def __init__(
self,
numpy_rng,
n_ins,
hidden_layers_sizes,
corruption_levels=[0.1, 0.1],
theano_rng=None,
n_outs=7
):
""" This class is made to support a variable number of layers.
:type numpy_rng: numpy.random.RandomState
:param numpy_rng: numpy random number generator used to draw initial
weights
:type theano_rng: theano.tensor.shared_randomstreams.RandomStreams
:param theano_rng: Theano random generator; if None is given one is
generated based on a seed drawn from `rng`
:type n_ins: int
:param n_ins: dimension of the input to the sdA
:type n_layers_sizes: list of ints
:param n_layers_sizes: intermediate layers size, must contain
at least one value
:type n_outs: int
:param n_outs: dimension of the output of the network
:type corruption_levels: list of float
:param corruption_levels: amount of corruption to use for each
layer
"""
self.sigmoid_layers = []
self.dA_layers = []
self.params = []
self.n_layers = len(hidden_layers_sizes)
assert self.n_layers > 0
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
# allocate symbolic variables for the data
self.x = T.matrix('x') # the data is presented as rasterized images
self.y = T.iscalar('y') # the labels are presented as 1D vector of
# [int] labels
# end-snippet-1
# The SdA is an MLP, for which all weights of intermediate layers
# are shared with a different denoising autoencoders
# We will first construct the SdA as a deep multilayer perceptron,
# and when constructing each sigmoidal layer we also construct a
# denoising autoencoder that shares weights with that layer
# During pretraining we will train these autoencoders (which will
# lead to chainging the weights of the MLP as well)
# During finetunining we will finish training the SdA by doing
# stochastich gradient descent on the MLP
# start-snippet-2
for i in xrange(self.n_layers):
# construct the sigmoidal layer
# the size of the input is either the number of hidden units of
# the layer below or the input size if we are on the first layer
if i == 0:
input_size = n_ins
else:
input_size = hidden_layers_sizes[i - 1]
# the input to this layer is either the activation of the hidden
# layer below or the input of the SdA if you are on the first
# layer
if i == 0:
layer_input = self.x
else:
layer_input = self.sigmoid_layers[-1].output
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=T.nnet.sigmoid)
# add the layer to our list of layers
self.sigmoid_layers.append(sigmoid_layer)
# its arguably a philosophical question...
# but we are going to only declare that the parameters of the
# sigmoid_layers are parameters of the StackedDAA
# the visible biases in the dA are parameters of those
# dA, but not the SdA
self.params.extend(sigmoid_layer.params)
# Construct a denoising autoencoder that shared weights with this
# layer
dA_layer = dA(numpy_rng=numpy_rng,
theano_rng=theano_rng,
input=layer_input,
n_visible=input_size,
n_hidden=hidden_layers_sizes[i],
theta=sigmoid_layer.theta)
self.dA_layers.append(dA_layer)
# end-snippet-2
# We now need to add a logistic layer on top of the MLP
self.logLayer = LogisticRegression(
input=self.sigmoid_layers[-1].output,
n_in=hidden_layers_sizes[-1],
n_out=n_outs
)
# construct a function that implements one step of finetunining
# compute the cost for second phase of training,
# defined as the negative log likelihood
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
# compute the gradients with respect to the model parameters
# symbolic variable that points to the number of errors made on the
# minibatch given by self.x and self.y
self.errors = self.logLayer.errors(self.y)
self.predict = self.logLayer.predict()
class SdA(object):
"""Stacked denoising auto-encoder class (SdA)
A stacked denoising autoencoder model is obtained by stacking several
dAs. The hidden layer of the dA at layer `i` becomes the input of
the dA at layer `i+1`. The first layer dA gets as input the input of
the SdA, and the hidden layer of the last dA represents the output.
Note that after pretraining, the SdA is dealt with as a normal MLP,
the dAs are only used to initialize the weights.
"""
def __init__(
self,
numpy_rng,
n_ins,
hidden_layers_sizes,
corruption_levels=[0.1, 0.1],
theano_rng=None,
n_outs=7
):
""" This class is made to support a variable number of layers.
:type numpy_rng: numpy.random.RandomState
:param numpy_rng: numpy random number generator used to draw initial
weights
:type theano_rng: theano.tensor.shared_randomstreams.RandomStreams
:param theano_rng: Theano random generator; if None is given one is
generated based on a seed drawn from `rng`
:type n_ins: int
:param n_ins: dimension of the input to the sdA
:type n_layers_sizes: list of ints
:param n_layers_sizes: intermediate layers size, must contain
at least one value
:type n_outs: int
:param n_outs: dimension of the output of the network
:type corruption_levels: list of float
:param corruption_levels: amount of corruption to use for each
layer
"""
self.sigmoid_layers = []
self.dA_layers = []
self.params = []
self.n_layers = len(hidden_layers_sizes)
assert self.n_layers > 0
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
# allocate symbolic variables for the data
self.x = T.matrix('x') # the data is presented as rasterized images
self.y = T.iscalar('y') # the labels are presented as 1D vector of
# [int] labels
# end-snippet-1
# The SdA is an MLP, for which all weights of intermediate layers
# are shared with a different denoising autoencoders
# We will first construct the SdA as a deep multilayer perceptron,
# and when constructing each sigmoidal layer we also construct a
# denoising autoencoder that shares weights with that layer
# During pretraining we will train these autoencoders (which will
# lead to chainging the weights of the MLP as well)
# During finetunining we will finish training the SdA by doing
# stochastich gradient descent on the MLP
# start-snippet-2
for i in xrange(self.n_layers):
# construct the sigmoidal layer
# the size of the input is either the number of hidden units of
# the layer below or the input size if we are on the first layer
if i == 0:
input_size = n_ins
else:
input_size = hidden_layers_sizes[i - 1]
# the input to this layer is either the activation of the hidden
# layer below or the input of the SdA if you are on the first
# layer
if i == 0:
layer_input = self.x
else:
layer_input = self.sigmoid_layers[-1].output
sigmoid_layer = HiddenLayer(rng=numpy_rng,
input=layer_input,
n_in=input_size,
n_out=hidden_layers_sizes[i],
activation=T.nnet.sigmoid)
# add the layer to our list of layers
self.sigmoid_layers.append(sigmoid_layer)
# its arguably a philosophical question...
# but we are going to only declare that the parameters of the
# sigmoid_layers are parameters of the StackedDAA
# the visible biases in the dA are parameters of those
# dA, but not the SdA
self.params.extend(sigmoid_layer.params)
# Construct a denoising autoencoder that shared weights with this
# layer
dA_layer = dA(numpy_rng=numpy_rng,
theano_rng=theano_rng,
input=layer_input,
n_visible=input_size,
n_hidden=hidden_layers_sizes[i],
theta=sigmoid_layer.theta)
self.dA_layers.append(dA_layer)
# end-snippet-2
# We now need to add a logistic layer on top of the MLP
self.logLayer = LogisticRegression(
input=self.sigmoid_layers[-1].output,
n_in=hidden_layers_sizes[-1],
n_out=n_outs
)
# construct a function that implements one step of finetunining
# compute the cost for second phase of training,
# defined as the negative log likelihood
self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
# compute the gradients with respect to the model parameters
# symbolic variable that points to the number of errors made on the
# minibatch given by self.x and self.y
self.errors = self.logLayer.errors(self.y)
self.predict = self.logLayer.predict()
def pretraining_functions(self, train_set_x, window_size):
''' Generates a list of functions, each of them implementing one
step in trainnig the dA corresponding to the layer with same index.
The function will require as input the index, and to train
a dA you just need to iterate, calling the corresponding function on
all indexes.
:type train_set_x: theano.tensor.TensorType
:param train_set_x: Shared variable that contains all datapoints used
for training the dA
:type window_size: int
:param window_size: size of a window
'''
# index
index = T.lscalar('index')
theta_value = T.vector('theta')
x = T.matrix('x') # the data is presented as 3D vector
corruption_level = T.scalar('corruption') # % of corruption to use
n_train_samples = train_set_x.get_value(borrow=True).shape[0] - window_size + 1
# creates a function that computes the average cost on the training set
def train_fn_vis(cur_dA, conj_cost):
train_losses = [conj_cost(i) for i in xrange(n_train_samples)]
this_train_loss = float(numpy.mean(train_losses))
cur_dA.train_cost_array.append([])
cur_dA.train_cost_array[-1].append(cur_dA.epoch)
cur_dA.train_cost_array[-1].append(this_train_loss)
cur_dA.epoch += 1
return theano.shared(this_train_loss)
# creates a function that computes the average gradient of cost with
# respect to theta
def train_fn_grad_vis(conj_grad):
grad = conj_grad(0)
for i in xrange(1, n_train_samples):
grad += conj_grad(i)
return theano.shared(grad / n_train_samples)
pretrain_fns = []
pretrain_updates = []
for cur_dA in self.dA_layers:
# get the cost and the updates list
cost = cur_dA.get_cost(corruption_level)
# compile a theano function that returns the cost
conj_cost = theano.function(
inputs=[
index,
theano.Param(corruption_level, default=0.2),
],
outputs=cost,
givens={
self.x: train_set_x[index: index + window_size]
},
on_unused_input='warn'
)
# compile a theano function that returns the gradient with respect to theta
conj_grad = theano.function(
inputs=[
index,
theano.Param(corruption_level, default=0.2),
],
outputs=T.grad(cost, cur_dA.theta),
givens={
self.x: train_set_x[index: index + window_size]
},
on_unused_input='warn'
)
cur_dA.train_cost_array = []
cur_dA.epoch = 0
train_result = train_fn_vis(cur_dA, conj_cost)
train_fn = theano.function(
inputs=[theta_value],
outputs=train_result,
updates=[(cur_dA.theta, theta_value)]
)
train_grad_result = train_fn_grad_vis(conj_grad)
train_fn_grad = theano.function(
inputs=[theta_value],
outputs=train_grad_result,
updates=[(cur_dA.theta, theta_value)]
)
# append `fn` to the list of functions
pretrain_fns.append(train_fn)
pretrain_updates.append(train_fn_grad)
return pretrain_fns, pretrain_updates
def build_finetune_functions(self, datasets, window_size):
'''Generates a function `train` that implements one step of
finetuning, a function `validate` that computes the error on
a batch from the validation set, and a function `test` that
computes the error on a batch from the testing set
:type datasets: list of pairs of theano.tensor.TensorType
:param datasets: It is a list that contain all the datasets;
the has to contain three pairs, `train`,
`valid`, `test` in this order, where each pair
is formed of two Theano variables, one for the
datapoints, the other for the labels
:type window_size: int
:param window_size: size of window
'''
(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 examples for validation and testing
n_train_samples = train_set_x.get_value(borrow=True).shape[0] - window_size + 1
n_valid_samples = valid_set_x.get_value(borrow=True).shape[0] - window_size + 1
n_test_samples = test_set_x.get_value(borrow=True).shape[0] - window_size + 1
index = T.lscalar('index') # index to a sample
validate_model = theano.function(
[index],
outputs=self.errors,
givens={
self.x: valid_set_x[index: index + window_size],
self.y: valid_set_y[index + window_size - 1]
},
name='valid'
)
test_model = theano.function(
[index],
outputs=self.errors,
givens={
self.x: test_set_x[index: index + window_size],
self.y: test_set_y[index + window_size - 1]
},
name='test'
)
# compile a theano function that returns the cost of a minibatch
conj_cost = theano.function(
[index],
outputs=[self.finetune_cost, self.errors],
givens={
self.x: train_set_x[index: index + window_size],
self.y: train_set_y[index + window_size - 1]
},
name="conj_cost"
)
# compile a theano function that returns the gradient with respect to theta
conj_grad = theano.function(
[index],
outputs=T.grad(self.finetune_cost, self.logLayer.theta),
givens={
self.x: train_set_x[index: index + window_size],
self.y: train_set_y[index + window_size - 1]
},
name="conj_grad"
)
self.logLayer.train_cost_array = []
self.logLayer.train_error_array = []
self.logLayer.epoch = 0
# creates a function that computes the average cost on the training set
def train_fn(theta_value):
self.logLayer.theta.set_value(theta_value, borrow=True)
cur_train_cost = []
cur_train_error =[]
for i in xrange(n_train_samples):
sample_cost, sample_error = conj_cost(i)
cur_train_cost.append(sample_cost)
cur_train_error.append(sample_error)
this_train_loss = float(numpy.mean(cur_train_cost))
self.logLayer.train_cost_array.append([])
self.logLayer.train_cost_array[-1].append(self.logLayer.epoch)
self.logLayer.train_cost_array[-1].append(this_train_loss)
self.logLayer.train_error_array.append([])
self.logLayer.train_error_array[-1].append(self.logLayer.epoch)
self.logLayer.train_error_array[-1].append(float(numpy.mean(cur_train_error)*100))
self.logLayer.epoch += 1
return this_train_loss
# creates a function that computes the average gradient of cost with
# respect to theta
def train_fn_grad(theta_value):
self.logLayer.theta.set_value(theta_value, borrow=True)
grad = conj_grad(0)
for i in xrange(1, n_train_samples):
grad += conj_grad(i)
return grad / n_train_samples
self.logLayer.validation_scores = [numpy.inf, 0]
self.logLayer.valid_error_array = []
self.logLayer.test_error_array = []
# creates the validation function
def callback(theta_value):
self.logLayer.theta.set_value(theta_value, borrow=True)
#compute the validation loss
validation_losses = [validate_model(i)
for i in xrange(n_valid_samples)]
this_validation_loss = numpy.mean(validation_losses)
print('validation error %f %%' % (this_validation_loss * 100.,))
# check if it is better then best validation score got until now
if this_validation_loss < self.logLayer.validation_scores[0]:
# if so, replace the old one, and compute the score on the
# testing dataset
self.logLayer.validation_scores[0] = this_validation_loss
test_losses = [test_model(i)
for i in xrange(n_test_samples)]
self.logLayer.validation_scores[1] = numpy.mean(test_losses)
return train_fn, train_fn_grad, callback