Mercurial > pylearn
annotate nnet_ops.py @ 444:9cfc2fc0f4d1
New tests for softmax.
author | Pascal Lamblin <lamblinp@iro.umontreal.ca> |
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date | Fri, 22 Aug 2008 17:33:19 -0400 |
parents | 060c12314734 |
children | 23960ee12b52 |
rev | line source |
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419
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1 ## This file contain ops that are not currently integrated in the core of threano. |
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2 ## Not all of those ops have been thoroughly tested. |
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3 |
24 | 4 import theano |
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5 from theano import tensor, scalar |
24 | 6 import numpy |
7 | |
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8 ############ |
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9 # |
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10 # SCALAR OPS |
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11 # |
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12 |
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13 class ScalarSigmoid(scalar.UnaryScalarOp): |
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14 @staticmethod |
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15 def st_impl(x): |
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16 if x < -30.0: |
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17 return 0.0 |
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18 if x > 30.0: |
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19 return 1.0 |
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20 return 1.0 / (1.0 + numpy.exp(-x)) |
24 | 21 def impl(self, x): |
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22 return ScalarSigmoid.st_impl(x) |
24 | 23 def grad(self, (x,), (gz,)): |
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24 y = scalar_sigmoid(x) |
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25 return [gz * y * (1.0 - y)] |
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26 def c_code(self, node, name, (x,), (z,), sub): |
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27 if node.inputs[0].type in [scalar.float32, scalar.float64]: |
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28 return """%(z)s = |
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29 %(x)s < -30.0 |
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30 ? 0.0 |
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31 : %(x)s > 30.0 |
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32 ? 1.0 |
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33 : 1.0 /(1.0+exp(-%(x)s));""" % locals() |
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34 raise NotImplementedError('only floatingpoint is implemented') |
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35 scalar_sigmoid = ScalarSigmoid(scalar.upgrade_to_float, name='scalar_sigmoid') |
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36 sigmoid = tensor.Elemwise(scalar_sigmoid, name='sigmoid') |
24 | 37 |
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38 class ScalarSoftplus(scalar.UnaryScalarOp): |
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39 @staticmethod |
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40 def static_impl(x): |
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41 if x < -30.0: |
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42 return 0.0 |
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43 if x > 30.0: |
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44 return x |
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45 return numpy.log1p(numpy.exp(x)) |
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46 def impl(self, x): |
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47 return ScalarSoftplus.static_impl(x) |
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48 def grad(self, (x,), (gz,)): |
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49 return [gz * scalar_sigmoid(x)] |
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50 def c_code(self, node, name, (x,), (z,), sub): |
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51 if node.inputs[0].type in [scalar.float32, scalar.float64]: |
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52 return """%(z)s = |
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53 %(x)s < -30.0 |
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54 ? 0.0 |
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55 : %(x)s > 30.0 |
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56 ? %(x)s |
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57 : log1p(exp(%(x)s));""" % locals() |
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58 raise NotImplementedError('only floating point x is implemented') |
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59 scalar_softplus = ScalarSoftplus(scalar.upgrade_to_float, name='scalar_softplus') |
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60 softplus = tensor.Elemwise(scalar_softplus, name='softplus') |
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61 |
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62 |
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63 ############ |
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64 # |
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65 # TENSOR OPS |
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66 # |
24 | 67 |
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68 |
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69 class SoftmaxWithBias(theano.Op): |
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70 """ |
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71 An L{Op} for the output of neural-net multiclass classifiers. |
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72 |
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73 @type x: is a matrix of floats (32 or 64) |
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74 @type b: is a [row] vector of floats (32 or 64), length is number of cols in x |
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75 |
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76 This L{Op}'s output is softmax(x+b). |
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77 softmax(x[i]) is the i'th distribution over len(x[i]) options. |
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78 """ |
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79 |
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80 nin = 2 |
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81 nout = 1 |
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82 def __init__(self, **kwargs): |
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83 theano.Op.__init__(self, **kwargs) |
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84 |
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85 def make_node(self, x, b): |
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86 x = tensor.as_tensor(x) |
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87 b = tensor.as_tensor(b) |
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88 if x.type.ndim != 2 \ |
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89 or x.type.dtype not in ['float32', 'float64']: |
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90 raise ValueError('x must be 2-d tensor of floats') |
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91 if b.type.ndim != 1 \ |
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92 or x.type.dtype not in ['float32', 'float64']: |
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93 raise ValueError('b must be 1-d tensor of floats') |
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94 |
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95 sm = x.type.make_result() |
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96 return theano.Apply(self, [x, b], [sm]) |
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97 |
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98 def perform(self, node, input_storage, output_storage): |
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99 x, b = input_storage |
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100 if b.shape[0] != x.shape[1]: |
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101 raise ValueError('b must have same number of columns as x') |
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102 |
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103 sm = numpy.zeros_like(x) |
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104 for i in xrange(sm.shape[0]): |
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105 row = x[i] + b |
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106 sm[i] = numpy.exp(row - numpy.max(row)) |
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107 sm[i] *= 1.0 / numpy.sum(sm[i]) |
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108 output_storage[0][0] = sm |
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109 |
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110 def grad(self, (x, b), (g_sm,)): |
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111 sm = softmax_with_bias(x, b) |
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112 dx = SoftmaxWithBiasDx()(g_sm, sm) |
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113 db = tensor.sum(dx, axis = 0) |
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114 return dx, db |
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115 |
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116 def c_headers(self): |
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117 return ['<iostream>'] |
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118 |
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119 @staticmethod |
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120 def c_code_template(): |
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121 # this implementation was lifted from |
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122 # /u/bergstrj/cvs/bergstrj/src/feb07/nn.cxx |
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123 |
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124 #TODO: put this into a templated function, in the support code |
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125 #TODO: declare the max of each row as an Op output |
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126 |
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127 #TODO: set error messages for failures in this code |
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128 |
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129 #TODO: use this to accept float32 and int32: node.inputs[0].type.dtype_specs()[1] |
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130 init_decl = """ |
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131 npy_intp* Nx = %(x)s->dimensions; |
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132 |
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133 if (%(x)s->nd != 2) |
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134 { |
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135 PyErr_SetString(PyExc_ValueError, "a not 2d tensor"); |
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136 %(fail)s; |
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137 } |
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138 if (%(b)s->nd != 1) |
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139 { |
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140 PyErr_SetString(PyExc_ValueError, "b not 1d tensor"); |
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141 %(fail)s; |
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142 } |
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143 if (%(x)s->descr->type_num != PyArray_DOUBLE) |
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144 { |
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145 PyErr_SetString(PyExc_TypeError, "a not float64"); |
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146 %(fail)s; |
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147 } |
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148 if (%(b)s->descr->type_num != PyArray_DOUBLE) |
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149 { |
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150 PyErr_SetString(PyExc_TypeError, "b not float64"); |
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151 %(fail)s; |
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152 } |
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153 if ((%(x)s->dimensions[1] != %(b)s->dimensions[0])) |
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154 { |
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155 PyErr_SetString(PyExc_ValueError, "dimension mismatch in arguments"); |
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156 %(fail)s; |
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157 } |
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158 |
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159 if ((NULL == %(sm)s) |
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160 || (%(sm)s->dimensions[0] != %(x)s->dimensions[0]) |
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161 || (%(sm)s->dimensions[1] != %(x)s->dimensions[1])) |
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162 { |
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163 if (NULL != %(sm)s) Py_XDECREF(%(sm)s); |
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164 %(sm)s = (PyArrayObject*)PyArray_SimpleNew(2, PyArray_DIMS(%(x)s), type_num_%(x)s); |
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165 if(!%(sm)s) { |
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166 // The normal cleanup code will take care of %(nll)s |
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167 // Py_XDECREF(%(nll)s); %(nll)s=NULL; |
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168 PyErr_SetString(PyExc_MemoryError, "failed to alloc sm output"); |
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169 %(fail)s |
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170 } |
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171 } |
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172 """ |
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173 |
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174 begin_row_loop = """ |
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175 for (size_t i = 0; i < Nx[0]; ++i) |
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176 { |
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177 size_t j; |
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178 double sum = 0.0; |
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179 bool discount_max = false; |
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180 |
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181 const double* __restrict__ x_i = (double*)(%(x)s->data + %(x)s->strides[0] * i); |
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182 const double* __restrict__ b_i = (double*)(%(b)s->data); |
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183 double* __restrict__ sm_i = (double*)(%(sm)s->data + %(sm)s->strides[0] * i); |
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184 """ |
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185 |
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186 inside_row_loop = """ |
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187 npy_intp Sx = %(x)s->strides[1]/sizeof(double); |
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188 npy_intp Sb = %(b)s->strides[0]/sizeof(double); |
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189 npy_intp Ssm = %(sm)s->strides[1]/sizeof(double); |
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190 |
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191 size_t row_max_j=0; |
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192 double row_max = x_i[0] + b_i[0]; |
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193 // Get the maximum value of the row |
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194 for (j = 0; j < Nx[1]; ++j) |
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195 { |
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196 double row_ij = x_i[j * Sx] + b_i[j * Sb]; |
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197 row_max_j = (row_ij > row_max) ? j : row_max_j; |
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198 row_max = (row_ij > row_max) ? row_ij : row_max; |
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199 } |
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200 |
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201 for (j = 0; j < Nx[1]; ++j) |
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202 { |
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203 double row_ij = x_i[j * Sx] + b_i[j * Sb]; |
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204 double sm_ij = exp(row_ij - row_max); |
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205 sum += sm_ij; |
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206 sm_i[j * Ssm] = sm_ij; |
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207 } |
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208 if ( (0.0 == sum) || (isinf(sum))) |
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209 { |
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210 //that was our best... |
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211 %(fail)s; |
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212 } |
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213 |
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214 //cblas_dscal(x.N, 1.0 / sum, &mat_at(s,i,0), s.n); |
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215 double sum_inv = 1.0 / sum; |
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216 for (j = 0; j < Nx[1]; ++j) |
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217 { |
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218 sm_i[j * Ssm] *= sum_inv; |
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219 } |
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220 |
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221 if (y_i >= Nx[1]) |
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222 { |
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223 %(fail)s; |
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224 } |
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225 """ |
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226 |
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227 end_row_loop = """ |
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228 } |
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229 """ |
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230 |
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231 return (init_decl, begin_row_loop, inside_row_loop, end_row_loop) |
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232 |
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233 |
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234 def c_code(self, node, name, (x, b), (sm,), sub): |
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235 code_template = ''.join(self.c_code_template()) |
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236 return code_template % dict(locals(), **sub) |
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237 |
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238 softmax_with_bias = SoftmaxWithBias() |
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239 |
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240 |
442
b3315b252824
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241 class SoftmaxWithBiasDx(theano.Op): |
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242 nin = 2 |
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243 nout = 1 |
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244 """Gradient wrt x of the SoftmaxWithBias Op""" |
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245 |
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246 def __init__(self, **kwargs): |
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247 theano.Op.__init__(self, **kwargs) |
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248 |
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249 def make_node(self, dy, sm, **kwargs): |
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250 dy = tensor.as_tensor(dy) |
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251 sm = tensor.as_tensor(sm) |
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252 return theano.Apply(self, [dy, sm], [sm.type.make_result()]) |
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253 |
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254 def perform(self, node, input_storage, output_storage): |
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255 dy, sm = input_storage |
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256 dx = numpy.zeros_like(sm) |
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257 #dx[i,j] = - (\sum_k dy[i,k] sm[i,k]) sm[i,j] + dy[i,j] sm[i,j] |
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258 for i in xrange(sm.shape[0]): |
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259 dy_times_sm_i = dy[i] * sm[i] |
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260 dx[i] = dy_times_sm_i - sum(dy_times_sm_i) * sm[i] |
442
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261 output_storage[0][0] = dx |
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262 |
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263 def grad(self, *args): |
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264 raise NotImplementedError() |
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265 |
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266 def c_code(self, node, name, (dy, sm), (dx,), sub): |
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267 return ''' |
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268 if ((%(dy)s->descr->type_num != PyArray_DOUBLE) |
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269 || (%(sm)s->descr->type_num != PyArray_DOUBLE)) |
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270 { |
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271 PyErr_SetString(PyExc_TypeError, "types should be float64, float64"); |
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272 %(fail)s; |
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273 } |
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274 if ((%(dy)s->nd != 2) |
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275 || (%(sm)s->nd != 2)) |
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276 { |
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277 PyErr_SetString(PyExc_ValueError, "rank error"); |
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278 %(fail)s; |
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279 } |
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280 if (%(dy)s->dimensions[0] != %(sm)s->dimensions[0]) |
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281 { |
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|
282 PyErr_SetString(PyExc_ValueError, "dimension mismatch"); |
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|
283 %(fail)s; |
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|
284 } |
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|
285 if ((NULL == %(dx)s) |
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286 || (%(dx)s->dimensions[0] != %(sm)s->dimensions[0]) |
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287 || (%(dx)s->dimensions[1] != %(sm)s->dimensions[1])) |
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|
288 { |
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|
289 Py_XDECREF(%(dx)s); |
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|
290 %(dx)s = (PyArrayObject*) PyArray_SimpleNew(2, PyArray_DIMS(%(sm)s), |
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|
291 type_num_%(sm)s); |
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|
292 if (!%(dx)s) |
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293 { |
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|
294 PyErr_SetString(PyExc_MemoryError, "failed to alloc dx output"); |
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|
295 %(fail)s; |
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|
296 } |
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|
297 } |
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298 |
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299 for (size_t i = 0; i < %(dx)s->dimenstions[0]; ++i) |
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300 { |
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|
301 const double* __restrict__ dy_i = (double*) (%(dy)s->data + %(dy)s->strides[0] * i); |
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302 npy_intp Sdy = %(dy)s->strides[1]/sizeof(double); |
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303 const double* __restrict__ sm_i = (double*) (%(sm)s->data + %(sm)s->strides[0] * i); |
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304 npy_intp Ssm = %(sm)s->strides[1]/sizeof(double); |
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|
305 const double* __restrict__ dx_i = (double*) (%(dx)s->data + %(dx)s->strides[0] * i); |
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|
306 npy_intp Sdx = %(dx)s->strides[1]/sizeof(double); |
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|
307 |
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|
308 double sum_dy_times_sm = 0.; |
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309 for (size_t j = 0; j < %(dx)s->dimensions[1]; ++j) |
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|
310 { |
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|
311 dx_i[j * Sdx] = dy_i[j * Sdy] * sm_i[j * Ssm]; |
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|
312 sum_dy_times_sm += dx_i[j * Sdx]; |
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|
313 } |
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|
314 for (size_t j = 0; j < %(dx)s->dimensions[1]; ++j) |
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|
315 { |
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|
316 dx_i[j * Sdx] -= sum_dy_times_sm * sm_i[j * Ssm]; |
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|
317 } |
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Finished derivative of softmax gradient.
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|
318 } |
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|
319 ''' % dict(locals(), **sub) |
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|
320 |
443
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Hopefully last bugfix in Softmax
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|
321 def softmax(x, **kwargs): |
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|
322 b = tensor.zeros_like(x[0,:]) |
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323 return softmax_with_bias(x, b, **kwargs) |
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|
324 |
440
18dbc1c11647
Work on softmax operators
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419
diff
changeset
|
325 |
117
3ef569b92fba
ported nnet_ops to new theano
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diff
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|
326 class CrossentropySoftmax1HotWithBias(theano.Op): |
70
76e5c0f37165
better docs & precondition testing for cross_entropy_softmax_1hot & friends
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|
327 """A special compound L{Op} for the output of neural-net classifiers. |
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|
328 |
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|
329 @type x: is a matrix of floats (32 or 64) |
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|
330 @type b: is a [row] vector of floats (32 or 64), length is number of cols in x |
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|
331 @type y_idx: a [column] vector of int (32 or 64), length is number of rows in x |
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|
332 |
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|
333 @precondition: every entry in y_idx is a valid (non-negative) column index into x |
24 | 334 |
70
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diff
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|
335 This L{Op} has two outputs: |
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|
336 - KL(softmax(x+b), y) |
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|
337 - softmax(x+b) |
24 | 338 |
440
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Work on softmax operators
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|
339 |
24 | 340 softmax(x[i]) is the i'th distribution over len(x[i]) options |
70
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|
341 |
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|
342 y_idx[i] is an integer index, encoding a 1-hot distribution. |
440
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|
343 |
70
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|
344 In practice, when we're trying to do classification, we have one row in x |
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|
345 and y_idx per example, and y[i] is the index of the (correct) class of the |
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|
346 i'th example. |
24 | 347 |
348 """ | |
70
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|
349 nin=3 |
24 | 350 nout=2 |
117
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|
351 def __init__(self, **kwargs): |
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|
352 theano.Op.__init__(self, **kwargs) |
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|
353 |
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ported nnet_ops to new theano
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|
354 def make_node(self, x, b, y_idx): |
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|
355 x = tensor.as_tensor(x) |
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|
356 b = tensor.as_tensor(b) |
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|
357 y_idx = tensor.as_tensor(y_idx) |
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|
358 if x.type.ndim != 2 \ |
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|
359 or x.type.dtype not in ['float32', 'float64']: |
30
bf0145fa73e8
added c implementation for CrossentropySoftmax1Hot
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diff
changeset
|
360 raise ValueError('x must be 2-d tensor of floats') |
117
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|
361 if b.type.ndim != 1 \ |
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|
362 or x.type.dtype not in ['float32', 'float64']: |
121 | 363 raise ValueError('b must be 1-d tensor of floats') |
117
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|
364 if y_idx.type.ndim != 1 \ |
185
3d953844abd3
support for more int types in crossentropysoftmax1hot
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365 or y_idx.type.dtype not in ['int8', 'int16', 'int32', 'int64']: |
121 | 366 raise ValueError('y_idx must be 1-d tensor of ints') |
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367 |
24 | 368 # TODO: Is this correct? It used to be y, not y_idx |
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369 nll = tensor.Tensor(x.type.dtype, |
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370 y_idx.type.broadcastable).make_result() |
24 | 371 # nll = Tensor(x.dtype, y.broadcastable) |
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372 sm = x.type.make_result() |
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373 return theano.Apply(self, [x, b, y_idx], [nll, sm]) |
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374 def perform(self, node, input_storage, output_storage): |
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375 x, b, y_idx = input_storage |
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376 if b.shape[0] != x.shape[1]: |
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377 raise ValueError('b must have same number of columns as x') |
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378 if y_idx.shape[0] != x.shape[0]: |
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379 raise ValueError('y_idx must have same number of rows as x') |
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380 |
24 | 381 sm = numpy.zeros_like(x) # softmax |
382 nll = numpy.zeros(x.shape[0]) #nll(y | softmax(x)) | |
383 for i in xrange(sm.shape[0]): | |
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384 row = x[i] + b |
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385 sm[i] = numpy.exp(row - numpy.max(row)) #softmax |
24 | 386 sm[i] *= 1.0 / numpy.sum(sm[i]) #vector scale |
387 nll[i] = -numpy.log( sm[i, y_idx[i]]) #cross-entropy | |
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388 output_storage[0][0] = nll |
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389 output_storage[1][0] = sm |
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390 def grad(self, (x, b, y_idx), (g_nll, g_sm)): |
24 | 391 if g_sm is not None: |
392 raise NotImplementedError() | |
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393 nll, sm = crossentropy_softmax_1hot_with_bias(x, b, y_idx) |
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394 dx = CrossentropySoftmax1HotWithBiasDx()(g_nll, sm, y_idx) |
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395 db = tensor.sum(dx, axis = [0]) |
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396 return dx, db, None |
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397 |
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398 def c_headers(self): return ['<iostream>'] |
440
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399 |
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400 @staticmethod |
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401 def c_code_template(): |
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402 # this implementation was lifted from |
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403 # /u/bergstrj/cvs/bergstrj/src/feb07/nn.cxx |
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404 |
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405 #TODO: put this into a templated function, in the support code |
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406 #TODO: declare the max of each row as an Op output |
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407 |
32 | 408 #TODO: set error messages for failures in this code |
409 | |
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410 #TODO: use this to accept float32 and int32: node.inputs[0].type.dtype_specs()[1] |
440
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411 (init_decl, begin_row_loop, inside_row_loop, end_row_loop) = \ |
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412 SoftmaxWithBias.c_code_template() |
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413 return (init_decl, |
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414 """ |
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415 if (%(y_idx)s->nd != 1) |
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416 { |
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417 PyErr_SetString(PyExc_ValueError, "y_idx not 1d tensor"); |
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418 %(fail)s; |
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419 } |
185
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420 if ((%(y_idx)s->descr->type_num != PyArray_INT64) |
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421 && (%(y_idx)s->descr->type_num != PyArray_INT32) |
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422 && (%(y_idx)s->descr->type_num != PyArray_INT16) |
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423 && (%(y_idx)s->descr->type_num != PyArray_INT8)) |
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424 { |
185
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425 PyErr_SetString(PyExc_TypeError, "y_idx not int8, int16, int32, or int64"); |
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426 %(fail)s; |
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427 } |
440
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428 if (%(x)s->dimensions[0] != %(y_idx)s->dimensions[0]) |
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429 { |
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430 PyErr_SetString(PyExc_ValueError, "dimension mismatch in arguments"); |
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431 %(fail)s; |
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432 } |
34 | 433 |
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434 if ((NULL == %(nll)s) //initial condition |
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435 || (%(nll)s->dimensions[0] != %(y_idx)s->dimensions[0])) |
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436 { |
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437 if (NULL != %(nll)s) Py_XDECREF(%(nll)s); |
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438 %(nll)s = (PyArrayObject*)PyArray_SimpleNew(1, PyArray_DIMS(%(y_idx)s), type_num_%(x)s); |
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439 if(!%(nll)s) |
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440 { |
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441 PyErr_SetString(PyExc_MemoryError, "failed to alloc nll output"); |
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442 %(fail)s; |
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443 } |
34 | 444 } |
440
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445 """, |
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446 begin_row_loop, |
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447 """ |
185
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448 const %(y_idx_type)s y_i = ((%(y_idx_type)s*)(%(y_idx)s->data + %(y_idx)s->strides[0] * i))[0]; |
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449 double* __restrict__ nll_i = (double*)(%(nll)s->data + %(nll)s->strides[0] * i); |
440
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450 """, |
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451 inside_row_loop, |
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452 """ |
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453 nll_i[0] = - x_i[y_i*Sx] |
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454 - b_i[y_i*Sb] |
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455 + log(sum); |
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456 """, |
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457 end_row_loop) |
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458 |
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459 |
440
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460 def c_code(self, node, name, (x, b, y_idx), (nll, sm), sub): |
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461 y_idx_type = node.inputs[2].type.dtype_specs()[1] |
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462 code_template = ''.join(self.c_code_template()) |
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463 return code_template % dict(locals(), **sub) |
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464 |
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465 crossentropy_softmax_1hot_with_bias = CrossentropySoftmax1HotWithBias() |
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466 |
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467 class CrossentropySoftmax1HotWithBiasDx (theano.Op): |
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468 nin=3 |
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469 nout=1 |
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470 """Gradient wrt x of the CrossentropySoftmax1Hot Op""" |
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471 def __init__(self, **kwargs): |
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472 theano.Op.__init__(self,**kwargs) |
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473 def make_node(self, dy, sm, y_idx,**kwargs): |
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474 dy = tensor.as_tensor(dy) |
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475 sm = tensor.as_tensor(sm) |
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476 y_idx = tensor.as_tensor(y_idx) |
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477 return theano.Apply(self, [dy, sm, y_idx],[sm.type.make_result()]) |
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478 def perform(self, node, input_storage, output_storage): |
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479 dy,sm,y_idx = input_storage |
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480 dx = numpy.zeros_like(sm) |
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481 for i in xrange(sm.shape[0]): |
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482 dx[i] = dy[i] * sm[i] #vector scale |
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483 dx[i, y_idx[i]] -= dy[i] #scalar decrement |
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484 output_storage[0][0] = dx |
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485 def grad(self, *args): |
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486 raise NotImplementedError() |
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487 def c_code(self, node, name, (dnll, sm, y_idx), (dx,), sub): |
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488 y_idx_type = node.inputs[2].type.dtype_specs()[1] |
32 | 489 return """ |
490 | |
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491 if ((%(dnll)s->descr->type_num != PyArray_DOUBLE) |
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492 || (%(sm)s->descr->type_num != PyArray_DOUBLE) |
185
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493 ) |
67
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494 { |
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495 PyErr_SetString(PyExc_TypeError, "types should be float64, float64, int64"); |
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496 %(fail)s; |
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497 } |
185
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498 if ((%(y_idx)s->descr->type_num != PyArray_INT64) |
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499 && (%(y_idx)s->descr->type_num != PyArray_INT32) |
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500 && (%(y_idx)s->descr->type_num != PyArray_INT16) |
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501 && (%(y_idx)s->descr->type_num != PyArray_INT8)) |
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502 { |
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503 PyErr_SetString(PyExc_TypeError, "y_idx not int8, int16, int32, or int64"); |
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504 %(fail)s; |
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505 } |
67
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506 if ((%(dnll)s->nd != 1) |
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507 || (%(sm)s->nd != 2) |
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508 || (%(y_idx)s->nd != 1)) |
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509 { |
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510 PyErr_SetString(PyExc_ValueError, "rank error"); |
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511 %(fail)s; |
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512 } |
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513 if ((%(dnll)s->dimensions[0] != %(sm)s->dimensions[0]) |
68 | 514 || (%(dnll)s->dimensions[0] != %(y_idx)s->dimensions[0])) |
67
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515 { |
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516 PyErr_SetString(PyExc_ValueError, "dimension mismatch"); |
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517 %(fail)s; |
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518 } |
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519 if ((NULL == %(dx)s) |
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520 || (%(dx)s->dimensions[0] != %(sm)s->dimensions[0]) |
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521 || (%(dx)s->dimensions[1] != %(sm)s->dimensions[1])) |
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522 { |
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523 if (NULL != %(dx)s) Py_XDECREF(%(dx)s); |
68 | 524 %(dx)s = (PyArrayObject*)PyArray_SimpleNew(2, PyArray_DIMS(%(sm)s), type_num_%(sm)s); |
67
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525 if(!%(dx)s) { |
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526 PyErr_SetString(PyExc_MemoryError, "failed to alloc dx output"); |
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527 %(fail)s |
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528 } |
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529 } |
24 | 530 |
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531 for (size_t i = 0; i < %(dx)s->dimensions[0]; ++i) |
32 | 532 { |
533 const double dnll_i = ((double*)(%(dnll)s->data + %(dnll)s->strides[0] * i))[0]; | |
534 | |
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535 const %(y_idx_type)s y_i = ((%(y_idx_type)s*)(%(y_idx)s->data + %(y_idx)s->strides[0] * i))[0]; |
32 | 536 |
537 const double* __restrict__ sm_i = (double*)(%(sm)s->data + %(sm)s->strides[0] * i); | |
538 npy_intp Ssm = %(sm)s->strides[1]/sizeof(double); | |
539 | |
540 double* __restrict__ dx_i = (double*)(%(dx)s->data + %(dx)s->strides[0] * i); | |
541 npy_intp Sdx = %(dx)s->strides[1]/sizeof(double); | |
542 | |
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543 for (size_t j = 0; j < %(dx)s->dimensions[1]; ++j) |
32 | 544 { |
545 dx_i[j * Sdx] = dnll_i * sm_i[j * Ssm]; | |
546 } | |
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547 if (y_i >= %(dx)s->dimensions[1]) |
32 | 548 { |
549 %(fail)s; | |
550 } | |
551 dx_i[y_i * Sdx] -= dnll_i; | |
552 } | |
553 """ % dict(locals(), **sub) | |
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554 |
70
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555 def crossentropy_softmax_1hot(x, y_idx, **kwargs): |
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556 b = tensor.zeros_like(x[0,:]) |
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557 return crossentropy_softmax_1hot_with_bias(x, b, y_idx, **kwargs) |
382
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558 |
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559 def binary_crossentropy(output, target): |
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560 """ |
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561 Compute the crossentropy of binary output wrt binary target. |
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562 @note: We do not sum, crossentropy is computed by component. |
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563 @todo: Rewrite as a scalar, and then broadcast to tensor. |
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564 """ |
383 | 565 return -(target * tensor.log(output) + (1 - target) * tensor.log(1 - output)) |
419
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566 |
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567 |
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568 |
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569 class Prepend_scalar_constant_to_each_row(theano.Op): |
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570 def __init__(self, val = 0): |
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571 if isinstance(val, float): |
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572 val = scalar.constant(val) |
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573 self.val = val |
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574 |
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575 def make_node(self, mat): |
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576 #check type of input |
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577 if not isinstance(mat,theano.Result) or not mat.type==tensor.matrix().type: |
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578 raise TypeError("Expected a matrix as input") |
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579 x = tensor.as_tensor(mat) |
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|
580 y = tensor.as_tensor(self.val) |
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581 if x.type.dtype != y.type.dtype: |
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582 TypeError("the value to prepend don't have the same type as the matrix") |
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|
583 |
419
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|
584 node = theano.Apply(op=self, inputs=[mat], outputs=[tensor.matrix()]) |
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585 return node |
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586 |
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diff
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|
587 def perform(self, node, (mat, ), (output, )): |
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588 new_shape=(mat.shape[0],mat.shape[1]+1) |
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diff
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|
589 if output[0] == None: |
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diff
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|
590 output[0]=numpy.empty(new_shape,dtype=mat.dtype) |
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|
591 out=output[0] |
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diff
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|
592 else: |
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diff
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|
593 if output[0].shape!=new_shape: |
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|
594 try: |
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diff
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|
595 output[0].resize(new_shape) |
43d9aa93934e
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596 except: |
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597 output[0]=numpy.empty(new_shape, dtype=mat.dtype) |
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|
598 out=output[0] |
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diff
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599 |
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diff
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|
600 out[:,0].fill(self.val.data) |
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|
601 out[:,1:]=mat |
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parents:
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diff
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|
602 |
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|
603 def grad(self, (mat,), (goutput,)): |
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|
604 return goutput[:,1:] |
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|
605 |
443
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|
606 class Prepend_scalar_to_each_row(theano.Op): |
419
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607 def make_node(self, val, mat): |
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608 #check type of input |
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609 if isinstance(val, float): |
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610 val = scalar.constant(val) |
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611 if not isinstance(mat,theano.Result) or not mat.type==tensor.matrix().type: |
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612 raise TypeError("Expected a matrix as input") |
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613 x = tensor.as_tensor(mat) |
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614 y = tensor.as_tensor(val) |
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615 if x.type.dtype != y.type.dtype: |
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diff
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|
616 TypeError("the value to prepend don't have the same type as the matrix") |
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|
617 |
419
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618 node = theano.Apply(op=self, inputs=[val,mat], outputs=[tensor.matrix()]) |
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619 return node |
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620 |
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621 def perform(self, node, (val,mat), (output, )): |
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622 new_shape=(mat.shape[0],mat.shape[1]+1) |
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623 if output[0] == None: |
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624 output[0]=numpy.empty(new_shape,dtype=mat.dtype) |
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625 out=output[0] |
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626 else: |
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627 if output[0].shape!=new_shape: |
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628 try: |
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629 output[0].resize(new_shape) |
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630 except: |
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631 output[0]=numpy.empty(new_shape, dtype=mat.dtype) |
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632 out=output[0] |
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633 out[:,0].fill(val) |
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634 out[:,1:]=mat |
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635 |
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636 def grad(self, (val, mat), (goutput,)): |
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637 return goutput[:,0], goutput[:,1:] |
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638 |
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639 prepend_scalar_to_each_row = Prepend_scalar_to_each_row() |
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640 prepend_0_to_each_row = Prepend_scalar_constant_to_each_row(0.) |
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641 prepend_1_to_each_row = Prepend_scalar_constant_to_each_row(1.) |
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642 |
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643 class solve(theano.Op): |
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644 """ |
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645 Find the solution to the linear equation Ax=b, |
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646 where A is a 2d matrix and b is a 1d or 2d matrix. |
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647 It use numpy.solve to find the solution. |
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648 """ |
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649 |
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650 def make_node(self, A, b): |
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651 if not isinstance(A, theano.Result) or not A.type==tensor.matrix().type: |
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652 raise TypeError("We expected that A had a matrix type") |
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653 if not isinstance(B, theano.Result) or not B.type==tensor.matrix().type: |
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654 raise TypeError("We expected that B had a matrix type") |
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655 |
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656 node = theano.Apply(op=self, inputs=[A, B], outputs=[tensor.matrix()]) |
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657 return node |
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658 |
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659 def perform(self, node, (A, B), (output, )): |
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660 ret=numpy.solve(A,B) |
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661 output[0]=ret |
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662 |
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663 def grad(self, (theta, A, B), (gtheta,)): |
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664 raise NotImplementedError() |
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665 |
443
060c12314734
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666 |