def test_DBN(finetune_lr=0.01, pretraining_epochs=100,
pretrain_lr=0.01, k=1, training_epochs=100,
batch_size=5):
"""
Demonstrates how to train and main a Deep Belief Network.
This is demonstrated on MNIST.
:type finetune_lr: float
:param finetune_lr: learning rate used in the finetune stage
:type pretraining_epochs: int
:param pretraining_epochs: number of epoch to do pretraining
:type pretrain_lr: float
:param pretrain_lr: learning rate to be used during pre-training
:type k: int
:param k: number of Gibbs steps in CD/PCD
:type training_epochs: int
:param training_epochs: maximal number of iterations ot run the optimizer
:type dataset: string
:param dataset: path the the pickled dataset
:type batch_size: int
:param batch_size: the size of a minibatch
"""
datasets = loadFeaturedData()
# datasets = load10secData()
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
# numpy random generator
numpy_rng = numpy.random.RandomState(123)
print '... building the model'
# construct the Deep Belief Network
# Test for debugging
# dbn = DBN(numpy_rng=numpy_rng, n_ins=10,
# hidden_layers_sizes=[100, 100, 100],
# n_outs=2)
dbn = DBN(numpy_rng=numpy_rng, n_ins=10,
hidden_layers_sizes=[50, 50],
n_outs=2)
# start-snippet-2
#########################
# PRETRAINING THE MODEL #
#########################
print '... getting the pretraining functions'
pretraining_fns = dbn.pretraining_functions(train_set_x=train_set_x,
batch_size=batch_size,
k=k)
print '... pre-training the model'
start_time = time.clock()
## Pre-train layer-wise
for i in xrange(dbn.n_layers):
# go through pretraining epochs
for epoch in xrange(pretraining_epochs):
# go through the training set
c = []
for batch_index in xrange(n_train_batches):
c.append(pretraining_fns[i](index=batch_index,
lr=pretrain_lr))
print 'Pre-training layer %i, epoch %d, cost ' % (i, epoch),
print numpy.mean(c)
end_time = time.clock()
# end-snippet-2
print >> sys.stderr, ('The pretraining code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
########################
# FINETUNING THE MODEL #
########################
# get the training, validation and testing function for the model
print '... getting the finetuning functions'
train_fn, validate_model, test_model, test_confmatrix = dbn.build_finetune_functions(
datasets=datasets,
batch_size=batch_size,
learning_rate=finetune_lr
)
print '... finetuning the model'
# early-stopping parameters
patience = 4 * n_train_batches # 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
# minibatches before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = numpy.inf
test_score = 0.
start_time = time.clock()
done_looping = False
epoch = 0
while (epoch < training_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_fn(minibatch_index)
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
validation_losses = validate_model()
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
# main it on the main set
test_losses = test_model()
test_score = numpy.mean(test_losses)
test_confmatrices = test_confmatrix()
test_confelement = numpy.sum(test_confmatrices, axis=0)
true_pos = test_confelement[0]
true_neg = test_confelement[1]
false_pos = test_confelement[2]
false_neg = test_confelement[3]
print(('epoch %i, minibatch %i/%i, main error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
print(('TP %i, TN %i, FP %i, FN %i') % (true_pos, true_neg, false_pos, false_neg))
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print(
(
'Optimization complete with best validation score of %f %%, '
'obtained at iteration %i, '
'with main performance %f %% '
) % (best_validation_loss * 100., best_iter + 1, test_score * 100.)
)
print(('TP %i, TN %i, FP %i, FN %i') % (true_pos, true_neg, false_pos, false_neg))
print >> sys.stderr, ('The fine tuning code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time)
/ 60.))
import numpy
import matplotlib.pyplot as plt
import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
from data_process import loadFeaturedData
from logistic_sgd import LogisticRegression
from mlp_hh import MLP
if __name__ == '__main__':
datasets = loadFeaturedData()
train_set_x, train_set_y = datasets[0]
test_set_x, test_set_y = datasets[1]
batch_size = 5
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# allocate symbolic variables for the data
def test_DBN(finetune_lr=0.01,
pretraining_epochs=100,
pretrain_lr=0.01,
k=1,
training_epochs=100,
batch_size=5):
"""
Demonstrates how to train and main a Deep Belief Network.
This is demonstrated on MNIST.
:type finetune_lr: float
:param finetune_lr: learning rate used in the finetune stage
:type pretraining_epochs: int
:param pretraining_epochs: number of epoch to do pretraining
:type pretrain_lr: float
:param pretrain_lr: learning rate to be used during pre-training
:type k: int
:param k: number of Gibbs steps in CD/PCD
:type training_epochs: int
:param training_epochs: maximal number of iterations ot run the optimizer
:type dataset: string
:param dataset: path the the pickled dataset
:type batch_size: int
:param batch_size: the size of a minibatch
"""
datasets = loadFeaturedData()
# datasets = load10secData()
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
# numpy random generator
numpy_rng = numpy.random.RandomState(123)
print '... building the model'
# construct the Deep Belief Network
# Test for debugging
# dbn = DBN(numpy_rng=numpy_rng, n_ins=10,
# hidden_layers_sizes=[100, 100, 100],
# n_outs=2)
dbn = DBN(numpy_rng=numpy_rng,
n_ins=10,
hidden_layers_sizes=[50, 50],
n_outs=2)
# start-snippet-2
#########################
# PRETRAINING THE MODEL #
#########################
print '... getting the pretraining functions'
pretraining_fns = dbn.pretraining_functions(train_set_x=train_set_x,
batch_size=batch_size,
k=k)
print '... pre-training the model'
start_time = time.clock()
## Pre-train layer-wise
for i in xrange(dbn.n_layers):
# go through pretraining epochs
for epoch in xrange(pretraining_epochs):
# go through the training set
c = []
for batch_index in xrange(n_train_batches):
c.append(pretraining_fns[i](index=batch_index, lr=pretrain_lr))
print 'Pre-training layer %i, epoch %d, cost ' % (i, epoch),
print numpy.mean(c)
end_time = time.clock()
# end-snippet-2
print >> sys.stderr, ('The pretraining code for file ' +
os.path.split(__file__)[1] + ' ran for %.2fm' %
((end_time - start_time) / 60.))
########################
# FINETUNING THE MODEL #
########################
# get the training, validation and testing function for the model
print '... getting the finetuning functions'
train_fn, validate_model, test_model, test_confmatrix = dbn.build_finetune_functions(
datasets=datasets, batch_size=batch_size, learning_rate=finetune_lr)
print '... finetuning the model'
# early-stopping parameters
patience = 4 * n_train_batches # 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
# minibatches before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = numpy.inf
test_score = 0.
start_time = time.clock()
done_looping = False
epoch = 0
while (epoch < training_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_fn(minibatch_index)
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
validation_losses = validate_model()
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
# main it on the main set
test_losses = test_model()
test_score = numpy.mean(test_losses)
test_confmatrices = test_confmatrix()
test_confelement = numpy.sum(test_confmatrices, axis=0)
true_pos = test_confelement[0]
true_neg = test_confelement[1]
false_pos = test_confelement[2]
false_neg = test_confelement[3]
print(('epoch %i, minibatch %i/%i, main error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
print(('TP %i, TN %i, FP %i, FN %i') %
(true_pos, true_neg, false_pos, false_neg))
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print(('Optimization complete with best validation score of %f %%, '
'obtained at iteration %i, '
'with main performance %f %% ') %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print(('TP %i, TN %i, FP %i, FN %i') %
(true_pos, true_neg, false_pos, false_neg))
print >> sys.stderr, ('The fine tuning code for file ' +
os.path.split(__file__)[1] + ' ran for %.2fm' %
((end_time - start_time) / 60.))
def test_DBN(finetune_lr=0.01,
pretraining_epochs=100,
pretrain_lr=0.1,
k=1,
training_epochs=100,
batch_size=5):
datasets = loadFeaturedData()
train_set_x, train_set_y = datasets[0]
test_set_x, test_set_y = datasets[1]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size
# numpy random generator
numpy_rng = numpy.random.RandomState(123)
print '... building the model'
dbn = DBN(numpy_rng=numpy_rng,
n_ins=10,
hidden_layers_sizes=[100],
n_outs=2)
#########################
# PRETRAINING THE MODEL #
#########################
print '... getting the pretraining functions'
pretraining_fns = dbn.pretraining_functions(train_set_x=train_set_x,
batch_size=batch_size,
k=k)
print '... pre-training the model'
start_time = time.clock()
# Pre-train layer-wise
for i in xrange(dbn.n_layers):
# go through pretraining epochs
for epoch in xrange(pretraining_epochs):
# go through the training set
c = []
for batch_index in xrange(n_train_batches):
c.append(pretraining_fns[i](index=batch_index, lr=pretrain_lr))
print 'Pre-training layer %i, epoch %d, cost ' % (i, epoch),
print numpy.mean(c)
end_time = time.clock()
# end-snippet-2
print >> sys.stderr, ('The pretraining code for file ' +
os.path.split(__file__)[1] + ' ran for %.2fm' %
((end_time - start_time) / 60.))
########################
# FINETUNING THE MODEL #
########################
# get the training, validation and testing function for the model
print '... getting the finetuning functions'
train_fn, test_model, test_confmatrix = dbn.build_finetune_functions(
datasets=datasets, batch_size=batch_size, learning_rate=finetune_lr)
index = T.lscalar('index') # index to a [mini]batch
# Custom function
stopcheck_model = theano.function(
inputs=[index],
outputs=dbn.finetune_cost,
givens={
dbn.x: test_set_x[index * batch_size:(index + 1) * batch_size],
dbn.y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
train_check = theano.function(
inputs=[index],
outputs=dbn.errors,
givens={
dbn.x: train_set_x[index * batch_size:(index + 1) * batch_size],
dbn.y: train_set_y[index * batch_size:(index + 1) * batch_size]
})
print '... finetuning the model'
test_score = 0.
start_time = time.clock()
tr_cost = []
te_cost = []
epoch = 0
while (epoch < training_epochs):
epoch = epoch + 1
# go through the training set
d = []
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_fn(minibatch_index)
d.append(minibatch_avg_cost)
print(('In epoch %d, ') % (epoch))
print 'Training cost = ', numpy.mean(d)
tr_cost.append(numpy.mean(d))
tc = []
for test_batch_index in xrange(n_test_batches):
testing_cost = stopcheck_model(test_batch_index)
tc.append(testing_cost)
print 'Testing cost = ', numpy.mean(tc)
te_cost.append(numpy.mean(tc))
train_losses = [train_check(i) for i in xrange(n_train_batches)]
print 'Training error = ', numpy.mean(train_losses) * 100, ' %'
test_losses = test_model()
test_score = numpy.mean(test_losses)
test_confmatrices = test_confmatrix()
test_confelement = numpy.sum(test_confmatrices, axis=0)
true_pos = test_confelement[0]
true_neg = test_confelement[1]
false_pos = test_confelement[2]
false_neg = test_confelement[3]
f_score = (true_pos + true_neg) / float(true_pos + true_neg + false_pos +
5 * false_neg)
print(('Test error: %f %%, F-score: %f') % (test_score * 100., f_score))
print(('TP %i, TN %i, FP %i, FN %i') %
(true_pos, true_neg, false_pos, false_neg))
end_time = time.clock()
print >> sys.stderr, ('The fine tuning code for file ' +
os.path.split(__file__)[1] + ' ran for %.2fm' %
((end_time - start_time) / 60.))
x_axis = numpy.arange(training_epochs)
plt.plot(x_axis, numpy.array(tr_cost), '+', x_axis, numpy.array(te_cost),
'.')
plt.show()
def test_DBN(finetune_lr=0.01, pretraining_epochs=100,
pretrain_lr=0.1, k=1, training_epochs=100,
batch_size=5):
datasets = loadFeaturedData()
train_set_x, train_set_y = datasets[0]
test_set_x, test_set_y = datasets[1]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size
# numpy random generator
numpy_rng = numpy.random.RandomState(123)
print '... building the model'
dbn = DBN(numpy_rng=numpy_rng, n_ins=10,
hidden_layers_sizes=[100],
n_outs=2)
#########################
# PRETRAINING THE MODEL #
#########################
print '... getting the pretraining functions'
pretraining_fns = dbn.pretraining_functions(train_set_x=train_set_x,
batch_size=batch_size,
k=k)
print '... pre-training the model'
start_time = time.clock()
# Pre-train layer-wise
for i in xrange(dbn.n_layers):
# go through pretraining epochs
for epoch in xrange(pretraining_epochs):
# go through the training set
c = []
for batch_index in xrange(n_train_batches):
c.append(pretraining_fns[i](index=batch_index,
lr=pretrain_lr))
print 'Pre-training layer %i, epoch %d, cost ' % (i, epoch),
print numpy.mean(c)
end_time = time.clock()
# end-snippet-2
print >> sys.stderr, ('The pretraining code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
########################
# FINETUNING THE MODEL #
########################
# get the training, validation and testing function for the model
print '... getting the finetuning functions'
train_fn, test_model, test_confmatrix = dbn.build_finetune_functions(
datasets=datasets,
batch_size=batch_size,
learning_rate=finetune_lr
)
index = T.lscalar('index') # index to a [mini]batch
# Custom function
stopcheck_model = theano.function(
inputs=[index],
outputs=dbn.finetune_cost,
givens={
dbn.x: test_set_x[index * batch_size:(index + 1) * batch_size],
dbn.y: test_set_y[index * batch_size:(index + 1) * batch_size]
}
)
train_check = theano.function(
inputs=[index],
outputs=dbn.errors,
givens={
dbn.x: train_set_x[index * batch_size: (index + 1) * batch_size],
dbn.y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
print '... finetuning the model'
test_score = 0.
start_time = time.clock()
tr_cost = []
te_cost = []
epoch = 0
while (epoch < training_epochs):
epoch = epoch + 1
# go through the training set
d = []
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_fn(minibatch_index)
d.append(minibatch_avg_cost)
print(('In epoch %d, ') % (epoch))
print 'Training cost = ', numpy.mean(d)
tr_cost.append(numpy.mean(d))
tc = []
for test_batch_index in xrange(n_test_batches):
testing_cost = stopcheck_model(test_batch_index)
tc.append(testing_cost)
print 'Testing cost = ', numpy.mean(tc)
te_cost.append(numpy.mean(tc))
train_losses = [train_check(i) for i in xrange(n_train_batches)]
print 'Training error = ', numpy.mean(train_losses)*100, ' %'
test_losses = test_model()
test_score = numpy.mean(test_losses)
test_confmatrices = test_confmatrix()
test_confelement = numpy.sum(test_confmatrices, axis=0)
true_pos = test_confelement[0]
true_neg = test_confelement[1]
false_pos = test_confelement[2]
false_neg = test_confelement[3]
f_score = (true_pos + true_neg)/float(true_pos + true_neg + false_pos + 5*false_neg)
print(('Test error: %f %%, F-score: %f') % (test_score * 100., f_score))
print(('TP %i, TN %i, FP %i, FN %i') % (true_pos, true_neg, false_pos, false_neg))
end_time = time.clock()
print >> sys.stderr, ('The fine tuning code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time)
/ 60.))
x_axis = numpy.arange(training_epochs)
plt.plot(x_axis, numpy.array(tr_cost), '+', x_axis, numpy.array(te_cost), '.')
plt.show()
import numpy
import matplotlib.pyplot as plt
import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
from data_process import loadFeaturedData
from logistic_sgd import LogisticRegression
from mlp_hh import MLP
if __name__ == '__main__':
datasets = loadFeaturedData()
train_set_x, train_set_y = datasets[0]
test_set_x, test_set_y = datasets[1]
batch_size = 5
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# allocate symbolic variables for the data