Mercurial > ift6266
view code_tutoriel/convolutional_mlp.py @ 489:ee9836baade3
merge
author | dumitru@dumitru.mtv.corp.google.com |
---|---|
date | Mon, 31 May 2010 19:07:59 -0700 |
parents | 4bc5eeec6394 |
children |
line wrap: on
line source
""" 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)