ift6266: baseline/conv_mlp/convolutional

annotate baseline/conv_mlp/convolutional_mlp.py @ 238:9fc641d7adda

Possibilite de restreindre la taille des ensemble d'entrainement, valid et test afin de pouvoir tester le code rapidement

author	SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
date	Mon, 15 Mar 2010 13:22:20 -0400
parents	168aae8a6419
children	a491d3600a77

rev	line source
146 33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	1 """
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	2 This tutorial introduces the LeNet5 neural network architecture using Theano. LeNet5 is a
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	3 convolutional neural network, good for classifying images. This tutorial shows how to build the
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	4 architecture, and comes with all the hyper-parameters you need to reproduce the paper's MNIST
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	5 results.
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	6
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	7 The best results are obtained after X iterations of the main program loop, which takes ***
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	8 minutes on my workstation (an Intel Core i7, circa July 2009), and *** minutes on my GPU (an
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	9 NVIDIA GTX 285 graphics processor).
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	10
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	11 This implementation simplifies the model in the following ways:
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	12
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	13 - LeNetConvPool doesn't implement location-specific gain and bias parameters
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	14 - LeNetConvPool doesn't implement pooling by average, it implements pooling by max.
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	15 - Digit classification is implemented with a logistic regression rather than an RBF network
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	16 - LeNet5 was not fully-connected convolutions at second layer
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	17
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	18 References:
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	19 - Y. LeCun, L. Bottou, Y. Bengio and P. Haffner: Gradient-Based Learning Applied to Document
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	20 Recognition, Proceedings of the IEEE, 86(11):2278-2324, November 1998.
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	21 http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	22 """
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	23
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	24 import numpy, theano, cPickle, gzip, time
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	25 import theano.tensor as T
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	26 import theano.sandbox.softsign
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	27 import pylearn.datasets.MNIST
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	28 from pylearn.io import filetensor as ft
196 168aae8a6419 Fix import errors (conv and downsample are no longer in sandbox). Arnaud Bergeron <abergeron@gmail.com> parents: 169 diff changeset	29 from theano.tensor.signal import downsample
168aae8a6419 Fix import errors (conv and downsample are no longer in sandbox). Arnaud Bergeron <abergeron@gmail.com> parents: 169 diff changeset	30 from theano.tensor.nnet import conv
146 33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	31
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	32 class LeNetConvPoolLayer(object):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	33
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	34 def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2,2)):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	35 """
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	36 Allocate a LeNetConvPoolLayer with shared variable internal parameters.
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	37 :type rng: numpy.random.RandomState
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	38 :param rng: a random number generator used to initialize weights
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	39 :type input: theano.tensor.dtensor4
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	40 :param input: symbolic image tensor, of shape image_shape
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	41 :type filter_shape: tuple or list of length 4
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	42 :param filter_shape: (number of filters, num input feature maps,
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	43 filter height,filter width)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	44 :type image_shape: tuple or list of length 4
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	45 :param image_shape: (batch size, num input feature maps,
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	46 image height, image width)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	47 :type poolsize: tuple or list of length 2
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	48 :param poolsize: the downsampling (pooling) factor (#rows,#cols)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	49 """
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	50 assert image_shape[1]==filter_shape[1]
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	51 self.input = input
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	52
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	53 # initialize weight values: the fan-in of each hidden neuron is
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	54 # restricted by the size of the receptive fields.
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	55 fan_in = numpy.prod(filter_shape[1:])
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	56 W_values = numpy.asarray( rng.uniform( \
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	57 low = -numpy.sqrt(3./fan_in), \
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	58 high = numpy.sqrt(3./fan_in), \
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	59 size = filter_shape), dtype = theano.config.floatX)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	60 self.W = theano.shared(value = W_values)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	61
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	62 # the bias is a 1D tensor -- one bias per output feature map
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	63 b_values = numpy.zeros((filter_shape[0],), dtype= theano.config.floatX)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	64 self.b = theano.shared(value= b_values)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	65
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	66 # convolve input feature maps with filters
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	67 conv_out = conv.conv2d(input, self.W,
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	68 filter_shape=filter_shape, image_shape=image_shape)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	69
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	70 # downsample each feature map individually, using maxpooling
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	71 pooled_out = downsample.max_pool2D(conv_out, poolsize, ignore_border=True)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	72
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	73 # add the bias term. Since the bias is a vector (1D array), we first
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	74 # reshape it to a tensor of shape (1,n_filters,1,1). Each bias will thus
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	75 # be broadcasted across mini-batches and feature map width & height
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	76 self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	77
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	78 # store parameters of this layer
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	79 self.params = [self.W, self.b]
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	80
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	81
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	82 class SigmoidalLayer(object):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	83 def __init__(self, rng, input, n_in, n_out):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	84 """
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	85 Typical hidden layer of a MLP: units are fully-connected and have
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	86 sigmoidal activation function. Weight matrix W is of shape (n_in,n_out)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	87 and the bias vector b is of shape (n_out,).
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	88
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	89 Hidden unit activation is given by: sigmoid(dot(input,W) + b)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	90
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	91 :type rng: numpy.random.RandomState
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	92 :param rng: a random number generator used to initialize weights
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	93 :type input: theano.tensor.dmatrix
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	94 :param input: a symbolic tensor of shape (n_examples, n_in)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	95 :type n_in: int
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	96 :param n_in: dimensionality of input
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	97 :type n_out: int
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	98 :param n_out: number of hidden units
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	99 """
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	100 self.input = input
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	101
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	102 W_values = numpy.asarray( rng.uniform( \
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	103 low = -numpy.sqrt(6./(n_in+n_out)), \
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	104 high = numpy.sqrt(6./(n_in+n_out)), \
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	105 size = (n_in, n_out)), dtype = theano.config.floatX)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	106 self.W = theano.shared(value = W_values)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	107
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	108 b_values = numpy.zeros((n_out,), dtype= theano.config.floatX)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	109 self.b = theano.shared(value= b_values)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	110
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	111 self.output = T.tanh(T.dot(input, self.W) + self.b)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	112 self.params = [self.W, self.b]
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	113
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	114
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	115 class LogisticRegression(object):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	116 """Multi-class Logistic Regression Class
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	117
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	118 The logistic regression is fully described by a weight matrix :math:`W`
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	119 and bias vector :math:`b`. Classification is done by projecting data
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	120 points onto a set of hyperplanes, the distance to which is used to
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	121 determine a class membership probability.
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	122 """
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	123
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	124 def __init__(self, input, n_in, n_out):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	125 """ Initialize the parameters of the logistic regression
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	126 :param input: symbolic variable that describes the input of the
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	127 architecture (one minibatch)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	128 :type n_in: int
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	129 :param n_in: number of input units, the dimension of the space in
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	130 which the datapoints lie
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	131 :type n_out: int
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	132 :param n_out: number of output units, the dimension of the space in
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	133 which the labels lie
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	134 """
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	135
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	136 # initialize with 0 the weights W as a matrix of shape (n_in, n_out)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	137 self.W = theano.shared( value=numpy.zeros((n_in,n_out),
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	138 dtype = theano.config.floatX) )
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	139 # initialize the baises b as a vector of n_out 0s
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	140 self.b = theano.shared( value=numpy.zeros((n_out,),
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	141 dtype = theano.config.floatX) )
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	142 # compute vector of class-membership probabilities in symbolic form
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	143 self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W)+self.b)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	144
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	145 # compute prediction as class whose probability is maximal in
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	146 # symbolic form
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	147 self.y_pred=T.argmax(self.p_y_given_x, axis=1)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	148
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	149 # list of parameters for this layer
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	150 self.params = [self.W, self.b]
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	151
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	152 def negative_log_likelihood(self, y):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	153 """Return the mean of the negative log-likelihood of the prediction
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	154 of this model under a given target distribution.
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	155 :param y: corresponds to a vector that gives for each example the
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	156 correct label
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	157 Note: we use the mean instead of the sum so that
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	158 the learning rate is less dependent on the batch size
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	159 """
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	160 return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y])
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	161
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	162 def errors(self, y):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	163 """Return a float representing the number of errors in the minibatch
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	164 over the total number of examples of the minibatch ; zero one
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	165 loss over the size of the minibatch
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	166 """
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	167 # check if y has same dimension of y_pred
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	168 if y.ndim != self.y_pred.ndim:
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	169 raise TypeError('y should have the same shape as self.y_pred',
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	170 ('y', target.type, 'y_pred', self.y_pred.type))
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	171
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	172 # check if y is of the correct datatype
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	173 if y.dtype.startswith('int'):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	174 # the T.neq operator returns a vector of 0s and 1s, where 1
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	175 # represents a mistake in prediction
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	176 return T.mean(T.neq(self.y_pred, y))
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	177 else:
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	178 raise NotImplementedError()
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	179
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	180
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	181 def load_dataset(fname,batch=20):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	182
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	183 # repertoire qui contient les donnees NIST
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	184 # le repertoire suivant va fonctionner si vous etes connecte sur un ordinateur
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	185 # du reseau DIRO
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	186 datapath = '/data/lisa/data/nist/by_class/'
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	187 # le fichier .ft contient chiffres NIST dans un format efficace. Les chiffres
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	188 # sont stockes dans une matrice de NxD, ou N est le nombre d'images, est D est
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	189 # le nombre de pixels par image (32x32 = 1024). Chaque pixel de l'image est une
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	190 # valeur entre 0 et 255, correspondant a un niveau de gris. Les valeurs sont
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	191 # stockees comme des uint8, donc des bytes.
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	192 f = open(datapath+'digits/digits_train_data.ft')
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	193 # Verifier que vous avez assez de memoire pour loader les donnees au complet
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	194 # dans le memoire. Sinon, utilisez ft.arraylike, une classe construite
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	195 # specialement pour des fichiers qu'on ne souhaite pas loader dans RAM.
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	196 d = ft.read(f)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	197
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	198 # NB: N'oubliez pas de diviser les valeurs des pixels par 255. si jamais vous
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	199 # utilisez les donnees commes entrees dans un reseaux de neurones et que vous
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	200 # voulez des entres entre 0 et 1.
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	201 # digits_train_data.ft contient les images, digits_train_labels.ft contient les
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	202 # etiquettes
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	203 f = open(datapath+'digits/digits_train_labels.ft')
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	204 labels = ft.read(f)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	205
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	206
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	207 # Load the dataset
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	208 #f = gzip.open(fname,'rb')
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	209 #train_set, valid_set, test_set = cPickle.load(f)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	210 #f.close()
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	211
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	212 # make minibatches of size 20
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	213 batch_size = batch # sized of the minibatch
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	214
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	215 # Dealing with the training set
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	216 # get the list of training images (x) and their labels (y)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	217 (train_set_x, train_set_y) = (d[:4000,:],labels[:4000])
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	218 # initialize the list of training minibatches with empty list
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	219 train_batches = []
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	220 for i in xrange(0, len(train_set_x), batch_size):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	221 # add to the list of minibatches the minibatch starting at
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	222 # position i, ending at position i+batch_size
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	223 # a minibatch is a pair ; the first element of the pair is a list
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	224 # of datapoints, the second element is the list of corresponding
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	225 # labels
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	226 train_batches = train_batches + \
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	227 [(train_set_x[i:i+batch_size], train_set_y[i:i+batch_size])]
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	228
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	229 #print train_batches[500]
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	230
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	231 # Dealing with the validation set
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	232 (valid_set_x, valid_set_y) = (d[4000:5000,:],labels[4000:5000])
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	233 # initialize the list of validation minibatches
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	234 valid_batches = []
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	235 for i in xrange(0, len(valid_set_x), batch_size):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	236 valid_batches = valid_batches + \
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	237 [(valid_set_x[i:i+batch_size], valid_set_y[i:i+batch_size])]
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	238
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	239 # Dealing with the testing set
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	240 (test_set_x, test_set_y) = (d[5000:6000,:],labels[5000:6000])
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	241 # initialize the list of testing minibatches
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	242 test_batches = []
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	243 for i in xrange(0, len(test_set_x), batch_size):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	244 test_batches = test_batches + \
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	245 [(test_set_x[i:i+batch_size], test_set_y[i:i+batch_size])]
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	246
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	247 return train_batches, valid_batches, test_batches
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	248
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	249
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	250 def evaluate_lenet5(learning_rate=0.1, n_iter=1, batch_size=20, n_kern0=20,n_kern1=50,filter_shape=5,n_layer=3, dataset='mnist.pkl.gz'):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	251 rng = numpy.random.RandomState(23455)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	252
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	253 print 'Before load dataset'
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	254 train_batches, valid_batches, test_batches = load_dataset(dataset,batch_size)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	255 print 'After load dataset'
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	256
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	257 ishape = (32,32) # this is the size of NIST images
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	258 n_kern2=80
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	259
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	260 # allocate symbolic variables for the data
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	261 x = T.matrix('x') # rasterized images
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	262 y = T.lvector() # the labels are presented as 1D vector of [long int] labels
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	263
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	264
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	265 ######################
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	266 # BUILD ACTUAL MODEL #
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	267 ######################
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	268
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	269 # Reshape matrix of rasterized images of shape (batch_size,28*28)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	270 # to a 4D tensor, compatible with our LeNetConvPoolLayer
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	271 layer0_input = x.reshape((batch_size,1,32,32))
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	272
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	273 # Construct the first convolutional pooling layer:
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	274 # filtering reduces the image size to (32-5+1,32-5+1)=(28,28)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	275 # maxpooling reduces this further to (28/2,28/2) = (14,14)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	276 # 4D output tensor is thus of shape (20,20,14,14)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	277 layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	278 image_shape=(batch_size,1,32,32),
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	279 filter_shape=(n_kern0,1,filter_shape,filter_shape), poolsize=(2,2))
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	280
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	281 if(n_layer>2):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	282
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	283 # Construct the second convolutional pooling layer
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	284 # filtering reduces the image size to (14-5+1,14-5+1)=(10,10)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	285 # maxpooling reduces this further to (10/2,10/2) = (5,5)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	286 # 4D output tensor is thus of shape (20,50,5,5)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	287 fshape=(32-filter_shape+1)/2
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	288 layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	289 image_shape=(batch_size,n_kern0,fshape,fshape),
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	290 filter_shape=(n_kern1,n_kern0,filter_shape,filter_shape), poolsize=(2,2))
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	291
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	292 else:
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	293
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	294 fshape=(32-filter_shape+1)/2
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	295 layer1_input = layer0.output.flatten(2)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	296 # construct a fully-connected sigmoidal layer
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	297 layer1 = SigmoidalLayer(rng, input=layer1_input,n_in=n_kern0fshapefshape, n_out=500)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	298
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	299 layer2 = LogisticRegression(input=layer1.output, n_in=500, n_out=10)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	300 cost = layer2.negative_log_likelihood(y)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	301 test_model = theano.function([x,y], layer2.errors(y))
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	302 params = layer2.params+ layer1.params + layer0.params
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	303
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	304
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	305 if(n_layer>3):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	306
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	307 fshape=(32-filter_shape+1)/2
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	308 fshape2=(fshape-filter_shape+1)/2
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	309 fshape3=(fshape2-filter_shape+1)/2
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	310 layer2 = LeNetConvPoolLayer(rng, input=layer1.output,
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	311 image_shape=(batch_size,n_kern1,fshape2,fshape2),
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	312 filter_shape=(n_kern2,n_kern1,filter_shape,filter_shape), poolsize=(2,2))
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	313
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	314 layer3_input = layer2.output.flatten(2)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	315
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	316 layer3 = SigmoidalLayer(rng, input=layer3_input,
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	317 n_in=n_kern2fshape3fshape3, n_out=500)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	318
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	319
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	320 layer4 = LogisticRegression(input=layer3.output, n_in=500, n_out=10)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	321
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	322 cost = layer4.negative_log_likelihood(y)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	323
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	324 test_model = theano.function([x,y], layer4.errors(y))
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	325
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	326 params = layer4.params+ layer3.params+ layer2.params+ layer1.params + layer0.params
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	327
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	328
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	329 elif(n_layer>2):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	330
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	331 fshape=(32-filter_shape+1)/2
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	332 fshape2=(fshape-filter_shape+1)/2
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	333
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	334 # the SigmoidalLayer being fully-connected, it operates on 2D matrices of
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	335 # shape (batch_size,num_pixels) (i.e matrix of rasterized images).
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	336 # This will generate a matrix of shape (20,3244) = (20,512)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	337 layer2_input = layer1.output.flatten(2)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	338
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	339 # construct a fully-connected sigmoidal layer
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	340 layer2 = SigmoidalLayer(rng, input=layer2_input,
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	341 n_in=n_kern1fshape2fshape2, n_out=500)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	342
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	343
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	344 # classify the values of the fully-connected sigmoidal layer
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	345 layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	346
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	347 # the cost we minimize during training is the NLL of the model
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	348 cost = layer3.negative_log_likelihood(y)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	349
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	350 # create a function to compute the mistakes that are made by the model
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	351 test_model = theano.function([x,y], layer3.errors(y))
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	352
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	353 # create a list of all model parameters to be fit by gradient descent
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	354 params = layer3.params+ layer2.params+ layer1.params + layer0.params
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	355
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	356
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	357
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	358
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	359
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	360 # create a list of gradients for all model parameters
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	361 grads = T.grad(cost, params)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	362
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	363 # train_model is a function that updates the model parameters by SGD
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	364 # Since this model has many parameters, it would be tedious to manually
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	365 # create an update rule for each model parameter. We thus create the updates
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	366 # dictionary by automatically looping over all (params[i],grads[i]) pairs.
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	367 updates = {}
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	368 for param_i, grad_i in zip(params, grads):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	369 updates[param_i] = param_i - learning_rate * grad_i
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	370 train_model = theano.function([x, y], cost, updates=updates)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	371
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	372
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	373 ###############
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	374 # TRAIN MODEL #
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	375 ###############
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	376
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	377 n_minibatches = len(train_batches)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	378
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	379 # early-stopping parameters
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	380 patience = 10000 # look as this many examples regardless
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	381 patience_increase = 2 # wait this much longer when a new best is
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	382 # found
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	383 improvement_threshold = 0.995 # a relative improvement of this much is
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	384 # considered significant
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	385 validation_frequency = n_minibatches # go through this many
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	386 # minibatche before checking the network
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	387 # on the validation set; in this case we
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	388 # check every epoch
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	389
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	390 best_params = None
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	391 best_validation_loss = float('inf')
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	392 best_iter = 0
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	393 test_score = 0.
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	394 start_time = time.clock()
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	395
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	396 # have a maximum of `n_iter` iterations through the entire dataset
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	397 for iter in xrange(n_iter * n_minibatches):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	398
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	399 # get epoch and minibatch index
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	400 epoch = iter / n_minibatches
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	401 minibatch_index = iter % n_minibatches
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	402
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	403 # get the minibatches corresponding to `iter` modulo
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	404 # `len(train_batches)`
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	405 x,y = train_batches[ minibatch_index ]
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	406
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	407 if iter %100 == 0:
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	408 print 'training @ iter = ', iter
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	409 cost_ij = train_model(x,y)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	410
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	411 if (iter+1) % validation_frequency == 0:
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	412
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	413 # compute zero-one loss on validation set
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	414 this_validation_loss = 0.
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	415 for x,y in valid_batches:
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	416 # sum up the errors for each minibatch
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	417 this_validation_loss += test_model(x,y)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	418
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	419 # get the average by dividing with the number of minibatches
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	420 this_validation_loss /= len(valid_batches)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	421 print('epoch %i, minibatch %i/%i, validation error %f %%' % \
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	422 (epoch, minibatch_index+1, n_minibatches, \
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	423 this_validation_loss*100.))
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	424
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	425
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	426 # if we got the best validation score until now
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	427 if this_validation_loss < best_validation_loss:
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	428
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	429 #improve patience if loss improvement is good enough
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	430 if this_validation_loss < best_validation_loss * \
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	431 improvement_threshold :
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	432 patience = max(patience, iter * patience_increase)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	433
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	434 # save best validation score and iteration number
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	435 best_validation_loss = this_validation_loss
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	436 best_iter = iter
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	437
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	438 # test it on the test set
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	439 test_score = 0.
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	440 for x,y in test_batches:
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	441 test_score += test_model(x,y)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	442 test_score /= len(test_batches)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	443 print((' epoch %i, minibatch %i/%i, test error of best '
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	444 'model %f %%') %
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	445 (epoch, minibatch_index+1, n_minibatches,
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	446 test_score*100.))
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	447
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	448 if patience <= iter :
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	449 break
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	450
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	451 end_time = time.clock()
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	452 print('Optimization complete.')
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	453 print('Best validation score of %f %% obtained at iteration %i,'\
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	454 'with test performance %f %%' %
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	455 (best_validation_loss * 100., best_iter, test_score*100.))
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	456 print('The code ran for %f minutes' % ((end_time-start_time)/60.))
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	457
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	458 return (best_validation_loss * 100., test_score*100., (end_time-start_time)/60., best_iter)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	459
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	460 if __name__ == '__main__':
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	461 evaluate_lenet5()
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	462
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	463 def experiment(state, channel):
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	464 print 'start experiment'
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	465 (best_validation_loss, test_score, minutes_trained, iter) = evaluate_lenet5(state.learning_rate, state.n_iter, state.batch_size, state.n_kern0, state.n_kern1, state.filter_shape, state.n_layer)
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	466 print 'end experiment'
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	467
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	468 state.best_validation_loss = best_validation_loss
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	469 state.test_score = test_score
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	470 state.minutes_trained = minutes_trained
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	471 state.iter = iter
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	472
33038ab4e799 Reseau a convolution Jeremy Eustache <jeremy.eustache@voila.fr> parents: diff changeset	473 return channel.COMPLETE

Mercurial > ift6266

annotate baseline/conv_mlp/convolutional_mlp.py @ 238:9fc641d7adda