ift6266: code_tutoriel/convolutional

annotate code_tutoriel/convolutional_mlp.py @ 462:f59af1648d83

cleaner le techreport

author	Yoshua Bengio <bengioy@iro.umontreal.ca>
date	Fri, 28 May 2010 08:49:36 -0600
parents	4bc5eeec6394
children

rev	line source
165 4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	1 """
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	2 This tutorial introduces the LeNet5 neural network architecture using Theano. LeNet5 is a
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	3 convolutional neural network, good for classifying images. This tutorial shows how to build the
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	4 architecture, and comes with all the hyper-parameters you need to reproduce the paper's MNIST
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	5 results.
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	6
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	7
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	8 This implementation simplifies the model in the following ways:
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	9
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	10 - LeNetConvPool doesn't implement location-specific gain and bias parameters
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	11 - LeNetConvPool doesn't implement pooling by average, it implements pooling by max.
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	12 - Digit classification is implemented with a logistic regression rather than an RBF network
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	13 - LeNet5 was not fully-connected convolutions at second layer
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	14
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	15 References:
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	16 - Y. LeCun, L. Bottou, Y. Bengio and P. Haffner: Gradient-Based Learning Applied to Document
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	17 Recognition, Proceedings of the IEEE, 86(11):2278-2324, November 1998.
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	18 http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	19 """
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	20
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	21 import numpy, time, cPickle, gzip
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	22
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	23 import theano
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	24 import theano.tensor as T
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	25 from theano.tensor.signal import downsample
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	26 from theano.tensor.nnet import conv
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	27
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	28 from logistic_sgd import LogisticRegression, load_data
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	29 from mlp import HiddenLayer
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	30
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	31
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	32 class LeNetConvPoolLayer(object):
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	33 """Pool Layer of a convolutional network """
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	34
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	35 def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2,2)):
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	36 """
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	37 Allocate a LeNetConvPoolLayer with shared variable internal parameters.
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	38
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	39 :type rng: numpy.random.RandomState
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	40 :param rng: a random number generator used to initialize weights
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	41
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	42 :type input: theano.tensor.dtensor4
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	43 :param input: symbolic image tensor, of shape image_shape
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	44
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	45 :type filter_shape: tuple or list of length 4
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	46 :param filter_shape: (number of filters, num input feature maps,
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	47 filter height,filter width)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	48
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	49 :type image_shape: tuple or list of length 4
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	50 :param image_shape: (batch size, num input feature maps,
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	51 image height, image width)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	52
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	53 :type poolsize: tuple or list of length 2
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	54 :param poolsize: the downsampling (pooling) factor (#rows,#cols)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	55 """
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	56
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	57 assert image_shape[1]==filter_shape[1]
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	58 self.input = input
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	59
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	60 # initialize weights to temporary values until we know the shape of the output feature
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	61 # maps
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	62 W_values = numpy.zeros(filter_shape, dtype=theano.config.floatX)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	63 self.W = theano.shared(value = W_values)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	64
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	65 # the bias is a 1D tensor -- one bias per output feature map
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	66 b_values = numpy.zeros((filter_shape[0],), dtype= theano.config.floatX)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	67 self.b = theano.shared(value= b_values)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	68
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	69 # convolve input feature maps with filters
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	70 conv_out = conv.conv2d(input = input, filters = self.W,
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	71 filter_shape=filter_shape, image_shape=image_shape)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	72
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	73 # there are "num input feature maps * filter height * filter width" inputs
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	74 # to each hidden unit
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	75 fan_in = numpy.prod(filter_shape[1:])
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	76 # each unit in the lower layer receives a gradient from:
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	77 # "num output feature maps * filter height * filter width" / pooling size
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	78 fan_out = filter_shape[0] * numpy.prod(filter_shape[2:]) / numpy.prod(poolsize)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	79 # replace weight values with random weights
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	80 W_bound = numpy.sqrt(6./(fan_in + fan_out))
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	81 self.W.value = numpy.asarray(
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	82 rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	83 dtype = theano.config.floatX)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	84
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	85 # downsample each feature map individually, using maxpooling
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	86 pooled_out = downsample.max_pool2D( input = conv_out,
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	87 ds = poolsize, ignore_border=True)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	88
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	89 # add the bias term. Since the bias is a vector (1D array), we first
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	90 # reshape it to a tensor of shape (1,n_filters,1,1). Each bias will thus
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	91 # be broadcasted across mini-batches and feature map width & height
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	92 self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	93
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	94 # store parameters of this layer
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	95 self.params = [self.W, self.b]
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	96
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	97
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	98
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	99 def evaluate_lenet5(learning_rate=0.1, n_epochs=200, dataset='mnist.pkl.gz', nkerns=[20,50]):
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	100 """ Demonstrates lenet on MNIST dataset
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	101
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	102 :type learning_rate: float
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	103 :param learning_rate: learning rate used (factor for the stochastic
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	104 gradient)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	105
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	106 :type n_epochs: int
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	107 :param n_epochs: maximal number of epochs to run the optimizer
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	108
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	109 :type dataset: string
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	110 :param dataset: path to the dataset used for training /testing (MNIST here)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	111
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	112 :type nkerns: list of ints
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	113 :param nkerns: number of kernels on each layer
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	114 """
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	115
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	116 rng = numpy.random.RandomState(23455)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	117
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	118 datasets = load_data(dataset)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	119
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	120 train_set_x, train_set_y = datasets[0]
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	121 valid_set_x, valid_set_y = datasets[1]
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	122 test_set_x , test_set_y = datasets[2]
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	123
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	124
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	125 batch_size = 500 # size of the minibatch
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	126
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	127 # compute number of minibatches for training, validation and testing
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	128 n_train_batches = train_set_x.value.shape[0] / batch_size
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	129 n_valid_batches = valid_set_x.value.shape[0] / batch_size
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	130 n_test_batches = test_set_x.value.shape[0] / batch_size
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	131
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	132 # allocate symbolic variables for the data
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	133 index = T.lscalar() # index to a [mini]batch
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	134 x = T.matrix('x') # the data is presented as rasterized images
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	135 y = T.ivector('y') # the labels are presented as 1D vector of
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	136 # [int] labels
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	137
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	138
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	139 ishape = (28,28) # this is the size of MNIST images
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	140
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	141 ######################
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	142 # BUILD ACTUAL MODEL #
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	143 ######################
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	144 print '... building the model'
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	145
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	146 # Reshape matrix of rasterized images of shape (batch_size,28*28)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	147 # to a 4D tensor, compatible with our LeNetConvPoolLayer
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	148 layer0_input = x.reshape((batch_size,1,28,28))
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	149
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	150 # Construct the first convolutional pooling layer:
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	151 # filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	152 # maxpooling reduces this further to (24/2,24/2) = (12,12)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	153 # 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	154 layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	155 image_shape=(batch_size,1,28,28),
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	156 filter_shape=(nkerns[0],1,5,5), poolsize=(2,2))
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	157
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	158 # Construct the second convolutional pooling layer
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	159 # filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	160 # maxpooling reduces this further to (8/2,8/2) = (4,4)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	161 # 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	162 layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	163 image_shape=(batch_size,nkerns[0],12,12),
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	164 filter_shape=(nkerns[1],nkerns[0],5,5), poolsize=(2,2))
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	165
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	166 # the TanhLayer being fully-connected, it operates on 2D matrices of
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	167 # shape (batch_size,num_pixels) (i.e matrix of rasterized images).
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	168 # This will generate a matrix of shape (20,3244) = (20,512)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	169 layer2_input = layer1.output.flatten(2)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	170
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	171 # construct a fully-connected sigmoidal layer
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	172 layer2 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[1]44,
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	173 n_out=500, activation = T.tanh)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	174
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	175 # classify the values of the fully-connected sigmoidal layer
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	176 layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	177
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	178 # the cost we minimize during training is the NLL of the model
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	179 cost = layer3.negative_log_likelihood(y)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	180
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	181 # create a function to compute the mistakes that are made by the model
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	182 test_model = theano.function([index], layer3.errors(y),
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	183 givens = {
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	184 x: test_set_x[indexbatch_size:(index+1)batch_size],
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	185 y: test_set_y[indexbatch_size:(index+1)batch_size]})
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	186
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	187 validate_model = theano.function([index], layer3.errors(y),
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	188 givens = {
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	189 x: valid_set_x[indexbatch_size:(index+1)batch_size],
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	190 y: valid_set_y[indexbatch_size:(index+1)batch_size]})
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	191
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	192 # create a list of all model parameters to be fit by gradient descent
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	193 params = layer3.params+ layer2.params+ layer1.params + layer0.params
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	194
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	195 # create a list of gradients for all model parameters
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	196 grads = T.grad(cost, params)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	197
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	198 # train_model is a function that updates the model parameters by SGD
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	199 # Since this model has many parameters, it would be tedious to manually
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	200 # create an update rule for each model parameter. We thus create the updates
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	201 # dictionary by automatically looping over all (params[i],grads[i]) pairs.
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	202 updates = {}
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	203 for param_i, grad_i in zip(params, grads):
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	204 updates[param_i] = param_i - learning_rate * grad_i
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	205
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	206 train_model = theano.function([index], cost, updates=updates,
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	207 givens = {
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	208 x: train_set_x[indexbatch_size:(index+1)batch_size],
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	209 y: train_set_y[indexbatch_size:(index+1)batch_size]})
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	210
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	211
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	212 ###############
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	213 # TRAIN MODEL #
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	214 ###############
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	215 print '... training'
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	216 # early-stopping parameters
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	217 patience = 10000 # look as this many examples regardless
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	218 patience_increase = 2 # wait this much longer when a new best is
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	219 # found
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	220 improvement_threshold = 0.995 # a relative improvement of this much is
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	221 # considered significant
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	222 validation_frequency = min(n_train_batches, patience/2)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	223 # go through this many
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	224 # minibatche before checking the network
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	225 # on the validation set; in this case we
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	226 # check every epoch
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	227
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	228 best_params = None
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	229 best_validation_loss = float('inf')
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	230 best_iter = 0
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	231 test_score = 0.
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	232 start_time = time.clock()
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	233
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	234 epoch = 0
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	235 done_looping = False
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	236
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	237 while (epoch < n_epochs) and (not done_looping):
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	238 epoch = epoch + 1
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	239 for minibatch_index in xrange(n_train_batches):
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	240
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	241 iter = epoch * n_train_batches + minibatch_index
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	242
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	243 if iter %100 == 0:
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	244 print 'training @ iter = ', iter
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	245 cost_ij = train_model(minibatch_index)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	246
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	247 if (iter+1) % validation_frequency == 0:
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	248
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	249 # compute zero-one loss on validation set
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	250 validation_losses = [validate_model(i) for i in xrange(n_valid_batches)]
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	251 this_validation_loss = numpy.mean(validation_losses)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	252 print('epoch %i, minibatch %i/%i, validation error %f %%' % \
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	253 (epoch, minibatch_index+1, n_train_batches, \
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	254 this_validation_loss*100.))
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	255
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	256
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	257 # if we got the best validation score until now
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	258 if this_validation_loss < best_validation_loss:
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	259
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	260 #improve patience if loss improvement is good enough
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	261 if this_validation_loss < best_validation_loss * \
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	262 improvement_threshold :
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	263 patience = max(patience, iter * patience_increase)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	264
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	265 # save best validation score and iteration number
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	266 best_validation_loss = this_validation_loss
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	267 best_iter = iter
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	268
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	269 # test it on the test set
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	270 test_losses = [test_model(i) for i in xrange(n_test_batches)]
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	271 test_score = numpy.mean(test_losses)
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	272 print((' epoch %i, minibatch %i/%i, test error of best '
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	273 'model %f %%') %
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	274 (epoch, minibatch_index+1, n_train_batches,
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	275 test_score*100.))
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	276
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	277 if patience <= iter :
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	278 done_looping = False
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	279 break
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	280
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	281 end_time = time.clock()
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	282 print('Optimization complete.')
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	283 print('Best validation score of %f %% obtained at iteration %i,'\
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	284 'with test performance %f %%' %
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	285 (best_validation_loss * 100., best_iter, test_score*100.))
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	286 print('The code ran for %f minutes' % ((end_time-start_time)/60.))
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	287
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	288 if __name__ == '__main__':
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	289 evaluate_lenet5()
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	290
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	291 def experiment(state, channel):
4bc5eeec6394 Updating the tutorial code to the latest revisions. Dumitru Erhan <dumitru.erhan@gmail.com> parents: diff changeset	292 evaluate_lenet5(state.learning_rate, dataset=state.dataset)

Mercurial > ift6266

annotate code_tutoriel/convolutional_mlp.py @ 462:f59af1648d83