ift6266: code_tutoriel/DBN.py comparison

comparison code_tutoriel/DBN.py @ 165:4bc5eeec6394

Updating the tutorial code to the latest revisions.

author	Dumitru Erhan <dumitru.erhan@gmail.com>
date	Fri, 26 Feb 2010 13:55:27 -0500
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+:4bc5eeec6394
+"""
+"""
+import os
+import numpy, time, cPickle, gzip
+import theano
+import theano.tensor as T
+from theano.tensor.shared_randomstreams import RandomStreams
+from logistic_sgd import LogisticRegression, load_data
+from mlp import HiddenLayer
+from rbm import RBM
+class DBN(object):
+"""
+"""
+def __init__(self, numpy_rng, theano_rng = None, n_ins = 784,
+hidden_layers_sizes = [500,500], n_outs = 10):
+"""This class is made to support a variable number of layers.
+:type numpy_rng: numpy.random.RandomState
+:param numpy_rng: numpy random number generator used to draw initial
+weights
+:type theano_rng: theano.tensor.shared_randomstreams.RandomStreams
+:param theano_rng: Theano random generator; if None is given one is
+generated based on a seed drawn from `rng`
+:type n_ins: int
+:param n_ins: dimension of the input to the DBN
+:type n_layers_sizes: list of ints
+:param n_layers_sizes: intermidiate layers size, must contain
+at least one value
+:type n_outs: int
+:param n_outs: dimension of the output of the network
+"""
+self.sigmoid_layers = []
+self.rbm_layers     = []
+self.params         = []
+self.n_layers       = len(hidden_layers_sizes)
+assert self.n_layers > 0
+if not theano_rng:
+theano_rng = RandomStreams(numpy_rng.randint(2**30))
+# allocate symbolic variables for the data
+self.x  = T.matrix('x')  # the data is presented as rasterized images
+self.y  = T.ivector('y') # the labels are presented as 1D vector of
+# [int] labels
+# The DBN is an MLP, for which all weights of intermidiate layers are shared with a
+# different RBM.  We will first construct the DBN as a deep multilayer perceptron, and
+# when constructing each sigmoidal layer we also construct an RBM that shares weights
+# with that layer. During pretraining we will train these RBMs (which will lead
+# to chainging the weights of the MLP as well) During finetuning we will finish
+# training the DBN by doing stochastic gradient descent on the MLP.
+for i in xrange( self.n_layers ):
+# construct the sigmoidal layer
+# the size of the input is either the number of hidden units of the layer below or
+# the input size if we are on the first layer
+if i == 0 :
+input_size = n_ins
+else:
+input_size = hidden_layers_sizes[i-1]
+# the input to this layer is either the activation of the hidden layer below or the
+# input of the DBN if you are on the first layer
+if i == 0 :
+layer_input = self.x
+else:
+layer_input = self.sigmoid_layers[-1].output
+sigmoid_layer = HiddenLayer(rng   = numpy_rng,
+input = layer_input,
+n_in  = input_size,
+n_out = hidden_layers_sizes[i],
+activation = T.nnet.sigmoid)
+# add the layer to our list of layers
+self.sigmoid_layers.append(sigmoid_layer)
+# its arguably a philosophical question...  but we are going to only declare that
+# the parameters of the sigmoid_layers are parameters of the DBN. The visible
+# biases in the RBM are parameters of those RBMs, but not of the DBN.
+self.params.extend(sigmoid_layer.params)
+# Construct an RBM that shared weights with this layer
+rbm_layer = RBM(numpy_rng = numpy_rng, theano_rng = theano_rng,
+input = layer_input,
+n_visible = input_size,
+n_hidden  = hidden_layers_sizes[i],
+W = sigmoid_layer.W,
+hbias = sigmoid_layer.b)
+self.rbm_layers.append(rbm_layer)
+# We now need to add a logistic layer on top of the MLP
+self.logLayer = LogisticRegression(\
+input = self.sigmoid_layers[-1].output,\
+n_in = hidden_layers_sizes[-1], n_out = n_outs)
+self.params.extend(self.logLayer.params)
+# construct a function that implements one step of fine-tuning compute the cost for
+# second phase of training, defined as the negative log likelihood
+self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
+# compute the gradients with respect to the model parameters
+# symbolic variable that points to the number of errors made on the
+# minibatch given by self.x and self.y
+self.errors = self.logLayer.errors(self.y)
+def pretraining_functions(self, train_set_x, batch_size):
+''' Generates a list of functions, for performing one step of gradient descent at a
+given layer. The function will require as input the minibatch index, and to train an
+RBM you just need to iterate, calling the corresponding function on all minibatch
+indexes.
+:type train_set_x: theano.tensor.TensorType
+:param train_set_x: Shared var. that contains all datapoints used for training the RBM
+:type batch_size: int
+:param batch_size: size of a [mini]batch
+'''
+# index to a [mini]batch
+index            = T.lscalar('index')   # index to a minibatch
+learning_rate    = T.scalar('lr')    # learning rate to use
+# number of batches
+n_batches = train_set_x.value.shape[0] / batch_size
+# begining of a batch, given `index`
+batch_begin = index * batch_size
+# ending of a batch given `index`
+batch_end = batch_begin+batch_size
+pretrain_fns = []
+for rbm in self.rbm_layers:
+# get the cost and the updates list
+# TODO: change cost function to reconstruction error
+cost,updates = rbm.cd(learning_rate, persistent=None)
+# compile the theano function
+fn = theano.function(inputs = [index,
+theano.Param(learning_rate, default = 0.1)],
+outputs = cost,
+updates = updates,
+givens  = {self.x :train_set_x[batch_begin:batch_end]})
+# append `fn` to the list of functions
+pretrain_fns.append(fn)
+return pretrain_fns
+def build_finetune_functions(self, datasets, batch_size, learning_rate):
+'''Generates a function `train` that implements one step of finetuning, a function
+`validate` that computes the error on a batch from the validation set, and a function
+`test` that computes the error on a batch from the testing set
+:type datasets: list of pairs of theano.tensor.TensorType
+:param datasets: It is a list that contain all the datasets;  the has to contain three
+pairs, `train`, `valid`, `test` in this order, where each pair is formed of two Theano
+variables, one for the datapoints, the other for the labels
+:type batch_size: int
+:param batch_size: size of a minibatch
+:type learning_rate: float
+:param learning_rate: learning rate used during finetune stage
+'''
+(train_set_x, train_set_y) = datasets[0]
+(valid_set_x, valid_set_y) = datasets[1]
+(test_set_x , test_set_y ) = datasets[2]
+# compute number of minibatches for training, validation and testing
+n_valid_batches = valid_set_x.value.shape[0] / batch_size
+n_test_batches  = test_set_x.value.shape[0]  / batch_size
+index   = T.lscalar('index')    # index to a [mini]batch
+# compute the gradients with respect to the model parameters
+gparams = T.grad(self.finetune_cost, self.params)
+# compute list of fine-tuning updates
+updates = {}
+for param, gparam in zip(self.params, gparams):
+updates[param] = param - gparam*learning_rate
+train_fn = theano.function(inputs = [index],
+outputs =   self.finetune_cost,
+updates = updates,
+givens  = {
+self.x : train_set_x[index*batch_size:(index+1)*batch_size],
+self.y : train_set_y[index*batch_size:(index+1)*batch_size]})
+test_score_i = theano.function([index], self.errors,
+givens = {
+self.x: test_set_x[index*batch_size:(index+1)*batch_size],
+self.y: test_set_y[index*batch_size:(index+1)*batch_size]})
+valid_score_i = theano.function([index], self.errors,
+givens = {
+self.x: valid_set_x[index*batch_size:(index+1)*batch_size],
+self.y: valid_set_y[index*batch_size:(index+1)*batch_size]})
+# Create a function that scans the entire validation set
+def valid_score():
+return [valid_score_i(i) for i in xrange(n_valid_batches)]
+# Create a function that scans the entire test set
+def test_score():
+return [test_score_i(i) for i in xrange(n_test_batches)]
+return train_fn, valid_score, test_score
+def test_DBN( finetune_lr = 0.1, pretraining_epochs = 10, \
+pretrain_lr = 0.1, training_epochs = 1000, \
+dataset='mnist.pkl.gz'):
+"""
+Demonstrates how to train and test a Deep Belief Network.
+This is demonstrated on MNIST.
+:type learning_rate: float
+:param learning_rate: learning rate used in the finetune stage
+:type pretraining_epochs: int
+:param pretraining_epochs: number of epoch to do pretraining
+:type pretrain_lr: float
+:param pretrain_lr: learning rate to be used during pre-training
+:type n_iter: int
+:param n_iter: maximal number of iterations ot run the optimizer
+:type dataset: string
+:param dataset: path the the pickled dataset
+"""
+print 'finetune_lr = ', finetune_lr
+print 'pretrain_lr = ', pretrain_lr
+datasets = load_data(dataset)
+train_set_x, train_set_y = datasets[0]
+valid_set_x, valid_set_y = datasets[1]
+test_set_x , test_set_y  = datasets[2]
+batch_size = 20    # size of the minibatch
+# compute number of minibatches for training, validation and testing
+n_train_batches = train_set_x.value.shape[0] / batch_size
+# numpy random generator
+numpy_rng = numpy.random.RandomState(123)
+print '... building the model'
+# construct the Deep Belief Network
+dbn = DBN(numpy_rng = numpy_rng, n_ins = 28*28,
+hidden_layers_sizes = [1000,1000,1000],
+n_outs = 10)
+#########################
+# PRETRAINING THE MODEL #
+#########################
+print '... getting the pretraining functions'
+pretraining_fns = dbn.pretraining_functions(
+train_set_x   = train_set_x,
+batch_size    = batch_size )
+print '... pre-training the model'
+start_time = time.clock()
+## Pre-train layer-wise
+for i in xrange(dbn.n_layers):
+# go through pretraining epochs
+for epoch in xrange(pretraining_epochs):
+# go through the training set
+c = []
+for batch_index in xrange(n_train_batches):
+c.append(pretraining_fns[i](index = batch_index,
+lr = pretrain_lr ) )
+print 'Pre-training layer %i, epoch %d, cost '%(i,epoch),numpy.mean(c)
+end_time = time.clock()
+print ('Pretraining took %f minutes' %((end_time-start_time)/60.))
+########################
+# FINETUNING THE MODEL #
+########################
+# get the training, validation and testing function for the model
+print '... getting the finetuning functions'
+train_fn, validate_model, test_model = dbn.build_finetune_functions (
+datasets = datasets, batch_size = batch_size,
+learning_rate = finetune_lr)
+print '... finetunning the model'
+# early-stopping parameters
+patience              = 10000 # look as this many examples regardless
+patience_increase     = 2.    # wait this much longer when a new best is
+# found
+improvement_threshold = 0.995 # a relative improvement of this much is
+# considered significant
+validation_frequency  = min(n_train_batches, patience/2)
+# go through this many
+# minibatche before checking the network
+# on the validation set; in this case we
+# check every epoch
+best_params          = None
+best_validation_loss = float('inf')
+test_score           = 0.
+start_time = time.clock()
+done_looping = False
+epoch = 0
+while (epoch < training_epochs) and (not done_looping):
+epoch = epoch + 1
+for minibatch_index in xrange(n_train_batches):
+minibatch_avg_cost = train_fn(minibatch_index)
+iter    = epoch * n_train_batches + minibatch_index
+if (iter+1) % validation_frequency == 0:
+validation_losses = validate_model()
+this_validation_loss = numpy.mean(validation_losses)
+print('epoch %i, minibatch %i/%i, validation error %f %%' % \
+(epoch, minibatch_index+1, n_train_batches, \
+this_validation_loss*100.))
+# if we got the best validation score until now
+if this_validation_loss < best_validation_loss:
+#improve patience if loss improvement is good enough
+if this_validation_loss < best_validation_loss *  \
+improvement_threshold :
+patience = max(patience, iter * patience_increase)
+# save best validation score and iteration number
+best_validation_loss = this_validation_loss
+best_iter = iter
+# test it on the test set
+test_losses = test_model()
+test_score = numpy.mean(test_losses)
+print(('     epoch %i, minibatch %i/%i, test error of best '
+'model %f %%') %
+(epoch, minibatch_index+1, n_train_batches,
+test_score*100.))
+if patience <= iter :
+done_looping = True
+break
+end_time = time.clock()
+print(('Optimization complete with best validation score of %f %%,'
+'with test performance %f %%') %
+(best_validation_loss * 100., test_score*100.))
+print ('The code ran for %f minutes' % ((end_time-start_time)/60.))
+if __name__ == '__main__':
+pretrain_lr = numpy.float(os.sys.argv[1])
+finetune_lr = numpy.float(os.sys.argv[2])
+test_DBN(pretrain_lr=pretrain_lr, finetune_lr=finetune_lr)

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comparison code_tutoriel/DBN.py @ 165:4bc5eeec6394