"""Post training:- Second phase MLP.

A multilayer perceptron is a feedforward artificial neural network model

that has one layer or more of hidden units and nonlinear activations.

Intermediate layers usually have as activation function thanh or the

sigmoid function (defined here by a ``SigmoidalLayer`` class) while the

top layer is a softamx layer (defined here by a ``LogisticRegression``

class).

"""

def __init__(self,

input,

n_in,

n_hidden,

n_out,

rng=None,

is_binary=False,

params_first_phase=None):

"""

Initialize the parameters for the multilayer perceptron

:type rng: numpy.random.RandomState

:param rng: a random number generator used to initialize weights

:type input: theano.tensor.TensorType

:param input: symbolic variable that describes the input of the

architecture (one minibatch)

:type n_in: int

:param n_in: number of input units, the dimension of the space in

which the datapoints lie

:type n_hidden: int

:param n_hidden: number of hidden units

:type n_out: int

:param n_out: number of output units, the dimension of the space in which

the labels lie.

"""

self.input = input

if rng == None:

rng = numpy.random.RandomState(1234)

if DEBUGGING:

theano.config.compute_test_value = 'raise'

self.input.tag.test_value = numpy.random.rand(1800, n_in)

# Since we are dealing with a one hidden layer MLP, this will

# translate into a TanhLayer connected to the LogisticRegression

# layer; this can be replaced by a SigmoidalLayer, or a layer

# implementing any other nonlinearity

self.hiddenLayer = HiddenLayer(rng=rng, input=self.input,

n_in=n_in, n_out=n_hidden,

activation=T.tanh)

self.state = "train"

self.is_binary = is_binary

self.out_dir = "out/"

# The logistic regression layer gets as input the hidden units

# of the hidden layer

self.logRegressionLayer = LogisticRegressionLayer(

input=self.hiddenLayer.output,

n_in=n_hidden,

n_out=n_out,

is_binary=is_binary,

rng=rng)

# L1 norm ; one regularization option is to enforce L1 norm to

# be small

self.L1 = abs(self.hiddenLayer.W).sum() \

+ abs(self.logRegressionLayer.W).sum()

# square of L2 norm ; one regularization option is to enforce

# square of L2 norm to be small

self.L2_sqr = (self.hiddenLayer.W ** 2).sum() \

+ (self.logRegressionLayer.W ** 2).sum()

# negative log likelihood of the MLP is given by the

# crossentropy of the output of the model, computed in the

# logistic regression layer

if is_binary:

self.crossentropy = self.logRegressionLayer.crossentropy

else:

self.crossentropy = self.logRegressionLayer.crossentropy_categorical

# negative log likelihood of the MLP is given by the negative

# log likelihood of the output of the model, computed in the

# logistic regression layer

self.negative_log_likelihood = self.logRegressionLayer.negative_log_likelihood

# same holds for the function computing the number of errors

self.errors = self.logRegressionLayer.errors

# Class memberships

self.class_memberships = self.logRegressionLayer.get_class_memberships(self.hiddenLayer.get_outputs(self.input))

self.train_pkl_file = "post_train_n_hidden_" + str(n_hidden) + "n_out_" + str(n_out)

self.test_pkl_file = "post_test_n_hidden_" + str(n_hidden) + "n_out_" + str(n_out)

if params_first_phase is not None:

self.params = params_first_phase

# the parameters of the model are the parameters of the two layer it is

# made out of

self.params = self.hiddenLayer.params + self.logRegressionLayer.params

self.data_dict = { 'Ws':[],

'costs':[],

'test_probs':[],

'train_probs':[],

'test_scores':[]}

def _shared_dataset(self, data_x, name="x"):

shared_x = theano.shared(numpy.asarray(list(data_x), dtype=theano.config.floatX))

shared_x.name = name

return shared_x

def save_data(self):

output = None

if self.state == "train":

output = open(self.out_dir + self.train_pkl_file + ".pkl", 'wb')

else:

output = open(self.out_dir + self.test_pkl_file + ".pkl", 'wb')

pkl.dump(self.data_dict, output)

self.data_dict['Ws'] = []

self.data_dict['costs'] = []

self.data_dict['test_scores'] = []

def posttrain(self,

learning_rate=0.1,

L1_reg=0.00,

L2_reg=0.0001,

n_epochs=80,

data=None,

labels=None,

cost_type=Costs.Crossentropy,

save_exp_data=False,

batch_size=20):

if data is None:

raise Exception("Post-training can't start without pretraining class membership probabilities.")

if labels is None:

raise Exception("Post-training can not start without posttraining class labels.")

self.state = "train"

self.learning_rate = learning_rate

train_set_x = self._shared_dataset(data, name="training_set")

train_set_y = labels

# compute number of minibatches for training

n_examples = train_set_x.get_value(borrow=True).shape[0]

n_train_batches = n_examples / batch_size

######################

# BUILD ACTUAL MODEL #

######################

print '...postraining the model'

# allocate symbolic variables for the data

index = T.lscalar('index') # index to a [mini]batch

y = T.ivector('y') # the labels are presented as 1D vector of int32

mode = "FAST_COMPILE" #DEBUG_MODE"

if DEBUGGING:

self.input.tag.test_value = numpy.random.rand(self)

index.tag.test_value = 0

y.tag.test_value = numpy.ones(n_examples)

mode = "DEBUG_MODE"

# the cost we minimize during training is the negative log likelihood of

# the model plus the regularization terms (L1 and L2); cost is expressed

# here symbolically.

cost = None

if cost_type == Costs.NegativeLikelihood:

cost = self.negative_log_likelihood(y) \

+ L1_reg * self.L1 \

+ L2_reg * self.L2_sqr

elif cost_type == Costs.Crossentropy:

cost = self.crossentropy(y) \

+ L1_reg * self.L1 \

+ L2_reg * self.L2_sqr

gparams = []

for param in self.params:

gparam = T.grad(cost, param)

gparams.append(gparam)

# specify how to update the parameters of the model as selfa dictionary

updates = {}

# given two list the zip A = [a1, a2, a3, a4] and B = [b1, b2, b3, b4] of

# same length, zip generates a list C of same size, where each element

# is a pair formed from the two lists :

# C = [(a1, b1), (a2, b2), (a3, b3) , (a4, b4)]

for param, gparam in zip(self.params, gparams):

updates[param] = param - learning_rate * gparam

# compiling a Theano function `train_model` that returns the cost, butx

# in the same time updates the parameter of the model based on the rules

# defined in `updates`

# p_y_given_x = self.class_memberships

train_model = theano.function(inputs=[index],

outputs=cost,

updates = updates,

givens = {

self.input: train_set_x[index * batch_size:(index + 1) * batch_size],

y: train_set_y[index * batch_size: (index + 1) * batch_size]

},

mode=mode)

if DEBUGGING:

theano.printing.debugprint(train_model)

epoch = 0

costs = []

Ws = []

while (epoch < n_epochs):

for minibatch_index in xrange(n_train_batches):

print "Postraining in Minibatch %i " % (minibatch_index)

minibatch_avg_cost = train_model(minibatch_index)

costs.append(float(minibatch_avg_cost))

Ws.append(self.params[2])

epoch +=1

if save_exp_data:

self.data_dict['Ws'].append(Ws)

self.data_dict['costs'].append([costs])

self.save_data()

return costs

def posttest(self,

data=None,

labels=None,

save_exp_data = False,

batch_size=20):

if data is None:

raise Exception("Post-training can't start without pretraining class membership probabilities.")

if labels is None:

raise Exception("Post-training can not start without posttraining class-membership probabilities.")

test_set_x = shared_dataset(data)

test_set_y = labels

self.state = "test"

# compute number of minibatches for training, validation and testing

n_examples = test_set_x.get_value(borrow=True).shape[0]

n_test_batches = n_examples / batch_size

print '...post-testing the model'

# allocate symbolic variables for the data

index = T.lscalar() # index to a [mini]batch

y = T.ivector('y') # the labels are presented as 1D vector of

# [int] labels

mode = "FAST_RUN"

if DEBUGGING:

theano.config.compute_test_value = 'raise'

index.tag.test_value = 0

y.tag.test_value = numpy.ones(n_examples)

mode = "DEBUG_MODE"

# the cost we minimize during training is the negative log likelihood of

# the model plus the regularization terms (L1 and L2); cost is expressed

# here symbolically

# compiling a Theano function `test_model` that returns the cost, but

# in the same time updates the parameter of the model based on the rules

# defined in `updates`

test_model = theano.function(inputs=[index],

outputs=self.errors(y),

givens={

self.input: test_set_x[index * batch_size:(index + 1) * batch_size],

y: test_set_y[index * batch_size: (index + 1) * batch_size]},

mode=mode)

###############

# TEST MODEL #

###############

test_losses = []

for minibatch_index in xrange(n_test_batches):

test_losses.append(float(test_model(minibatch_index)))

test_score = numpy.mean(test_losses)

print("Minibatch %i, mean test error %f" % (minibatch_index, test_losses[minibatch_index] * 100))

print "In the end test score is %f" %(test_score * 100)

if save_exp_data:

self.data_dict['test_scores'].append(test_losses)

self.save_data()