ift6266: baseline_algorithms/conv_mlp/convolutional

comparison baseline_algorithms/conv_mlp/convolutional_mlp.py @ 166:17ae5a1a4dd1

Moving the convolutional MLP code into baseline

author	Dumitru Erhan <dumitru.erhan@gmail.com>
date	Fri, 26 Feb 2010 14:03:24 -0500
parents	conv_mlp/convolutional_mlp.py@33038ab4e799
children

comparison

equal deleted inserted replaced

-:4bc5eeec6394
+:17ae5a1a4dd1
+"""
+This tutorial introduces the LeNet5 neural network architecture using Theano.  LeNet5 is a
+convolutional neural network, good for classifying images. This tutorial shows how to build the
+architecture, and comes with all the hyper-parameters you need to reproduce the paper's MNIST
+results.
+The best results are obtained after X iterations of the main program loop, which takes ***
+minutes on my workstation (an Intel Core i7, circa July 2009), and *** minutes on my GPU (an
+NVIDIA GTX 285 graphics processor).
+This implementation simplifies the model in the following ways:
+- LeNetConvPool doesn't implement location-specific gain and bias parameters
+- LeNetConvPool doesn't implement pooling by average, it implements pooling by max.
+- Digit classification is implemented with a logistic regression rather than an RBF network
+- LeNet5 was not fully-connected convolutions at second layer
+References:
+- Y. LeCun, L. Bottou, Y. Bengio and P. Haffner: Gradient-Based Learning Applied to Document
+Recognition, Proceedings of the IEEE, 86(11):2278-2324, November 1998.
+http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
+"""
+import numpy, theano, cPickle, gzip, time
+import theano.tensor as T
+import theano.sandbox.softsign
+import pylearn.datasets.MNIST
+from pylearn.io import filetensor as ft
+from theano.sandbox import conv, downsample
+class LeNetConvPoolLayer(object):
+def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2,2)):
+"""
+Allocate a LeNetConvPoolLayer with shared variable internal parameters.
+:type rng: numpy.random.RandomState
+:param rng: a random number generator used to initialize weights
+:type input: theano.tensor.dtensor4
+:param input: symbolic image tensor, of shape image_shape
+:type filter_shape: tuple or list of length 4
+:param filter_shape: (number of filters, num input feature maps,
+filter height,filter width)
+:type image_shape: tuple or list of length 4
+:param image_shape: (batch size, num input feature maps,
+image height, image width)
+:type poolsize: tuple or list of length 2
+:param poolsize: the downsampling (pooling) factor (#rows,#cols)
+"""
+assert image_shape[1]==filter_shape[1]
+self.input = input
+# initialize weight values: the fan-in of each hidden neuron is
+# restricted by the size of the receptive fields.
+fan_in =  numpy.prod(filter_shape[1:])
+W_values = numpy.asarray( rng.uniform( \
+low = -numpy.sqrt(3./fan_in), \
+high = numpy.sqrt(3./fan_in), \
+size = filter_shape), dtype = theano.config.floatX)
+self.W = theano.shared(value = W_values)
+# the bias is a 1D tensor -- one bias per output feature map
+b_values = numpy.zeros((filter_shape[0],), dtype= theano.config.floatX)
+self.b = theano.shared(value= b_values)
+# convolve input feature maps with filters
+conv_out = conv.conv2d(input, self.W,
+filter_shape=filter_shape, image_shape=image_shape)
+# downsample each feature map individually, using maxpooling
+pooled_out = downsample.max_pool2D(conv_out, poolsize, ignore_border=True)
+# add the bias term. Since the bias is a vector (1D array), we first
+# reshape it to a tensor of shape (1,n_filters,1,1). Each bias will thus
+# be broadcasted across mini-batches and feature map width & height
+self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
+# store parameters of this layer
+self.params = [self.W, self.b]
+class SigmoidalLayer(object):
+def __init__(self, rng, input, n_in, n_out):
+"""
+Typical hidden layer of a MLP: units are fully-connected and have
+sigmoidal activation function. Weight matrix W is of shape (n_in,n_out)
+and the bias vector b is of shape (n_out,).
+Hidden unit activation is given by: sigmoid(dot(input,W) + b)
+:type rng: numpy.random.RandomState
+:param rng: a random number generator used to initialize weights
+:type input: theano.tensor.dmatrix
+:param input: a symbolic tensor of shape (n_examples, n_in)
+:type n_in: int
+:param n_in: dimensionality of input
+:type n_out: int
+:param n_out: number of hidden units
+"""
+self.input = input
+W_values = numpy.asarray( rng.uniform( \
+low = -numpy.sqrt(6./(n_in+n_out)), \
+high = numpy.sqrt(6./(n_in+n_out)), \
+size = (n_in, n_out)), dtype = theano.config.floatX)
+self.W = theano.shared(value = W_values)
+b_values = numpy.zeros((n_out,), dtype= theano.config.floatX)
+self.b = theano.shared(value= b_values)
+self.output = T.tanh(T.dot(input, self.W) + self.b)
+self.params = [self.W, self.b]
+class LogisticRegression(object):
+"""Multi-class Logistic Regression Class
+The logistic regression is fully described by a weight matrix :math:`W`
+and bias vector :math:`b`. Classification is done by projecting data
+points onto a set of hyperplanes, the distance to which is used to
+determine a class membership probability.
+"""
+def __init__(self, input, n_in, n_out):
+""" Initialize the parameters of the logistic regression
+:param input: symbolic variable that describes the input of the
+architecture (one minibatch)
+:type n_in: int
+:param n_in: number of input units, the dimension of the space in
+which the datapoints lie
+:type n_out: int
+:param n_out: number of output units, the dimension of the space in
+which the labels lie
+"""
+# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
+self.W = theano.shared( value=numpy.zeros((n_in,n_out),
+dtype = theano.config.floatX) )
+# initialize the baises b as a vector of n_out 0s
+self.b = theano.shared( value=numpy.zeros((n_out,),
+dtype = theano.config.floatX) )
+# compute vector of class-membership probabilities in symbolic form
+self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W)+self.b)
+# compute prediction as class whose probability is maximal in
+# symbolic form
+self.y_pred=T.argmax(self.p_y_given_x, axis=1)
+# list of parameters for this layer
+self.params = [self.W, self.b]
+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.
+:param y: corresponds to a vector that gives for each example the
+correct label
+Note: we use the mean instead of the sum so that
+the learning rate is less dependent on the batch size
+"""
+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 ; zero one
+loss over the size 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 load_dataset(fname,batch=20):
+# repertoire qui contient les donnees NIST
+# le repertoire suivant va fonctionner si vous etes connecte sur un ordinateur
+# du reseau DIRO
+datapath = '/data/lisa/data/nist/by_class/'
+# le fichier .ft contient chiffres NIST dans un format efficace. Les chiffres
+# sont stockes dans une matrice de NxD, ou N est le nombre d'images, est D est
+# le nombre de pixels par image (32x32 = 1024). Chaque pixel de l'image est une
+# valeur entre 0 et 255, correspondant a un niveau de gris. Les valeurs sont
+# stockees comme des uint8, donc des bytes.
+f = open(datapath+'digits/digits_train_data.ft')
+# Verifier que vous avez assez de memoire pour loader les donnees au complet
+# dans le memoire. Sinon, utilisez ft.arraylike, une classe construite
+# specialement pour des fichiers qu'on ne souhaite pas loader dans RAM.
+d = ft.read(f)
+# NB: N'oubliez pas de diviser les valeurs des pixels par 255. si jamais vous
+# utilisez les donnees commes entrees dans un reseaux de neurones et que vous
+# voulez des entres entre 0 et 1.
+# digits_train_data.ft contient les images, digits_train_labels.ft contient les
+# etiquettes
+f = open(datapath+'digits/digits_train_labels.ft')
+labels = ft.read(f)
+# Load the dataset
+#f = gzip.open(fname,'rb')
+#train_set, valid_set, test_set = cPickle.load(f)
+#f.close()
+# make minibatches of size 20
+batch_size = batch   # 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) = (d[:4000,:],labels[:4000])
+# 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])]
+#print train_batches[500]
+# Dealing with the validation set
+(valid_set_x, valid_set_y) = (d[4000:5000,:],labels[4000:5000])
+# 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) = (d[5000:6000,:],labels[5000:6000])
+# 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])]
+return train_batches, valid_batches, test_batches
+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'):
+rng = numpy.random.RandomState(23455)
+print 'Before load dataset'
+train_batches, valid_batches, test_batches = load_dataset(dataset,batch_size)
+print 'After load dataset'
+ishape = (32,32)     # this is the size of NIST images
+n_kern2=80
+# allocate symbolic variables for the data
+x = T.matrix('x')  # rasterized images
+y = T.lvector()  # the labels are presented as 1D vector of [long int] labels
+######################
+# BUILD ACTUAL MODEL #
+######################
+# Reshape matrix of rasterized images of shape (batch_size,28*28)
+# to a 4D tensor, compatible with our LeNetConvPoolLayer
+layer0_input = x.reshape((batch_size,1,32,32))
+# Construct the first convolutional pooling layer:
+# filtering reduces the image size to (32-5+1,32-5+1)=(28,28)
+# maxpooling reduces this further to (28/2,28/2) = (14,14)
+# 4D output tensor is thus of shape (20,20,14,14)
+layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
+image_shape=(batch_size,1,32,32),
+filter_shape=(n_kern0,1,filter_shape,filter_shape), poolsize=(2,2))
+if(n_layer>2):
+	# Construct the second convolutional pooling layer
+	# filtering reduces the image size to (14-5+1,14-5+1)=(10,10)
+	# maxpooling reduces this further to (10/2,10/2) = (5,5)
+	# 4D output tensor is thus of shape (20,50,5,5)
+	fshape=(32-filter_shape+1)/2
+	layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
+		image_shape=(batch_size,n_kern0,fshape,fshape),
+		filter_shape=(n_kern1,n_kern0,filter_shape,filter_shape), poolsize=(2,2))
+else:
+	fshape=(32-filter_shape+1)/2
+	layer1_input = layer0.output.flatten(2)
+		# construct a fully-connected sigmoidal layer
+	layer1 = SigmoidalLayer(rng, input=layer1_input,n_in=n_kern0*fshape*fshape, n_out=500)
+	layer2 = LogisticRegression(input=layer1.output, n_in=500, n_out=10)
+	cost = layer2.negative_log_likelihood(y)
+	test_model = theano.function([x,y], layer2.errors(y))
+	params = layer2.params+ layer1.params + layer0.params
+if(n_layer>3):
+	fshape=(32-filter_shape+1)/2
+	fshape2=(fshape-filter_shape+1)/2
+	fshape3=(fshape2-filter_shape+1)/2
+	layer2 = LeNetConvPoolLayer(rng, input=layer1.output,
+		image_shape=(batch_size,n_kern1,fshape2,fshape2),
+		filter_shape=(n_kern2,n_kern1,filter_shape,filter_shape), poolsize=(2,2))
+	layer3_input = layer2.output.flatten(2)
+	layer3 = SigmoidalLayer(rng, input=layer3_input,
+					n_in=n_kern2*fshape3*fshape3, n_out=500)
+	layer4 = LogisticRegression(input=layer3.output, n_in=500, n_out=10)
+	cost = layer4.negative_log_likelihood(y)
+	test_model = theano.function([x,y], layer4.errors(y))
+	params = layer4.params+ layer3.params+ layer2.params+ layer1.params + layer0.params
+elif(n_layer>2):
+	fshape=(32-filter_shape+1)/2
+	fshape2=(fshape-filter_shape+1)/2
+	# the SigmoidalLayer being fully-connected, it operates on 2D matrices of
+	# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
+	# This will generate a matrix of shape (20,32*4*4) = (20,512)
+	layer2_input = layer1.output.flatten(2)
+	# construct a fully-connected sigmoidal layer
+	layer2 = SigmoidalLayer(rng, input=layer2_input,
+					n_in=n_kern1*fshape2*fshape2, n_out=500)
+	# classify the values of the fully-connected sigmoidal layer
+	layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
+	# the cost we minimize during training is the NLL of the model
+	cost = layer3.negative_log_likelihood(y)
+	# create a function to compute the mistakes that are made by the model
+	test_model = theano.function([x,y], layer3.errors(y))
+	# create a list of all model parameters to be fit by gradient descent
+	params = layer3.params+ layer2.params+ layer1.params + layer0.params
+# create a list of gradients for all model parameters
+grads = T.grad(cost, params)
+# train_model is a function that updates the model parameters by SGD
+# Since this model has many parameters, it would be tedious to manually
+# create an update rule for each model parameter. We thus create the updates
+# dictionary by automatically looping over all (params[i],grads[i])  pairs.
+updates = {}
+for param_i, grad_i in zip(params, grads):
+updates[param_i] = param_i - learning_rate * grad_i
+train_model = theano.function([x, y], cost, updates=updates)
+###############
+# TRAIN MODEL #
+###############
+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')
+best_iter            = 0
+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 ]
+if iter %100 == 0:
+print 'training @ iter = ', iter
+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)
+# 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:
+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.')
+print('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.))
+return (best_validation_loss * 100., test_score*100., (end_time-start_time)/60., best_iter)
+if __name__ == '__main__':
+evaluate_lenet5()
+def experiment(state, channel):
+print 'start experiment'
+(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)
+print 'end experiment'
+state.best_validation_loss = best_validation_loss
+state.test_score = test_score
+state.minutes_trained = minutes_trained
+state.iter = iter
+return channel.COMPLETE

Mercurial > ift6266

comparison baseline_algorithms/conv_mlp/convolutional_mlp.py @ 166:17ae5a1a4dd1