def evaluate_lenet5(learning_rate=0.01, n_epochs=10000,
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.))
return best_params
def evaluate_lenet5(learning_rate=0.01,
n_epochs=10000,
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.))
return best_params
