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annotate nnet_ops.py @ 34:1b152f46ad0c

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consolidated code
author bergstrj@iro.umontreal.ca
date Thu, 17 Apr 2008 12:49:48 -0400
parents 039c0f249859
children 810a8e3c85e1
rev   line source
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1 import theano
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2 from theano import tensor, gof, scalar
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3 import numpy
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4
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5 class ScalarSigmoid(scalar.UnaryScalarOp):
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6 def impl(self, x):
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7 return 1.0 / (1 + numpy.exp(-x))
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8 def grad(self, (x,), (gz,)):
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9 return gz * scalar_sigmoid(x) * (1.0 - scalar_sigmoid(x)),
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10 def c_foreach(self, (x,), (z,)):
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11 return "%(z)s = 1.0 / (1 + exp(-%(x)s));" % locals()
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12 scalar_sigmoid = gof.op.constructor(ScalarSigmoid)
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13 Sigmoid, sigmoid, SigmoidInplace, sigmoid_inplace \
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14 = theano.tensor.broadcast(ScalarSigmoid, 'Sigmoid')
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15
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16
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17
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18 class CrossentropySoftmax1Hot(gof.op.Op):
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19 """A special compound Op for the output of neural-net classifiers.
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20
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21 This Op has two outputs:
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22 - KL(softmax(x), y)
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23 - softmax(x)
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24
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25 x[i] is assumed to be a dense vector
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26 softmax(x[i]) is the i'th distribution over len(x[i]) options
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27 y[i] is an integer index, encoding a 1-hot distribution
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28
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29 """
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30 nin=2
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31 nout=2
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32 def __init__(self, x, b, y_idx, **kwargs):
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33 x = tensor._as_tensor(x)
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34 b = tensor._as_tensor(b)
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35 y_idx = tensor._as_tensor(y_idx)
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36 if len(x.broadcastable) != 2 \
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37 or x.dtype not in ['float32', 'float64']:
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38 raise ValueError('x must be 2-d tensor of floats')
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39 if len(b.broadcastable) != 1 \
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40 or x.dtype not in ['float32', 'float64']:
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41 raise ValueError('x must be 1-d tensor of floats')
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42 if len(y_idx.broadcastable) != 1 \
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43 or y_idx.dtype not in ['int32', 'int64']:
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44 raise ValueError('x must be 1-d tensor of ints')
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45
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46 # TODO: Is this correct? It used to be y, not y_idx
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47 nll = tensor.Tensor(x.dtype, y_idx.broadcastable)
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48 # nll = Tensor(x.dtype, y.broadcastable)
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49 sm = tensor.Tensor(x.dtype, x.broadcastable)
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50 self.inputs = [x, b, y_idx]
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51 self.outputs = [nll, sm]
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52 def perform(self):
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53 x, b, y_idx = [i.data for i in self.inputs]
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54 if b.shape[0] != x.shape[1]:
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55 raise ValueError('b must have same shape as x[0]')
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56
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57 sm = numpy.zeros_like(x) # softmax
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58 nll = numpy.zeros(x.shape[0]) #nll(y | softmax(x))
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59 for i in xrange(sm.shape[0]):
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60 row = x[i] + b
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61 sm[i] = numpy.exp(row - numpy.max(row)) #softmax
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62 sm[i] *= 1.0 / numpy.sum(sm[i]) #vector scale
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63 nll[i] = -numpy.log( sm[i, y_idx[i]]) #cross-entropy
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64 self.outputs[0].data = nll
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65 self.outputs[1].data = sm
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66 def grad(self, (x, b, y_idx), (g_nll, g_sm)):
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67 if g_sm is not None:
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68 raise NotImplementedError()
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69 nll, sm = crossentropy_softmax_1hot(x, b, y_idx)
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70 dx = CrossentropySoftmax1HotDx(g_nll, sm, y_idx).outputs[0]
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71 db = tensor.Sum(dx, axis = [0]).outputs[0]
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72 return dx, db, None
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73
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74 def c_validate_cleanup(self, (x, b, y_idx), (nll, sm), sub):
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75 """Not sure..."""
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76 return ""
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77 def c_support_code(self):
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78 return """
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79 """
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80 def c_code(self, (x, b, y_idx), (nll, sm), sub):
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81 # this implementation was lifted from
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82 # /u/bergstrj/cvs/bergstrj/src/feb07/nn.cxx
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83
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84 #TODO: put this into a templated function, in the support code
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85 #TODO: declare the max of each row as an Op output
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86
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87 #TODO: set error messages for failures in this code
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88
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89 return """
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90 npy_intp* Nx = %(x)s->dimensions;
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91
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92 if (%(x)s->nd != 2) { %(fail)s }
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93 if (%(b)s->nd != 1) { %(fail)s }
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94 if (%(y_idx)s->nd != 1) { %(fail)s }
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95 if (%(x)s->descr->type_num != PyArray_DOUBLE) { %(fail)s}
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96 if (%(b)s->descr->type_num != PyArray_DOUBLE) { %(fail)s}
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97 if (%(y_idx)s->descr->type_num != PyArray_INT64) { %(fail)s}
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98
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99 %(nll)s = (PyArrayObject*)PyArray_SimpleNew(1, PyArray_DIMS(%(y_idx)s), type_num_%(x)s);
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100 if(!%(nll)s){%(fail)s}
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101
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102 %(sm)s = (PyArrayObject*)PyArray_SimpleNew(2, PyArray_DIMS(%(x)s), type_num_%(x)s);
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103 if(!%(sm)s) {
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104 // The normal cleanup code will take care of %(nll)s
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105 // Py_XDECREF(%(nll)s); %(nll)s=NULL;
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106 %(fail)s
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107 }
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108 if (%(x)s->dimensions[1] != %(b)s->dimensions[0]) {%(fail)s}
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109 if (%(sm)s->dimensions[0] != %(x)s->dimensions[0]) {%(fail)s}
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110 if (%(sm)s->dimensions[1] != %(x)s->dimensions[1]) {%(fail)s}
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111
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112 for (size_t i = 0; i < Nx[0]; ++i)
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113 {
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114 size_t j;
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115 double sum = 0.0;
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116 bool discount_max = false;
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117
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118 const double* __restrict__ x_i = (double*)(%(x)s->data + %(x)s->strides[0] * i);
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119 const double* __restrict__ b_i = (double*)(%(b)s->data);
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120 const long int y_i = ((long int*)(%(y_idx)s->data + %(y_idx)s->strides[0] * i))[0];
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121 double* __restrict__ sm_i = (double*)(%(sm)s->data + %(sm)s->strides[0] * i);
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122 double* __restrict__ nll_i = (double*)(%(nll)s->data + %(nll)s->strides[0] * i);
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123
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124 npy_intp Sx = %(x)s->strides[1]/sizeof(double);
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125 npy_intp Sb = %(b)s->strides[0]/sizeof(double);
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126 npy_intp Ssm = %(sm)s->strides[1]/sizeof(double);
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127
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128 size_t row_max_j=0;
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129 double row_max = x_i[0] + b_i[0];
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130 //try to compute sum and sm the easy way
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131 for (j = 0; j < Nx[1]; ++j)
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132 {
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133 double row_ij = x_i[j * Sx] + b_i[j * Sb];
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134 row_max_j = (row_ij > row_max) ? j : row_max_j;
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135 row_max = (row_ij > row_max) ? row_ij : row_max;
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136
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137 double sm_ij = exp(row_ij);
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138 sum += sm_ij;
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139 sm_i[j * Ssm] = sm_ij;
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140 }
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141 if ((0.0 == sum) || (isinf(sum)))
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142 {
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143 //our cheap trick didn't work... try again and do it better.
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144 discount_max = true;
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145 sum = 0.0; //reset sum and recompute....
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146 for (j = 0; j < Nx[1]; ++j)
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147 {
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148 double row_ij = x_i[j * Sx] + b_i[j * Sb];
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149
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150 double sm_ij = exp(row_ij - row_max);
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151 sum += sm_ij;
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152 sm_i[j * Ssm] = sm_ij;
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153 }
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154 if ( (0.0 == sum) || (isinf(sum)))
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155 {
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156 //that was our best...
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157 %(fail)s;
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158 }
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159 //if we still can't sum it up, we're screwed.
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160 //So far, this assertion has never failed...
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161 }
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162
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163 //cblas_dscal(x.N, 1.0 / sum, &mat_at(s,i,0), s.n);
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164 double sum_inv = 1.0 / sum;
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165 for (j = 0; j < Nx[1]; ++j)
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166 {
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167 sm_i[j * Ssm] *= sum_inv;
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168 }
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169
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170 if (y_i >= Nx[1])
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171 {
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172 %(fail)s;
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173 }
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174
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175 nll_i[0] = - x_i[y_i*Sx]
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176 - b_i[y_i*Sb]
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177 + (discount_max ? row_max : 0.0)
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178 + log(sum);
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179 //mat_at(y,i,0) = -log( mat_at(s,i,t[i])); //less accurate?
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180 //mat_at(y,i,0) = - mat_at(x,i,t[i]) - mat_at(b,0,t[i]) + (discount_max ? maxi : 0.0) + log(sum);
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181 }
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182 """ % dict(locals(), **sub)
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183
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184
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185
25
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186 crossentropy_softmax_1hot = gof.op.constructor(CrossentropySoftmax1Hot)
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187
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188 class CrossentropySoftmax1HotDx (gof.op.Op):
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189 nin=3
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190 nout=1
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191 """Gradient wrt x of the CrossentropySoftmax1Hot Op"""
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192 def __init__(self, dy, sm, y_idx,**kwargs):
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193 dy = tensor._as_tensor(dy)
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194 sm = tensor._as_tensor(sm)
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195 y_idx = tensor._as_tensor(y_idx)
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196 self.inputs = [dy, sm, y_idx]
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197 self.outputs = [tensor.Tensor(sm.dtype, sm.broadcastable)]
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198 def perform(self):
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199 dy,sm,y_idx = [i.data for i in self.inputs]
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200 dx = numpy.zeros_like(sm)
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201 for i in xrange(sm.shape[0]):
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202 dx[i] = dy[i] * sm[i] #vector scale
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203 dx[i, y_idx[i]] -= dy[i] #scalar decrement
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204 self.outputs[0].data = dx
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205 def grad(self, *args):
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206 raise NotImplementedError()
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207 def c_validate_update(self, (dnll, sm, y_idx), (dx,), sub):
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208 """Allocate output storage"""
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209 return """
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210 if (%(dnll)s->nd != 1) { %(fail)s }
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211 if (%(sm)s->nd != 2) { %(fail)s }
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212 if (%(y_idx)s->nd != 1) { %(fail)s }
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213 if (%(dnll)s->descr->type_num != PyArray_DOUBLE) { %(fail)s}
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214 if (%(sm)s->descr->type_num != PyArray_DOUBLE) { %(fail)s}
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215 if (%(y_idx)s->descr->type_num != PyArray_INT64) { %(fail)s}
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216
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217 %(dx)s = (PyArrayObject*)PyArray_SimpleNew(2, PyArray_DIMS(%(sm)s), type_num_%(sm)s);
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218 if(!%(dx)s){%(fail)s}
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219
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220 """ % dict(locals(), **sub)
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221 def c_validate_cleanup(self, inputs, outputs, sub):
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222 """Not sure..."""
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223 return ""
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224 def c_support_code(self):
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225 return """
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226 """
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227 def c_code(self, (dnll, sm, y_idx), (dx,), sub):
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228 return """
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229 npy_intp* shape = %(dx)s->dimensions;
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230 if (%(dnll)s->dimensions[0] != %(sm)s->dimensions[0]) {%(fail)s}
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231 if (%(dnll)s->dimensions[0] != %(y_idx)s->dimensions[0]) {%(fail)s}
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232 if (%(dnll)s->dimensions[0] != %(dx)s->dimensions[0]) {%(fail)s}
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233
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234 if (%(sm)s->dimensions[1] != %(dx)s->dimensions[1]) {%(fail)s}
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235
32
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236 for (size_t i = 0; i < shape[0]; ++i)
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237 {
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238 const double dnll_i = ((double*)(%(dnll)s->data + %(dnll)s->strides[0] * i))[0];
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239
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240 const long int y_i = ((long int*)(%(y_idx)s->data + %(y_idx)s->strides[0] * i))[0];
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241
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242 const double* __restrict__ sm_i = (double*)(%(sm)s->data + %(sm)s->strides[0] * i);
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243 npy_intp Ssm = %(sm)s->strides[1]/sizeof(double);
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244
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245 double* __restrict__ dx_i = (double*)(%(dx)s->data + %(dx)s->strides[0] * i);
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246 npy_intp Sdx = %(dx)s->strides[1]/sizeof(double);
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247
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248 for (size_t j = 0; j < shape[1]; ++j)
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249 {
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250 dx_i[j * Sdx] = dnll_i * sm_i[j * Ssm];
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251 }
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252 if (y_i >= shape[1])
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253 {
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254 %(fail)s;
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255 }
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256 dx_i[y_i * Sdx] -= dnll_i;
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257 }
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258 """ % dict(locals(), **sub)