 |
| parents | c635d1df51a1 |
| children |
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| 1446:6e50d209b5f1 | 1447:fbe470217937 |
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| 58 | 58 |
| 59 layer = Kouh2008.new_filters_expbounds(rng, x, n_in, n_out, n_terms, dtype='float64') | 59 layer = Kouh2008.new_filters_expbounds(rng, x, n_in, n_out, n_terms, dtype='float64') |
| 60 out = LogisticRegression.new(layer.output, n_out, 2) | 60 out = LogisticRegression.new(layer.output, n_out, 2) |
| 61 cost = out.nll(y).sum() | 61 cost = out.nll(y).sum() |
| 62 #joint optimization except for one of the linear filters | 62 #joint optimization except for one of the linear filters |
| 63 out.w.value += 0.1 * rng.rand(*out.w.value.shape) | 63 out.w.set_value((out.w.get_value(borrow=True) + |
| 64 0.1 * rng.rand(*out.w.get_value(borrow=True).shape)), | |
| 65 borrow=True) | |
| 64 params = layer.params[:-2] | 66 params = layer.params[:-2] |
| 65 mode = None | 67 mode = None |
| 66 updates = [(p, p - numpy.asarray(0.001, dtype=dtype)*gp) for p,gp in zip(params, tensor.grad(cost, params)) ] | 68 updates = [(p, p - numpy.asarray(0.001, dtype=dtype)*gp) for p,gp in zip(params, tensor.grad(cost, params)) ] |
| 67 for p, newp in updates: | 69 for p, newp in updates: |
| 68 if p is layer.r: | 70 if p is layer.r: |