class MLP(object):
def __init__(self, rng, input, n_in, n_hidden, n_out,discriminant_threshold):
self.hiddenLayer = HiddenLayer(
rng=rng,
input=input,
n_in=n_in,
n_out=n_hidden,
activation=T.tanh
)
self.logRegressionLayer = LogisticRegression(
input=self.hiddenLayer.output,
n_in=n_hidden,
n_out=n_out,
discriminant_threshold = discriminant_threshold
)
self.L1 = (
abs(self.hiddenLayer.W).sum()
+ abs(self.logRegressionLayer.W).sum()
)
self.L2_sqr = (
(self.hiddenLayer.W ** 2).sum()
+ (self.logRegressionLayer.W ** 2).sum()
)
self.negative_log_likelihood = (
self.logRegressionLayer.negative_log_likelihood
)
self.errors = self.logRegressionLayer.errors
self.params = self.hiddenLayer.params + self.logRegressionLayer.params
self.layers = [self.hiddenLayer, self.logRegressionLayer]
self.ypred = self.logRegressionLayer.y_pred
self.py_given_x = self.logRegressionLayer.p_y_given_x
def output(self, x):
print "x"
#print x.eval()
y = self.hiddenLayer.outputer(x)
print "y"
#print y.eval()
z = self.logRegressionLayer.output(y)
print "z"
#print z
return z
def asimov_errors(self, y):
# check if y has same dimension of y_pred
if y.ndim != self.logRegressionLayer.y_pred.ndim:
raise TypeError(
'y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', self.y_pred.type)
)
# check if y is of the correct datatype
if y.dtype.startswith('int'):
S = T.sum(T.eq(y,1))
B = T.sum(T.eq(y,0))#*10000 # TODO: cross-section scaling
s = T.sum(T.and_(T.eq(y,1),T.eq(self.logRegressionLayer.y_pred,1)))
b = T.sum(T.and_(T.eq(y,0),T.eq(self.logRegressionLayer.y_pred,1)))#*10000 TODO: cross-section scaling
return(S,B,s,b)
# represents a mistake in prediction
else:
raise NotImplementedError()
def prediction(self,y):
p_y_and_y = self.logRegressionLayer.p_y_given_x[:,1], y
return p_y_and_y
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,
theano_rng=None,
n_ins=5,
hidden_layers_sizes=[28, 28],#original [500,500]
n_outs=2,
corruption_levels=[0.1, 0.1],
discriminant_threshold = 0.5
):
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.ivector('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)
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],
W=sigmoid_layer.W,
bhid=sigmoid_layer.b)
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,
discriminant_threshold = discriminant_threshold
)
self.params.extend(self.logLayer.params)
# 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)
self.errors = self.logLayer.errors(self.y)
self.py_given_x = self.logLayer.p_y_given_x
def output(self, x):
x = self.sigmoid_layers[0].outputer(x)
x = self.sigmoid_layers[1].outputer(x)
x = self.logLayer.output(x)
return x
def asimov_errors(self, y):
# check if y has same dimension of y_pred
if y.ndim != self.logLayer.y_pred.ndim:
raise TypeError(
'y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', self.y_pred.type)
)
# check if y is of the correct datatype
if y.dtype.startswith('int'):
S = T.sum(T.eq(y,1))
B = T.sum(T.eq(y,0))#*10000 # TODO: cross-section scaling
s = T.sum(T.and_(T.eq(y,1),T.eq(self.logLayer.y_pred,1)))
b = T.sum(T.and_(T.eq(y,0),T.eq(self.logLayer.y_pred,1)))#*10000 TODO: cross-section scaling
return(S,B,s,b)
# represents a mistake in prediction
else:
raise NotImplementedError()
def prediction(self,y):
p_y_and_y = self.logLayer.p_y_given_x[:,1], y
return p_y_and_y
def pretraining_functions(self, train_set_x, batch_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 minibatch index, and to train
a dA you just need to iterate, calling the corresponding function on
all minibatch indexes.
:type train_set_x: theano.tensor.TensorType
:param train_set_x: Shared variable that contains all datapoints used
for training the dA
:type batch_size: int
:param batch_size: size of a [mini]batch
:type learning_rate: float
:param learning_rate: learning rate used during training for any of
the dA layers
'''
# index to a [mini]batch
index = T.lscalar('index') # index to a minibatch
corruption_level = T.scalar('corruption') # % of corruption to use
learning_rate = T.scalar('lr') # learning rate to use
# begining of a batch, given `index`
batch_begin = index * batch_size
# ending of a batch given `index`
batch_end = batch_begin + batch_size
pretrain_fns = []
for dA in self.dA_layers:
# get the cost and the updates list
cost, updates = dA.get_cost_updates(corruption_level,
learning_rate)
# compile the theano function
fn = theano.function(
inputs=[
index,
theano.Param(corruption_level, default=0.2),
theano.Param(learning_rate, default=0.1)
],
outputs=cost,
updates=updates,
givens={
self.x: train_set_x[batch_begin: batch_end]
}
)
# append `fn` to the list of functions
pretrain_fns.append(fn)
return pretrain_fns
def build_finetune_functions(self, datasets, batch_size, learning_rate):
'''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 batch_size: int
:param batch_size: size of a minibatch
:type learning_rate: float
:param learning_rate: learning rate used during finetune stage
'''
(train_set_x, train_set_y, train_set_w) = datasets[0]
(valid_set_x, valid_set_y, valid_set_w) = datasets[1]
(test_set_x, test_set_y, test_set_w) = datasets[2]
# compute number of minibatches for training, validation and testing
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_valid_batches /= batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_test_batches /= batch_size
index = T.lscalar('index') # index to a [mini]batch
# compute the gradients with respect to the model parameters
gparams = T.grad(self.finetune_cost, self.params)
# compute list of fine-tuning updates
updates = [
(param, param - gparam * learning_rate)
for param, gparam in zip(self.params, gparams)
]
train_fn = theano.function(
inputs=[index],
outputs=self.finetune_cost,
updates=updates,
givens={
self.x: train_set_x[
index * batch_size: (index + 1) * batch_size
],
self.y: train_set_y[
index * batch_size: (index + 1) * batch_size
]
},
name='train'
)
test_score_i = theano.function(
[index],
self.errors,
givens={
self.x: test_set_x[
index * batch_size: (index + 1) * batch_size
],
self.y: test_set_y[
index * batch_size: (index + 1) * batch_size
]
},
name='test'
)
test_asimov_model = theano.function(
inputs=[index],
outputs=self.asimov_errors(self.y),
givens={
self.x: test_set_x[index * batch_size: (index + 1) * batch_size],
self.y: test_set_y[index * batch_size: (index + 1) * batch_size]
#w: test_set_w[index * batch_size: (index + 1) * batch_size] #JP
}
)
get_prediction_model = theano.function(
inputs=[index],
outputs=self.prediction(self.y),
givens={
self.x: test_set_x[index * batch_size: (index + 1) * batch_size],
self.y: test_set_y[index * batch_size: (index + 1) * batch_size]
#w: test_set_w[index * batch_size: (index + 1) * batch_size] #JP
}
)
valid_score_i = theano.function(
[index],
self.errors,
givens={
self.x: valid_set_x[
index * batch_size: (index + 1) * batch_size
],
self.y: valid_set_y[
index * batch_size: (index + 1) * batch_size
]
},
name='valid'
)
# Create a function that scans the entire validation set
def valid_score():
return [valid_score_i(i) for i in xrange(n_valid_batches)]
# Create a function that scans the entire test set
def test_score():
return [test_score_i(i) for i in xrange(n_test_batches)]
def asimov_score():
return np.sum([test_asimov_model(i)
for i in xrange(n_test_batches)], axis = 0)
def prediction_score():
return np.vstack([np.reshape(np.ravel(get_prediction_model(i), order='F'),(-1,2))
for i in xrange(n_test_batches)])
return train_fn, valid_score, test_score, asimov_score, prediction_score
