ift6266: code_tutoriel/convolutional

comparison code_tutoriel/convolutional_mlp.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
+"""
+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.
+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, time, cPickle, gzip
+import theano
+import theano.tensor as T
+from theano.tensor.signal import downsample
+from theano.tensor.nnet import conv
+from logistic_sgd import LogisticRegression, load_data
+from mlp import HiddenLayer
+class LeNetConvPoolLayer(object):
+"""Pool Layer of a convolutional network """
+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 weights to temporary values until we know the shape of the output feature
+# maps
+W_values = numpy.zeros(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 = input, filters = self.W,
+filter_shape=filter_shape, image_shape=image_shape)
+# there are "num input feature maps * filter height * filter width" inputs
+# to each hidden unit
+fan_in = numpy.prod(filter_shape[1:])
+# each unit in the lower layer receives a gradient from:
+# "num output feature maps * filter height * filter width" / pooling size
+fan_out = filter_shape[0] * numpy.prod(filter_shape[2:]) / numpy.prod(poolsize)
+# replace weight values with random weights
+W_bound = numpy.sqrt(6./(fan_in + fan_out))
+self.W.value = numpy.asarray(
+rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
+dtype = theano.config.floatX)
+# downsample each feature map individually, using maxpooling
+pooled_out = downsample.max_pool2D( input = conv_out,
+ds = 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]
+def evaluate_lenet5(learning_rate=0.1, n_epochs=200, dataset='mnist.pkl.gz', nkerns=[20,50]):
+""" Demonstrates lenet on MNIST dataset
+:type learning_rate: float
+:param learning_rate: learning rate used (factor for the stochastic
+gradient)
+:type n_epochs: int
+:param n_epochs: maximal number of epochs to run the optimizer
+:type dataset: string
+:param dataset: path to the dataset used for training /testing (MNIST here)
+:type nkerns: list of ints
+:param nkerns: number of kernels on each layer
+"""
+rng = numpy.random.RandomState(23455)
+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 = 500    # size of the minibatch
+# compute number of minibatches for training, validation and testing
+n_train_batches = train_set_x.value.shape[0] / batch_size
+n_valid_batches = valid_set_x.value.shape[0] / batch_size
+n_test_batches  = test_set_x.value.shape[0]  / batch_size
+# allocate symbolic variables for the data
+index = T.lscalar()    # index to a [mini]batch
+x     = T.matrix('x')  # the data is presented as rasterized images
+y     = T.ivector('y') # the labels are presented as 1D vector of
+# [int] labels
+ishape = (28,28)     # this is the size of MNIST images
+######################
+# BUILD ACTUAL MODEL #
+######################
+print '... building the 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,1,28,28))
+# Construct the first convolutional pooling layer:
+# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
+# maxpooling reduces this further to (24/2,24/2) = (12,12)
+# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
+layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
+image_shape=(batch_size,1,28,28),
+filter_shape=(nkerns[0],1,5,5), poolsize=(2,2))
+# Construct the second convolutional pooling layer
+# filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
+# maxpooling reduces this further to (8/2,8/2) = (4,4)
+# 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
+layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
+image_shape=(batch_size,nkerns[0],12,12),
+filter_shape=(nkerns[1],nkerns[0],5,5), poolsize=(2,2))
+# the TanhLayer 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 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[1]*4*4,
+n_out=500, activation = T.tanh)
+# 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([index], layer3.errors(y),
+givens = {
+x: test_set_x[index*batch_size:(index+1)*batch_size],
+y: test_set_y[index*batch_size:(index+1)*batch_size]})
+validate_model = theano.function([index], layer3.errors(y),
+givens = {
+x: valid_set_x[index*batch_size:(index+1)*batch_size],
+y: valid_set_y[index*batch_size:(index+1)*batch_size]})
+# 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([index], cost, updates=updates,
+givens = {
+x: train_set_x[index*batch_size:(index+1)*batch_size],
+y: train_set_y[index*batch_size:(index+1)*batch_size]})
+###############
+# TRAIN MODEL #
+###############
+print '... training'
+# 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')
+best_iter            = 0
+test_score           = 0.
+start_time = time.clock()
+epoch = 0
+done_looping = False
+while (epoch < n_epochs) and (not done_looping):
+epoch = epoch + 1
+for minibatch_index in xrange(n_train_batches):
+iter = epoch * n_train_batches + minibatch_index
+if iter %100 == 0:
+print 'training @ iter = ', iter
+cost_ij = train_model(minibatch_index)
+if (iter+1) % validation_frequency == 0:
+# compute zero-one loss on validation set
+validation_losses = [validate_model(i) for i in xrange(n_valid_batches)]
+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(i) for i in xrange(n_test_batches)]
+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 = False
+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.))
+if __name__ == '__main__':
+evaluate_lenet5()
+def experiment(state, channel):
+evaluate_lenet5(state.learning_rate, dataset=state.dataset)

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comparison code_tutoriel/convolutional_mlp.py @ 165:4bc5eeec6394