def base(train_dp, valid_dp, logger, learning_rate):
# learning_rate = 0.01
rng = numpy.random.RandomState([2016,02,26])
max_epochs = 1000
cost = CECost()
stats = list()
test_dp = deepcopy(valid_dp)
train_dp.reset()
valid_dp.reset()
test_dp.reset()
# NETWORK TOPOLOGY:
model = MLP(cost=cost)
model.add_layer(Relu(idim=125, odim=125, irange=1.6, rng=rng))
model.add_layer(Softmax(idim=125, odim=19, rng=rng))
# define the optimiser, here stochasitc gradient descent
# with fixed learning rate and max_epochs
lr_scheduler = LearningRateFixed(
learning_rate=learning_rate, max_epochs=max_epochs)
optimiser = SGDOptimiser(lr_scheduler=lr_scheduler)
logger.info('Training started...')
tr_stats_b, valid_stats_b = optimiser.train(model, train_dp, valid_dp)
logger.info('Testing the model on test set:')
tst_cost, tst_accuracy = optimiser.validate(model, test_dp)
logger.info('ACL test set accuracy is %.2f %%, cost (%s) is %.3f' %
(tst_accuracy*100., cost.get_name(), tst_cost))
fft=True,
name='RLAx')
for dt in pandas.date_range("2015-01-10", "2015-10-10"):
print "date: " + str(dt)
train_dp.reset()
test_dp.reset()
valid_dp.reset()
rng = numpy.random.RandomState([dt.year, dt.month, dt.day])
# define the model structure, here just one linear layer
# and mean square error cost
cost = CECost()
model = MLP(cost=cost)
model.add_layer(ConvRelu_Opt(1, 1, rng=rng, stride=(1, 1)))
model.add_layer(Sigmoid(idim=122, odim=122, rng=rng))
model.add_layer(Softmax(idim=122, odim=19, rng=rng))
#one can stack more layers here
# print map(lambda x: (x.idim, x.odim), model.layers)
lr_scheduler = LearningRateFixed(learning_rate=0.01, max_epochs=500)
optimiser = SGDOptimiser(lr_scheduler=lr_scheduler)
tr_stats, valid_stats = optimiser.train(model, train_dp, valid_dp)
tst_cost, tst_accuracy = optimiser.validate(model, test_dp)
seeds.append((tr_stats, valid_stats, (tst_cost, tst_accuracy)))
end = time.time()
print "scipy.correlate time: " + str(end - start)
logger.setLevel(logging.INFO)
from mlp.layers import MLP, Sigmoid, Linear, Softmax, Relu #import required layer types
from mlp.conv import ConvLinear, ConvRelu, ConvSigmoid
from mlp.maxpooling import ConvMaxPool2D
from mlp.optimisers import SGDOptimiser, Optimiser #import the optimiser
from mlp.dataset import MNISTDataProvider #import data provider #Ruslan Burakov - s1569105
from mlp.costs import CECost, MSECost #import the cost we want to use for optimisation
from mlp.schedulers import LearningRateFixed
rng = numpy.random.RandomState([2015, 10, 10])
# define the model structure, here just one linear layer
# and mean square error cost
cost = CECost()
tsk8_2_model = MLP(cost=cost)
"""
num_inp_feat_maps,
num_out_feat_maps,
image_shape=(28, 28),
kernel_shape=(5, 5),
stride=(1, 1),
irange=0.2,
rng=None,
conv_fwd=my_conv_fwd,
conv_bck=my_conv_bck,
conv_grad=my_conv_grad)
"""
tsk8_2_model.add_layer(
ConvRelu(num_inp_feat_maps=1,
num_out_feat_maps=5,
#some hyper-parameters
nhid = 100
learning_rate = 0.07
max_epochs = 30
cost = CECost()
stats = list()
test_dp = deepcopy(valid_dp)
train_dp.reset()
valid_dp.reset()
test_dp.reset()
#define the model
model = MLP(cost=cost)
#model.add_layer(ComplexLinear(idim=125, odim=125, irange=1.6, rng=rng))
#model.add_layer(Sigmoid(idim=2*125, odim=125, irange=1.6, rng=rng))
model.add_layer(Sigmoid(idim=125, odim=125, irange=1.6, rng=rng))
model.add_layer(Softmax(idim=125, odim=19, rng=rng))
# define the optimiser, here stochasitc gradient descent
# with fixed learning rate and max_epochs
lr_scheduler = LearningRateFixed(learning_rate=learning_rate, max_epochs=max_epochs)
optimiser = SGDOptimiser(lr_scheduler=lr_scheduler)
logger.info('Training started...')
tr_stats, valid_stats = optimiser.train(model, train_dp, valid_dp)
logger.info('Testing the model on test set:')
logger.setLevel(logging.INFO)
from mlp.layers import MLP, Sigmoid, Linear, Softmax #import required layer types
from mlp.conv import ConvLinear, ConvRelu, ConvSigmoid
from mlp.maxpooling import ConvMaxPool2D
from mlp.optimisers import SGDOptimiser, Optimiser #import the optimiser
from mlp.dataset import MNISTDataProvider #import data provider #Ruslan Burakov - s1569105
from mlp.costs import CECost, MSECost #import the cost we want to use for optimisation
from mlp.schedulers import LearningRateFixed
rng = numpy.random.RandomState([2015, 10, 10])
# define the model structure, here just one linear layer
# and mean square error cost
tsk8_1_cost = CECost()
tsk8_1_model = MLP(cost=tsk8_1_cost)
"""
num_inp_feat_maps,
num_out_feat_maps,
image_shape=(28, 28),
kernel_shape=(5, 5),
stride=(1, 1),
irange=0.2,
rng=None,
conv_fwd=my_conv_fwd,
conv_bck=my_conv_bck,
conv_grad=my_conv_grad)
"""
tsk8_1_model.add_layer(
ConvSigmoid(num_inp_feat_maps=1,
num_out_feat_maps=1,
#some hyper-parameters
nhid = 100
learning_rate = 0.07
max_epochs = 30
cost = CECost()
stats = list()
test_dp = deepcopy(valid_dp)
train_dp.reset()
valid_dp.reset()
test_dp.reset()
#define the model
model = MLP(cost=cost)
#model.add_layer(ComplexLinear(idim=125, odim=125, irange=1.6, rng=rng))
#model.add_layer(Sigmoid(idim=2*125, odim=125, irange=1.6, rng=rng))
model.add_layer(Sigmoid(idim=125, odim=125, irange=1.6, rng=rng))
model.add_layer(Softmax(idim=125, odim=19, rng=rng))
# define the optimiser, here stochasitc gradient descent
# with fixed learning rate and max_epochs
lr_scheduler = LearningRateFixed(learning_rate=learning_rate,
max_epochs=max_epochs)
optimiser = SGDOptimiser(lr_scheduler=lr_scheduler)
logger.info('Training started...')
tr_stats, valid_stats = optimiser.train(model, train_dp, valid_dp)
logger.info('Testing the model on test set:')
valid_dp = MACLDataProvider(dset="test", batch_size=1140, max_num_batches=1, randomize=False, fft=True, name="RLAx")
test_dp = MACLDataProvider(dset="valid", batch_size=1140, max_num_batches=1, randomize=False, fft=True, name="RLAx")
for dt in pandas.date_range("2015-01-10", "2015-10-10"):
print "date: " + str(dt)
train_dp.reset()
test_dp.reset()
valid_dp.reset()
rng = numpy.random.RandomState([dt.year, dt.month, dt.day])
# define the model structure, here just one linear layer
# and mean square error cost
cost = CECost()
model = MLP(cost=cost)
model.add_layer(ConvRelu_Opt(1, 1, rng=rng, stride=(1, 1)))
model.add_layer(Sigmoid(idim=122, odim=122, rng=rng))
model.add_layer(Softmax(idim=122, odim=19, rng=rng))
# one can stack more layers here
# print map(lambda x: (x.idim, x.odim), model.layers)
lr_scheduler = LearningRateFixed(learning_rate=0.01, max_epochs=500)
optimiser = SGDOptimiser(lr_scheduler=lr_scheduler)
tr_stats, valid_stats = optimiser.train(model, train_dp, valid_dp)
tst_cost, tst_accuracy = optimiser.validate(model, test_dp)
seeds.append((tr_stats, valid_stats, (tst_cost, tst_accuracy)))
end = time.time()
print "scipy.correlate time: " + str(end - start)
from mlp.layers import MLP, Sigmoid, Linear, Softmax, Relu #import required layer types
from mlp.conv import ConvLinear, ConvRelu, ConvSigmoid
from mlp.maxpooling import ConvMaxPool2D
from mlp.optimisers import SGDOptimiser, Optimiser#import the optimiser
from mlp.dataset import MNISTDataProvider #import data provider #Ruslan Burakov - s1569105
from mlp.costs import CECost, MSECost #import the cost we want to use for optimisation
from mlp.schedulers import LearningRateFixed
rng = numpy.random.RandomState([2015,10,10])
# define the model structure, here just one linear layer
# and mean square error cost
cost = CECost()
tsk8_1_1_model = MLP(cost=cost)
"""
num_inp_feat_maps,
num_out_feat_maps,
image_shape=(28, 28),
kernel_shape=(5, 5),
stride=(1, 1),
irange=0.2,
rng=None,
conv_fwd=my_conv_fwd,
conv_bck=my_conv_bck,
conv_grad=my_conv_grad)
"""
tsk8_1_1_model.add_layer(ConvSigmoid(num_inp_feat_maps=1,
num_out_feat_maps=1,
image_shape=(28,28),
def spretrain(self, model, train_iterator,
valid_iterator=None, noise=False):
self.noise_stack = [model.rng.binomial(1, 0.25,
(train_iterator.batch_size,
f.odim)) for f in model.layers]
converged = False
tr_stats, valid_stats = [], []
cost = MSECost()
model_out = MLP(cost=cost)
init_layer = Linear(idim=model.layers[0].idim,
odim=model.layers[0].idim*2,
rng=model.rng)
model_out.add_layer(init_layer)
nl_layer = Sigmoid(idim=model.layers[0].idim*2,
odim=model.layers[0].idim,
rng=model.rng)
model_out.add_layer(nl_layer)
output_layer = Linear(idim=model.layers[0].idim,
odim=model.layers[0].idim,
rng=model.rng)
model_out.add_layer(output_layer)
# do the initial validation
train_iterator.reset()
train_iterator_tmp = self.label_switch(train_iterator)
tr_nll, tr_acc = self.validate(
model_out, train_iterator_tmp, self.l1_weight, self.l2_weight)
logger.info('Epoch %i: SpecPreTraining cost (%s) for initial model is %.3f. Accuracy is %.2f%%'
% (self.lr_scheduler.epoch, model_out.cost.get_name(), tr_nll, tr_acc * 100.))
tr_stats.append((tr_nll, tr_acc))
layers = model.layers
layers_out = list()
train_iterator.reset()
fprop_list = self.fft_label_switch(deepcopy(train_iterator))
# print fprop_list
while not converged:
train_iterator.reset()
tstart = time.clock()
tr_nll, tr_acc = self.spretrain_epoch(model=model_out,
train_iterator=(train_iterator),
learning_rate=self.lr_scheduler.get_rate(),
to_layer=0,
fprop_list=fprop_list)
tstop = time.clock()
tr_stats.append((tr_nll, tr_acc))
logger.info('Epoch %i: PreTraining cost (%s) is %.3f. Accuracy is %.2f%%'
% (self.lr_scheduler.epoch + 1, model_out.cost.get_name(), tr_nll, tr_acc * 100.))
self.lr_scheduler.get_next_rate(None)
vstop = time.clock()
train_speed = train_iterator.num_examples_presented() / \
(tstop - tstart)
tot_time = vstop - tstart
converged = (self.lr_scheduler.get_rate() == 0)
# reseting epochs to zero could have just done lr_shed.epoch =0
# but I foucsed most my time on cleaning up the conv code
return model_out, tr_stats, valid_stats
def pretrain_discriminative(self, model, train_iterator, valid_iterator=None):
converged = False
cost_name = model.cost.get_name()
tr_stats, valid_stats = [], []
layer_num = len(model.layers)
cost = CECost()
model_out = MLP(cost=cost)
init_layer = model.layers[0]
layers = model.layers
model_out.add_layer(init_layer)
bleugh = layers_dict[layers[-1].get_name()](idim=init_layer.odim,
odim=layers[-1].odim, rng=model.rng, irange=layers[-1].irange)
model_out.add_layer((layers_dict[layers[-1].get_name()](idim=init_layer.odim,
odim=layers[-1].odim, rng=model.rng, irange=layers[-1].irange)))
# do the initial validation
train_iterator.reset()
tr_nll, tr_acc = self.validate(
model_out, train_iterator, self.l1_weight, self.l2_weight)
logger.info('Epoch %i: Training cost (%s) for initial model is %.3f. Accuracy is %.2f%%'
% (self.lr_scheduler.epoch, cost_name, tr_nll, tr_acc * 100.))
tr_stats.append((tr_nll, tr_acc))
if valid_iterator is not None:
valid_iterator.reset()
valid_nll, valid_acc = self.validate(
model, valid_iterator, self.l1_weight, self.l2_weight)
logger.info('Epoch %i: Validation cost (%s) for initial model is %.3f. Accuracy is %.2f%%'
% (self.lr_scheduler.epoch, cost_name, valid_nll, valid_acc * 100.))
valid_stats.append((valid_nll, valid_acc))
for to_layer in range(len(layers)):
if(to_layer > 0 and len(layers) > 2 and to_layer < len(layers) - 1):
model_out.remove_top_layer()
model_out.layers[
len(model_out.layers) - 1].odim = layers[to_layer].idim
tmp_layer = copy(layers[to_layer])
model_out.add_layer(tmp_layer)
# This is here to allow the final layer having a different dim
# to the hidden layers output since the weight matrix needs
# to be reshaped and reinstantiated. Thus I had to modify code
# in layers.py to cater for the global variables that allowed me
# to do so. I believe this may have been overlooked
model_out.add_layer(layers_dict[layers[-1].get_name()](idim=tmp_layer.odim,
odim=layers[-1].odim, rng=model.rng, irange=layers[-1].irange))
while not converged:
train_iterator.reset()
tstart = time.clock()
tr_nll, tr_acc = self.pretrain_discriminative_epoch(model=model_out,
train_iterator=train_iterator,
learning_rate=self.lr_scheduler.get_rate(),
to_layer=to_layer)
tstop = time.clock()
tr_stats.append((tr_nll, tr_acc))
logger.info('Epoch %i: PreTraining cost (%s) is %.3f. Accuracy is %.2f%%'
% (self.lr_scheduler.epoch + 1, cost_name, tr_nll, tr_acc * 100.))
vstart = time.clock()
if valid_iterator is not None:
valid_iterator.reset()
valid_nll, valid_acc = self.validate(model, valid_iterator,
self.l1_weight, self.l2_weight)
logger.info('Epoch %i: PreValidation cost (%s) is %.3f. Accuracy is %.2f%%'
% (self.lr_scheduler.epoch + 1, cost_name, valid_nll, valid_acc * 100.))
self.lr_scheduler.get_next_rate(valid_acc)
valid_stats.append((valid_nll, valid_acc))
else:
self.lr_scheduler.get_next_rate(None)
vstop = time.clock()
train_speed = train_iterator.num_examples_presented() / \
(tstop -
tstart)
valid_speed = valid_iterator.num_examples_presented() / \
(vstop -
vstart)
tot_time = vstop - tstart
# pps = presentations per second
logger.info("Epoch %i: Took %.0f seconds. PreTraining speed %.0f pps. "
"Validation speed %.0f pps."
% (self.lr_scheduler.epoch, tot_time, train_speed, valid_speed))
# we stop training when learning rate, as returned by lr scheduler, is 0
# this is implementation dependent and depending on lr schedule could happen,
# for example, when max_epochs has been reached or if the progress between
# two consecutive epochs is too small, etc.
converged = (self.lr_scheduler.get_rate() == 0)
self.lr_scheduler = copy(self.cache_l)
self.dp_scheduler = copy(self.cache_d)
converged = False
return model_out, tr_stats, valid_stats
def pretrain(self, model, train_iterator,
valid_iterator=None, noise=False):
"""
Returns the layers. Since I was a bit scared of making it return the model
and then ran out of time when it came to cleaning up this code....
the code base was super cool but it was time consuming workinground things
here and there
"""
# Whilst the slides say not to noise the learned represantations when
# Carrying out a denoising autoencoder in eached learned representation
# makes sense when inductively carrying out the definition of a single
# unit autoencoder. Nonetheless I did it in the way the slides point out.
# yet my version can still be run by passing wrong=True to fprop
self.noise_stack = [model.rng.binomial(
1, 0.25, (train_iterator.batch_size, f.odim)) for f in model.layers]
converged = False
cost_name = model.cost.get_name()
tr_stats, valid_stats = [], []
cost = MSECost()
model_out = MLP(cost=cost)
init_layer = Sigmoid(
idim=model.layers[0].idim, odim=model.layers[0].odim, rng=model.rng)
model_out.add_layer(init_layer)
output_layer = Linear(
idim=init_layer.odim, odim=125, rng=init_layer.rng)
model_out.add_layer(output_layer)
# do the initial validation
train_iterator.reset()
train_iterator_tmp = self.label_switch(train_iterator)
tr_nll, tr_acc = self.validate(
model_out, train_iterator_tmp, self.l1_weight, self.l2_weight)
logger.info('Epoch %i: PreTraining cost (%s) for initial model is %.3f. Accuracy is %.2f%%'
% (self.lr_scheduler.epoch, cost_name, tr_nll, tr_acc * 100.))
tr_stats.append((tr_nll, tr_acc))
if valid_iterator is not None:
valid_iterator.reset()
valid_iterator_tmp = self.label_switch(valid_iterator)
valid_nll, valid_acc = self.validate(
model_out, valid_iterator_tmp, self.l1_weight, self.l2_weight)
logger.info('Epoch %i: PreValidation cost (%s) for initial model is %.3f. Accuracy is %.2f%%'
% (self.lr_scheduler.epoch, cost_name, valid_nll, valid_acc * 100.))
valid_stats.append((valid_nll, valid_acc))
layers = model.layers
layers_out = list()
fprop_list = None
print len(layers)
final = False
noise_layer = -1
if noise:
noise_layer = 0
for to_layer in range(len(layers)):
# This is very ugly yes but I invested my time in conv
if(to_layer > 0 and len(layers) > 2 and to_layer < len(layers) - 1):
train_iterator.reset()
model_out.remove_top_layer()
fprop_list = self.fprop_label_switch(
(train_iterator), model_out)
if noise:
noise_layer = to_layer
tmp_layer = Sigmoid(idim=model_out.layers[len(model_out.layers) - 1].odim,
odim=layers[to_layer].odim,
rng=init_layer.rng)
model_out.add_layer(tmp_layer)
output_layer = Linear(idim=tmp_layer.odim,
odim=tmp_layer.idim,
rng=init_layer.rng)
model_out.add_layer(output_layer)
elif to_layer == len(layers) - 1:
final = True
train_iterator.reset()
model_out.remove_top_layer()
fprop_list = None
output_layer = layers[-1]
model_out.add_layer(output_layer)
model_out.cost = CECost()
noise_layer = -1
while not converged:
train_iterator.reset()
tstart = time.clock()
tr_nll, tr_acc = self.pretrain_epoch(model=model_out,
train_iterator=(
train_iterator),
learning_rate=self.lr_scheduler.get_rate(),
to_layer=to_layer,
fprop_list=fprop_list,
final=final,
noise_up_layer=noise_layer)
tstop = time.clock()
tr_stats.append((tr_nll, tr_acc))
logger.info('Epoch %i: PreTraining cost (%s) is %.3f. Accuracy is %.2f%%'
% (self.lr_scheduler.epoch + 1, cost_name, tr_nll, tr_acc * 100.))
vstart = time.clock()
if valid_iterator is not None:
valid_iterator.reset()
if fprop_list is not None:
valid_iterator_tmp = fprop_list
elif not final:
valid_iterator_tmp = self.label_switch(valid_iterator)
else:
valid_iterator_tmp = valid_iterator
valid_nll, valid_acc = self.validate(model_out, valid_iterator_tmp,
self.l1_weight, self.l2_weight)
logger.info('Epoch %i: PreValidation cost (%s) is %.3f. Accuracy is %.2f%%'
% (self.lr_scheduler.epoch + 1, cost_name, valid_nll, valid_acc * 100.))
self.lr_scheduler.get_next_rate(valid_acc)
valid_stats.append((valid_nll, valid_acc))
else:
self.lr_scheduler.get_next_rate(None)
vstop = time.clock()
train_speed = train_iterator.num_examples_presented() / \
(tstop -
tstart)
valid_speed = valid_iterator.num_examples_presented() / \
(vstop -
vstart)
tot_time = vstop - tstart
# pps = presentations per second
logger.info("Epoch %i: Took %.0f seconds. PreTraining speed %.0f pps. "
"Validation speed %.0f pps."
% (self.lr_scheduler.epoch, tot_time, train_speed, valid_speed))
converged = (self.lr_scheduler.get_rate() == 0)
# reseting epochs to zero could have just done lr_shed.epoch =0
# but I foucsed most my time on cleaning up the conv code
self.lr_scheduler.epoch = 0
if self.dp_scheduler is not None:
self.dp_scheduler.epoch = 0
converged = False
layers_out.append(model_out.layers[to_layer])
return layers_out, tr_stats, valid_stats