"""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

# 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))

# initialize weights with random weights

W_bound = numpy.sqrt(6. / (fan_in + fan_out))

self.W = theano.shared(numpy.asarray(

rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),

dtype=theano.config.floatX),

borrow=True)

# 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, borrow=True)

# convolve input feature maps with filters

conv_out = conv.conv2d(input=input, filters=self.W,

filter_shape=filter_shape, image_shape=image_shape)

# downsample each feature map individually, using maxpooling

pooled_out = downsample.max_pool_2d(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

""" Function that loads the dataset into shared variables

The reason we store our dataset in shared variables is to allow

Theano to copy it into the GPU memory (when code is run on GPU).

Since copying data into the GPU is slow, copying a minibatch everytime

is needed (the default behaviour if the data is not in a shared

variable) would lead to a large decrease in performance.

"""

data_x, data_y = data_xy

shared_x = theano.shared(numpy.asarray(data_x, dtype=theano.config.floatX))

shared_y = theano.shared(numpy.asarray(data_y, dtype=theano.config.floatX))

# When storing data on the GPU it has to be stored as floats

# therefore we will store the labels as ``floatX`` as well

# (``shared_y`` does exactly that). But during our computations

# we need them as ints (we use labels as index, and if they are

# floats it doesn't make sense) therefore instead of returning

# ``shared_y`` we will have to cast it to int. This little hack

# lets us get around this issue

dataset='ml2013final_train.dat',

nkerns=[20, 50], batch_size=500):

""" 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

:type nkerns: list of ints

:param nkerns: number of kernels on each layer

"""

print "learning_rate = ", learning_rate

rng = numpy.random.RandomState(23455)

X, Y = shared_dataset(preprocess(dataset))

# get validation set

length = Y.owner.inputs[0].get_value(borrow=True).shape[0]

kf = KFold(length, n_folds=5) # keep 80% for training and 20% for validation (test set = validation set here)

for i, (train_index, valid_index) in enumerate(kf):

if i == 0:

train_set_x, valid_set_x = X[train_index], X[valid_index]

train_set_y, valid_set_y = Y[train_index], Y[valid_index]

train_set_x = theano.shared(numpy.asarray(train_set_x.eval(), dtype=theano.config.floatX))

train_set_y = theano.shared(numpy.asarray(train_set_y.eval(), dtype=theano.config.floatX))

train_set_y = T.cast(train_set_y, 'int32')

valid_set_x = theano.shared(numpy.asarray(valid_set_x.eval(), dtype=theano.config.floatX))

valid_set_y = theano.shared(numpy.asarray(valid_set_y.eval(), dtype=theano.config.floatX))

valid_set_y = T.cast(valid_set_y, 'int32')

# set test set = validation set

test_set_x = valid_set_x

test_set_y = valid_set_y

# compute number of minibatches for training, validation and testing

n_train_batches = train_set_x.get_value(borrow=True).shape[0]

n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]

n_test_batches = test_set_x.get_value(borrow=True).shape[0]

n_train_batches /= batch_size

n_valid_batches /= batch_size

n_test_batches /= 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 the image

######################

# 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=12)

# 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 list by automatically looping over all

# (params[i],grads[i]) pairs.

updates = []

for param_i, grad_i in zip(params, grads):

updates.append((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 = numpy.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 - 1) * 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 epoch > 30:

# Save the model

print 'saving model..'

save_file = open('cnn_%s.txt' % epoch, 'wb')

cPickle.dump(params, save_file, -1)

save_file.close()

if patience <= iter:

done_looping = True

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 + 1, test_score * 100.))

print >> sys.stderr, ('The code for file ' +

os.path.split(__file__)[1] +