"""Pool Layer of a convolutional network """

def __init__(self, rng, input, filter_shape, image_shape, filter_pad=0, 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)

input_shuffled = input.dimshuffle(1, 2, 3, 0) # bc01 to c01b

filters_shuffled = self.W.dimshuffle(1, 2, 3, 0) # bc01 to c01b

conv_op = FilterActs(pad=filter_pad, stride=1, partial_sum=1)

contiguous_input = gpu_contiguous(input_shuffled)

contiguous_filters = gpu_contiguous(filters_shuffled)

conv_out_shuffled = conv_op(contiguous_input, contiguous_filters)

# downsample each feature map individually, using maxpooling

# pooled_out = downsample.max_pool_2d(input=conv_out,

# ds=poolsize, ignore_border=True)

pool_op = MaxPool(ds=poolsize[0], stride=poolsize[0])

pooled_out_shuffled = pool_op(conv_out_shuffled)

pooled_out = pooled_out_shuffled.dimshuffle(3, 0, 1, 2) # c01b to bc01

# 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

dataset='cifar-10-batches-py',

nkerns=[32, 64, 128], 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 /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]

# Example of how to reshape and display input

# a=train_set_x[0].reshape((3,1024,1)).eval()

# make_filter_fig(fname='results/input.png',

# filters=a,

# combine_chans=True)

# 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 = (32, 32) # this is the size of MNIST images

nChannels = 3 # the number of channels

print '... building the model'

# Reshape matrix of rasterized images of shape (batch_size,28*28)

# to a 4D tensor, compatible with our LeNetConvPoolLayer

reshaped_input = x.reshape((batch_size, 3, 32, 32))

# Construct the first convolutional pooling layer:

# filtering reduces the image size to (32-5+1+4,32-5+1+4)=(32,32)

# maxpooling reduces this further to (32/2,32/2) = (16,16)

# 4D output tensor is thus of shape (batch_size,nkerns[0],16,16)

conv0 = LeNetConvPoolLayer(

rng, input=reshaped_input,

image_shape=(batch_size, 3, 32, 32),

filter_shape=(nkerns[0], 3, 5, 5),

filter_pad=2,

poolsize=(2, 2))

# conv0_vis = HiddenLayer(rng, input=conv0.output.flatten(2),

# n_in=nkerns[0] * 16 * 16,

# n_out=3 * 32 * 32, activation=T.tanh)

# print conv0_vis.W.eval().shape # (8192, 3072)

# Construct the second convolutional pooling layer

# filtering reduces the image size to (16-5+1+2,16-5+1+2)=(14,14)

# maxpooling reduces this further to (14/2,14/2) = (7,7)

# 4D output tensor is thus of shape (nkerns[0],nkerns[1],7,7)

conv1 = LeNetConvPoolLayer(

rng, input=conv0.output,

image_shape=(batch_size, nkerns[0], 16, 16),

filter_shape=(nkerns[1], nkerns[0], 5, 5),

filter_pad=1,

poolsize=(2, 2))

# the HiddenLayer 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 (batch_size,128*4*4) = (batch_size,2048)

hidden_input = conv1.output.flatten(2)

# construct a fully-connected sigmoidal layer

hidden = HiddenLayer(rng, input=hidden_input, n_in=nkerns[1] * 7 * 7,

n_out=1024, activation=T.tanh)

hidden_vis = HiddenLayer(rng, input=hidden.output, n_in=1024,

n_out=3072, activation=T.nnet.sigmoid)

# classify the values of the fully-connected sigmoidal layer

softmax = LogisticRegression(input=hidden.output, n_in=1024, n_out=10)

softmax_vis = HiddenLayer(rng, input=softmax.p_y_given_x,

n_in=10, n_out=3072,

activation=T.nnet.sigmoid)

# the cost we minimize during training is the NLL of the model

cost = softmax.negative_log_likelihood(y)

hidden_vis_cost = hidden_vis.reconstruction_cost(x)

softmax_vis_cost = softmax_vis.reconstruction_cost(x)

# create a function to compute the mistakes that are made by the model

test_model = theano.function([index], softmax.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], softmax.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 = softmax.params + hidden.params + conv1.params + conv0.params

hidden_vis_params = hidden_vis.params

softmax_vis_params = softmax_vis.params

# create a list of gradients for all model parameters

grads = T.grad(cost, params)

hidden_vis_grads = T.grad(hidden_vis_cost, hidden_vis_params)

softmax_vis_grads = T.grad(softmax_vis_cost, softmax_vis_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))

for param_i, grad_i in zip(hidden_vis_params, hidden_vis_grads):

updates.append((param_i, param_i - learning_rate * grad_i))

for param_i, grad_i in zip(softmax_vis_params, softmax_vis_grads):

updates.append((param_i, param_i - learning_rate * grad_i))

train_model = theano.function(

inputs=[index],

outputs=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]})

print '... training'

patience = 1000 # 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

costs = []

valid = []

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

cost_ij = train_model(minibatch_index)

costs.append(cost_ij)

if iter % 100 == 0:

print('Step %d Cost %f' % (iter, cost_ij))

make_filter_fig(fname='results/hidden.png',

filters=hidden_vis.W.T.eval().reshape((3,1024,1024)),

filter_start=0,

num_filters=16*16,

combine_chans=True)

make_filter_fig(fname='results/softmax.png',

filters=softmax_vis.W.T.eval().reshape((3,1024,10)),

filter_start=0,

num_filters=10,

combine_chans=True)

# rs = conv0_vis.W.reshape((3, nkerns[0] * 16 * 16, 32*32)) # (3,8192,1024)

# rs2 = rs.dimshuffle(0,2,1)

# make_filter_fig(fname='results/conv0.png',

# filters=rs2.eval(),

# filter_start=0,

# num_filters=16*16,

# combine_chans=True)

# rs = conv0_vis.W.T # (3072,8192)

# rs2 = rs.reshape((3, 1024, 8192))

# make_filter_fig(fname='results/conv0-alt.png',

# filters=rs2.eval(),

# filter_start=0,

# num_filters=16*16,

# combine_chans=True)

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)

valid.append(this_validation_loss * 100.)

print('epoch %i, minibatch %i/%i, validation error %.2f%%' % \

(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

best_params = params

# 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(('New Best! epoch %i, minibatch %i/%i, test error of best '

'model %.2f %%') %

(epoch, minibatch_index + 1, n_train_batches,

test_score * 100.))

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] +

' ran for %.2fm' % ((end_time - start_time) / 60.))