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 #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,
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,
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),
