Mercurial > pylearn
annotate nnet_ops.py @ 264:a1793a5e9523
we can now load and save in a file, see test class in the file for an example, but basically it's model1.save(filename) or learn_algo(filename) to load
author | Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca> |
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date | Wed, 04 Jun 2008 17:00:44 -0400 |
parents | f6a7eb1b7970 |
children | e4473d9697d7 |
rev | line source |
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24 | 1 import theano |
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2 from theano import tensor, scalar |
24 | 3 import numpy |
4 | |
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5 ############ |
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6 # |
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7 # SCALAR OPS |
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8 # |
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9 |
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10 class ScalarSigmoid(scalar.UnaryScalarOp): |
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11 @staticmethod |
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12 def st_impl(x): |
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13 if x < -30.0: |
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14 return 0.0 |
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15 if x > 30.0: |
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16 return 1.0 |
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17 return 1.0 / (1.0 + numpy.exp(-x)) |
24 | 18 def impl(self, x): |
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19 return ScalarSigmoid.st_impl(x) |
24 | 20 def grad(self, (x,), (gz,)): |
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21 y = scalar_sigmoid(x) |
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22 return [gz * y * (1.0 - y)] |
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23 def c_code(self, node, name, (x,), (z,), sub): |
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24 if node.inputs[0].type in [scalar.float32, scalar.float64]: |
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25 return """%(z)s = |
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26 %(x)s < -30.0 |
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27 ? 0.0 |
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28 : %(x)s > 30.0 |
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29 ? 1.0 |
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30 : 1.0 /(1.0+exp(-%(x)s));""" % locals() |
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31 raise NotImplementedError('only floatingpoint is implemented') |
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32 scalar_sigmoid = ScalarSigmoid(scalar.upgrade_to_float, name='scalar_sigmoid') |
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33 sigmoid = tensor.Elemwise(scalar_sigmoid, name='sigmoid') |
24 | 34 |
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35 class ScalarSoftplus(scalar.UnaryScalarOp): |
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36 @staticmethod |
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37 def static_impl(x): |
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38 if x < -30.0: |
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39 return 0.0 |
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40 if x > 30.0: |
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41 return x |
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42 return numpy.log1p(numpy.exp(x)) |
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43 def impl(self, x): |
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44 return ScalarSoftplus.static_impl(x) |
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45 def grad(self, (x,), (gz,)): |
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46 return [gz * scalar_sigmoid(x)] |
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47 def c_code(self, node, name, (x,), (z,), sub): |
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48 if node.inputs[0].type in [scalar.float32, scalar.float64]: |
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49 return """%(z)s = |
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50 %(x)s < -30.0 |
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51 ? 0.0 |
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52 : %(x)s > 30.0 |
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53 ? %(x)s |
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54 : log1p(exp(%(x)s));""" % locals() |
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55 raise NotImplementedError('only floating point x is implemented') |
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56 scalar_softplus = ScalarSoftplus(scalar.upgrade_to_float, name='scalar_softplus') |
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57 softplus = tensor.Elemwise(scalar_softplus, name='softplus') |
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58 |
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59 |
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60 ############ |
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61 # |
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62 # TENSOR OPS |
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63 # |
24 | 64 |
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65 class CrossentropySoftmax1HotWithBias(theano.Op): |
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66 """A special compound L{Op} for the output of neural-net classifiers. |
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67 |
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68 @type x: is a matrix of floats (32 or 64) |
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69 @type b: is a [row] vector of floats (32 or 64), length is number of cols in x |
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70 @type y_idx: a [column] vector of int (32 or 64), length is number of rows in x |
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71 |
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72 @precondition: every entry in y_idx is a valid (non-negative) column index into x |
24 | 73 |
70
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74 This L{Op} has two outputs: |
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75 - KL(softmax(x+b), y) |
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76 - softmax(x+b) |
24 | 77 |
70
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78 |
24 | 79 softmax(x[i]) is the i'th distribution over len(x[i]) options |
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80 |
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81 y_idx[i] is an integer index, encoding a 1-hot distribution. |
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82 |
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83 In practice, when we're trying to do classification, we have one row in x |
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84 and y_idx per example, and y[i] is the index of the (correct) class of the |
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85 i'th example. |
24 | 86 |
87 """ | |
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88 nin=3 |
24 | 89 nout=2 |
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90 def __init__(self, **kwargs): |
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91 theano.Op.__init__(self, **kwargs) |
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92 |
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93 def make_node(self, x, b, y_idx): |
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94 x = tensor.as_tensor(x) |
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95 b = tensor.as_tensor(b) |
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96 y_idx = tensor.as_tensor(y_idx) |
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97 if x.type.ndim != 2 \ |
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98 or x.type.dtype not in ['float32', 'float64']: |
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99 raise ValueError('x must be 2-d tensor of floats') |
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100 if b.type.ndim != 1 \ |
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101 or x.type.dtype not in ['float32', 'float64']: |
121 | 102 raise ValueError('b must be 1-d tensor of floats') |
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103 if y_idx.type.ndim != 1 \ |
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104 or y_idx.type.dtype not in ['int8', 'int16', 'int32', 'int64']: |
121 | 105 raise ValueError('y_idx must be 1-d tensor of ints') |
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106 |
24 | 107 # TODO: Is this correct? It used to be y, not y_idx |
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108 nll = tensor.Tensor(x.type.dtype, |
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109 y_idx.type.broadcastable).make_result() |
24 | 110 # nll = Tensor(x.dtype, y.broadcastable) |
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111 sm = x.type.make_result() |
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112 return theano.Apply(self, [x, b, y_idx], [nll, sm]) |
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113 def perform(self, node, input_storage, output_storage): |
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114 x, b, y_idx = input_storage |
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115 if b.shape[0] != x.shape[1]: |
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116 raise ValueError('b must have same number of columns as x') |
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117 if y_idx.shape[0] != x.shape[0]: |
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118 raise ValueError('y_idx must have same number of rows as x') |
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119 |
24 | 120 sm = numpy.zeros_like(x) # softmax |
121 nll = numpy.zeros(x.shape[0]) #nll(y | softmax(x)) | |
122 for i in xrange(sm.shape[0]): | |
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123 row = x[i] + b |
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124 sm[i] = numpy.exp(row - numpy.max(row)) #softmax |
24 | 125 sm[i] *= 1.0 / numpy.sum(sm[i]) #vector scale |
126 nll[i] = -numpy.log( sm[i, y_idx[i]]) #cross-entropy | |
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127 output_storage[0][0] = nll |
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128 output_storage[1][0] = sm |
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129 def grad(self, (x, b, y_idx), (g_nll, g_sm)): |
24 | 130 if g_sm is not None: |
131 raise NotImplementedError() | |
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132 nll, sm = crossentropy_softmax_1hot_with_bias(x, b, y_idx) |
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133 dx = CrossentropySoftmax1HotWithBiasDx()(g_nll, sm, y_idx) |
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134 db = tensor.sum(dx, axis = [0]) |
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135 return dx, db, None |
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136 |
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137 def c_headers(self): return ['<iostream>'] |
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138 def c_code(self, node, name, (x, b, y_idx), (nll, sm), sub): |
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139 # this implementation was lifted from |
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140 # /u/bergstrj/cvs/bergstrj/src/feb07/nn.cxx |
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141 |
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142 #TODO: put this into a templated function, in the support code |
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143 #TODO: declare the max of each row as an Op output |
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144 |
32 | 145 #TODO: set error messages for failures in this code |
146 | |
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147 #TODO: use this to accept float32 and int32: node.inputs[0].type.dtype_specs()[1] |
185
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148 y_idx_type = node.inputs[2].type.dtype_specs()[1] |
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149 |
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150 return """ |
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151 npy_intp* Nx = %(x)s->dimensions; |
34 | 152 |
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153 if (%(x)s->nd != 2) |
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154 { |
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155 PyErr_SetString(PyExc_ValueError, "a not 2d tensor"); |
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156 %(fail)s; |
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157 } |
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158 if (%(b)s->nd != 1) |
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159 { |
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160 PyErr_SetString(PyExc_ValueError, "b not 1d tensor"); |
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161 %(fail)s; |
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162 } |
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163 if (%(y_idx)s->nd != 1) |
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164 { |
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165 PyErr_SetString(PyExc_ValueError, "y_idx not 1d tensor"); |
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166 %(fail)s; |
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167 } |
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168 if (%(x)s->descr->type_num != PyArray_DOUBLE) |
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169 { |
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170 PyErr_SetString(PyExc_TypeError, "a not float64"); |
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171 %(fail)s; |
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172 } |
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173 if (%(b)s->descr->type_num != PyArray_DOUBLE) |
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174 { |
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175 PyErr_SetString(PyExc_TypeError, "b not float64"); |
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176 %(fail)s; |
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177 } |
185
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178 if ((%(y_idx)s->descr->type_num != PyArray_INT64) |
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179 && (%(y_idx)s->descr->type_num != PyArray_INT32) |
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180 && (%(y_idx)s->descr->type_num != PyArray_INT16) |
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181 && (%(y_idx)s->descr->type_num != PyArray_INT8)) |
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182 { |
185
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183 PyErr_SetString(PyExc_TypeError, "y_idx not int8, int16, int32, or int64"); |
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184 %(fail)s; |
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185 } |
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186 if ((%(x)s->dimensions[1] != %(b)s->dimensions[0]) |
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187 || (%(x)s->dimensions[0] != %(y_idx)s->dimensions[0])) |
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188 { |
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189 PyErr_SetString(PyExc_ValueError, "dimension mismatch in arguments"); |
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190 %(fail)s; |
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191 } |
34 | 192 |
67
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193 if ((NULL == %(nll)s) //initial condition |
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194 || (%(nll)s->dimensions[0] != %(y_idx)s->dimensions[0])) |
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195 { |
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196 if (NULL != %(nll)s) Py_XDECREF(%(nll)s); |
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197 %(nll)s = (PyArrayObject*)PyArray_SimpleNew(1, PyArray_DIMS(%(y_idx)s), type_num_%(x)s); |
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198 if(!%(nll)s) |
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199 { |
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200 PyErr_SetString(PyExc_MemoryError, "failed to alloc nll output"); |
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201 %(fail)s; |
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202 } |
34 | 203 } |
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204 if ((NULL == %(sm)s) |
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205 || (%(sm)s->dimensions[0] != %(x)s->dimensions[0]) |
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206 || (%(sm)s->dimensions[1] != %(x)s->dimensions[1])) |
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207 { |
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208 if (NULL != %(sm)s) Py_XDECREF(%(sm)s); |
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209 %(sm)s = (PyArrayObject*)PyArray_SimpleNew(2, PyArray_DIMS(%(x)s), type_num_%(x)s); |
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210 if(!%(sm)s) { |
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211 // The normal cleanup code will take care of %(nll)s |
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212 // Py_XDECREF(%(nll)s); %(nll)s=NULL; |
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213 PyErr_SetString(PyExc_MemoryError, "failed to alloc sm output"); |
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214 %(fail)s |
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215 } |
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216 } |
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217 |
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218 for (size_t i = 0; i < Nx[0]; ++i) |
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219 { |
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220 size_t j; |
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221 double sum = 0.0; |
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222 bool discount_max = false; |
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223 |
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224 const double* __restrict__ x_i = (double*)(%(x)s->data + %(x)s->strides[0] * i); |
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225 const double* __restrict__ b_i = (double*)(%(b)s->data); |
185
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226 const %(y_idx_type)s y_i = ((%(y_idx_type)s*)(%(y_idx)s->data + %(y_idx)s->strides[0] * i))[0]; |
30
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227 double* __restrict__ sm_i = (double*)(%(sm)s->data + %(sm)s->strides[0] * i); |
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228 double* __restrict__ nll_i = (double*)(%(nll)s->data + %(nll)s->strides[0] * i); |
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229 |
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230 npy_intp Sx = %(x)s->strides[1]/sizeof(double); |
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231 npy_intp Sb = %(b)s->strides[0]/sizeof(double); |
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232 npy_intp Ssm = %(sm)s->strides[1]/sizeof(double); |
24 | 233 |
30
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234 size_t row_max_j=0; |
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235 double row_max = x_i[0] + b_i[0]; |
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236 //try to compute sum and sm the easy way |
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237 for (j = 0; j < Nx[1]; ++j) |
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238 { |
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239 double row_ij = x_i[j * Sx] + b_i[j * Sb]; |
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240 row_max_j = (row_ij > row_max) ? j : row_max_j; |
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241 row_max = (row_ij > row_max) ? row_ij : row_max; |
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242 |
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243 double sm_ij = exp(row_ij); |
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244 sum += sm_ij; |
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245 sm_i[j * Ssm] = sm_ij; |
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246 } |
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247 if ((0.0 == sum) || (isinf(sum))) |
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248 { |
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249 //our cheap trick didn't work... try again and do it better. |
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250 discount_max = true; |
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251 sum = 0.0; //reset sum and recompute.... |
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252 for (j = 0; j < Nx[1]; ++j) |
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253 { |
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254 double row_ij = x_i[j * Sx] + b_i[j * Sb]; |
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255 |
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256 double sm_ij = exp(row_ij - row_max); |
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257 sum += sm_ij; |
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258 sm_i[j * Ssm] = sm_ij; |
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259 } |
32 | 260 if ( (0.0 == sum) || (isinf(sum))) |
261 { | |
262 //that was our best... | |
263 %(fail)s; | |
264 } | |
30
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265 //if we still can't sum it up, we're screwed. |
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266 //So far, this assertion has never failed... |
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267 } |
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268 |
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269 //cblas_dscal(x.N, 1.0 / sum, &mat_at(s,i,0), s.n); |
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270 double sum_inv = 1.0 / sum; |
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271 for (j = 0; j < Nx[1]; ++j) |
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272 { |
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273 sm_i[j * Ssm] *= sum_inv; |
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274 } |
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275 |
32 | 276 if (y_i >= Nx[1]) |
277 { | |
278 %(fail)s; | |
279 } | |
30
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280 |
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281 nll_i[0] = - x_i[y_i*Sx] |
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282 - b_i[y_i*Sb] |
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283 + (discount_max ? row_max : 0.0) |
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284 + log(sum); |
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285 //mat_at(y,i,0) = -log( mat_at(s,i,t[i])); //less accurate? |
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286 //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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287 } |
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288 """ % dict(locals(), **sub) |
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289 crossentropy_softmax_1hot_with_bias = CrossentropySoftmax1HotWithBias() |
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290 |
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291 class CrossentropySoftmax1HotWithBiasDx (theano.Op): |
30
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292 nin=3 |
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293 nout=1 |
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294 """Gradient wrt x of the CrossentropySoftmax1Hot Op""" |
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295 def __init__(self, **kwargs): |
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296 theano.Op.__init__(self,**kwargs) |
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297 def make_node(self, dy, sm, y_idx,**kwargs): |
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298 dy = tensor.as_tensor(dy) |
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299 sm = tensor.as_tensor(sm) |
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300 y_idx = tensor.as_tensor(y_idx) |
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301 return theano.Apply(self, [dy, sm, y_idx],[sm.type.make_result()]) |
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302 def perform(self, node, input_storage, output_storage): |
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303 dy,sm,y_idx = input_storage |
30
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304 dx = numpy.zeros_like(sm) |
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305 for i in xrange(sm.shape[0]): |
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306 dx[i] = dy[i] * sm[i] #vector scale |
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307 dx[i, y_idx[i]] -= dy[i] #scalar decrement |
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308 output_storage[0][0] = dx |
30
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309 def grad(self, *args): |
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310 raise NotImplementedError() |
181
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311 def c_code(self, node, name, (dnll, sm, y_idx), (dx,), sub): |
185
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312 y_idx_type = node.inputs[2].type.dtype_specs()[1] |
32 | 313 return """ |
314 | |
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315 if ((%(dnll)s->descr->type_num != PyArray_DOUBLE) |
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316 || (%(sm)s->descr->type_num != PyArray_DOUBLE) |
185
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317 ) |
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318 { |
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319 PyErr_SetString(PyExc_TypeError, "types should be float64, float64, int64"); |
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320 %(fail)s; |
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321 } |
185
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322 if ((%(y_idx)s->descr->type_num != PyArray_INT64) |
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323 && (%(y_idx)s->descr->type_num != PyArray_INT32) |
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324 && (%(y_idx)s->descr->type_num != PyArray_INT16) |
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325 && (%(y_idx)s->descr->type_num != PyArray_INT8)) |
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326 { |
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327 PyErr_SetString(PyExc_TypeError, "y_idx not int8, int16, int32, or int64"); |
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328 %(fail)s; |
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329 } |
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330 if ((%(dnll)s->nd != 1) |
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331 || (%(sm)s->nd != 2) |
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332 || (%(y_idx)s->nd != 1)) |
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333 { |
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334 PyErr_SetString(PyExc_ValueError, "rank error"); |
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335 %(fail)s; |
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336 } |
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337 if ((%(dnll)s->dimensions[0] != %(sm)s->dimensions[0]) |
68 | 338 || (%(dnll)s->dimensions[0] != %(y_idx)s->dimensions[0])) |
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339 { |
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340 PyErr_SetString(PyExc_ValueError, "dimension mismatch"); |
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341 %(fail)s; |
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342 } |
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343 if ((NULL == %(dx)s) |
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344 || (%(dx)s->dimensions[0] != %(sm)s->dimensions[0]) |
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345 || (%(dx)s->dimensions[1] != %(sm)s->dimensions[1])) |
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346 { |
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347 if (NULL != %(dx)s) Py_XDECREF(%(dx)s); |
68 | 348 %(dx)s = (PyArrayObject*)PyArray_SimpleNew(2, PyArray_DIMS(%(sm)s), type_num_%(sm)s); |
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349 if(!%(dx)s) { |
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350 PyErr_SetString(PyExc_MemoryError, "failed to alloc dx output"); |
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351 %(fail)s |
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352 } |
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353 } |
24 | 354 |
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355 for (size_t i = 0; i < %(dx)s->dimensions[0]; ++i) |
32 | 356 { |
357 const double dnll_i = ((double*)(%(dnll)s->data + %(dnll)s->strides[0] * i))[0]; | |
358 | |
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359 const %(y_idx_type)s y_i = ((%(y_idx_type)s*)(%(y_idx)s->data + %(y_idx)s->strides[0] * i))[0]; |
32 | 360 |
361 const double* __restrict__ sm_i = (double*)(%(sm)s->data + %(sm)s->strides[0] * i); | |
362 npy_intp Ssm = %(sm)s->strides[1]/sizeof(double); | |
363 | |
364 double* __restrict__ dx_i = (double*)(%(dx)s->data + %(dx)s->strides[0] * i); | |
365 npy_intp Sdx = %(dx)s->strides[1]/sizeof(double); | |
366 | |
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367 for (size_t j = 0; j < %(dx)s->dimensions[1]; ++j) |
32 | 368 { |
369 dx_i[j * Sdx] = dnll_i * sm_i[j * Ssm]; | |
370 } | |
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371 if (y_i >= %(dx)s->dimensions[1]) |
32 | 372 { |
373 %(fail)s; | |
374 } | |
375 dx_i[y_i * Sdx] -= dnll_i; | |
376 } | |
377 """ % dict(locals(), **sub) | |
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378 |
70
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379 def crossentropy_softmax_1hot(x, y_idx, **kwargs): |
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380 b = tensor.zeros_like(x[0,:]) |
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381 return crossentropy_softmax_1hot_with_bias(x, b, y_idx, **kwargs) |
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382 |