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output_losses.py
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output_losses.py
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import numpy as np
import theano
import theano.tensor as T
class LogisticRegression(object):
"""Multi-class Logistic Regression loss dangler."""
def __init__(self, linear_layer):
"""Dangle a logistic regression from the given linear layer.
The given linear layer should be a HiddenLayer (or subclass) object,
for HiddenLayer as defined in LayerNet.py."""
self.input_layer = linear_layer
def loss_func(self, y):
"""Return the multiclass logistic regression loss for y.
The class labels in y are assumed to be in correspondence with the
set of column indices for self.input_layer.linear_output.
"""
p_y_given_x = T.nnet.softmax(self.input_layer.linear_output)
loss = -T.mean(T.log(p_y_given_x)[T.arange(y.shape[0]),y])
return loss
def errors(self, y):
"""Compute the number of wrong predictions by self.input_layer.
Predicted class labels are computed as the indices of the columns of
self.input_layer.linear_output which are maximal. Wrong predictions are
those for which max indices do not match their corresponding y values.
"""
# Compute class memberships predicted by self.input_layer
y_pred = T.argmax(self.input_layer.linear_output, axis=1)
errs = 0
# check if y has same dimension of y_pred
if y.ndim != y_pred.ndim:
raise TypeError('y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', y_pred.type))
# check if y is of the correct datatype
if y.dtype.startswith('int'):
# the T.neq operator returns a vector of 0s and 1s, where 1
# represents a mistake in prediction
errs = T.sum(T.neq(y_pred, y))
else:
raise NotImplementedError()
return errs
class LogRegSS(object):
"""Multi-class semi-supervised Logistic Regression loss dangler."""
def __init__(self, linear_layer):
"""Dangle a logistic regression from the given linear layer.
The given linear layer should be a HiddenLayer (or subclass) object,
for HiddenLayer as defined in LayerNet.py."""
self.input_layer = linear_layer
def safe_softmax_ss(self, x):
"""Softmax that shouldn't overflow."""
e_x = T.exp(x - T.max(x, axis=1, keepdims=True))
x_sm = e_x / T.sum(e_x, axis=1, keepdims=True)
return x_sm
def loss_func(self, y):
"""Return the multiclass logistic regression loss for y.
The class labels in y are assumed to be in correspondence with the
set of column indices for self.input_layer.linear_output.
"""
row_idx = T.arange(y.shape[0])
row_mask = T.neq(y, 0).reshape((y.shape[0], 1))
p_y_given_x = self.safe_softmax_ss(self.input_layer.linear_output)
wacky_mat = (p_y_given_x * row_mask) + (1. - row_mask)
loss = -T.sum(T.log(wacky_mat[row_idx,y])) / T.sum(row_mask)
return loss
def errors(self, y):
"""Compute the number of wrong predictions by self.input_layer.
Predicted class labels are computed as the indices of the columns of
self.input_layer.linear_output which are maximal. Wrong predictions are
those for which max indices do not match their corresponding y values.
"""
# Compute class memberships predicted by self.input_layer
y_pred = T.argmax(self.input_layer.linear_output[:,1:], axis=1)
y_pred = y_pred + 1
errs = 0
# check if y has same dimension of y_pred
if y.ndim != y_pred.ndim:
raise TypeError('y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', y_pred.type))
# check if y is of the correct datatype
if y.dtype.startswith('int'):
# the T.neq operator returns a vector of 0s and 1s, where 1
# represents a mistake in prediction
errs = T.sum(T.neq(y_pred, y) * T.neq(y, 0))
else:
raise NotImplementedError()
return errs
class MCL2Hinge(object):
"""Multi-class one-vs-all L2 hinge loss dangler."""
def __init__(self, linear_layer):
"""Dangle a squred hinge loss from the given linear layer.
The given linear layer should be a HiddenLayer (or subclass) object,
for HiddenLayer as defined in LayerNet.py."""
self.input_layer = linear_layer
def loss_func(self, y):
"""Return the multiclass squared hinge loss for y.
The class labels in y are assumed to be in correspondence with the
set of column indices for self.input_layer.linear_output.
"""
y_hat = self.input_layer.linear_output
margin_pos = T.maximum(0.0, (1.0 - y_hat))
margin_neg = T.maximum(0.0, (1.0 + y_hat))
obs_idx = T.arange(y.shape[0])
loss_pos = T.sum(margin_pos[obs_idx,y]**2.0)
loss_neg = T.sum(margin_neg**2.0) - T.sum(margin_neg[obs_idx,y]**2.0)
loss = (loss_pos + loss_neg) / y.shape[0]
return loss
def errors(self, y):
"""Compute the number of wrong predictions by self.input_layer.
Predicted class labels are computed as the indices of the columns of
self.input_layer.linear_output which are maximal. Wrong predictions are
those for which max indices do not match their corresponding y values.
"""
# Compute class memberships predicted by self.input_layer
y_pred = T.argmax(self.input_layer.linear_output, axis=1)
errs = 0
# check if y has same dimension of y_pred
if y.ndim != y_pred.ndim:
raise TypeError('y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', y_pred.type))
# check if y is of the correct datatype
if y.dtype.startswith('int'):
# the T.neq operator returns a vector of 0s and 1s, where 1
# represents a mistake in prediction
errs = T.sum(T.neq(y_pred, y))
else:
raise NotImplementedError()
return errs
class MCL2HingeSS(object):
"""Multi-class one-vs-all L2 hinge loss dangler.
For this loss, class index 0 is never penalized, and errors for inputs
with class index 0 are similarly ignored. This is for semi-supervised
training, constrained by Theano's programming model."""
def __init__(self, linear_layer):
"""Dangle a squred hinge loss from the given linear layer.
The given linear layer should be a HiddenLayer (or subclass) object,
for HiddenLayer as defined in LayerNet.py."""
self.input_layer = linear_layer
def loss_func(self, y):
"""Return the multiclass squared hinge loss for y.
The class labels in y are assumed to be in correspondence with the
set of column indices for self.input_layer.linear_output.
"""
y_hat = self.input_layer.linear_output
row_idx = T.arange(y.shape[0])
row_mask = T.neq(y, 0).reshape((y_hat.shape[0], 1))
margin_pos = T.maximum(0.0, (1.0 - y_hat)) * row_mask
margin_neg = T.maximum(0.0, (1.0 + y_hat)) * row_mask
loss_pos = T.sum(margin_pos[row_idx,y]**2.0)
loss_neg = T.sum(margin_neg**2.0) - T.sum(margin_neg[row_idx,y]**2.0)
loss = (loss_pos + loss_neg) / T.sum(row_mask)
return loss
def errors(self, y):
"""Compute the number of wrong predictions by self.input_layer.
Predicted class labels are computed as the indices of the columns of
self.input_layer.linear_output which are maximal. Wrong predictions are
those for which max indices do not match their corresponding y values.
"""
# Compute class memberships predicted by self.input_layer
y_pred = T.argmax(self.input_layer.linear_output[:,1:], axis=1)
y_pred = y_pred + 1
errs = 0
# check if y has same dimension of y_pred
if y.ndim != y_pred.ndim:
raise TypeError('y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', y_pred.type))
# check if y is of the correct datatype
if y.dtype.startswith('int'):
# the T.neq operator returns a vector of 0s and 1s, where 1
# represents a mistake in prediction
errs = T.sum(T.neq(y_pred, y) * T.neq(y, 0))
else:
raise NotImplementedError()
return errs