ift6266: deep/convolutional_dae/stacked_convolutional

annotate deep/convolutional_dae/stacked_convolutional_dae.py @ 272:f6d9b6b89c2a

ajouté : module de préparation de batches en fonction d'un ratio de classes

author	Guillaume Sicard <guitch21@gmail.com>
date	Mon, 22 Mar 2010 08:34:48 -0400
parents	0c0f0b3f6a93
children

rev	line source
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	1 import numpy
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	2 import theano
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	3 import time
247 4d109b648c31 Fixed dataset import. Removed unuseful code from da_conv. Keys parameters are now passed as arguments. humel parents: 215 diff changeset	4 import sys
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	5 import theano.tensor as T
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	6 from theano.tensor.shared_randomstreams import RandomStreams
259 3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	7 #import theano.sandbox.softsign
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	8
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	9 from theano.tensor.signal import downsample
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	10 from theano.tensor.nnet import conv
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	11
247 4d109b648c31 Fixed dataset import. Removed unuseful code from da_conv. Keys parameters are now passed as arguments. humel parents: 215 diff changeset	12 from ift6266 import datasets
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	13 from ift6266.baseline.log_reg.log_reg import LogisticRegression
247 4d109b648c31 Fixed dataset import. Removed unuseful code from da_conv. Keys parameters are now passed as arguments. humel parents: 215 diff changeset	14
248 7e6fecabb656 Optimized the call of ConvOp by specifying additional parameters. Specified image shape of the da_conv layer. humel parents: 247 diff changeset	15 batch_size = 100
7e6fecabb656 Optimized the call of ConvOp by specifying additional parameters. Specified image shape of the da_conv layer. humel parents: 247 diff changeset	16
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	17 class SigmoidalLayer(object):
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	18 def __init__(self, rng, input, n_in, n_out):
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	19
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	20 self.input = input
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	21
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	22 W_values = numpy.asarray( rng.uniform( \
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	23 low = -numpy.sqrt(6./(n_in+n_out)), \
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	24 high = numpy.sqrt(6./(n_in+n_out)), \
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	25 size = (n_in, n_out)), dtype = theano.config.floatX)
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	26 self.W = theano.shared(value = W_values)
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	27
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	28 b_values = numpy.zeros((n_out,), dtype= theano.config.floatX)
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	29 self.b = theano.shared(value= b_values)
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	30
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	31 self.output = T.tanh(T.dot(input, self.W) + self.b)
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	32 self.params = [self.W, self.b]
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	33
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	34 class dA_conv(object):
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	35
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	36 def __init__(self, input, filter_shape, corruption_level = 0.1,
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	37 shared_W = None, shared_b = None, image_shape = None,
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	38 poolsize = (2,2)):
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	39
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	40 theano_rng = RandomStreams()
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	41
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	42 fan_in = numpy.prod(filter_shape[1:])
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	43 fan_out = filter_shape[0] * numpy.prod(filter_shape[2:])
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	44
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	45 center = theano.shared(value = 1, name="center")
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	46 scale = theano.shared(value = 2, name="scale")
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	47
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	48 if shared_W != None and shared_b != None :
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	49 self.W = shared_W
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	50 self.b = shared_b
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	51 else:
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	52 initial_W = numpy.asarray( numpy.random.uniform(
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	53 low = -numpy.sqrt(6./(fan_in+fan_out)),
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	54 high = numpy.sqrt(6./(fan_in+fan_out)),
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	55 size = filter_shape), dtype = theano.config.floatX)
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	56 initial_b = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	57 self.W = theano.shared(value = initial_W, name = "W")
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	58 self.b = theano.shared(value = initial_b, name = "b")
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	59
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	60
215 334d2444000d Changes that enable using this code when floatX=float32 Dumitru Erhan <dumitru.erhan@gmail.com> parents: 200 diff changeset	61 initial_b_prime= numpy.zeros((filter_shape[1],),dtype=theano.config.floatX)
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	62
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	63 self.b_prime = theano.shared(value = initial_b_prime, name = "b_prime")
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	64
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	65 self.x = input
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	66
215 334d2444000d Changes that enable using this code when floatX=float32 Dumitru Erhan <dumitru.erhan@gmail.com> parents: 200 diff changeset	67 self.tilde_x = theano_rng.binomial( self.x.shape, 1, 1 - corruption_level,dtype=theano.config.floatX) * self.x
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	68
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	69 conv1_out = conv.conv2d(self.tilde_x, self.W, filter_shape=filter_shape,
259 3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	70 image_shape=image_shape, border_mode='valid')
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	71
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	72 self.y = T.tanh(conv1_out + self.b.dimshuffle('x', 0, 'x', 'x'))
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	73
259 3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	74 da_filter_shape = [ filter_shape[1], filter_shape[0],
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	75 filter_shape[2], filter_shape[3] ]
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	76 initial_W_prime = numpy.asarray( numpy.random.uniform( \
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	77 low = -numpy.sqrt(6./(fan_in+fan_out)), \
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	78 high = numpy.sqrt(6./(fan_in+fan_out)), \
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	79 size = da_filter_shape), dtype = theano.config.floatX)
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	80 self.W_prime = theano.shared(value = initial_W_prime, name = "W_prime")
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	81
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	82 conv2_out = conv.conv2d(self.y, self.W_prime,
259 3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	83 filter_shape = da_filter_shape,
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	84 border_mode='full')
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	85
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	86 self.z = (T.tanh(conv2_out + self.b_prime.dimshuffle('x', 0, 'x', 'x'))+center) / scale
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	87
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	88 scaled_x = (self.x + center) / scale
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	89
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	90 self.L = - T.sum( scaled_xT.log(self.z) + (1-scaled_x)T.log(1-self.z), axis=1 )
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	91
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	92 self.cost = T.mean(self.L)
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	93
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	94 self.params = [ self.W, self.b, self.b_prime ]
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	95
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	96 class LeNetConvPoolLayer(object):
247 4d109b648c31 Fixed dataset import. Removed unuseful code from da_conv. Keys parameters are now passed as arguments. humel parents: 215 diff changeset	97
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	98 def __init__(self, rng, input, filter_shape, image_shape=None, poolsize=(2,2)):
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	99 self.input = input
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	100
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	101 W_values = numpy.zeros(filter_shape, dtype=theano.config.floatX)
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	102 self.W = theano.shared(value=W_values)
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	103
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	104 b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	105 self.b = theano.shared(value=b_values)
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	106
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	107 conv_out = conv.conv2d(input, self.W,
259 3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	108 filter_shape=filter_shape, image_shape=image_shape)
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	109
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	110
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	111 fan_in = numpy.prod(filter_shape[1:])
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	112 fan_out = filter_shape[0] * numpy.prod(filter_shape[2:]) / numpy.prod(poolsize)
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	113
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	114 W_bound = numpy.sqrt(6./(fan_in + fan_out))
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	115 self.W.value = numpy.asarray(
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	116 rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	117 dtype = theano.config.floatX)
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	118
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	119
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	120 pooled_out = downsample.max_pool2D(conv_out, poolsize, ignore_border=True)
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	121
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	122 self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	123 self.params = [self.W, self.b]
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	124
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	125
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	126 class SdA():
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	127 def __init__(self, input, n_ins_mlp, conv_hidden_layers_sizes,
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	128 mlp_hidden_layers_sizes, corruption_levels, rng, n_out,
259 3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	129 pretrain_lr, finetune_lr, img_shape):
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	130
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	131 self.layers = []
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	132 self.pretrain_functions = []
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	133 self.params = []
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	134 self.conv_n_layers = len(conv_hidden_layers_sizes)
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	135 self.mlp_n_layers = len(mlp_hidden_layers_sizes)
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	136
215 334d2444000d Changes that enable using this code when floatX=float32 Dumitru Erhan <dumitru.erhan@gmail.com> parents: 200 diff changeset	137 self.x = T.matrix('x') # the data is presented as rasterized images
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	138 self.y = T.ivector('y') # the labels are presented as 1D vector of
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	139
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	140 for i in xrange( self.conv_n_layers ):
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	141 filter_shape=conv_hidden_layers_sizes[i][0]
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	142 image_shape=conv_hidden_layers_sizes[i][1]
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	143 max_poolsize=conv_hidden_layers_sizes[i][2]
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	144
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	145 if i == 0 :
259 3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	146 layer_input=self.x.reshape((self.x.shape[0], 1) + img_shape)
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	147 else:
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	148 layer_input=self.layers[-1].output
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	149
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	150 layer = LeNetConvPoolLayer(rng, input=layer_input,
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	151 image_shape=image_shape,
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	152 filter_shape=filter_shape,
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	153 poolsize=max_poolsize)
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	154 print 'Convolutional layer', str(i+1), 'created'
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	155
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	156 self.layers += [layer]
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	157 self.params += layer.params
215 334d2444000d Changes that enable using this code when floatX=float32 Dumitru Erhan <dumitru.erhan@gmail.com> parents: 200 diff changeset	158
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	159 da_layer = dA_conv(corruption_level = corruption_levels[0],
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	160 input = layer_input,
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	161 shared_W = layer.W, shared_b = layer.b,
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	162 filter_shape = filter_shape,
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	163 image_shape = image_shape )
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	164
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	165 gparams = T.grad(da_layer.cost, da_layer.params)
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	166
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	167 updates = {}
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	168 for param, gparam in zip(da_layer.params, gparams):
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	169 updates[param] = param - gparam * pretrain_lr
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	170
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	171 update_fn = theano.function([self.x], da_layer.cost, updates = updates)
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	172
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	173 self.pretrain_functions += [update_fn]
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	174
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	175 for i in xrange( self.mlp_n_layers ):
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	176 if i == 0 :
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	177 input_size = n_ins_mlp
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	178 else:
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	179 input_size = mlp_hidden_layers_sizes[i-1]
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	180
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	181 if i == 0 :
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	182 if len( self.layers ) == 0 :
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	183 layer_input=self.x
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	184 else :
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	185 layer_input = self.layers[-1].output.flatten(2)
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	186 else:
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	187 layer_input = self.layers[-1].output
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	188
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	189 layer = SigmoidalLayer(rng, layer_input, input_size,
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	190 mlp_hidden_layers_sizes[i] )
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	191
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	192 self.layers += [layer]
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	193 self.params += layer.params
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	194
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	195 print 'MLP layer', str(i+1), 'created'
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	196
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	197 self.logLayer = LogisticRegression(input=self.layers[-1].output, \
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	198 n_in=mlp_hidden_layers_sizes[-1], n_out=n_out)
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	199 self.params += self.logLayer.params
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	200
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	201 cost = self.logLayer.negative_log_likelihood(self.y)
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	202
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	203 gparams = T.grad(cost, self.params)
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	204
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	205 updates = {}
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	206 for param,gparam in zip(self.params, gparams):
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	207 updates[param] = param - gparam*finetune_lr
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	208
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	209 self.finetune = theano.function([self.x, self.y], cost, updates = updates)
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	210
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	211 self.errors = self.logLayer.errors(self.y)
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	212
259 3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	213 def sgd_optimization_mnist(learning_rate=0.1, pretraining_epochs = 1,
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	214 pretrain_lr = 0.1, training_epochs = 1000,
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	215 kernels = [[4,5,5], [4,3,3]], mlp_layers=[500],
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	216 corruption_levels = [0.2, 0.2, 0.2],
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	217 batch_size = batch_size, img_shape=(28, 28),
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	218 max_pool_layers = [[2,2], [2,2]],
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	219 dataset=datasets.mnist(5000)):
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	220
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	221 # allocate symbolic variables for the data
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	222 index = T.lscalar() # index to a [mini]batch
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	223 x = T.matrix('x') # the data is presented as rasterized images
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	224 y = T.ivector('y') # the labels are presented as 1d vector of
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	225 # [int] labels
247 4d109b648c31 Fixed dataset import. Removed unuseful code from da_conv. Keys parameters are now passed as arguments. humel parents: 215 diff changeset	226
259 3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	227 layer0_input = x.reshape((x.shape[0],1)+img_shape)
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	228
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	229 rng = numpy.random.RandomState(1234)
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	230 conv_layers=[]
259 3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	231 init_layer = [[kernels[0][0],1,kernels[0][1],kernels[0][2]],
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	232 None, # do not specify the batch size since it can
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	233 # change for the last one and then theano will
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	234 # crash.
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	235 max_pool_layers[0]]
247 4d109b648c31 Fixed dataset import. Removed unuseful code from da_conv. Keys parameters are now passed as arguments. humel parents: 215 diff changeset	236 conv_layers.append(init_layer)
248 7e6fecabb656 Optimized the call of ConvOp by specifying additional parameters. Specified image shape of the da_conv layer. humel parents: 247 diff changeset	237
259 3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	238 conv_n_out = (img_shape[0]-kernels[0][2]+1)/max_pool_layers[0][0]
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	239
247 4d109b648c31 Fixed dataset import. Removed unuseful code from da_conv. Keys parameters are now passed as arguments. humel parents: 215 diff changeset	240 for i in range(1,len(kernels)):
259 3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	241 layer = [[kernels[i][0],kernels[i-1][0],kernels[i][1],kernels[i][2]],
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	242 None, # same comment as for init_layer
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	243 max_pool_layers[i] ]
247 4d109b648c31 Fixed dataset import. Removed unuseful code from da_conv. Keys parameters are now passed as arguments. humel parents: 215 diff changeset	244 conv_layers.append(layer)
4d109b648c31 Fixed dataset import. Removed unuseful code from da_conv. Keys parameters are now passed as arguments. humel parents: 215 diff changeset	245 conv_n_out = (conv_n_out - kernels[i][2]+1)/max_pool_layers[i][0]
259 3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	246
247 4d109b648c31 Fixed dataset import. Removed unuseful code from da_conv. Keys parameters are now passed as arguments. humel parents: 215 diff changeset	247 network = SdA(input = layer0_input, n_ins_mlp = kernels[-1][0]conv_n_out*2,
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	248 conv_hidden_layers_sizes = conv_layers,
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	249 mlp_hidden_layers_sizes = mlp_layers,
259 3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	250 corruption_levels = corruption_levels, n_out = 62,
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	251 rng = rng , pretrain_lr = pretrain_lr,
3919c71e3091 Make img_size a parameter, and remove the passing of the image size to the ConvOp. This will have to get back in later somehow. Arnaud Bergeron <abergeron@gmail.com> parents: 248 diff changeset	252 finetune_lr = learning_rate, img_shape=img_shape)
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	253
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	254 test_model = theano.function([network.x, network.y], network.errors)
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	255
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	256 start_time = time.clock()
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	257 for i in xrange(len(network.layers)-len(mlp_layers)):
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	258 for epoch in xrange(pretraining_epochs):
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	259 for x, y in dataset.train(batch_size):
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	260 c = network.pretrain_functions[i](x)
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	261 print 'pre-training convolution layer %i, epoch %d, cost '%(i,epoch), c
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	262
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	263 patience = 10000 # look as this many examples regardless
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	264 patience_increase = 2. # WAIT THIS MUCH LONGER WHEN A NEW BEST IS
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	265 # FOUND
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	266 improvement_threshold = 0.995 # a relative improvement of this much is
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	267
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	268 validation_frequency = patience/2
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	269
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	270 best_params = None
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	271 best_validation_loss = float('inf')
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	272 test_score = 0.
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	273 start_time = time.clock()
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	274
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	275 done_looping = False
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	276 epoch = 0
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	277 iter = 0
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	278
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	279 while (epoch < training_epochs) and (not done_looping):
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	280 epoch = epoch + 1
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	281 for x, y in dataset.train(batch_size):
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	282
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	283 cost_ij = network.finetune(x, y)
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	284 iter += 1
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	285
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	286 if iter % validation_frequency == 0:
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	287 validation_losses = [test_model(xv, yv) for xv, yv in dataset.valid(batch_size)]
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	288 this_validation_loss = numpy.mean(validation_losses)
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	289 print('epoch %i, iter %i, validation error %f %%' % \
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	290 (epoch, iter, this_validation_loss*100.))
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	291
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	292 # if we got the best validation score until now
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	293 if this_validation_loss < best_validation_loss:
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	294
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	295 #improve patience if loss improvement is good enough
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	296 if this_validation_loss < best_validation_loss * \
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	297 improvement_threshold :
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	298 patience = max(patience, iter * patience_increase)
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	299
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	300 # save best validation score and iteration number
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	301 best_validation_loss = this_validation_loss
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	302 best_iter = iter
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	303
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	304 # test it on the test set
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	305 test_losses = [test_model(xt, yt) for xt, yt in dataset.test(batch_size)]
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	306 test_score = numpy.mean(test_losses)
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	307 print((' epoch %i, iter %i, test error of best '
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	308 'model %f %%') %
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	309 (epoch, iter, test_score*100.))
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	310
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	311 if patience <= iter :
200 3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	312 done_looping = True
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	313 break
3f2cc90ad51c Adapt the sdae code for ift6266.datasets input. Arnaud Bergeron <abergeron@gmail.com> parents: 167 diff changeset	314
138 128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	315 end_time = time.clock()
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	316 print(('Optimization complete with best validation score of %f %%,'
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	317 'with test performance %f %%') %
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	318 (best_validation_loss * 100., test_score*100.))
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	319 print ('The code ran for %f minutes' % ((end_time-start_time)/60.))
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	320
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	321 if __name__ == '__main__':
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	322 sgd_optimization_mnist()
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders Owner <salahmeister@gmail.com> parents: diff changeset	323

Mercurial > ift6266

annotate deep/convolutional_dae/stacked_convolutional_dae.py @ 272:f6d9b6b89c2a