ift6266: baseline_algorithms/mlp/mlp

comparison baseline_algorithms/mlp/mlp_nist.py @ 110:93b4b84d86cf

added simple mlp file

author	XavierMuller
date	Tue, 16 Feb 2010 17:12:35 -0500
parents
children	f341a4efb44a

comparison

equal deleted inserted replaced

-:9c45e0071b52
+:93b4b84d86cf
+"""
+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 pdb
+import numpy
+import pylab
+import theano
+import theano.tensor as T
+import time
+import theano.tensor.nnet
+from pylearn.io import filetensor as ft
+data_path = '/data/lisa/data/nist/by_class/'
+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 -6./sqrt(n_in+n_hidden) and 6./sqrt(n_in+n_hidden)
+# 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 -6./sqrt(n_hidden+n_out) and 6./sqrt(n_hidden+n_out)
+# 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 jobman_mlp(state,channel):
+#    (validation_error,test_error,nb_exemples,time)=mlp_full_nist(state.learning_rate,\
+#                                                                state.n_iter,\
+#                                                                state.batch_size,\
+#                                                                state.nb_hidden_units)
+#   state.validation_error = validation_error
+#   state.test_error = test_error
+#   state.nb_exemples = nb_exemples
+#  state.time=time
+# return channel.COMPLETE
+def mlp_full_nist(      verbose = False,\
+train_data = 'all/all_train_data.ft',\
+train_labels = 'all/all_train_labels.ft',\
+test_data = 'all/all_test_data.ft',\
+test_labels = 'all/all_test_labels.ft',\
+learning_rate=0.01,\
+L1_reg = 0.00,\
+L2_reg = 0.0001,\
+nb_max_exemples=1000000,\
+batch_size=20,\
+nb_hidden = 500,\
+nb_targets = 62):
+f = open(data_path+train_data)
+g= open(data_path+train_labels)
+h = open(data_path+test_data)
+i= open(data_path+test_labels)
+raw_train_data = ft.read(f)
+raw_train_labels = ft.read(g)
+raw_test_data = ft.read(h)
+raw_test_labels = ft.read(i)
+f.close()
+g.close()
+i.close()
+h.close()
+#create a validation set the same size as the test size
+#use the end of the training array for this purpose
+#discard the last remaining so we get a %batch_size number
+test_size=len(raw_test_labels)
+test_size = int(test_size/batch_size)
+test_size*=batch_size
+train_size = len(raw_train_data)
+train_size = int(train_size/batch_size)
+train_size*=batch_size
+validation_size =test_size
+offset = train_size-test_size
+if verbose == True:
+print 'train size = %d' %train_size
+print 'test size = %d' %test_size
+print 'valid size = %d' %validation_size
+print 'offset = %d' %offset
+train_set = (raw_train_data,raw_train_labels)
+train_batches = []
+for i in xrange(0, train_size-test_size, batch_size):
+train_batches = train_batches + \
+[(raw_train_data[i:i+batch_size], raw_train_labels[i:i+batch_size])]
+test_batches = []
+for i in xrange(0, test_size, batch_size):
+test_batches = test_batches + \
+[(raw_test_data[i:i+batch_size], raw_test_labels[i:i+batch_size])]
+validation_batches = []
+for i in xrange(0, test_size, batch_size):
+validation_batches = validation_batches + \
+[(raw_train_data[offset+i:offset+i+batch_size], raw_train_labels[offset+i:offset+i+batch_size])]
+ishape     = (32,32) # this is the size of NIST 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,32*32)),\
+n_in=32*32,\
+n_hidden=nb_hidden,\
+n_out=nb_targets)
+# 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)
+#conditions for stopping the adaptation:
+#1) we have reached  nb_max_exemples (this is rounded up to be a multiple of the train size)
+#2) validation error is going up (probable overfitting)
+# This means we no longer stop on slow convergence as low learning rates stopped
+# too fast.
+patience              =nb_max_exemples/batch_size
+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/4
+best_params          = None
+best_validation_loss = float('inf')
+best_iter            = 0
+test_score           = 0.
+start_time = time.clock()
+n_iter = nb_max_exemples/batch_size  # nb of max times we are allowed to run through all exemples
+n_iter = n_iter/n_minibatches + 1
+n_iter=max(1,n_iter) # run at least once on short debug call
+# have a maximum of `n_iter` iterations through the entire dataset
+if verbose == True:
+print 'looping at most %d times through the data set' %n_iter
+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 ]
+# convert to float
+x_float = x/255.0
+cost_ij = train_model(x_float,y)
+if (iter+1) % validation_frequency == 0:
+# compute zero-one loss on validation set
+this_validation_loss = 0.
+for x,y in validation_batches:
+# sum up the errors for each minibatch
+x_float = x/255.0
+this_validation_loss += test_model(x_float,y)
+# get the average by dividing with the number of minibatches
+this_validation_loss /= len(validation_batches)
+if verbose == True:
+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)
+elif verbose == True:
+print 'slow convergence stop'
+# save best validation score and iteration number
+best_validation_loss = this_validation_loss
+best_iter = iter
+# test it on the test set
+test_score = 0.
+for x,y in test_batches:
+x_float=x/255.0
+test_score += test_model(x_float,y)
+test_score /= len(test_batches)
+if verbose == True:
+print(('     epoch %i, minibatch %i/%i, test error of best '
+'model %f %%') %
+(epoch, minibatch_index+1, n_minibatches,
+test_score*100.))
+#if the validation error is going up, we are overfitting
+#stop converging
+elif this_validation_loss > best_validation_loss:
+#calculate the test error at this point and exit
+# test it on the test set
+if verbose==True:
+print ' We are diverging'
+best_iter = iter
+test_score = 0.
+for x,y in test_batches:
+x_float=x/255.0
+test_score += test_model(x_float,y)
+test_score /= len(test_batches)
+if verbose == True:
+print ' validation error is going up, stopping now'
+print(('     epoch %i, minibatch %i/%i, test error of best '
+'model %f %%') %
+(epoch, minibatch_index+1, n_minibatches,
+test_score*100.))
+break
+if patience <= iter :
+break
+end_time = time.clock()
+if verbose == True:
+print(('Optimization complete. Best validation score of %f %% '
+'obtained at iteration %i, with test performance %f %%') %
+(best_validation_loss * 100., best_iter, test_score*100.))
+print ('The code ran for %f minutes' % ((end_time-start_time)/60.))
+print iter
+return (best_validation_loss * 100.,test_score*100.,best_iter*batch_size,(end_time-start_time)/60)
+if __name__ == '__main__':
+mlp_full_mnist()
+def jobman_mlp_full_nist(state,channel):
+(validation_error,test_error,nb_exemples,time)=mlp_full_nist(learning_rate=state.learning_rate,\
+nb_max_exemples=state.nb_max_exemples,\
+nb_hidden=state.nb_hidden)
+state.validation_error=validation_error
+state.test_error=test_error
+state.nb_exemples=nb_exemples
+state.time=time
+return channel.COMPLETE

Mercurial > ift6266

comparison baseline_algorithms/mlp/mlp_nist.py @ 110:93b4b84d86cf