ift6266: code_tutoriel/mlp.py comparison

comparison code_tutoriel/mlp.py @ 0:fda5f787baa6

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author	Dumitru Erhan <dumitru.erhan@gmail.com>
date	Thu, 21 Jan 2010 11:26:43 -0500
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children	bcc87d3e33a3

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+:fda5f787baa6
+"""
+This tutorial introduces the multilayer perceptron using Theano.
+A multilayer perceptron is a logistic regressor where
+instead of feeding the input to the logistic regression you insert a
+intermidiate layer, called the hidden layer, that has a nonlinear
+activation function (usually tanh or sigmoid) . One can use many such
+hidden layers making the architecture deep. The tutorial will also tackle
+the problem of MNIST digit classification.
+.. math::
+f(x) = G( b^{(2)} + W^{(2)}( s( b^{(1)} + W^{(1)} x))),
+References:
+- textbooks: "Pattern Recognition and Machine Learning" -
+Christopher M. Bishop, section 5
+TODO: recommended preprocessing, lr ranges, regularization ranges (explain
+to do lr first, then add regularization)
+"""
+__docformat__ = 'restructedtext en'
+import numpy, cPickle, gzip
+import theano
+import theano.tensor as T
+import time
+import theano.tensor.nnet
+class MLP(object):
+"""Multi-Layer Perceptron Class
+A multilayer perceptron is a feedforward artificial neural network model
+that has one layer or more of hidden units and nonlinear activations.
+Intermidiate layers usually have as activation function thanh or the
+sigmoid function  while the top layer is a softamx layer.
+"""
+def __init__(self, input, n_in, n_hidden, n_out):
+"""Initialize the parameters for the multilayer perceptron
+:param input: symbolic variable that describes the input of the
+architecture (one minibatch)
+:param n_in: number of input units, the dimension of the space in
+which the datapoints lie
+:param n_hidden: number of hidden units
+:param n_out: number of output units, the dimension of the space in
+which the labels lie
+"""
+# initialize the parameters theta = (W1,b1,W2,b2) ; note that this
+# example contains only one hidden layer, but one can have as many
+# layers as he/she wishes, making the network deeper. The only
+# problem making the network deep this way is during learning,
+# backpropagation being unable to move the network from the starting
+# point towards; this is where pre-training helps, giving a good
+# starting point for backpropagation, but more about this in the
+# other tutorials
+# `W1` is initialized with `W1_values` which is uniformely sampled
+# from -1/sqrt(n_in) and 1/sqrt(n_in)
+# the output of uniform if converted using asarray to dtype
+# theano.config.floatX so that the code is runable on GPU
+W1_values = numpy.asarray( numpy.random.uniform( \
+low = -numpy.sqrt(6./(n_in+n_hidden)), high = numpy.sqrt(6./(n_in+n_hidden)), \
+size = (n_in, n_hidden)), dtype = theano.config.floatX)
+# `W2` is initialized with `W2_values` which is uniformely sampled
+# from -1/sqrt(n_hidden) and 1/sqrt(n_hidden)
+# the output of uniform if converted using asarray to dtype
+# theano.config.floatX so that the code is runable on GPU
+W2_values = numpy.asarray( numpy.random.uniform(
+low = numpy.sqrt(6./(n_hidden+n_out)), high= numpy.sqrt(6./(n_hidden+n_out)),\
+size= (n_hidden, n_out)), dtype = theano.config.floatX)
+self.W1 = theano.shared( value = W1_values )
+self.b1 = theano.shared( value = numpy.zeros((n_hidden,),
+dtype= theano.config.floatX))
+self.W2 = theano.shared( value = W2_values )
+self.b2 = theano.shared( value = numpy.zeros((n_out,),
+dtype= theano.config.floatX))
+# symbolic expression computing the values of the hidden layer
+self.hidden = T.tanh(T.dot(input, self.W1)+ self.b1)
+# symbolic expression computing the values of the top layer
+self.p_y_given_x= T.nnet.softmax(T.dot(self.hidden, self.W2)+self.b2)
+# compute prediction as class whose probability is maximal in
+# symbolic form
+self.y_pred = T.argmax( self.p_y_given_x, axis =1)
+# L1 norm ; one regularization option is to enforce L1 norm to
+# be small
+self.L1     = abs(self.W1).sum() + abs(self.W2).sum()
+# square of L2 norm ; one regularization option is to enforce
+# square of L2 norm to be small
+self.L2_sqr = (self.W1**2).sum() + (self.W2**2).sum()
+def negative_log_likelihood(self, y):
+"""Return the mean of the negative log-likelihood of the prediction
+of this model under a given target distribution.
+.. math::
+\frac{1}{|\mathcal{D}|}\mathcal{L} (\theta=\{W,b\}, \mathcal{D}) =
+\frac{1}{|\mathcal{D}|}\sum_{i=0}^{|\mathcal{D}|} \log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\
+\ell (\theta=\{W,b\}, \mathcal{D})
+:param y: corresponds to a vector that gives for each example the
+:correct label
+"""
+return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y])
+def errors(self, y):
+"""Return a float representing the number of errors in the minibatch
+over the total number of examples of the minibatch
+"""
+# check if y has same dimension of y_pred
+if y.ndim != self.y_pred.ndim:
+raise TypeError('y should have the same shape as self.y_pred',
+('y', target.type, 'y_pred', self.y_pred.type))
+# check if y is of the correct datatype
+if y.dtype.startswith('int'):
+# the T.neq operator returns a vector of 0s and 1s, where 1
+# represents a mistake in prediction
+return T.mean(T.neq(self.y_pred, y))
+else:
+raise NotImplementedError()
+def sgd_optimization_mnist( learning_rate=0.01, L1_reg = 0.00, \
+L2_reg = 0.0001, n_iter=100):
+"""
+Demonstrate stochastic gradient descent optimization for a multilayer
+perceptron
+This is demonstrated on MNIST.
+:param learning_rate: learning rate used (factor for the stochastic
+gradient
+:param n_iter: number of iterations ot run the optimizer
+:param L1_reg: L1-norm's weight when added to the cost (see
+regularization)
+:param L2_reg: L2-norm's weight when added to the cost (see
+regularization)
+"""
+# Load the dataset
+f = gzip.open('mnist.pkl.gz','rb')
+train_set, valid_set, test_set = cPickle.load(f)
+f.close()
+# make minibatches of size 20
+batch_size = 20    # sized of the minibatch
+# Dealing with the training set
+# get the list of training images (x) and their labels (y)
+(train_set_x, train_set_y) = train_set
+# initialize the list of training minibatches with empty list
+train_batches = []
+for i in xrange(0, len(train_set_x), batch_size):
+# add to the list of minibatches the minibatch starting at
+# position i, ending at position i+batch_size
+# a minibatch is a pair ; the first element of the pair is a list
+# of datapoints, the second element is the list of corresponding
+# labels
+train_batches = train_batches + \
+[(train_set_x[i:i+batch_size], train_set_y[i:i+batch_size])]
+# Dealing with the validation set
+(valid_set_x, valid_set_y) = valid_set
+# initialize the list of validation minibatches
+valid_batches = []
+for i in xrange(0, len(valid_set_x), batch_size):
+valid_batches = valid_batches + \
+[(valid_set_x[i:i+batch_size], valid_set_y[i:i+batch_size])]
+# Dealing with the testing set
+(test_set_x, test_set_y) = test_set
+# initialize the list of testing minibatches
+test_batches = []
+for i in xrange(0, len(test_set_x), batch_size):
+test_batches = test_batches + \
+[(test_set_x[i:i+batch_size], test_set_y[i:i+batch_size])]
+ishape     = (28,28) # this is the size of MNIST images
+# allocate symbolic variables for the data
+x = T.fmatrix()  # the data is presented as rasterized images
+y = T.lvector()  # the labels are presented as 1D vector of
+# [long int] labels
+# construct the logistic regression class
+classifier = MLP( input=x.reshape((batch_size,28*28)),\
+n_in=28*28, n_hidden = 500, n_out=10)
+# the cost we minimize during training is the negative log likelihood of
+# the model plus the regularization terms (L1 and L2); cost is expressed
+# here symbolically
+cost = classifier.negative_log_likelihood(y) \
++ L1_reg * classifier.L1 \
++ L2_reg * classifier.L2_sqr
+# compiling a theano function that computes the mistakes that are made by
+# the model on a minibatch
+test_model = theano.function([x,y], classifier.errors(y))
+# compute the gradient of cost with respect to theta = (W1, b1, W2, b2)
+g_W1 = T.grad(cost, classifier.W1)
+g_b1 = T.grad(cost, classifier.b1)
+g_W2 = T.grad(cost, classifier.W2)
+g_b2 = T.grad(cost, classifier.b2)
+# specify how to update the parameters of the model as a dictionary
+updates = \
+{ classifier.W1: classifier.W1 - learning_rate*g_W1 \
+, classifier.b1: classifier.b1 - learning_rate*g_b1 \
+, classifier.W2: classifier.W2 - learning_rate*g_W2 \
+, classifier.b2: classifier.b2 - learning_rate*g_b2 }
+# compiling a theano function `train_model` that returns the cost, but in
+# the same time updates the parameter of the model based on the rules
+# defined in `updates`
+train_model = theano.function([x, y], cost, updates = updates )
+n_minibatches        = len(train_batches)
+# 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  = n_minibatches  # 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()
+# have a maximum of `n_iter` iterations through the entire dataset
+for iter in xrange(n_iter* n_minibatches):
+# get epoch and minibatch index
+epoch           = iter / n_minibatches
+minibatch_index =  iter % n_minibatches
+# get the minibatches corresponding to `iter` modulo
+# `len(train_batches)`
+x,y = train_batches[ minibatch_index ]
+cost_ij = train_model(x,y)
+if (iter+1) % validation_frequency == 0:
+# compute zero-one loss on validation set
+this_validation_loss = 0.
+for x,y in valid_batches:
+# sum up the errors for each minibatch
+this_validation_loss += test_model(x,y)
+# get the average by dividing with the number of minibatches
+this_validation_loss /= len(valid_batches)
+print('epoch %i, minibatch %i/%i, validation error %f %%' % \
+(epoch, minibatch_index+1, n_minibatches, \
+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)
+best_validation_loss = this_validation_loss
+# test it on the test set
+test_score = 0.
+for x,y in test_batches:
+test_score += test_model(x,y)
+test_score /= len(test_batches)
+print(('     epoch %i, minibatch %i/%i, test error of best '
+'model %f %%') %
+(epoch, minibatch_index+1, n_minibatches,
+test_score*100.))
+if patience <= iter :
+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__':
+sgd_optimization_mnist()

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comparison code_tutoriel/mlp.py @ 0:fda5f787baa6