def adadelta(self, lr, tparams, grads, model_input, cost, givens=None):
"""
An adaptive learning rate optimizer
Parameters
----------
lr : Theano SharedVariable
Initial learning rate
tpramas: Theano SharedVariable
Model parameters
grads: Theano variable
Gradients of cost w.r.t to parameres
input: Theano variable of input, list.
cost: Theano variable
Objective fucntion to minimize
Notes
-----
For more information, see [ADADELTA]_.
.. [ADADELTA] Matthew D. Zeiler, *ADADELTA: An Adaptive Learning
Rate Method*, arXiv:1212.5701.
"""
zipped_grads = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_grad' % k)
for k, p in tparams.iteritems()]
running_up2 = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_rup2' % k)
for k, p in tparams.iteritems()]
running_grads2 = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_rgrad2' % k)
for k, p in tparams.iteritems()]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g ** 2))
for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function(model_input, cost, updates=zgup + rg2up,
name='adadelta_f_grad_shared', givens=givens)
updir = [-T.sqrt(ru2 + 1e-6) / T.sqrt(rg2 + 1e-6) * zg
for zg, ru2, rg2 in zip(zipped_grads,
running_up2,
running_grads2)]
ru2up = [(ru2, 0.95 * ru2 + 0.05 * (ud ** 2))
for ru2, ud in zip(running_up2, updir)]
param_up = [(p, p + ud) for p, ud in zip(tparams.values(), updir)]
f_update = theano.function([lr], [], updates=ru2up + param_up,
on_unused_input='ignore',
name='adadelta_f_update',
givens=givens)
return f_grad_shared, f_update
def adadelta(self, lr, tparams, grads, model_input, cost, givens=None):
"""
An adaptive learning rate optimizer
Parameters
----------
lr : tensorheano SharedVariable
Initial learning rate
tpramas: tensorheano SharedVariable
Model parameters
grads: tensorheano variable
Gradients of cost w.r.t to parameres
input: tensorheano variable of input, list.
cost: tensorheano variable
Objective fucntion to minimize
Notes
-----
For more information, see [ADADELtensorA]_.
.. [ADADELtensorA] Matthew D. Zeiler, *ADADELtensorA: An Adaptive Learning
Rate Method*, arXiv:1212.5701.
"""
zipped_grads = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_grad' % k)
for k, p in tparams.iteritems()]
running_up2 = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_rup2' % k)
for k, p in tparams.iteritems()]
running_grads2 = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_rgrad2' % k)
for k, p in tparams.iteritems()]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g ** 2))
for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function(model_input, cost, updates=zgup + rg2up,
name='adadelta_f_grad_shared', givens=givens)
updir = [-tensor.sqrt(ru2 + 1e-6) / tensor.sqrt(rg2 + 1e-6) * zg
for zg, ru2, rg2 in zip(zipped_grads,
running_up2,
running_grads2)]
ru2up = [(ru2, 0.95 * ru2 + 0.05 * (ud ** 2))
for ru2, ud in zip(running_up2, updir)]
param_up = [(p, p + ud) for p, ud in zip(tparams.values(), updir)]
f_update = theano.function([lr], [], updates=ru2up + param_up,
on_unused_input='ignore',
name='adadelta_f_update',
givens=givens)
return f_grad_shared, f_update
def getOutput(self, inputs):
"""
Get outputs of encoder layer.
Return all of the hidden status.
"""
(self.sentence, self.mask) = inputs
assert self.mask is not None
n_steps = self.sentence.shape[0]
if self.sentence.ndim == 3:
n_samples = self.sentence.shape[1]
else:
n_samples = 1
last_h = tensor.alloc(numpy_floatX(0.), n_samples, self.hidden_status_dim)
state_below = tensor.dot(self.sentence, self.node.get_params_W())
results, _ = theano.scan(self.node.node_update,
sequences=[self.mask, state_below],
outputs_info=[last_h],
name=self._p(self.prefix, '_scan'),
n_steps=n_steps)
hidden_status_outputs = results
return hidden_status_outputs
def get_output(self, inputs):
"""
Get outputs of encoder layer.
Return all of the hidden status.
"""
(self.sentence, self.mask, self.encoder_hidden_status, self.question_mask) = inputs
assert self.mask is not None
n_steps = self.sentence.shape[0]
if self.sentence.ndim == 3:
n_samples = self.sentence.shape[1]
else:
n_samples = 1
# last_s = tensor.dot(self.encoder_hidden_status[0, :, self.hidden_status_dim:],
# self.params[self._p(self.prefix, 'Ws')])
last_s = tensor.alloc(numpy_floatX(0.), n_samples, self.hidden_status_dim)
state_below = self.sentence
def upd(am_, x_, s_, h_, qm_):
c, alpha = self.attention_node.node_update(s_, h_, qm_)
x_ = tensor.dot(tensor.concatenate([x_, c], axis=1), self.node.get_params_W())
s = self.node.node_update(am_, x_, s_)
return s, c, alpha
results, _ = theano.scan(upd,
sequences=[self.mask, state_below],
outputs_info=[last_s, None, None],
non_sequences=[self.encoder_hidden_status, self.question_mask],
name=self._p(self.prefix, '_scan'),
n_steps=n_steps)
hidden_status_outputs, context_outputs, alpha_outputs = results
return hidden_status_outputs, context_outputs, alpha_outputs
def getOutput(self, inputs):
"""
Get outputs of encoder layer.
Return all of the hidden status.
"""
(self.sentence, self.mask) = inputs
assert self.mask is not None
n_steps = self.sentence.shape[0]
if self.sentence.ndim == 3:
n_samples = self.sentence.shape[1]
else:
n_samples = 1
last_h = tensor.alloc(numpy_floatX(0.), n_samples,
self.hidden_status_dim)
state_below = tensor.dot(self.sentence, self.node.get_params_W())
results, _ = theano.scan(self.node.node_update,
sequences=[self.mask, state_below],
outputs_info=[last_h],
name=self._p(self.prefix, '_scan'),
n_steps=n_steps)
hidden_status_outputs = results
return hidden_status_outputs
def get_output(self, inputs):
"""
Get outputs of encoder layer.
Return all of the hidden status.
"""
(self.sentence, self.mask) = inputs
assert self.mask is not None
n_steps = self.sentence.shape[0]
if self.sentence.ndim == 3:
n_samples = self.sentence.shape[1]
else:
n_samples = 1
hidden_states_list = [self.sentence]
for idx in range(self.n_layers) :
sentence = hidden_states_list[idx]
state_below = tensor.dot(sentence, self.node_list[idx].get_params_W())
last_h = tensor.alloc(numpy_floatX(0.), n_samples, self.hidden_status_dim)
results, _ = theano.scan(self.node_list[idx].node_update,
sequences=[self.mask, state_below],
outputs_info=[last_h],
name=self._p(self.prefix, '_scan'+str(idx)),
n_steps=n_steps)
hidden_states_list.append(results)
hidden_status_outputs = hidden_states_list
return hidden_status_outputs
def get_output(self, inputs):
"""
Get outputs of encoder layer.
Return all of the hidden status.
"""
(self.sentence, self.mask, self.question_children, self.question_children_mask, self.max_offset) = inputs
assert self.mask is not None
n_steps = self.sentence.shape[0]
if self.sentence.ndim == 3:
n_samples = self.sentence.shape[1]
else:
n_samples = 1
queue_buffer = tensor.alloc(numpy_floatX(0.), n_samples,
self.max_offset, self.hidden_status_dim)
state_below = tensor.dot(self.sentence, self.node.get_params_W())
non_seq = self.node.get_non_seq_parameter(n_samples)
self.question_children_mask= self.question_children_mask.dimshuffle([0, 1, 2, 'x'])
results, _ = theano.scan(self.node.node_update,
sequences=[self.mask, state_below, self.question_children, self.question_children_mask],
outputs_info=[queue_buffer, queue_buffer[:, -1, :]],
non_sequences=non_seq,
name=self._p(self.prefix, '_scan'),
n_steps=n_steps)
hidden_status_outputs = results[1]
return hidden_status_outputs
def get_output(self, inputs):
"""
Get outputs of encoder layer.
Return all of the hidden status.
"""
if len(inputs) == 4:
(self.sentence, self.mask, self.forward_hidden_status,
self.direction) = inputs
if len(inputs) == 5:
(self.sentence, self.mask, self.forward_hidden_status,
self.direction, _) = inputs
assert self.mask is not None
n_steps = self.sentence.shape[0]
if self.sentence.ndim == 3:
n_samples = self.sentence.shape[1]
else:
n_samples = 1
if len(inputs) == 4:
last_h = tensor.alloc(numpy_floatX(0.), n_samples,
self.layer_number, self.hidden_status_dim)
if len(inputs) == 5:
last_h = inputs[4]
last_h = tensor.alloc(last_h, self.layer_number, last_h.shape[0],
last_h.shape[1]).dimshuffle([1, 0, 2])
state_below = tensor.dot(
tensor.concatenate([
self.sentence,
tensor.alloc(self.forward_hidden_status[-1, :, :],
self.sentence.shape[0],
self.forward_hidden_status.shape[1],
self.hidden_status_dim)
],
axis=2), self.node.get_params_W())
results, _ = theano.scan(
self.node.node_update,
sequences=[self.mask, state_below, self.direction],
outputs_info=[last_h],
name=self._p(self.prefix, '_scan'),
n_steps=n_steps)
hidden_status_outputs = results
# p = printing.Print('hidden_status_outputs')
# hidden_status_outputs = p(hidden_status_outputs)
return hidden_status_outputs
def get_output(self, inputs):
"""
Get outputs of encoder layer.
Return all of the hidden status.
"""
(self.sentence, self.mask, self.encoder_hidden_status,
self.question_mask) = inputs
assert self.mask is not None
n_steps = self.sentence.shape[0]
if self.sentence.ndim == 3:
n_samples = self.sentence.shape[1]
else:
n_samples = 1
# last_s = tensor.dot(self.encoder_hidden_status[0, :, self.hidden_status_dim:],
# self.params[self._p(self.prefix, 'Ws')])
last_s = tensor.alloc(numpy_floatX(0.), n_samples,
self.hidden_status_dim)
state_below = self.sentence
def upd(am_, x_, s_, h_, qm_):
c, alpha = self.attention_node.node_update(s_, h_, qm_)
x_ = tensor.dot(tensor.concatenate([x_, c], axis=1),
self.node.get_params_W())
s = self.node.node_update(am_, x_, s_)
return s, c, alpha
results, _ = theano.scan(
upd,
sequences=[self.mask, state_below],
outputs_info=[last_s, None, None],
non_sequences=[self.encoder_hidden_status, self.question_mask],
name=self._p(self.prefix, '_scan'),
n_steps=n_steps)
hidden_status_outputs, context_outputs, alpha_outputs = results
return hidden_status_outputs, context_outputs, alpha_outputs
def rmsprop(self, lr, tparams, grads, model_input, cost, givens=None):
"""
A variant of SGD that scales the step size by running average of the
recent step norms.
Parameters
----------
lr : Theano SharedVariable
Initial learning rate
tpramas: Theano SharedVariable
Model parameters
grads: Theano variable
Gradients of cost w.r.t to parameres
x: Theano variable
Model inputs
mask: Theano variable
Sequence mask
y: Theano variable
Targets
cost: Theano variable
Objective fucntion to minimize
Notes
-----
For more information, see [Hint2014]_.
.. [Hint2014] Geoff Hinton, *Neural Networks for Machine Learning*,
lecture 6a,
http://cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
"""
zipped_grads = [
theano.shared(p.get_value() * numpy_floatX(0.), name='%s_grad' % k)
for k, p in tparams.iteritems()
]
running_grads = [
theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_rgrad' % k) for k, p in tparams.iteritems()
]
running_grads2 = [
theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_rgrad2' % k)
for k, p in tparams.iteritems()
]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
rgup = [(rg, 0.95 * rg + 0.05 * g)
for rg, g in zip(running_grads, grads)]
rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g**2))
for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function(model_input,
cost,
updates=zgup + rgup + rg2up,
name='rmsprop_f_grad_shared',
givens=givens)
updir = [
theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_updir' % k) for k, p in tparams.iteritems()
]
updir_new = [(ud, 0.9 * ud - 1e-4 * zg / T.sqrt(rg2 - rg**2 + 1e-4))
for ud, zg, rg, rg2 in zip(updir, zipped_grads,
running_grads, running_grads2)]
param_up = [(p, p + udn[1])
for p, udn in zip(tparams.values(), updir_new)]
f_update = theano.function([lr], [],
updates=updir_new + param_up,
on_unused_input='ignore',
name='rmsprop_f_update',
givens=givens)
return f_grad_shared, f_update
def rmsprop(self, lr, tparams, grads, model_input, cost, givens=None):
"""
A variant of SGD that scales the step size by running average of the
recent step norms.
Parameters
----------
lr : tensorheano SharedVariable
Initial learning rate
tpramas: tensorheano SharedVariable
Model parameters
grads: tensorheano variable
Gradients of cost w.r.t to parameres
x: tensorheano variable
Model inputs
mask: tensorheano variable
Sequence mask
y: tensorheano variable
tensorargets
cost: tensorheano variable
Objective fucntion to minimize
Notes
-----
For more information, see [Hint2014]_.
.. [Hint2014] Geoff Hinton, *Neural Networks for Machine Learning*,
lecture 6a,
http://cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
"""
zipped_grads = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_grad' % k)
for k, p in tparams.iteritems()]
running_grads = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_rgrad' % k)
for k, p in tparams.iteritems()]
running_grads2 = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_rgrad2' % k)
for k, p in tparams.iteritems()]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
rgup = [(rg, 0.95 * rg + 0.05 * g) for rg, g in zip(running_grads, grads)]
rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g ** 2))
for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function(model_input, cost,
updates=zgup + rgup + rg2up,
name='rmsprop_f_grad_shared',
givens=givens)
updir = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_updir' % k)
for k, p in tparams.iteritems()]
updir_new = [(ud, 0.9 * ud - 1e-4 * zg / tensor.sqrt(rg2 - rg ** 2 + 1e-4))
for ud, zg, rg, rg2 in zip(updir, zipped_grads, running_grads,
running_grads2)]
param_up = [(p, p + udn[1])
for p, udn in zip(tparams.values(), updir_new)]
f_update = theano.function([lr], [], updates=updir_new + param_up,
on_unused_input='ignore',
name='rmsprop_f_update', givens=givens)
return f_grad_shared, f_update