Mercurial > pylearn
annotate nnet_ops.py @ 29:46c5c90019c2
Changed apply_function so that it propagates methods of the source.
| author | bengioy@grenat.iro.umontreal.ca |
|---|---|
| date | Fri, 11 Apr 2008 15:46:18 -0400 |
| parents | b63e8c0bf21b |
| children | bf0145fa73e8 |
| rev | line source |
|---|---|
| 24 | 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() | |
|
25
b63e8c0bf21b
added __init__.py, fixed crossentropy_softmax_1hot function name
bergstrj@iro.umontreal.ca
parents:
24
diff
changeset
|
54 nll, sm = crossentropy_softmax_1hot(x, y_idx) |
| 24 | 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() | |
|
25
b63e8c0bf21b
added __init__.py, fixed crossentropy_softmax_1hot function name
bergstrj@iro.umontreal.ca
parents:
24
diff
changeset
|
77 crossentropy_softmax_1hot = gof.op.constructor(CrossentropySoftmax1Hot) |
| 24 | 78 |
| 79 #TODO: write a version of CrossentropySoftmax1Hot that accepts a bias for x, if | |
| 80 # this op needs to be faster. | |
| 81 |