Mercurial > pylearn
comparison pylearn/shared/layers/sigmoidal_layer.py @ 1405:f9e4d71aa353
Add L1 and L2² costs to sigmoidal layer
| author | Pascal Lamblin <lamblinp@iro.umontreal.ca> |
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| date | Wed, 26 Jan 2011 16:55:44 -0500 |
| parents | 912be602c3ac |
| children |
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| 1404:89017617ab36 | 1405:f9e4d71aa353 |
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| 18 :param w: a symbolic weight matrix of shape (n_in, n_out) | 18 :param w: a symbolic weight matrix of shape (n_in, n_out) |
| 19 :param b: symbolic bias terms of shape (n_out,) | 19 :param b: symbolic bias terms of shape (n_out,) |
| 20 :param squash: an squashing function | 20 :param squash: an squashing function |
| 21 """ | 21 """ |
| 22 output = squash_fn(tensor.dot(input, w) + b) | 22 output = squash_fn(tensor.dot(input, w) + b) |
| 23 l1 = abs(w).sum() | |
| 24 l2_sqr = (w**2).sum() | |
| 23 update_locals(self, locals()) | 25 update_locals(self, locals()) |
| 24 | 26 |
| 25 @classmethod | 27 @classmethod |
| 26 def new(cls, rng, input, n_in, n_out, squash_fn=tensor.tanh, dtype=None): | 28 def new(cls, rng, input, n_in, n_out, squash_fn=tensor.tanh, dtype=None): |
| 27 """Allocate a SigmoidLayer with weights to transform inputs with n_in dimensions, | 29 """Allocate a SigmoidLayer with weights to transform inputs with n_in dimensions, |