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comparison nnet_ops.py @ 27:e6c550cb2896

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Merging? what?
author bengioy@grenat.iro.umontreal.ca
date Fri, 11 Apr 2008 11:16:09 -0400
parents b63e8c0bf21b
children bf0145fa73e8
comparison
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26:672fe4b23032 27:e6c550cb2896
1 import theano
2 from theano import tensor, gof, scalar
3 import numpy
4
5 class ScalarSigmoid(scalar.UnaryScalarOp):
6 def impl(self, x):
7 return 1.0 / (1 + numpy.exp(-x))
8 def grad(self, (x,), (gz,)):
9 return gz * scalar_sigmoid(x) * (1.0 - scalar_sigmoid(x)),
10 def c_foreach(self, (x,), (z,)):
11 return "%(z)s = 1.0 / (1 + exp(-%(x)s));" % locals()
12 scalar_sigmoid = gof.op.constructor(ScalarSigmoid)
13 Sigmoid, sigmoid, SigmoidInplace, sigmoid_inplace \
14 = theano.tensor.broadcast(ScalarSigmoid, 'Sigmoid')
15
16
17
18 class CrossentropySoftmax1Hot(gof.op.Op):
19 """A special compound Op for the output of neural-net classifiers.
20
21 This Op has two outputs:
22 - KL(softmax(x), y)
23 - softmax(x)
24
25 x[i] is assumed to be a dense vector
26 softmax(x[i]) is the i'th distribution over len(x[i]) options
27 y[i] is an integer index, encoding a 1-hot distribution
28
29 """
30 nin=2
31 nout=2
32 def __init__(self, x, y_idx,**kwargs):
33 x = tensor._as_tensor(x)
34 y_idx = tensor._as_tensor(y_idx)
35 # TODO: Is this correct? It used to be y, not y_idx
36 nll = tensor.Tensor(x.dtype, y_idx.broadcastable)
37 # nll = Tensor(x.dtype, y.broadcastable)
38 sm = tensor.Tensor(x.dtype, x.broadcastable)
39 self.inputs = [x, y_idx]
40 self.outputs = [nll,sm]
41 def perform(self):
42 x, y_idx = [i.data for i in self.inputs]
43 sm = numpy.zeros_like(x) # softmax
44 nll = numpy.zeros(x.shape[0]) #nll(y | softmax(x))
45 for i in xrange(sm.shape[0]):
46 sm[i] = numpy.exp(x[i] - numpy.max(x[i])) #softmax
47 sm[i] *= 1.0 / numpy.sum(sm[i]) #vector scale
48 nll[i] = -numpy.log( sm[i, y_idx[i]]) #cross-entropy
49 self.outputs[0].data = nll
50 self.outputs[1].data = sm
51 def grad(self, (x, y_idx), (g_nll, g_sm)):
52 if g_sm is not None:
53 raise NotImplementedError()
54 nll, sm = crossentropy_softmax_1hot(x, y_idx)
55 dx = CrossentropySoftmax1Hot.Dx(g_nll, sm, y_idx).outputs[0]
56 return dx, None
57
58 class Dx (gof.op.Op):
59 nin=3
60 nout=1
61 """Gradient wrt x of the CrossentropySoftmax1Hot Op"""
62 def __init__(self, dy, sm, y_idx,**kwargs):
63 dy = tensor._as_tensor(dy)
64 sm = tensor._as_tensor(sm)
65 y_idx = tensor._as_tensor(y_idx)
66 self.inputs = [dy, sm, y_idx]
67 self.outputs = [tensor.Tensor(sm.dtype, sm.broadcastable)]
68 def perform(self):
69 dy,sm,y_idx = [i.data for i in self.inputs]
70 dx = numpy.zeros_like(sm)
71 for i in xrange(sm.shape[0]):
72 dx[i] = dy[i] * sm[i] #vector scale
73 dx[i, y_idx[i]] -= dy[i] #scalar decrement
74 self.outputs[0].data = dx
75 def grad(self, *args):
76 raise NotImplementedError()
77 crossentropy_softmax_1hot = gof.op.constructor(CrossentropySoftmax1Hot)
78
79 #TODO: write a version of CrossentropySoftmax1Hot that accepts a bias for x, if
80 # this op needs to be faster.
81