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annotate nnet_ops.py @ 441:a2e8de4669cd

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author Pascal Lamblin <lamblinp@iro.umontreal.ca>
date Thu, 21 Aug 2008 13:55:43 -0400
parents 18dbc1c11647
children b3315b252824
rev   line source
419
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
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parents: 383
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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
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4 import theano
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5 from theano import tensor, scalar
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6 import numpy
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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))
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21 def impl(self, x):
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22 return ScalarSigmoid.st_impl(x)
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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')
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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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f6a7eb1b7970 redo what James had done, so invert node and name
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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 #
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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 = nympy.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] = nll
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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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diff changeset
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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diff changeset
240
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241
117
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242 class CrossentropySoftmax1HotWithBias(theano.Op):
70
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243 """A special compound L{Op} for the output of neural-net classifiers.
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244
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245 @type x: is a matrix of floats (32 or 64)
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246 @type b: is a [row] vector of floats (32 or 64), length is number of cols in x
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247 @type y_idx: a [column] vector of int (32 or 64), length is number of rows in x
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248
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249 @precondition: every entry in y_idx is a valid (non-negative) column index into x
24
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250
70
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251 This L{Op} has two outputs:
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252 - KL(softmax(x+b), y)
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253 - softmax(x+b)
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254
440
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diff changeset
255
24
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256 softmax(x[i]) is the i'th distribution over len(x[i]) options
70
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diff changeset
257
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diff changeset
258 y_idx[i] is an integer index, encoding a 1-hot distribution.
440
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259
70
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diff changeset
260 In practice, when we're trying to do classification, we have one row in x
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diff changeset
261 and y_idx per example, and y[i] is the index of the (correct) class of the
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diff changeset
262 i'th example.
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263
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264 """
70
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265 nin=3
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266 nout=2
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diff changeset
267 def __init__(self, **kwargs):
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diff changeset
268 theano.Op.__init__(self, **kwargs)
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269
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diff changeset
270 def make_node(self, x, b, y_idx):
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diff changeset
271 x = tensor.as_tensor(x)
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272 b = tensor.as_tensor(b)
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diff changeset
273 y_idx = tensor.as_tensor(y_idx)
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diff changeset
274 if x.type.ndim != 2 \
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diff changeset
275 or x.type.dtype not in ['float32', 'float64']:
30
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diff changeset
276 raise ValueError('x must be 2-d tensor of floats')
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diff changeset
277 if b.type.ndim != 1 \
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diff changeset
278 or x.type.dtype not in ['float32', 'float64']:
121
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diff changeset
279 raise ValueError('b must be 1-d tensor of floats')
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diff changeset
280 if y_idx.type.ndim != 1 \
185
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281 or y_idx.type.dtype not in ['int8', 'int16', 'int32', 'int64']:
121
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282 raise ValueError('y_idx must be 1-d tensor of ints')
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283
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284 # TODO: Is this correct? It used to be y, not y_idx
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285 nll = tensor.Tensor(x.type.dtype,
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286 y_idx.type.broadcastable).make_result()
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287 # nll = Tensor(x.dtype, y.broadcastable)
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288 sm = x.type.make_result()
185
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289 return theano.Apply(self, [x, b, y_idx], [nll, sm])
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290 def perform(self, node, input_storage, output_storage):
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291 x, b, y_idx = input_storage
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292 if b.shape[0] != x.shape[1]:
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293 raise ValueError('b must have same number of columns as x')
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294 if y_idx.shape[0] != x.shape[0]:
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295 raise ValueError('y_idx must have same number of rows as x')
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296
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297 sm = numpy.zeros_like(x) # softmax
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298 nll = numpy.zeros(x.shape[0]) #nll(y | softmax(x))
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299 for i in xrange(sm.shape[0]):
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300 row = x[i] + b
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301 sm[i] = numpy.exp(row - numpy.max(row)) #softmax
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302 sm[i] *= 1.0 / numpy.sum(sm[i]) #vector scale
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303 nll[i] = -numpy.log( sm[i, y_idx[i]]) #cross-entropy
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304 output_storage[0][0] = nll
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305 output_storage[1][0] = sm
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306 def grad(self, (x, b, y_idx), (g_nll, g_sm)):
24
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307 if g_sm is not None:
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308 raise NotImplementedError()
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309 nll, sm = crossentropy_softmax_1hot_with_bias(x, b, y_idx)
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310 dx = CrossentropySoftmax1HotWithBiasDx()(g_nll, sm, y_idx)
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311 db = tensor.sum(dx, axis = [0])
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312 return dx, db, None
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313
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314 def c_headers(self): return ['<iostream>']
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315
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316 @staticmethod
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317 def c_code_template():
30
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318 # this implementation was lifted from
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319 # /u/bergstrj/cvs/bergstrj/src/feb07/nn.cxx
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320
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321 #TODO: put this into a templated function, in the support code
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322 #TODO: declare the max of each row as an Op output
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323
32
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diff changeset
324 #TODO: set error messages for failures in this code
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325
184
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diff changeset
326 #TODO: use this to accept float32 and int32: node.inputs[0].type.dtype_specs()[1]
440
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327 (init_decl, begin_row_loop, inside_row_loop, end_row_loop) = \
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328 SoftmaxWithBias.c_code_template()
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329 return (init_decl,
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330 """
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331 if (%(y_idx)s->nd != 1)
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332 {
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333 PyErr_SetString(PyExc_ValueError, "y_idx not 1d tensor");
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334 %(fail)s;
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335 }
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336 if ((%(y_idx)s->descr->type_num != PyArray_INT64)
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337 && (%(y_idx)s->descr->type_num != PyArray_INT32)
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338 && (%(y_idx)s->descr->type_num != PyArray_INT16)
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339 && (%(y_idx)s->descr->type_num != PyArray_INT8))
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340 {
185
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341 PyErr_SetString(PyExc_TypeError, "y_idx not int8, int16, int32, or int64");
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342 %(fail)s;
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343 }
440
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344 if (%(x)s->dimensions[0] != %(y_idx)s->dimensions[0])
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345 {
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346 PyErr_SetString(PyExc_ValueError, "dimension mismatch in arguments");
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347 %(fail)s;
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348 }
34
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349
67
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350 if ((NULL == %(nll)s) //initial condition
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351 || (%(nll)s->dimensions[0] != %(y_idx)s->dimensions[0]))
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352 {
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353 if (NULL != %(nll)s) Py_XDECREF(%(nll)s);
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354 %(nll)s = (PyArrayObject*)PyArray_SimpleNew(1, PyArray_DIMS(%(y_idx)s), type_num_%(x)s);
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355 if(!%(nll)s)
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356 {
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357 PyErr_SetString(PyExc_MemoryError, "failed to alloc nll output");
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358 %(fail)s;
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359 }
34
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360 }
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361 """,
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362 begin_row_loop,
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363 """
185
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diff changeset
364 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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365 double* __restrict__ nll_i = (double*)(%(nll)s->data + %(nll)s->strides[0] * i);
440
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diff changeset
366 """,
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367 inside_row_loop,
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diff changeset
368 """
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diff changeset
369 nll_i[0] = - x_i[y_i*Sx]
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diff changeset
370 - b_i[y_i*Sb]
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371 + log(sum);
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diff changeset
372 """,
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373 end_row_loop)
30
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374
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375
440
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diff changeset
376 def c_code(self, node, name, (x, b, y_idx), (nll, sm), sub):
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diff changeset
377 y_idx_type = node.inputs[2].type.dtype_specs()[1]
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diff changeset
378 code_template = ''.join(self.c_code_template())
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379 return code_template % dict(locals(), **sub)
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380
117
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diff changeset
381 crossentropy_softmax_1hot_with_bias = CrossentropySoftmax1HotWithBias()
30
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382
117
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diff changeset
383 class CrossentropySoftmax1HotWithBiasDx (theano.Op):
30
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384 nin=3
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385 nout=1
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386 """Gradient wrt x of the CrossentropySoftmax1Hot Op"""
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387 def __init__(self, **kwargs):
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diff changeset
388 theano.Op.__init__(self,**kwargs)
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389 def make_node(self, dy, sm, y_idx,**kwargs):
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390 dy = tensor.as_tensor(dy)
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391 sm = tensor.as_tensor(sm)
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diff changeset
392 y_idx = tensor.as_tensor(y_idx)
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diff changeset
393 return theano.Apply(self, [dy, sm, y_idx],[sm.type.make_result()])
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diff changeset
394 def perform(self, node, input_storage, output_storage):
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diff changeset
395 dy,sm,y_idx = input_storage
30
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396 dx = numpy.zeros_like(sm)
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397 for i in xrange(sm.shape[0]):
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398 dx[i] = dy[i] * sm[i] #vector scale
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399 dx[i, y_idx[i]] -= dy[i] #scalar decrement
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diff changeset
400 output_storage[0][0] = dx
30
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diff changeset
401 def grad(self, *args):
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402 raise NotImplementedError()
181
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diff changeset
403 def c_code(self, node, name, (dnll, sm, y_idx), (dx,), sub):
185
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diff changeset
404 y_idx_type = node.inputs[2].type.dtype_specs()[1]
32
039c0f249859 added C impl for softmax dx
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diff changeset
405 return """
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diff changeset
406
67
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diff changeset
407 if ((%(dnll)s->descr->type_num != PyArray_DOUBLE)
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408 || (%(sm)s->descr->type_num != PyArray_DOUBLE)
185
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diff changeset
409 )
67
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diff changeset
410 {
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diff changeset
411 PyErr_SetString(PyExc_TypeError, "types should be float64, float64, int64");
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diff changeset
412 %(fail)s;
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diff changeset
413 }
185
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diff changeset
414 if ((%(y_idx)s->descr->type_num != PyArray_INT64)
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diff changeset
415 && (%(y_idx)s->descr->type_num != PyArray_INT32)
3d953844abd3 support for more int types in crossentropysoftmax1hot
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 184
diff changeset
416 && (%(y_idx)s->descr->type_num != PyArray_INT16)
3d953844abd3 support for more int types in crossentropysoftmax1hot
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 184
diff changeset
417 && (%(y_idx)s->descr->type_num != PyArray_INT8))
3d953844abd3 support for more int types in crossentropysoftmax1hot
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 184
diff changeset
418 {
3d953844abd3 support for more int types in crossentropysoftmax1hot
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 184
diff changeset
419 PyErr_SetString(PyExc_TypeError, "y_idx not int8, int16, int32, or int64");
3d953844abd3 support for more int types in crossentropysoftmax1hot
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 184
diff changeset
420 %(fail)s;
3d953844abd3 support for more int types in crossentropysoftmax1hot
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 184
diff changeset
421 }
67
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
422 if ((%(dnll)s->nd != 1)
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
423 || (%(sm)s->nd != 2)
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
424 || (%(y_idx)s->nd != 1))
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
425 {
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
426 PyErr_SetString(PyExc_ValueError, "rank error");
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
427 %(fail)s;
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
428 }
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
429 if ((%(dnll)s->dimensions[0] != %(sm)s->dimensions[0])
68
315eb36ff954 fixed typo in crossentropy_dx.c_code
bergstra@is23.m
parents: 67
diff changeset
430 || (%(dnll)s->dimensions[0] != %(y_idx)s->dimensions[0]))
67
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
431 {
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
432 PyErr_SetString(PyExc_ValueError, "dimension mismatch");
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
433 %(fail)s;
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
434 }
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
435 if ((NULL == %(dx)s)
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
436 || (%(dx)s->dimensions[0] != %(sm)s->dimensions[0])
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
437 || (%(dx)s->dimensions[1] != %(sm)s->dimensions[1]))
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
438 {
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
439 if (NULL != %(dx)s) Py_XDECREF(%(dx)s);
68
315eb36ff954 fixed typo in crossentropy_dx.c_code
bergstra@is23.m
parents: 67
diff changeset
440 %(dx)s = (PyArrayObject*)PyArray_SimpleNew(2, PyArray_DIMS(%(sm)s), type_num_%(sm)s);
67
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
441 if(!%(dx)s) {
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
442 PyErr_SetString(PyExc_MemoryError, "failed to alloc dx output");
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
443 %(fail)s
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
444 }
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
445 }
24
2e8be9f5412b added nnet_ops
bergstrj@iro.umontreal.ca
parents:
diff changeset
446
67
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
447 for (size_t i = 0; i < %(dx)s->dimensions[0]; ++i)
32
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
448 {
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
449 const double dnll_i = ((double*)(%(dnll)s->data + %(dnll)s->strides[0] * i))[0];
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
450
185
3d953844abd3 support for more int types in crossentropysoftmax1hot
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 184
diff changeset
451 const %(y_idx_type)s y_i = ((%(y_idx_type)s*)(%(y_idx)s->data + %(y_idx)s->strides[0] * i))[0];
32
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
452
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
453 const double* __restrict__ sm_i = (double*)(%(sm)s->data + %(sm)s->strides[0] * i);
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
454 npy_intp Ssm = %(sm)s->strides[1]/sizeof(double);
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
455
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
456 double* __restrict__ dx_i = (double*)(%(dx)s->data + %(dx)s->strides[0] * i);
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
457 npy_intp Sdx = %(dx)s->strides[1]/sizeof(double);
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
458
67
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
459 for (size_t j = 0; j < %(dx)s->dimensions[1]; ++j)
32
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
460 {
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
461 dx_i[j * Sdx] = dnll_i * sm_i[j * Ssm];
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
462 }
67
810a8e3c85e1 fixed horrible memory leak from crossentropy...
bergstra@is23.m
parents: 34
diff changeset
463 if (y_i >= %(dx)s->dimensions[1])
32
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
464 {
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
465 %(fail)s;
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
466 }
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
467 dx_i[y_i * Sdx] -= dnll_i;
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
468 }
039c0f249859 added C impl for softmax dx
bergstrj@iro.umontreal.ca
parents: 30
diff changeset
469 """ % dict(locals(), **sub)
69
8c2607f387e6 added softplus, elaborated sigmoid
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 68
diff changeset
470
70
76e5c0f37165 better docs & precondition testing for cross_entropy_softmax_1hot & friends
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 69
diff changeset
471 def crossentropy_softmax_1hot(x, y_idx, **kwargs):
76e5c0f37165 better docs & precondition testing for cross_entropy_softmax_1hot & friends
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 69
diff changeset
472 b = tensor.zeros_like(x[0,:])
76e5c0f37165 better docs & precondition testing for cross_entropy_softmax_1hot & friends
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 69
diff changeset
473 return crossentropy_softmax_1hot_with_bias(x, b, y_idx, **kwargs)
382
b4efd192d880 Moved xent loss to nnet_ups
Joseph Turian <turian@gmail.com>
parents: 381
diff changeset
474
b4efd192d880 Moved xent loss to nnet_ups
Joseph Turian <turian@gmail.com>
parents: 381
diff changeset
475 def binary_crossentropy(output, target):
b4efd192d880 Moved xent loss to nnet_ups
Joseph Turian <turian@gmail.com>
parents: 381
diff changeset
476 """
b4efd192d880 Moved xent loss to nnet_ups
Joseph Turian <turian@gmail.com>
parents: 381
diff changeset
477 Compute the crossentropy of binary output wrt binary target.
b4efd192d880 Moved xent loss to nnet_ups
Joseph Turian <turian@gmail.com>
parents: 381
diff changeset
478 @note: We do not sum, crossentropy is computed by component.
b4efd192d880 Moved xent loss to nnet_ups
Joseph Turian <turian@gmail.com>
parents: 381
diff changeset
479 @todo: Rewrite as a scalar, and then broadcast to tensor.
b4efd192d880 Moved xent loss to nnet_ups
Joseph Turian <turian@gmail.com>
parents: 381
diff changeset
480 """
383
344d1f874af7 Small fix
Joseph Turian <turian@gmail.com>
parents: 382
diff changeset
481 return -(target * tensor.log(output) + (1 - target) * tensor.log(1 - output))
419
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
482
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
483
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
484
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
485 class Prepend_scalar_constant_to_each_row(theano.Op):
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
486 def __init__(self, val = 0):
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
487 if isinstance(val, float):
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
488 val = scalar.constant(val)
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
489 self.val = val
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
490
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
491 def make_node(self, mat):
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
492 #check type of input
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
493 if not isinstance(mat,theano.Result) or not mat.type==tensor.matrix().type:
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
494 raise TypeError("Expected a matrix as input")
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
495 x = tensor.as_tensor(mat)
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
496 y = tensor.as_tensor(self.val)
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
497 if x.type.dtype != y.type.dtype:
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
498 TypeError("the value to prepend don't have the same type as the matrix")
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
499
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
500 node = theano.Apply(op=self, inputs=[mat], outputs=[tensor.matrix()])
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
501 return node
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
502
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
503 def perform(self, node, (mat, ), (output, )):
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
504 new_shape=(mat.shape[0],mat.shape[1]+1)
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
505 if output[0] == None:
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
506 output[0]=numpy.empty(new_shape,dtype=mat.dtype)
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
507 out=output[0]
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
508 else:
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
509 if output[0].shape!=new_shape:
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
510 try:
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
511 output[0].resize(new_shape)
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
512 except:
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
513 output[0]=numpy.empty(new_shape, dtype=mat.dtype)
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
514 out=output[0]
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
515
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
516 out[:,0].fill(self.val.data)
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
517 out[:,1:]=mat
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
518
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
519 def grad(self, (mat,), (goutput,)):
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
520 return goutput[:,1:]
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
521
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
522 class Prepend_scalar_to_each_row(theano.Op):
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
523 def make_node(self, val, mat):
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
524 #check type of input
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
525 if isinstance(val, float):
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
526 val = scalar.constant(val)
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
527 if not isinstance(mat,theano.Result) or not mat.type==tensor.matrix().type:
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
528 raise TypeError("Expected a matrix as input")
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
529 x = tensor.as_tensor(mat)
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
530 y = tensor.as_tensor(val)
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
531 if x.type.dtype != y.type.dtype:
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
532 TypeError("the value to prepend don't have the same type as the matrix")
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
533
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
534 node = theano.Apply(op=self, inputs=[val,mat], outputs=[tensor.matrix()])
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
535 return node
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
536
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
537 def perform(self, node, (val,mat), (output, )):
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
538 new_shape=(mat.shape[0],mat.shape[1]+1)
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
539 if output[0] == None:
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
540 output[0]=numpy.empty(new_shape,dtype=mat.dtype)
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
541 out=output[0]
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
542 else:
43d9aa93934e added other_ops.py to nnet_ops; added basic tests, no docs.
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
543 if output[0].shape!=new_shape:
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544 try:
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545 output[0].resize(new_shape)
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546 except:
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547 output[0]=numpy.empty(new_shape, dtype=mat.dtype)
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548 out=output[0]
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549 out[:,0].fill(val)
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550 out[:,1:]=mat
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551
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552 def grad(self, (val, mat), (goutput,)):
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553 return goutput[:,0], goutput[:,1:]
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554
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555 prepend_scalar_to_each_row = Prepend_scalar_to_each_row()
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556 prepend_0_to_each_row = Prepend_scalar_constant_to_each_row(0.)
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557 prepend_1_to_each_row = Prepend_scalar_constant_to_each_row(1.)
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558
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559 class solve(theano.Op):
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560 """
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561 Find the solution to the linear equation Ax=b,
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parents: 383
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562 where A is a 2d matrix and b is a 1d or 2d matrix.
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563 It use numpy.solve to find the solution.
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564 """
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565
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566 def make_node(self, A, b):
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567 if not isinstance(A, theano.Result) or not A.type==tensor.matrix().type:
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parents: 383
diff changeset
568 raise TypeError("We expected that A had a matrix type")
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diff changeset
569 if not isinstance(B, theano.Result) or not B.type==tensor.matrix().type:
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570 raise TypeError("We expected that B had a matrix type")
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571
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diff changeset
572 node = theano.Apply(op=self, inputs=[A, B], outputs=[tensor.matrix()])
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573 return node
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574
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diff changeset
575 def perform(self, node, (A, B), (output, )):
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576 ret=numpy.solve(A,B)
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parents: 383
diff changeset
577 output[0]=ret
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parents: 383
diff changeset
578
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parents: 383
diff changeset
579 def grad(self, (theta, A, B), (gtheta,)):
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parents: 383
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580 raise NotImplementedError()
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diff changeset
581
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 383
diff changeset
582