Mercurial > ift6266

annotate deep/convolutional_dae/scdae.py @ 298:a222af1d0598

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- Adapt to scdae to input_shape change in pynnet - Use the proper dataset in run_exp
author Arnaud Bergeron <abergeron@gmail.com>
date Mon, 29 Mar 2010 17:36:22 -0400
parents 8108d271c30c
children be45e7db7cd4
rev   line source
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1 from pynnet import *
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2
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3 import numpy
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4 import theano
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5 import theano.tensor as T
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6
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7 from itertools import izip
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8 from ift6266.utils.seriestables import *
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9
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10 class cdae(LayerStack):
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11 def __init__(self, filter_size, num_filt, num_in, subsampling, corruption,
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12 dtype):
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13 LayerStack.__init__(self, [ConvAutoencoder(filter_size=filter_size,
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14 num_filt=num_filt,
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15 num_in=num_in,
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16 noisyness=corruption,
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17 dtype=dtype),
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18 MaxPoolLayer(subsampling)])
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19
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20 def build(self, input, input_shape=None):
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21 LayerStack.build(self, input, input_shape)
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22 self.cost = self.layers[0].cost
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23 self.pre_params = self.layers[0].pre_params
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24
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25 def scdae(filter_sizes, num_filts, subsamplings, corruptions, dtype):
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26 layers = []
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27 old_nfilt = 1
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28 for fsize, nfilt, subs, corr in izip(filter_sizes, num_filts,
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29 subsamplings, corruptions):
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30 layers.append(cdae(fsize, nfilt, old_nfilt, subs, corr, dtype))
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31 old_nfilt = nfilt
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32 return LayerStack(layers)
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33
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34 def mlp(layer_sizes, dtype):
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35 layers = []
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36 old_size = layer_sizes[0]
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37 for size in layer_sizes[1:]:
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38 layers.append(SimpleLayer(old_size, size, activation=nlins.tanh,
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39 dtype=dtype))
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40 old_size = size
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41 return LayerStack(layers)
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42
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43 def scdae_net(in_size, num_in, filter_sizes, num_filts, subsamplings,
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44 corruptions, layer_sizes, out_size, dtype):
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45 rl1 = ReshapeLayer((None,)+in_size)
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46 ls = scdae(num_in, filter_sizes, num_filts, subsamplings,
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47 corruptions, dtype)
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48 x = T.tensor4()
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49 ls.build(x, input_shape=(1,)+in_size)
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50 outs = numpy.prod(ls.output_shape)
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51 rl2 = ReshapeLayer((None, outs))
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52 layer_sizes = [outs]+layer_sizes
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53 ls2 = mlp(layer_sizes, dtype)
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54 lrl = SimpleLayer(layer_sizes[-1], out_size, activation=nlins.softmax)
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55 return NNet([rl1, ls, rl2, ls2, lrl], error=errors.nll)
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56
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57 def build_funcs(batch_size, img_size, filter_sizes, num_filters, subs,
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58 noise, mlp_sizes, out_size, dtype, pretrain_lr, train_lr):
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59
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60 n = scdae_net((1,)+img_size, batch_size, filter_sizes, num_filters, subs,
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61 noise, mlp_sizes, out_size, dtype)
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62
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63 n.save('start.net')
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64
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65 x = T.fmatrix('x')
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66 y = T.ivector('y')
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67
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68 def pretrainfunc(net, alpha):
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69 up = trainers.get_updates(net.pre_params, net.cost, alpha)
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70 return theano.function([x], net.cost, updates=up)
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71
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72 def trainfunc(net, alpha):
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73 up = trainers.get_updates(net.params, net.cost, alpha)
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74 return theano.function([x, y], net.cost, updates=up)
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75
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76 n.build(x, y, input_shape=(bsize, 1)+img_size)
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77 pretrain_funcs_opt = [pretrainfunc(l, pretrain_lr) for l in n.layers[1].layers]
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78 trainf_opt = trainfunc(n, train_lr)
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79 evalf_opt = theano.function([x, y], errors.class_error(n.output, y))
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80
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81 n.build(x, y)
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82 pretrain_funcs_reg = [pretrainfunc(l, 0.01) for l in n.layers[1].layers]
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83 trainf_reg = trainfunc(n, 0.1)
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84 evalf_reg = theano.function([x, y], errors.class_error(n.output, y))
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85
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86 def select_f(f1, f2, bsize):
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87 def f(x):
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88 if x.shape[0] == bsize:
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89 return f1(x)
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90 else:
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91 return f2(x)
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92 return f
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93
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94 pretrain_funcs = [select_f(p_opt, p_reg, batch_size) for p_opt, p_reg in zip(pretrain_funcs_opt, pretrain_funcs_reg)]
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95
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96 def select_f2(f1, f2, bsize):
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97 def f(x, y):
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98 if x.shape[0] == bsize:
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99 return f1(x, y)
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100 else:
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101 return f2(x, y)
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102 return f
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103
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104 trainf = select_f2(trainf_opt, trainf_reg, batch_size)
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105 evalf = select_f2(evalf_opt, evalf_reg, batch_size)
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106 return pretrain_funcs, trainf, evalf, n
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107
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108 def do_pretrain(pretrain_funcs, pretrain_epochs, serie):
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109 for layer, f in enumerate(pretrain_funcs):
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110 for epoch in xrange(pretrain_epochs):
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111 serie.append((layer, epoch), f())
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112
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113 def massage_funcs(pretrain_it, train_it, dset, batch_size, pretrain_funcs,
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114 trainf, evalf):
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115 def pretrain_f(f):
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116 def res():
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117 for x, y in pretrain_it:
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118 yield f(x)
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119 it = res()
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120 return lambda: it.next()
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121
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122 pretrain_fs = map(pretrain_f, pretrain_funcs)
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123
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124 def train_f(f):
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125 def dset_it():
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126 for x, y in train_it:
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127 yield f(x, y)
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128 it = dset_it()
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129 return lambda: it.next()
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130
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131 train = train_f(trainf)
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132
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133 def eval_f(f, dsetf):
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134 def res():
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135 c = 0
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136 i = 0
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137 for x, y in dsetf(batch_size):
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138 i += x.shape[0]
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139 c += f(x, y)*x.shape[0]
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140 return c/i
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141 return res
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142
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143 test = eval_f(evalf, dset.test)
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144 valid = eval_f(evalf, dset.valid)
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145
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146 return pretrain_fs, train, valid, test
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147
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148 def repeat_itf(itf, *args, **kwargs):
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149 while True:
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150 for e in itf(*args, **kwargs):
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151 yield e
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152
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153 def create_series():
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154 import tables
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155
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156 series = {}
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157 h5f = tables.openFile('series.h5', 'w')
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158
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159 series['recons_error'] = AccumulatorSeriesWrapper(
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160 base_series=ErrorSeries(error_name='reconstruction_error',
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161 table_name='reconstruction_error',
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162 hdf5_file=h5f,
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163 index_names=('layer', 'epoch'),
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164 title="Reconstruction error (mse)"),
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165 reduce_every=100)
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166
292
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167 series['train_error'] = AccumulatorSeriesWrapper(
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168 base_series=ErrorSeries(error_name='training_error',
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169 table_name='training_error',
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170 hdf5_file=h5f,
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171 index_names=('iter',),
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172 title='Training error (nll)'),
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173 reduce_every=100)
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174
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175 series['valid_error'] = ErrorSeries(error_name='valid_error',
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176 table_name='valid_error',
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177 hdf5_file=h5f,
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178 index_names=('iter',),
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179 title='Validation error (class)')
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180
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181 series['test_error'] = ErrorSeries(error_name='test_error',
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182 table_name='test_error',
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183 hdf5_file=h5f,
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184 index_names=('iter',),
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185 title='Test error (class)')
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186
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187 return series
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188
276
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189 if __name__ == '__main__':
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190 from ift6266 import datasets
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191 from sgd_opt import sgd_opt
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192 import sys, time
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193
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194 batch_size = 100
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195 dset = datasets.mnist()
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196
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197 pretrain_funcs, trainf, evalf, net = build_funcs(
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198 img_size = (28, 28),
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199 batch_size=batch_size, filter_sizes=[(5,5), (3,3)],
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200 num_filters=[4, 4], subs=[(2,2), (2,2)], noise=[0.2, 0.2],
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201 mlp_sizes=[500], out_size=10, dtype=numpy.float32,
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202 pretrain_lr=0.01, train_lr=0.1)
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203
298
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204 t_it = repeat_itf(dset.train, batch_size)
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205 pretrain_fs, train, valid, test = massage_funcs(
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206 t_it, t_it, dset, batch_size,
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207 pretrain_funcs, trainf, evalf)
276
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208
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209 print "pretraining ...",
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210 sys.stdout.flush()
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211 start = time.time()
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212 do_pretrain(pretrain_fs, 2500, DummySeries())
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213 end = time.time()
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214 print "done (in", end-start, "s)"
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215
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216 sgd_opt(train, valid, test, training_epochs=10000, patience=1000,
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217 patience_increase=2., improvement_threshold=0.995,
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218 validation_frequency=250)
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219