def get_updates(self, gradients):
"""
Compute the AdaDelta updates (see the paper for details).
Parameters
----------
gradients : dict
A dictionary mapping from the model's parameters to their
gradients.
Returns
-------
updates : OrderdDict
A dictionary mapping from the old model parameters, to their new
values after a single iteration of the learning rule.
"""
log.debug('Setting up ADADELTA for optimizer...')
updates = OrderedDict()
for param in gradients.keys():
# mean_squared_grad := E[g^2]_{t-1}
mean_square_grad = sharedX(param.get_value() * 0.)
# mean_square_dx := E[(\Delta x)^2]_{t-1}
mean_square_dx = sharedX(param.get_value() * 0.)
if param.name is not None:
mean_square_grad.name = 'mean_square_grad_' + param.name
mean_square_dx.name = 'mean_square_dx_' + param.name
# Accumulate gradient
new_mean_squared_grad = (
self.decay * mean_square_grad +
(1 - self.decay) * T.sqr(gradients[param])
)
# Compute update
epsilon = self.lr_scalers.get(param, 1.) * self.learning_rate
rms_dx_tm1 = T.sqrt(mean_square_dx + epsilon)
rms_grad_t = T.sqrt(new_mean_squared_grad + epsilon)
delta_x_t = - (rms_dx_tm1 / rms_grad_t) * gradients[param]
# Accumulate updates
new_mean_square_dx = (
self.decay * mean_square_dx +
(1 - self.decay) * T.sqr(delta_x_t)
)
# Apply update
updates[mean_square_grad] = new_mean_squared_grad
updates[mean_square_dx] = new_mean_square_dx
updates[param] = param + delta_x_t
return updates
def get_updates(self, gradients):
"""
Compute the AdaDelta updates (see the paper for details).
Parameters
----------
gradients : dict
A dictionary mapping from the model's parameters to their
gradients.
Returns
-------
updates : OrderdDict
A dictionary mapping from the old model parameters, to their new
values after a single iteration of the learning rule.
"""
log.debug('Setting up ADADELTA for optimizer...')
updates = OrderedDict()
for param in gradients.keys():
# mean_squared_grad := E[g^2]_{t-1}
mean_square_grad = sharedX(param.get_value() * 0.)
# mean_square_dx := E[(\Delta x)^2]_{t-1}
mean_square_dx = sharedX(param.get_value() * 0.)
if param.name is not None:
mean_square_grad.name = 'mean_square_grad_' + param.name
mean_square_dx.name = 'mean_square_dx_' + param.name
# Accumulate gradient
new_mean_squared_grad = (
self.decay * mean_square_grad +
(1 - self.decay) * T.sqr(gradients[param])
)
# Compute update
epsilon = self.lr_scalers.get(param, 1.) * self.learning_rate
rms_dx_tm1 = T.sqrt(mean_square_dx + epsilon)
rms_grad_t = T.sqrt(new_mean_squared_grad + epsilon)
delta_x_t = - (rms_dx_tm1 / rms_grad_t) * gradients[param]
# Accumulate updates
new_mean_square_dx = (
self.decay * mean_square_dx +
(1 - self.decay) * T.sqr(delta_x_t)
)
# Apply update
updates[mean_square_grad] = new_mean_squared_grad
updates[mean_square_dx] = new_mean_square_dx
updates[param] = param + delta_x_t
return updates
def __call__(self, shape, name=None):
"""
Create the shared variable with given shape as an Identity matrix.
Parameters
----------
shape : tuple
A tuple giving the shape information for this variable.
name : str, optional
The name to give the shared variable.
Returns
-------
shared variable
The shared variable with given shape and name as an Identity matrix.
"""
log.debug("Creating variable {!s} with shape {!s} as Identity".format(name, shape))
weights = numpy.eye(N=shape[0], M=int(numpy.prod(shape[1:])), k=0, dtype=config.floatX)
if self.add_noise:
if isinstance(self.add_noise, partial):
weights = self.add_noise(input=weights)
else:
log.error("Add noise to identity weights was not a functools.partial object. Ignoring...")
# multiply by gain factor
if self.gain != 1.:
log.debug("Multiplying {!s} by {!s}".format(name, self.gain))
val = weights * self.gain
return sharedX(value=val, name=name)
def get_weights_gaussian(shape,
mean=None,
std=None,
name="W",
rng=None,
gain=1.):
"""
This initializes a shared variable with the given shape for weights drawn from a
Gaussian distribution with mean and std.
Parameters
----------
shape : tuple
A tuple giving the shape information for this weight matrix.
mean : float
The mean to use for the Gaussian distribution.
std : float
The standard deviation to use dor the Gaussian distribution.
name : str
The name to give the shared variable.
rng : random
A given random number generator to use with .normal method.
gain : float
A multiplicative factor to affect the whole weights matrix.
Returns
-------
shared variable
The theano shared variable with given shape and drawn from a Gaussian distribution.
"""
default_mean = 0
default_std = 0.05
mean = mean or default_mean
std = std or default_std
log.debug(
"Creating weights %s with shape %s from Gaussian mean=%s, std=%s",
name, str(shape), str(mean), str(std))
if rng is None:
rng = numpy.random
if std != 0:
if isinstance(rng, type(numpy.random)):
val = numpy.asarray(rng.normal(loc=mean, scale=std, size=shape),
dtype=config.floatX)
else:
val = numpy.asarray(rng.normal(avg=mean, std=std,
size=shape).eval(),
dtype=config.floatX)
else:
val = as_floatX(mean * numpy.ones(shape, dtype=config.floatX))
# check if a theano rng was used
if isinstance(val, TensorVariable):
val = val.eval()
val = val * gain
# make it into a shared variable
return sharedX(value=val, name=name)
def get_weights_identity(shape, name="W", add_noise=None, gain=1.):
"""
This will return a weights matrix as close to the identity as possible. If a non-square shape, it will make
a matrix of the form (I 0)
Identity matrix for weights is useful for RNNs with ReLU! http://arxiv.org/abs/1504.00941
Parameters
----------
shape : tuple
Tuple giving the shape information for the weight matrix.
name : str
Name to give the shared variable.
add_noise : functools.partial
A partially applied noise function (just missing the input parameter) to add noise to the identity
initialization. Noise functions can be found in opendeep.utils.noise.
gain : float
A multiplicative factor to affect the whole weights matrix.
Returns
-------
shared variable
The theano shared variable identity matrix with given shape.
"""
log.debug("Creating Identity matrix weights %s with shape %s", name, str(shape))
weights = numpy.eye(N=shape[0], M=int(numpy.prod(shape[1:])), k=0, dtype=theano.config.floatX)
if add_noise:
if isinstance(add_noise, partial):
weights = add_noise(input=weights)
else:
log.error("Add noise to identity weights was not a functools.partial object. Ignoring...")
val = weights * gain
return sharedX(value=val, name=name)
def get_updates(self, gradients):
"""
Provides the symbolic (theano) description of the updates needed to
perform this learning rule. See Notes for side-effects.
Parameters
----------
gradients : dict
A dictionary mapping from the model's parameters to their
gradients.
Returns
-------
updates : OrderdDict
A dictionary mapping from the old model parameters, to their new
values after a single iteration of the learning rule.
Notes
-----
This method has the side effect of storing the moving average
of the square gradient in `self.mean_square_grads`. This is
necessary in order for the monitoring channels to be able
to track the value of these moving averages.
Therefore, this method should only get called once for each
instance of RMSProp.
"""
log.debug('Setting up RMSProp for optimizer...')
updates = OrderedDict()
for param in gradients:
# mean_squared_grad := E[g^2]_{t-1}
mean_square_grad = sharedX(param.get_value() * 0.)
if param.name is None:
raise ValueError("Model parameters must be named.")
mean_square_grad.name = 'mean_square_grad_' + param.name
if param.name in self.mean_square_grads:
log.warning("Calling get_updates more than once on the "
"gradients of `%s` may make monitored values "
"incorrect." % param.name)
# Store variable in self.mean_square_grads for monitoring.
self.mean_square_grads[param.name] = mean_square_grad
# Accumulate gradient
new_mean_squared_grad = (
self.decay * mean_square_grad +
(1 - self.decay) * T.sqr(gradients[param]))
# Compute update
scaled_lr = self.lr_scalers.get(param, 1.) * self.learning_rate
rms_grad_t = T.sqrt(new_mean_squared_grad)
rms_grad_t = T.maximum(rms_grad_t, self.epsilon)
delta_x_t = -scaled_lr * gradients[param] / rms_grad_t
# Apply update
updates[mean_square_grad] = new_mean_squared_grad
updates[param] = param + delta_x_t
return updates
def get_bias(shape, name="b", init_values=None):
"""
This creates a theano shared variable for the bias parameter - normally initialized to zeros,
but you can specify other values
Parameters
----------
shape : tuple
The shape to use for the bias vector/matrix.
name : str
The name to give the shared variable.
offset : float or array_like
Values to add to the zeros, if you want a nonzero bias initially.
Returns
-------
shared variable
The theano shared variable with given shape.
"""
default_init = 0
init_values = init_values or default_init
log.debug("Initializing bias %s variable with shape %s", name, str(shape))
# init to zeros plus the offset
val = as_floatX(numpy.ones(shape=shape, dtype=theano.config.floatX) * init_values)
return sharedX(value=val, name=name)
def get_updates(self, gradients):
"""
Provides the symbolic (theano) description of the updates needed to
perform this learning rule. See Notes for side-effects.
Parameters
----------
gradients : dict
A dictionary mapping from the model's parameters to their
gradients.
Returns
-------
updates : OrderdDict
A dictionary mapping from the old model parameters, to their new
values after a single iteration of the learning rule.
Notes
-----
This method has the side effect of storing the moving average
of the square gradient in `self.mean_square_grads`. This is
necessary in order for the monitoring channels to be able
to track the value of these moving averages.
Therefore, this method should only get called once for each
instance of RMSProp.
"""
log.debug('Setting up RMSProp for optimizer...')
updates = OrderedDict()
for param in gradients:
# mean_squared_grad := E[g^2]_{t-1}
mean_square_grad = sharedX(param.get_value() * 0.)
if param.name is None:
raise ValueError("Model parameters must be named.")
mean_square_grad.name = 'mean_square_grad_' + param.name
if param.name in self.mean_square_grads:
log.warning("Calling get_updates more than once on the "
"gradients of `%s` may make monitored values "
"incorrect." % param.name)
# Store variable in self.mean_square_grads for monitoring.
self.mean_square_grads[param.name] = mean_square_grad
# Accumulate gradient
new_mean_squared_grad = (self.decay * mean_square_grad +
(1 - self.decay) * T.sqr(gradients[param]))
# Compute update
scaled_lr = self.lr_scalers.get(param, 1.) * self.learning_rate
rms_grad_t = T.sqrt(new_mean_squared_grad)
rms_grad_t = T.maximum(rms_grad_t, self.epsilon)
delta_x_t = - scaled_lr * gradients[param] / rms_grad_t
# Apply update
updates[mean_square_grad] = new_mean_squared_grad
updates[param] = param + delta_x_t
return updates
def get_weights_uniform(shape, interval='montreal', name="W", rng=None, gain=1.):
"""
This initializes a shared variable with a given shape for weights drawn from a Uniform distribution with
low = -interval and high = interval.
Interval can either be a number to use, or a string key to one of the predefined formulas in the
_uniform_interval dictionary.
Parameters
----------
shape : tuple
A tuple giving the shape information for this weight matrix.
interval : float or str
Either a number for your own custom interval, or a string key to one of the predefined formulas.
name : str
The name to give the shared variable.
rng : random
The random number generator to use with a .uniform method.
gain : float
A multiplicative factor to affect the whole weights matrix.
Returns
-------
shared variable
The theano shared variable with given shape and name drawn from a uniform distribution.
Raises
------
NotImplementedError
If the string name for the interval couldn't be found in the dictionary.
"""
if rng is None:
rng = numpy.random
# If the interval parameter is a string, grab the appropriate formula from the function dictionary,
# and apply the appropriate shape numbers to it.
if isinstance(interval, six.string_types):
interval_func = _uniform_interval.get(interval)
if interval_func is None:
log.error('Could not find uniform interval formula %s, try one of %s instead.' %
(str(interval), str(_uniform_interval.keys())))
raise NotImplementedError('Could not find uniform interval formula %s, try one of %s instead.' %
(str(interval), str(_uniform_interval.keys())))
else:
log.debug("Creating weights %s with shape %s from Uniform distribution with formula name: %s",
name, str(shape), str(interval))
interval = interval_func(shape)
else:
log.debug("Creating weights %s with shape %s from Uniform distribution with given interval +- %s",
name, str(shape), str(interval))
# build the uniform weights tensor
val = as_floatX(rng.uniform(low=-interval, high=interval, size=shape))
# check if a theano rng was used
if isinstance(val, T.TensorVariable):
val = val.eval()
val = val * gain
# make it into a shared variable
return sharedX(value=val, name=name)
def get_weights_orthogonal(shape, name="W", rng=None, gain=1.):
"""
This returns orthonormal random values to initialize a weight matrix (using SVD).
Some discussion here:
http://www.reddit.com/r/MachineLearning/comments/2qsje7/how_do_you_initialize_your_neural_network_weights/
From Lasagne:
For n-dimensional shapes where n > 2, the n-1 trailing axes are flattened.
For convolutional layers, this corresponds to the fan-in, so this makes the initialization
usable for both dense and convolutional layers.
Parameters
----------
shape : tuple
Tuple giving the shape information for the weight matrix.
name : str
Name to give the shared variable.
rng : random
A given random number generator to use with .normal method.
gain : float
A multiplicative factor to affect the whole weights matrix.
Returns
-------
shared variable
The theano shared variable orthogonal matrix with given shape.
"""
log.debug("Creating Orthogonal matrix weights %s with shape %s", name,
str(shape))
if rng is None:
rng = numpy.random
if len(shape) == 1:
shape = (shape[0], shape[0])
else:
# flatten shapes bigger than 2
# From Lasagne: For n-dimensional shapes where n > 2, the n-1 trailing axes are flattened.
# For convolutional layers, this corresponds to the fan-in, so this makes the initialization
# usable for both dense and convolutional layers.
shape = (shape[0], numpy.prod(shape[1:]))
# Sample from the standard normal distribution
if isinstance(rng, type(numpy.random)):
a = numpy.asarray(rng.normal(loc=0., scale=1., size=shape),
dtype=config.floatX)
else:
a = numpy.asarray(rng.normal(avg=0., std=1., size=shape).eval(),
dtype=config.floatX)
u, _, _ = numpy.linalg.svd(a, full_matrices=False)
val = u * gain
return sharedX(value=val, name=name)
def get_updates(self, gradients):
"""
Based on Pylearn2
(https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/training_algorithms/learning_rule.py)
Implements momentum as described in Section 9 of
"A Practical Guide to Training Restricted Boltzmann Machines",
Geoffrey Hinton.
Parameters are updated by the formula:
inc := momentum * inc - learning_rate * d cost / d param
param := param + inc
Also has the option to implement Nesterov momentum (accelerated momentum), which works better in a lot of cases.
Parameters
----------
gradients : dict
A dictionary mapping from the model's parameters to their
gradients.
Returns
-------
updates : OrderdDict
A dictionary mapping from the old model parameters, to their new
values after a single iteration of the learning rule.
"""
log.debug(
'Setting up Stochastic Gradient Descent with momentum for optimizer...'
)
updates = OrderedDict()
for (param, gradient) in six.iteritems(gradients):
velocity = sharedX(param.get_value() * 0.)
assert param.dtype == velocity.dtype
assert gradient.dtype == param.dtype
if param.name is not None:
velocity.name = 'vel_' + param.name
scaled_lr = self.learning_rate * self.lr_scalers.get(param, 1.)
updates[velocity] = self.momentum * velocity - scaled_lr * gradient
inc = updates[velocity]
if self.nesterov_momentum:
log.debug('Using Nesterov momentum for parameter %s',
str(param))
inc = self.momentum * inc - scaled_lr * gradient
assert inc.dtype == velocity.dtype
updates[param] = param + inc
return updates
def get_weights_orthogonal(shape, name="W", rng=None, gain=1.):
"""
This returns orthonormal random values to initialize a weight matrix (using SVD).
Some discussion here:
http://www.reddit.com/r/MachineLearning/comments/2qsje7/how_do_you_initialize_your_neural_network_weights/
From Lasagne:
For n-dimensional shapes where n > 2, the n-1 trailing axes are flattened.
For convolutional layers, this corresponds to the fan-in, so this makes the initialization
usable for both dense and convolutional layers.
Parameters
----------
shape : tuple
Tuple giving the shape information for the weight matrix.
name : str
Name to give the shared variable.
rng : random
A given random number generator to use with .normal method.
gain : float
A multiplicative factor to affect the whole weights matrix.
Returns
-------
shared variable
The theano shared variable orthogonal matrix with given shape.
"""
log.debug("Creating Orthogonal matrix weights %s with shape %s", name, str(shape))
if rng is None:
rng = numpy.random
if len(shape) == 1:
shape = (shape[0], shape[0])
else:
# flatten shapes bigger than 2
# From Lasagne: For n-dimensional shapes where n > 2, the n-1 trailing axes are flattened.
# For convolutional layers, this corresponds to the fan-in, so this makes the initialization
# usable for both dense and convolutional layers.
shape = (shape[0], numpy.prod(shape[1:]))
# Sample from the standard normal distribution
if isinstance(rng, type(numpy.random)):
a = numpy.asarray(rng.normal(loc=0., scale=1., size=shape), dtype=theano.config.floatX)
else:
a = numpy.asarray(rng.normal(avg=0., std=1., size=shape).eval(), dtype=theano.config.floatX)
u, _, _ = numpy.linalg.svd(a, full_matrices=False)
val = u * gain
return sharedX(value=val, name=name)
def get_weights_gaussian(shape, mean=None, std=None, name="W", rng=None, gain=1.):
"""
This initializes a shared variable with the given shape for weights drawn from a
Gaussian distribution with mean and std.
Parameters
----------
shape : tuple
A tuple giving the shape information for this weight matrix.
mean : float
The mean to use for the Gaussian distribution.
std : float
The standard deviation to use dor the Gaussian distribution.
name : str
The name to give the shared variable.
rng : random
A given random number generator to use with .normal method.
gain : float
A multiplicative factor to affect the whole weights matrix.
Returns
-------
shared variable
The theano shared variable with given shape and drawn from a Gaussian distribution.
"""
default_mean = 0
default_std = 0.05
mean = mean or default_mean
std = std or default_std
log.debug("Creating weights %s with shape %s from Gaussian mean=%s, std=%s", name, str(shape), str(mean), str(std))
if rng is None:
rng = numpy.random
if std != 0:
if isinstance(rng, type(numpy.random)):
val = numpy.asarray(rng.normal(loc=mean, scale=std, size=shape), dtype=theano.config.floatX)
else:
val = numpy.asarray(rng.normal(avg=mean, std=std, size=shape).eval(), dtype=theano.config.floatX)
else:
val = as_floatX(mean * numpy.ones(shape, dtype=theano.config.floatX))
# check if a theano rng was used
if isinstance(val, T.TensorVariable):
val = val.eval()
val = val * gain
# make it into a shared variable
return sharedX(value=val, name=name)
def get_updates(self, gradients):
"""
Based on Pylearn2
(https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/training_algorithms/learning_rule.py)
Implements momentum as described in Section 9 of
"A Practical Guide to Training Restricted Boltzmann Machines",
Geoffrey Hinton.
Parameters are updated by the formula:
inc := momentum * inc - learning_rate * d cost / d param
param := param + inc
Also has the option to implement Nesterov momentum (accelerated momentum), which works better in a lot of cases.
Parameters
----------
gradients : dict
A dictionary mapping from the model's parameters to their
gradients.
Returns
-------
updates : OrderdDict
A dictionary mapping from the old model parameters, to their new
values after a single iteration of the learning rule.
"""
log.debug('Setting up Stochastic Gradient Descent with momentum for optimizer...')
updates = OrderedDict()
for (param, gradient) in iteritems(gradients):
velocity = sharedX(param.get_value() * 0.)
assert param.dtype == velocity.dtype
assert gradient.dtype == param.dtype
if param.name is not None:
velocity.name = 'vel_' + param.name
scaled_lr = self.learning_rate * self.lr_scalers.get(param, 1.)
updates[velocity] = self.momentum * velocity - scaled_lr * gradient
inc = updates[velocity]
if self.nesterov_momentum:
log.debug('Using Nesterov momentum for parameter %s', str(param))
inc = self.momentum * inc - scaled_lr * gradient
assert inc.dtype == velocity.dtype
updates[param] = param + inc
return updates
def __call__(self, shape, name=None):
"""
Parameters
----------
shape : tuple
Tuple giving the shape information for the weight matrix.
name : str
Name to give the shared variable.
Returns
-------
shared variable
The shared variable matrix with given shape.
"""
log.debug("Initializing bias %s variable with shape %s", name, str(shape))
# init to zeros plus the offset
val = as_floatX(numpy.ones(shape=shape, dtype=config.floatX) * self.init_values)
return sharedX(value=val, name=name)
def __call__(self, shape, name=None):
"""
Create the shared variable with given shape from the uniform distribution with interval described in __init__.
Parameters
----------
shape : tuple
A tuple giving the shape information for this variable.
name : str, optional
The name to give the shared variable.
Returns
-------
shared variable
The shared variable with given shape and name drawn from a uniform distribution.
"""
# if the min and max are determined by a function of shape
if hasattr(self, "interval_func"):
try:
interval = self.interval_func(shape)
if isinstance(interval, number_types):
self._parse_single_number(interval)
elif isinstance(interval, Iterable):
self._parse_tuple(interval)
except Exception as err:
msg = "Expected interval function to output a number or Iterable of numbers, found {!s}".format(type(interval))
log.error(msg)
raise AttributeError(msg)
# build the uniform weights tensor
log.debug("Creating variable {!s} with shape {!s} from Uniform interval [{!s}, {!s}]".format(
name, shape, self.min, self.max
))
val = as_floatX(self.rng.uniform(low=self.min, high=self.max, size=shape))
# check if a theano rng was used
if isinstance(val, TensorVariable):
val = val.eval()
# multiply by gain factor
if self.gain != 1.:
log.debug("Multiplying {!s} by {!s}".format(name, self.gain))
val = val * self.gain
# make it into a shared variable
return sharedX(value=val, name=name)
def __call__(self, shape, name=None):
"""
Parameters
----------
shape : tuple
Tuple giving the shape information for the weight matrix.
name : str
Name to give the shared variable.
Returns
-------
shared variable
The shared variable orthogonal matrix with given shape.
"""
log.debug("Creating Orthogonal matrix weights {!s} with shape {!s}".format(name, shape))
if len(shape) == 1:
shape = (shape[0], shape[0])
else:
# flatten shapes bigger than 2
# From Lasagne: For n-dimensional shapes where n > 2, the n-1 trailing axes are flattened.
# For convolutional layers, this corresponds to the fan-in, so this makes the initialization
# usable for both dense and convolutional layers.
shape = (shape[0], numpy.prod(shape[1:]))
# Sample from the standard normal distribution
if isinstance(self.rng, type(numpy.random)):
a = numpy.asarray(self.rng.normal(loc=0., scale=1., size=shape), dtype=config.floatX)
else:
a = numpy.asarray(self.rng.normal(avg=0., std=1., size=shape).eval(), dtype=config.floatX)
u, _, _ = numpy.linalg.svd(a, full_matrices=False)
# multiply by gain factor
if self.gain != 1.:
log.debug("Multiplying {!s} by {!s}".format(name, self.gain))
val = u * self.gain
return sharedX(value=val, name=name)
def __call__(self, shape, name=None):
"""
Create the shared variable with given shape from the Gaussian distribution described in __init__.
Parameters
----------
shape : tuple
A tuple giving the shape information for this variable.
name : str, optional
The name to give the shared variable.
Returns
-------
shared variable
The shared variable with given shape and name drawn from a Gaussian (normal) distribution.
"""
log.debug("Creating variable {!s} with shape {!s} from Gaussian mean={!s}, std={!s}".format(
name, shape, self.mean, self.std
))
if self.std != 0:
if isinstance(self.rng, type(numpy.random)):
val = numpy.asarray(self.rng.normal(loc=self.mean, scale=self.std, size=shape), dtype=config.floatX)
else:
val = numpy.asarray(self.rng.normal(avg=self.mean, std=self.std, size=shape).eval(), dtype=config.floatX)
else:
val = as_floatX(self.mean * numpy.ones(shape, dtype=config.floatX))
# check if a theano rng was used
if isinstance(val, TensorVariable):
val = val.eval()
# multiply by gain factor
if self.gain != 1.:
log.debug("Multiplying {!s} by {!s}".format(name, self.gain))
val = val * self.gain
# make it into a shared variable
return sharedX(value=val, name=name)
def __init__(self, dataset, loss, model=None,
epochs=10, batch_size=100, min_batch_size=1,
save_freq=None, stop_threshold=None, stop_patience=None,
learning_rate=.1, lr_decay="exponential", lr_decay_factor=.995,
momentum=0.5, momentum_decay="linear", momentum_factor=0, nesterov_momentum=True,
grad_clip=None, hard_clip=False):
"""
Initialize SGD.
Parameters
----------
dataset : Dataset
The :class:`opendeep.data.Dataset` to use when training the Model.
loss : Loss
The :class:`opendeep.optimization.loss.Loss` function to compare the model to a 'target' result.
model : Model
The :class:`opendeep.models.Model` to train. Needed if the Optimizer isn't being passed to a
Model's .train() method.
epochs : int
how many training iterations over the dataset to go.
batch_size : int
How many examples from the training dataset to use in parallel.
min_batch_size : int
The minimum number of examples required at a time (for things like time series, this would be > 1).
save_freq : int
How many epochs to train between each new save of the Model's parameters.
stop_threshold : float
The factor by how much the best validation training score needs to improve to determine early stopping.
stop_patience : int
The patience or number of epochs to wait after the stop_threshold has been reached before stopping.
learning_rate : float
The multiplicative amount to adjust parameters based on their gradient values.
lr_decay : str
The type of decay function to use for changing the learning rate over epochs. See
`opendeep.utils.decay` for options.
lr_decay_factor : float
The amount to use for the decay function when changing the learning rate over epochs. See
`opendeep.utils.decay` for its effect for given decay functions.
momentum : float
The momentum to use during gradient updates.
momentum_decay : str
The type of decay function to use for changing the momentum over epochs. See
`opendeep.utils.decay` for options.
momentum_factor : float
The amount to use for the decay function when changing the momentum over epochs. See
`opendeep.utils.decay` for its effect for given decay functions.
nesterov_momentum : bool
Whether or not to use Nesterov momentum.
grad_clip : float, optional
Whether to clip gradients. This will clip with a maximum of grad_clip or the parameter norm.
hard_clip : bool
Whether to use a hard cutoff or rescaling for clipping gradients.
"""
# superclass init
initial_parameters = locals().copy()
initial_parameters.pop('self')
super(SGD, self).__init__(**initial_parameters)
# Momentum - smoothing over the parameter changes (see Hinton)
if momentum:
self.momentum = sharedX(momentum, 'momentum')
if momentum_decay is not None and \
momentum_decay is not False and \
momentum_factor is not None:
self.momentum_decay = get_decay_function(momentum_decay,
self.momentum,
self.momentum.get_value(),
momentum_factor)
else:
self.momentum_decay = False
else:
self.momentum = 0
self.momentum_decay = False
self.nesterov_momentum = nesterov_momentum
def __init__(self, inputs_hook=None, hiddens_hook=None, params_hook=None, outdir='outputs/rnn/',
input_size=None, hidden_size=None, output_size=None,
layers=1,
activation='sigmoid', hidden_activation='relu',
mrg=RNG_MRG.MRG_RandomStreams(1),
weights_init='uniform', weights_interval='montreal', weights_mean=0, weights_std=5e-3,
bias_init=0.0,
r_weights_init='identity', r_weights_interval='montreal', r_weights_mean=0, r_weights_std=5e-3,
r_bias_init=0.0,
cost_function='mse', cost_args=None,
noise='dropout', noise_level=None, noise_decay=False, noise_decay_amount=.99,
direction='forward',
clip_recurrent_grads=False):
"""
Initialize a simple recurrent network.
Parameters
----------
inputs_hook : Tuple of (shape, variable)
Routing information for the model to accept inputs from elsewhere. This is used for linking
different models together (e.g. setting the Softmax model's input layer to the DAE's hidden layer gives a
newly supervised classification model). For now, it needs to include the shape information (normally the
dimensionality of the input i.e. n_in).
hiddens_hook : Tuple of (shape, variable)
Routing information for the model to accept its hidden representation from elsewhere. For recurrent nets,
this will be the initial starting value for hidden layers.
params_hook : List(theano shared variable)
A list of model parameters (shared theano variables) that you should use when constructing
this model (instead of initializing your own shared variables). This parameter is useful when you want to
have two versions of the model that use the same parameters.
outdir : str
The location to produce outputs from training or running the :class:`RNN`. If None, nothing will be saved.
input_size : int
The size (dimensionality) of the input. If shape is provided in `inputs_hook`, this is optional.
hidden_size : int
The size (dimensionality) of the hidden layers. If shape is provided in `hiddens_hook`, this is optional.
output_size : int
The size (dimensionality) of the output.
layers : int
The number of stacked hidden layers to use.
activation : str or callable
The nonlinear (or linear) activation to perform after the dot product from hiddens -> output layer.
This activation function should be appropriate for the output unit types, i.e. 'sigmoid' for binary.
See opendeep.utils.activation for a list of available activation functions. Alternatively, you can pass
your own function to be used as long as it is callable.
hidden_activation : str or callable
The activation to perform for the hidden layers.
See opendeep.utils.activation for a list of available activation functions. Alternatively, you can pass
your own function to be used as long as it is callable.
mrg : random
A random number generator that is used when adding noise.
I recommend using Theano's sandbox.rng_mrg.MRG_RandomStreams.
weights_init : str
Determines the method for initializing model weights. See opendeep.utils.nnet for options.
weights_interval : str or float
If Uniform `weights_init`, the +- interval to use. See opendeep.utils.nnet for options.
weights_mean : float
If Gaussian `weights_init`, the mean value to use.
weights_std : float
If Gaussian `weights_init`, the standard deviation to use.
bias_init : float
The initial value to use for the bias parameter. Most often, the default of 0.0 is preferred.
r_weights_init : str
Determines the method for initializing recurrent model weights. See opendeep.utils.nnet for options.
r_weights_interval : str or float
If Uniform `r_weights_init`, the +- interval to use. See opendeep.utils.nnet for options.
r_weights_mean : float
If Gaussian `r_weights_init`, the mean value to use.
r_weights_std : float
If Gaussian `r_weights_init`, the standard deviation to use.
r_bias_init : float
The initial value to use for the recurrent bias parameter. Most often, the default of 0.0 is preferred.
cost_function : str or callable
The function to use when calculating the output cost of the model.
See opendeep.utils.cost for options. You can also specify your own function, which needs to be callable.
cost_args : dict
Any additional named keyword arguments to pass to the specified `cost_function`.
noise : str
What type of noise to use for the hidden layers and outputs. See opendeep.utils.noise
for options. This should be appropriate for the unit activation, i.e. Gaussian for tanh or other
real-valued activations, etc.
noise_level : float
The amount of noise to use for the noise function specified by `hidden_noise`. This could be the
standard deviation for gaussian noise, the interval for uniform noise, the dropout amount, etc.
noise_decay : str or False
Whether to use `noise` scheduling (decay `noise_level` during the course of training),
and if so, the string input specifies what type of decay to use. See opendeep.utils.decay for options.
Noise decay (known as noise scheduling) effectively helps the model learn larger variance features first,
and then smaller ones later (almost as a kind of curriculum learning). May help it converge faster.
noise_decay_amount : float
The amount to reduce the `noise_level` after each training epoch based on the decay function specified
in `noise_decay`.
direction : str
The direction this recurrent model should go over its inputs. Can be 'forward', 'backward', or
'bidirectional'. In the case of 'bidirectional', it will make two passes over the sequence,
computing two sets of hiddens and merging them before running through the final decoder.
clip_recurrent_grads : False or float, optional
Whether to clip the gradients for the parameters that unroll over timesteps (such as the weights
connecting previous hidden states to the current hidden state, and not the weights from current
input to hiddens). If it is a float, the gradients for the weights will be hard clipped to the range
`+-clip_recurrent_grads`.
Raises
------
AssertionError
When asserting various properties of input parameters. See error messages.
"""
initial_parameters = locals().copy()
initial_parameters.pop('self')
super(RNN, self).__init__(**initial_parameters)
##################
# specifications #
##################
self.direction = direction
self.bidirectional = (direction == "bidirectional")
self.backward = (direction == "backward")
self.layers = layers
self.noise = noise
self.weights_init = weights_init
self.weights_mean = weights_mean
self.weights_std = weights_std
self.weights_interval = weights_interval
self.r_weights_init = r_weights_init
self.r_weights_mean = r_weights_mean
self.r_weights_std = r_weights_std
self.r_weights_interval = r_weights_interval
self.bias_init = bias_init
self.r_bias_init = r_bias_init
#########################################
# activation, cost, and noise functions #
#########################################
# recurrent hidden activation function!
self.hidden_activation_func = get_activation_function(hidden_activation)
# output activation function!
self.activation_func = get_activation_function(activation)
# Cost function
self.cost_function = get_cost_function(cost_function)
self.cost_args = cost_args or dict()
# Now deal with noise if we added it:
if self.noise:
log.debug('Adding %s noise switch.' % str(noise))
if noise_level is not None:
noise_level = sharedX(value=noise_level)
self.noise_func = get_noise(noise, noise_level=noise_level, mrg=mrg)
else:
self.noise_func = get_noise(noise, mrg=mrg)
# apply the noise as a switch!
# default to apply noise. this is for the cost and gradient functions to be computed later
# (not sure if the above statement is accurate such that gradient depends on initial value of switch)
self.noise_switch = sharedX(value=1, name="basiclayer_noise_switch")
# noise scheduling
if noise_decay and noise_level is not None:
self.noise_schedule = get_decay_function(noise_decay,
noise_level,
noise_level.get_value(),
noise_decay_amount)
###############
# inputs hook #
###############
# grab info from the inputs_hook
# in the case of an inputs_hook, recurrent will always work with the leading tensor dimension
# being the temporal dimension.
# input is 3D tensor of (timesteps, batch_size, data_dim)
# if input is 2D tensor, assume it is of the form (timesteps, data_dim) i.e. batch_size is 1. Convert to 3D.
# if input is > 3D tensor, assume it is of form (timesteps, batch_size, data...) and flatten to 3D.
if self.inputs_hook is not None:
self.input = self.inputs_hook[1]
if self.input.ndim == 1:
self.input = T.unbroadcast(self.input.dimshuffle(0, 'x', 'x'), [1, 2])
self.input_size = 1
elif self.input.ndim == 2:
self.input = T.unbroadcast(self.input.dimshuffle(0, 'x', 1), 1)
elif self.input.ndim == 3:
pass
elif self.input.ndim > 3:
self.input = self.input.flatten(3)
self.input_size = sum(self.input_size)
else:
raise NotImplementedError("Recurrent input with %d dimensions not supported!" % self.input.ndim)
else:
# Assume input coming from optimizer is (batches, timesteps, data)
# so, we need to reshape to (timesteps, batches, data)
xs = T.tensor3("Xs")
xs = xs.dimshuffle(1, 0, 2)
self.input = xs
# The target outputs for supervised training - in the form of (batches, timesteps, output) which is
# the same dimension ordering as the expected input from optimizer.
# therefore, we need to swap it like we did to input xs.
ys = T.tensor3("Ys")
ys = ys.dimshuffle(1, 0, 2)
self.target = ys
################
# hiddens hook #
################
# set an initial value for the recurrent hiddens from hook
if self.hiddens_hook is not None:
self.h_init = self.hiddens_hook[1]
self.hidden_size = self.hiddens_hook[0]
else:
# deal with h_init after parameters are made (have to make the same size as hiddens that are computed)
self.hidden_size = hidden_size
##################
# for generating #
##################
# symbolic scalar for how many recurrent steps to use during generation from the model
self.n_steps = T.iscalar("generate_n_steps")
self.output, self.hiddens, self.updates, self.cost, self.params = self.build_computation_graph()
def __init__(self, dataset, loss, model=None,
epochs=10, batch_size=100, min_batch_size=1,
save_freq=None, stop_threshold=None, stop_patience=None,
learning_rate=1e-6, lr_decay=None, lr_decay_factor=None,
decay=0.95, gamma_clip=1.8, damping=1e-7, grad_clip=None, hard_clip=False, start_var_reduction=0,
delta_clip=None, use_adagrad=False, skip_nan_inf=False,
upper_bound_tau=1e8, lower_bound_tau=1.5, use_corrected_grad=True):
"""
Initialize AdaSecant.
Parameters
----------
dataset : Dataset
The :class:`opendeep.data.Dataset` to use when training the Model.
loss : Loss
The :class:`opendeep.optimization.loss.Loss` function to compare the model to a 'target' result.
model : Model
The :class:`opendeep.models.Model` to train. Needed if the Optimizer isn't being passed to a
Model's .train() method.
epochs : int
how many training iterations over the dataset to go.
batch_size : int
How many examples from the training dataset to use in parallel.
min_batch_size : int
The minimum number of examples required at a time (for things like time series, this would be > 1).
save_freq : int
How many epochs to train between each new save of the Model's parameters.
stop_threshold : float
The factor by how much the best validation training score needs to improve to determine early stopping.
stop_patience : int
The patience or number of epochs to wait after the stop_threshold has been reached before stopping.
learning_rate : float
The multiplicative amount to adjust parameters based on their gradient values.
lr_decay : str
The type of decay function to use for changing the learning rate over epochs. See
`opendeep.utils.decay` for options.
lr_decay_factor : float
The amount to use for the decay function when changing the learning rate over epochs. See
`opendeep.utils.decay` for its effect for given decay functions.
decay : float, optional
Decay rate :math:`\\rho` in Algorithm 1 of the aforementioned
paper. Decay 0.95 seems to work fine for several tasks.
gamma_clip : float, optional
The clipping threshold for the gamma. In general 1.8 seems to
work fine for several tasks.
start_var_reduction: float, optional,
How many updates later should the variance reduction start from?
delta_clip: float, optional,
The threshold to clip the deltas after.
grad_clip: float, optional,
Apply gradient clipping for RNNs (not necessary for feedforward networks). But this is
a constraint on the norm of the gradient per layer.
hard_clip : bool
Whether to use a hard cutoff or rescaling for clipping gradients.
use_adagrad: bool, optional
Either to use clipped adagrad or not.
use_corrected_grad: bool, optional
Either to use correction for gradients (referred as variance
reduction in the workshop paper).
"""
# get everything together with the Optimizer class
initial_parameters = locals().copy()
initial_parameters.pop('self')
super(AdaSecant, self).__init__(**initial_parameters)
assert decay >= 0., "Decay needs to be >=0."
assert decay < 1., "Decay needs to be <1."
self.decay = sharedX(decay, "decay")
self.damping = damping
self.skip_nan_inf = skip_nan_inf
# if grad_clip:
# assert grad_clip > 0.
# assert grad_clip <= 1., "Norm of the gradients per layer can not be larger than 1."
# self.grad_clip = grad_clip
self.use_adagrad = use_adagrad
self.use_corrected_grad = use_corrected_grad
self.gamma_clip = gamma_clip
self.start_var_reduction = start_var_reduction
self.delta_clip = delta_clip
# We have to bound the tau to prevent it to
# grow to an arbitrarily large number, oftenwise
# that causes numerical instabilities for very deep
# networks. Note that once tau become very large, it will keep,
# increasing indefinitely.
self.lower_bound_tau = lower_bound_tau
self.upper_bound_tau = upper_bound_tau
def __init__(self,
inputs_hook=None,
hiddens_hook=None,
params_hook=None,
outdir='outputs/gsn/',
input_size=None,
hidden_size=1000,
layers=2,
walkbacks=4,
visible_activation='sigmoid',
hidden_activation='tanh',
input_sampling=True,
mrg=RNG_MRG.MRG_RandomStreams(1),
tied_weights=True,
weights_init='uniform',
weights_interval='montreal',
weights_mean=0,
weights_std=5e-3,
bias_init=0.0,
cost_function='binary_crossentropy',
cost_args=None,
add_noise=True,
noiseless_h1=True,
hidden_noise='gaussian',
hidden_noise_level=2,
input_noise='salt_and_pepper',
input_noise_level=0.4,
noise_decay='exponential',
noise_annealing=1,
image_width=None,
image_height=None,
**kwargs):
"""
Initialize a GSN.
Parameters
----------
inputs_hook : Tuple of (shape, variable)
Routing information for the model to accept inputs from elsewhere. This is used for linking
different models together (e.g. setting the Softmax model's input layer to the DAE's hidden layer gives a
newly supervised classification model). For now, it needs to include the shape information (normally the
dimensionality of the input i.e. n_in).
hiddens_hook : Tuple of (shape, variable)
Routing information for the model to accept its hidden representation from elsewhere.
This is used for linking different models together (e.g. setting the DAE model's hidden layers to the RNN's
output layer gives a generative recurrent model.) For now, it needs to include the shape
information (normally the dimensionality of the hiddens i.e. n_hidden).
params_hook : List(theano shared variable)
A list of model parameters (shared theano variables) that you should use when constructing
this model (instead of initializing your own shared variables). This parameter is useful when you want to
have two versions of the model that use the same parameters - such as a training model with dropout applied
to layers and one without for testing, where the parameters are shared between the two.
outdir : str
The directory you want outputs (parameters, images, etc.) to save to. If None, nothing will
be saved.
input_size : int
The size (dimensionality) of the input to the DAE. If shape is provided in `inputs_hook`, this is optional.
The :class:`Model` requires an `output_size`, which gets set to this value because the DAE is an
unsupervised model. The output is a reconstruction of the input.
hidden_size : int
The size (dimensionality) of the hidden layer for the DAE. Generally, you want it to be larger than
`input_size`, which is known as *overcomplete*.
visible_activation : str or callable
The nonlinear (or linear) visible activation to perform after the dot product from hiddens -> visible layer.
This activation function should be appropriate for the input unit types, i.e. 'sigmoid' for binary inputs.
See opendeep.utils.activation for a list of available activation functions. Alternatively, you can pass
your own function to be used as long as it is callable.
hidden_activation : str or callable
The nonlinear (or linear) hidden activation to perform after the dot product from visible -> hiddens layer.
See opendeep.utils.activation for a list of available activation functions. Alternatively, you can pass
your own function to be used as long as it is callable.
layers : int
The number of hidden layers to use.
walkbacks : int
The number of walkbacks to perform (the variable K in Bengio's paper above). A walkback is a Gibbs sample
from the DAE, which means the model generates inputs in sequence, where each generated input is compared
to the original input to create the reconstruction cost for training. For running the model, the very last
generated input in the Gibbs chain is used as the output.
input_sampling : bool
During walkbacks, whether to sample from the generated input to create a new starting point for the next
walkback (next step in the Gibbs chain). This generally makes walkbacks more effective by making the
process more stochastic - more likely to find spurious modes in the model's representation.
mrg : random
A random number generator that is used when adding noise into the network and for sampling from the input.
I recommend using Theano's sandbox.rng_mrg.MRG_RandomStreams.
tied_weights : bool
DAE has two weight matrices - W from input -> hiddens and V from hiddens -> input. This boolean
determines if V = W.T, which 'ties' V to W and reduces the number of parameters necessary during training.
weights_init : str
Determines the method for initializing model weights. See opendeep.utils.nnet for options.
weights_interval : str or float
If Uniform `weights_init`, the +- interval to use. See opendeep.utils.nnet for options.
weights_mean : float
If Gaussian `weights_init`, the mean value to use.
weights_std : float
If Gaussian `weights_init`, the standard deviation to use.
bias_init : float
The initial value to use for the bias parameter. Most often, the default of 0.0 is preferred.
cost_function : str or callable
The function to use when calculating the reconstruction cost of the model. This should be appropriate
for the type of input, i.e. use 'binary_crossentropy' for binary inputs, or 'mse' for real-valued inputs.
See opendeep.utils.cost for options. You can also specify your own function, which needs to be callable.
cost_args : dict
Any additional named keyword arguments to pass to the specified `cost_function`.
add_noise : bool
Whether to add noise (corrupt) the input before passing it through the computation graph during training.
This should most likely be set to the default of True, because this is a *denoising* autoencoder after all.
noiseless_h1 : bool
Whether to not add noise (corrupt) the hidden layer during computation.
hidden_noise : str
What type of noise to use for corrupting the hidden layer (if not `noiseless_h1`). See opendeep.utils.noise
for options. This should be appropriate for the hidden unit activation, i.e. Gaussian for tanh or other
real-valued activations, etc.
hidden_noise_level : float
The amount of noise to use for the noise function specified by `hidden_noise`. This could be the
standard deviation for gaussian noise, the interval for uniform noise, the dropout amount, etc.
input_noise : str
What type of noise to use for corrupting the input before computation (if `add_noise`).
See opendeep.utils.noise for options. This should be appropriate for the input units, i.e. salt-and-pepper
for binary units, etc.
input_noise_level : float
The amount of noise used to corrupt the input. This could be the masking probability for salt-and-pepper,
standard deviation for Gaussian, interval for Uniform, etc.
noise_decay : str or False
Whether to use `input_noise` scheduling (decay `input_noise_level` during the course of training),
and if so, the string input specifies what type of decay to use. See opendeep.utils.decay for options.
Noise decay (known as noise scheduling) effectively helps the DAE learn larger variance features first,
and then smaller ones later (almost as a kind of curriculum learning). May help it converge faster.
noise_annealing : float
The amount to reduce the `input_noise_level` after each training epoch based on the decay function specified
in `noise_decay`.
image_width : int
If the input should be represented as an image, the width of the input image. If not specified, it will be
close to the square factor of the `input_size`.
image_height : int
If the input should be represented as an image, the height of the input image. If not specified, it will be
close to the square factor of the `input_size`.
"""
# init Model to combine the defaults and config dictionaries with the initial parameters.
initial_parameters = locals().copy()
initial_parameters.pop('self')
super(GSN, self).__init__(**initial_parameters)
# when the input should be thought of as an image, either use the specified width and height,
# or try to make as square as possible.
if image_height is None and image_width is None:
(_h, _w) = closest_to_square_factors(self.input_size)
self.image_width = _w
self.image_height = _h
else:
self.image_height = image_height
self.image_width = image_width
############################
# Theano variables and RNG #
############################
if self.inputs_hook is None:
self.X = T.matrix('X')
else:
# inputs_hook is a (shape, input) tuple
self.X = self.inputs_hook[1]
##########################
# Network specifications #
##########################
# generally, walkbacks should be at least 2*layers
if layers % 2 == 0:
if walkbacks < 2 * layers:
log.warning(
'Not enough walkbacks for the layers! Layers is %s and walkbacks is %s. '
'Generaly want 2X walkbacks to layers', str(layers),
str(walkbacks))
else:
if walkbacks < 2 * layers - 1:
log.warning(
'Not enough walkbacks for the layers! Layers is %s and walkbacks is %s. '
'Generaly want 2X walkbacks to layers', str(layers),
str(walkbacks))
self.add_noise = add_noise
self.noise_annealing = as_floatX(
noise_annealing) # noise schedule parameter
self.hidden_noise_level = sharedX(hidden_noise_level,
dtype=theano.config.floatX)
self.hidden_noise = get_noise(name=hidden_noise,
noise_level=self.hidden_noise_level,
mrg=mrg)
self.input_noise_level = sharedX(input_noise_level,
dtype=theano.config.floatX)
self.input_noise = get_noise(name=input_noise,
noise_level=self.input_noise_level,
mrg=mrg)
self.walkbacks = walkbacks
self.tied_weights = tied_weights
self.layers = layers
self.noiseless_h1 = noiseless_h1
self.input_sampling = input_sampling
self.noise_decay = noise_decay
# if there was a hiddens_hook, unpack the hidden layers in the tensor
if self.hiddens_hook is not None:
hidden_size = self.hiddens_hook[0]
self.hiddens_flag = True
else:
self.hiddens_flag = False
# determine the sizes of each layer in a list.
# layer sizes, from h0 to hK (h0 is the visible layer)
hidden_size = list(raise_to_list(hidden_size))
if len(hidden_size) == 1:
self.layer_sizes = [self.input_size] + hidden_size * self.layers
else:
assert len(hidden_size) == self.layers, "Hiddens sizes and number of hidden layers mismatch." + \
"Hiddens %d and layers %d" % (len(hidden_size), self.layers)
self.layer_sizes = [self.input_size] + hidden_size
if self.hiddens_hook is not None:
self.hiddens = self.unpack_hiddens(self.hiddens_hook[1])
#########################
# Activation functions! #
#########################
# hidden unit activation
self.hidden_activation = get_activation_function(hidden_activation)
# Visible layer activation
self.visible_activation = get_activation_function(visible_activation)
# make sure the sampling functions are appropriate for the activation functions.
if is_binary(self.visible_activation):
self.visible_sampling = mrg.binomial
else:
# TODO: implement non-binary activation
log.error("Non-binary visible activation not supported yet!")
raise NotImplementedError(
"Non-binary visible activation not supported yet!")
# Cost function
self.cost_function = get_cost_function(cost_function)
self.cost_args = cost_args or dict()
###############
# Parameters! #
###############
# make sure to deal with params_hook!
if self.params_hook is not None:
# if tied weights, expect layers*2 + 1 params
if self.tied_weights:
assert len(self.params_hook) == 2*layers + 1, \
"Tied weights: expected {0!s} params, found {1!s}!".format(2*layers+1, len(self.params_hook))
self.weights_list = self.params_hook[:layers]
self.bias_list = self.params_hook[layers:]
# if untied weights, expect layers*3 + 1 params
else:
assert len(self.params_hook) == 3*layers + 1, \
"Untied weights: expected {0!s} params, found {1!s}!".format(3*layers+1, len(self.params_hook))
self.weights_list = self.params_hook[:2 * layers]
self.bias_list = self.params_hook[2 * layers:]
# otherwise, construct our params
else:
# initialize a list of weights and biases based on layer_sizes for the GSN
self.weights_list = [
get_weights(
weights_init=weights_init,
shape=(self.layer_sizes[i], self.layer_sizes[i + 1]),
name="W_{0!s}_{1!s}".format(i, i + 1),
rng=mrg,
# if gaussian
mean=weights_mean,
std=weights_std,
# if uniform
interval=weights_interval) for i in range(layers)
]
# add more weights if we aren't tying weights between layers (need to add for higher-lower layers now)
if not tied_weights:
self.weights_list.extend([
get_weights(
weights_init=weights_init,
shape=(self.layer_sizes[i + 1], self.layer_sizes[i]),
name="W_{0!s}_{1!s}".format(i + 1, i),
rng=mrg,
# if gaussian
mean=weights_mean,
std=weights_std,
# if uniform
interval=weights_interval)
for i in reversed(range(layers))
])
# initialize each layer bias to 0's.
self.bias_list = [
get_bias(shape=(self.layer_sizes[i], ),
name='b_' + str(i),
init_values=bias_init) for i in range(layers + 1)
]
# build the params of the model into a list
self.params = self.weights_list + self.bias_list
log.debug("gsn params: %s", str(self.params))
# using the properties, build the computational graph
self.cost, self.monitors, self.output, self.hiddens = self.build_computation_graph(
)
def __init__(self, inputs=None,
noise='dropout', noise_level=0.5, noise_decay=False, noise_decay_amount=0.99,
mrg=RNG_MRG.MRG_RandomStreams(1), switch=True):
"""
Parameters
----------
inputs : tuple(shape, `Theano.TensorType`)
tuple(shape, `Theano.TensorType`) describing the inputs to use for this layer.
`shape` will be a monad tuple representing known sizes for each dimension in the `Theano.TensorType`.
The length of `shape` should be equal to number of dimensions in `Theano.TensorType`, where the shape
element is an integer representing the size for its dimension, or None if the shape isn't known.
For example, if you have a matrix with unknown batch size but fixed feature size of 784, `shape` would
be: (None, 784). The full form of `inputs` would be:
[((None, 784), <TensorType(float32, matrix)>)].
noise : str
What type of noise to use for the output. See opendeep.utils.noise
for options. This should be appropriate for the unit activation, i.e. Gaussian for tanh or other
real-valued activations, etc.
noise_level : float
The amount of noise to use for the noise function specified by `noise`. This could be the
standard deviation for gaussian noise, the interval for uniform noise, the dropout amount, etc.
noise_decay : str or False
Whether to use `noise` scheduling (decay `noise_level` during the course of training),
and if so, the string input specifies what type of decay to use. See opendeep.utils.decay for options.
Noise decay (known as noise scheduling) effectively helps the model learn larger variance features first,
and then smaller ones later (almost as a kind of curriculum learning). May help it converge faster.
noise_decay_amount : float
The amount to reduce the `noise_level` after each training epoch based on the decay function specified
in `noise_decay`.
mrg : random
A random number generator that is used when adding noise.
I recommend using Theano's sandbox.rng_mrg.MRG_RandomStreams.
switch : boolean
Whether to create a switch to turn noise on during training and off during testing (True). If False,
noise will be applied at both training and testing times.
"""
super(Noise, self).__init__(inputs=inputs, outputs=inputs[0],
noise=noise, noise_level=noise_level,
noise_decay=noise_decay, noise_decay_amount=noise_decay_amount,
mrg=mrg, switch=switch)
# self.inputs is a list from superclass initialization, grab the first element
self.inputs = self.inputs[0][1]
log.debug('Adding %s noise switch.' % str(noise))
if noise_level is not None:
noise_level = sharedX(value=noise_level)
noise_func = get_noise(noise, noise_level=noise_level, mrg=mrg)
else:
noise_func = get_noise(noise, mrg=mrg)
# apply the noise as a switch!
# default to apply noise. this is for the cost and gradient functions to be computed later
# (not sure if the above statement is accurate such that gradient depends on initial value of switch)
if switch:
self.noise_switch = sharedX(value=1, name="noise_switch")
# noise scheduling
if noise_decay and noise_level is not None:
self.noise_schedule = get_decay_function(noise_decay,
noise_level,
noise_level.get_value(),
noise_decay_amount)
# apply noise to the inputs!
if switch:
self.outputs = Tswitch(self.noise_switch,
noise_func(input=self.inputs),
self.inputs)
else:
self.outputs = noise_func(input=self.inputs)
def __init__(self,
inputs_hook=None,
hiddens_hook=None,
params_hook=None,
outdir='outputs/gru/',
input_size=None,
hidden_size=None,
output_size=None,
activation='sigmoid',
hidden_activation='relu',
inner_hidden_activation='sigmoid',
mrg=RNG_MRG.MRG_RandomStreams(1),
weights_init='uniform',
weights_interval='montreal',
weights_mean=0,
weights_std=5e-3,
bias_init=0.0,
r_weights_init='identity',
r_weights_interval='montreal',
r_weights_mean=0,
r_weights_std=5e-3,
r_bias_init=0.0,
cost_function='mse',
cost_args=None,
noise='dropout',
noise_level=None,
noise_decay=False,
noise_decay_amount=.99,
forward=True,
clip_recurrent_grads=False):
"""
Initialize a simple recurrent network.
Parameters
----------
inputs_hook : Tuple of (shape, variable)
Routing information for the model to accept inputs from elsewhere. This is used for linking
different models together (e.g. setting the Softmax model's input layer to the DAE's hidden layer gives a
newly supervised classification model). For now, it needs to include the shape information (normally the
dimensionality of the input i.e. n_in).
hiddens_hook : Tuple of (shape, variable)
Routing information for the model to accept its hidden representation from elsewhere. For recurrent nets,
this will be the initial starting value for hidden layers.
params_hook : List(theano shared variable)
A list of model parameters (shared theano variables) that you should use when constructing
this model (instead of initializing your own shared variables). This parameter is useful when you want to
have two versions of the model that use the same parameters.
outdir : str
The location to produce outputs from training or running the :class:`RNN`. If None, nothing will be saved.
input_size : int
The size (dimensionality) of the input. If shape is provided in `inputs_hook`, this is optional.
hidden_size : int
The size (dimensionality) of the hidden layers. If shape is provided in `hiddens_hook`, this is optional.
output_size : int
The size (dimensionality) of the output.
activation : str or callable
The nonlinear (or linear) activation to perform after the dot product from hiddens -> output layer.
This activation function should be appropriate for the output unit types, i.e. 'sigmoid' for binary.
See opendeep.utils.activation for a list of available activation functions. Alternatively, you can pass
your own function to be used as long as it is callable.
hidden_activation : str or callable
The activation to perform for the hidden units.
See opendeep.utils.activation for a list of available activation functions. Alternatively, you can pass
your own function to be used as long as it is callable.
inner_hidden_activation : str or callable
The activation to perform for the hidden gates.
See opendeep.utils.activation for a list of available activation functions. Alternatively, you can pass
your own function to be used as long as it is callable.
mrg : random
A random number generator that is used when adding noise.
I recommend using Theano's sandbox.rng_mrg.MRG_RandomStreams.
weights_init : str
Determines the method for initializing model weights. See opendeep.utils.nnet for options.
weights_interval : str or float
If Uniform `weights_init`, the +- interval to use. See opendeep.utils.nnet for options.
weights_mean : float
If Gaussian `weights_init`, the mean value to use.
weights_std : float
If Gaussian `weights_init`, the standard deviation to use.
bias_init : float
The initial value to use for the bias parameter. Most often, the default of 0.0 is preferred.
r_weights_init : str
Determines the method for initializing recurrent model weights. See opendeep.utils.nnet for options.
r_weights_interval : str or float
If Uniform `r_weights_init`, the +- interval to use. See opendeep.utils.nnet for options.
r_weights_mean : float
If Gaussian `r_weights_init`, the mean value to use.
r_weights_std : float
If Gaussian `r_weights_init`, the standard deviation to use.
r_bias_init : float
The initial value to use for the recurrent bias parameter. Most often, the default of 0.0 is preferred.
cost_function : str or callable
The function to use when calculating the output cost of the model.
See opendeep.utils.cost for options. You can also specify your own function, which needs to be callable.
cost_args : dict
Any additional named keyword arguments to pass to the specified `cost_function`.
noise : str
What type of noise to use for the hidden layers and outputs. See opendeep.utils.noise
for options. This should be appropriate for the unit activation, i.e. Gaussian for tanh or other
real-valued activations, etc.
noise_level : float
The amount of noise to use for the noise function specified by `hidden_noise`. This could be the
standard deviation for gaussian noise, the interval for uniform noise, the dropout amount, etc.
noise_decay : str or False
Whether to use `noise` scheduling (decay `noise_level` during the course of training),
and if so, the string input specifies what type of decay to use. See opendeep.utils.decay for options.
Noise decay (known as noise scheduling) effectively helps the model learn larger variance features first,
and then smaller ones later (almost as a kind of curriculum learning). May help it converge faster.
noise_decay_amount : float
The amount to reduce the `noise_level` after each training epoch based on the decay function specified
in `noise_decay`.
forward : bool
The direction this recurrent model should go over its inputs. True means forward, False mean backward.
clip_recurrent_grads : False or float, optional
Whether to clip the gradients for the parameters that unroll over timesteps (such as the weights
connecting previous hidden states to the current hidden state, and not the weights from current
input to hiddens). If it is a float, the gradients for the weights will be hard clipped to the range
`+-clip_recurrent_grads`.
"""
initial_parameters = locals().copy()
initial_parameters.pop('self')
super(GRU, self).__init__(**initial_parameters)
##################
# specifications #
##################
#########################################
# activation, cost, and noise functions #
#########################################
# recurrent hidden activation function!
self.hidden_activation_func = get_activation_function(
hidden_activation)
self.inner_hidden_activation_func = get_activation_function(
inner_hidden_activation)
# output activation function!
activation_func = get_activation_function(activation)
# Cost function
cost_function = get_cost_function(cost_function)
cost_args = cost_args or dict()
# Now deal with noise if we added it:
if noise:
log.debug('Adding %s noise switch.' % str(noise))
if noise_level is not None:
noise_level = sharedX(value=noise_level)
noise_func = get_noise(noise, noise_level=noise_level, mrg=mrg)
else:
noise_func = get_noise(noise, mrg=mrg)
# apply the noise as a switch!
# default to apply noise. this is for the cost and gradient functions to be computed later
# (not sure if the above statement is accurate such that gradient depends on initial value of switch)
self.noise_switch = sharedX(value=1, name="gru_noise_switch")
# noise scheduling
if noise_decay and noise_level is not None:
self.noise_schedule = get_decay_function(
noise_decay, noise_level, noise_level.get_value(),
noise_decay_amount)
###############
# inputs hook #
###############
# grab info from the inputs_hook
# in the case of an inputs_hook, recurrent will always work with the leading tensor dimension
# being the temporal dimension.
# input is 3D tensor of (timesteps, batch_size, data_dim)
# if input is 2D tensor, assume it is of the form (timesteps, data_dim) i.e. batch_size is 1. Convert to 3D.
# if input is > 3D tensor, assume it is of form (timesteps, batch_size, data...) and flatten to 3D.
if self.inputs_hook is not None:
self.input = self.inputs_hook[1]
if self.input.ndim == 1:
self.input = T.unbroadcast(self.input.dimshuffle(0, 'x', 'x'),
[1, 2])
self.input_size = 1
elif self.input.ndim == 2:
self.input = T.unbroadcast(self.input.dimshuffle(0, 'x', 1), 1)
elif self.input.ndim > 3:
self.input = self.input.flatten(3)
self.input_size = sum(self.input_size)
else:
raise NotImplementedError(
"Recurrent input with %d dimensions not supported!" %
self.input.ndim)
xs = self.input
else:
# Assume input coming from optimizer is (batches, timesteps, data)
# so, we need to reshape to (timesteps, batches, data)
self.input = T.tensor3("Xs")
xs = self.input.dimshuffle(1, 0, 2)
# The target outputs for supervised training - in the form of (batches, timesteps, output) which is
# the same dimension ordering as the expected input from optimizer.
# therefore, we need to swap it like we did to input xs.
self.target = T.tensor3("Ys")
ys = self.target.dimshuffle(1, 0, 2)
################
# hiddens hook #
################
# set an initial value for the recurrent hiddens from hook
if self.hiddens_hook is not None:
h_init = self.hiddens_hook[1]
self.hidden_size = self.hiddens_hook[0]
else:
# deal with h_init after parameters are made (have to make the same size as hiddens that are computed)
self.hidden_size = hidden_size
##################
# for generating #
##################
# symbolic scalar for how many recurrent steps to use during generation from the model
self.n_steps = T.iscalar("generate_n_steps")
####################################################
# parameters - make sure to deal with params_hook! #
####################################################
if self.params_hook is not None:
(W_x_z, W_x_r, W_x_h, U_h_z, U_h_r, U_h_h, W_h_y, b_z, b_r, b_h,
b_y) = self.params_hook
recurrent_params = [U_h_z, U_h_r, U_h_h]
# otherwise, construct our params
else:
# all input-to-hidden weights
W_x_z, W_x_r, W_x_h = [
get_weights(
weights_init=weights_init,
shape=(self.input_size, self.hidden_size),
name="W_x_%s" % sub,
# if gaussian
mean=weights_mean,
std=weights_std,
# if uniform
interval=weights_interval) for sub in ['z', 'r', 'h']
]
# all hidden-to-hidden weights
U_h_z, U_h_r, U_h_h = [
get_weights(
weights_init=r_weights_init,
shape=(self.hidden_size, self.hidden_size),
name="U_h_%s" % sub,
# if gaussian
mean=r_weights_mean,
std=r_weights_std,
# if uniform
interval=r_weights_interval) for sub in ['z', 'r', 'h']
]
# hidden-to-output weights
W_h_y = get_weights(
weights_init=weights_init,
shape=(self.hidden_size, self.output_size),
name="W_h_y",
# if gaussian
mean=weights_mean,
std=weights_std,
# if uniform
interval=weights_interval)
# biases
b_z, b_r, b_h = [
get_bias(shape=(self.hidden_size, ),
name="b_%s" % sub,
init_values=r_bias_init) for sub in ['z', 'r', 'h']
]
# output bias
b_y = get_bias(shape=(self.output_size, ),
name="b_y",
init_values=bias_init)
# clip gradients if we are doing that
recurrent_params = [U_h_z, U_h_r, U_h_h]
if clip_recurrent_grads:
clip = abs(clip_recurrent_grads)
U_h_z, U_h_r, U_h_h = [
theano.gradient.grad_clip(p, -clip, clip)
for p in recurrent_params
]
# put all the parameters into our list, and make sure it is in the same order as when we try to load
# them from a params_hook!!!
self.params = [W_x_z, W_x_r, W_x_h
] + recurrent_params + [W_h_y, b_z, b_r, b_h, b_y]
# make h_init the right sized tensor
if not self.hiddens_hook:
h_init = T.zeros_like(T.dot(xs[0], W_x_h))
###############
# computation #
###############
# move some computation outside of scan to speed it up!
x_z = T.dot(xs, W_x_z) + b_z
x_r = T.dot(xs, W_x_r) + b_r
x_h = T.dot(xs, W_x_h) + b_h
# now do the recurrent stuff
self.hiddens, self.updates = theano.scan(
fn=self.recurrent_step,
sequences=[x_z, x_r, x_h],
outputs_info=[h_init],
non_sequences=[U_h_z, U_h_r, U_h_h],
go_backwards=not forward,
name="gru_scan",
strict=True)
# add noise (like dropout) if we wanted it!
if noise:
self.hiddens = T.switch(self.noise_switch,
noise_func(input=self.hiddens),
self.hiddens)
# now compute the outputs from the leftover (top level) hiddens
self.output = activation_func(T.dot(self.hiddens, W_h_y) + b_y)
# now to define the cost of the model - use the cost function to compare our output with the target value.
self.cost = cost_function(output=self.output, target=ys, **cost_args)
log.info("Initialized a GRU!")
def __init__(self, dataset, loss=None, model=None,
epochs=1000, batch_size=100, min_batch_size=1,
save_freq=10, stop_threshold=None, stop_patience=50,
learning_rate=1e-3, lr_decay=None, lr_decay_factor=None,
grad_clip=None, hard_clip=False,
**kwargs):
"""
Initialize the Optimizer.
Parameters
----------
dataset : Dataset
The :class:`opendeep.data.Dataset` to use when training the Model.
loss : Loss
The :class:`opendeep.optimization.loss.Loss` function to compare the model to a 'target' result.
model : Model
The :class:`opendeep.models.Model` to train. Needed if the Optimizer isn't being passed to a
Model's .train() method.
epochs : int
How many training iterations over the dataset to go.
batch_size : int
How many examples from the training dataset to use in parallel.
min_batch_size : int
The minimum number of examples required at a time (for things like time series, this would be > 1).
save_freq : int, optional
How many epochs to train between each new save of the Model's parameters.
stop_threshold : float, optional
The factor by how much the best validation training score needs to improve to determine early stopping.
stop_patience : int, optional
The patience or number of epochs to wait after the stop_threshold has been reached before stopping.
learning_rate : float
The multiplicative amount to adjust parameters based on their gradient values.
lr_decay : str
The decay function to use for changing the learning rate over epochs. See
`opendeep.utils.decay` for classes of decay and documentation.
lr_decay_factor : float
The amount of decay to use for the ``lr_decay`` type of decay.
grad_clip : float, optional
Whether to clip gradients. This will clip the norm of the gradients either with a hard cutoff or rescaling.
hard_clip : bool
Whether to use a hard cutoff or rescaling for clipping gradients.
"""
log.info("Initializing optimizer %s", str(self.__class__.__name__))
# Deal with early stopping None initializations (no early stopping).
if not stop_threshold:
stop_threshold = numpy.inf
if not save_freq:
save_freq = 1000000
if not stop_patience:
stop_patience = 1
# Put all init parameters in self.args so we can log the initial configuration.
self.args = locals().copy()
self.args.pop('self')
kwargs = self.args.pop('kwargs')
self.args = add_kwargs_to_dict(kwargs, self.args)
# log the arguments
log.info("Optimizer config args: %s", str(self.args))
# if the optimizer wasn't initialized with a Model (train() being called from the model class itself),
# just return. (This seems kinda hacky but hey, people wanted .train() to happen from Model and there
# wasn't really a better way unless the epoch looping logic was in that method for Model. That wasn't
# the best option because other methods besides stochastic ones can exist for optimizers in the future.
# TODO: fix this up - feels like a hack just to make model.train() work...
if not model:
return
# Otherwise, things are proceeding as normal. Carry on...
assert isinstance(model, Model), "Optimizer input model needs to be a Model class! " \
"Found %s" % str(model.__class__.__name__)
assert isinstance(dataset, Dataset), "Optimizer input dataset needs to be a Dataset class! " \
"Found %s" % str(dataset.__class__.__name__)
# deal with loss expression/targets
if loss is not None:
assert isinstance(loss, Loss), "Optimizer input loss needs to be a Loss class! " \
"Found %s" % str(loss.__class__.__name__)
if isinstance(loss, Loss):
self.loss_targets = loss.get_targets()
self.loss_expression = loss.get_loss()
else:
assert model.get_loss() is not None, "No Loss specified, and the model does not have one implemented."
if isinstance(model.get_loss(), tuple):
self.loss_targets = raise_to_list(model.get_loss()[0])
self.loss_expression = model.get_loss()[1]
else:
self.loss_targets = None
self.loss_expression = model.get_loss()
model_inputs = raise_to_list(model.get_inputs())
n_model_inputs = len(model_inputs)
model_targets = self.loss_targets or []
for input in model_inputs:
if input in model_targets:
model_targets.remove(input)
n_model_targets = len(model_targets)
self.unsupervised = (n_model_targets is 0)
# make sure the number of inputs/targets matches up with the dataset properties
# train
assert n_model_inputs == len(raise_to_list(dataset.train_inputs)), \
"Dataset has %d train inputs, while model expects %d" % \
(len(raise_to_list(dataset.train_inputs)), n_model_inputs)
if not self.unsupervised:
assert n_model_targets == len(raise_to_list(dataset.train_targets) or []), \
"Dataset has %d train targets, while model expects %d" % \
(len(raise_to_list(dataset.train_targets) or []), n_model_targets)
# valid
if dataset.valid_inputs is not None:
assert n_model_inputs == len(raise_to_list(dataset.valid_inputs)), \
"Dataset has %d valid inputs, while model expects %d" % \
(len(raise_to_list(dataset.valid_inputs)), n_model_inputs)
if not self.unsupervised:
assert n_model_targets == len(raise_to_list(dataset.valid_targets) or []), \
"Dataset has %d valid targets, while model expects %d" % \
(len(raise_to_list(dataset.valid_targets) or []), n_model_targets)
# test
if dataset.test_inputs is not None:
assert n_model_inputs == len(raise_to_list(dataset.test_inputs)), \
"Dataset has %d test inputs, while model expects %d" % \
(len(raise_to_list(dataset.test_inputs)), n_model_inputs)
if not self.unsupervised:
assert n_model_targets == len(raise_to_list(dataset.test_targets) or []), \
"Dataset has %d test targets, while model expects %d" % \
(len(raise_to_list(dataset.test_targets) or []), n_model_targets)
# now we are happy, we can add them to `self`
self.model = model
self.dataset = dataset
self.loss = loss
# Learning rate - how drastic of a step do the parameters change
self.learning_rate = sharedX(learning_rate, 'learning_rate')
# whether to scale individual model parameters' learning rates.
self.lr_scalers = self.model.get_lr_scalers()
# whether to decay
if lr_decay:
self.learning_rate_decay = get_decay_function(lr_decay,
self.learning_rate,
learning_rate,
lr_decay_factor)
else:
self.learning_rate_decay = False
# rest of initial parameters needed for training.
self.batch_size = batch_size
self.min_batch_size = min_batch_size
self.n_epoch = epochs
self.save_frequency = save_freq
self.early_stop_threshold = stop_threshold
self.early_stop_length = stop_patience
self.grad_clip = grad_clip
self.hard_clip = hard_clip
def __init__(self,
inputs_hook=None,
hiddens_hook=None,
params_hook=None,
outdir='outputs/rnn/',
input_size=None,
hidden_size=None,
output_size=None,
layers=1,
activation='sigmoid',
hidden_activation='relu',
mrg=RNG_MRG.MRG_RandomStreams(1),
weights_init='uniform',
weights_interval='montreal',
weights_mean=0,
weights_std=5e-3,
bias_init=0.0,
r_weights_init='identity',
r_weights_interval='montreal',
r_weights_mean=0,
r_weights_std=5e-3,
r_bias_init=0.0,
cost_function='mse',
cost_args=None,
noise='dropout',
noise_level=None,
noise_decay=False,
noise_decay_amount=.99,
direction='forward',
clip_recurrent_grads=False):
"""
Initialize a simple recurrent network.
Parameters
----------
inputs_hook : Tuple of (shape, variable)
Routing information for the model to accept inputs from elsewhere. This is used for linking
different models together (e.g. setting the Softmax model's input layer to the DAE's hidden layer gives a
newly supervised classification model). For now, it needs to include the shape information (normally the
dimensionality of the input i.e. n_in).
hiddens_hook : Tuple of (shape, variable)
Routing information for the model to accept its hidden representation from elsewhere. For recurrent nets,
this will be the initial starting value for hidden layers.
params_hook : List(theano shared variable)
A list of model parameters (shared theano variables) that you should use when constructing
this model (instead of initializing your own shared variables). This parameter is useful when you want to
have two versions of the model that use the same parameters.
outdir : str
The location to produce outputs from training or running the :class:`RNN`. If None, nothing will be saved.
input_size : int
The size (dimensionality) of the input. If shape is provided in `inputs_hook`, this is optional.
hidden_size : int
The size (dimensionality) of the hidden layers. If shape is provided in `hiddens_hook`, this is optional.
output_size : int
The size (dimensionality) of the output.
layers : int
The number of stacked hidden layers to use.
activation : str or callable
The nonlinear (or linear) activation to perform after the dot product from hiddens -> output layer.
This activation function should be appropriate for the output unit types, i.e. 'sigmoid' for binary.
See opendeep.utils.activation for a list of available activation functions. Alternatively, you can pass
your own function to be used as long as it is callable.
hidden_activation : str or callable
The activation to perform for the hidden layers.
See opendeep.utils.activation for a list of available activation functions. Alternatively, you can pass
your own function to be used as long as it is callable.
mrg : random
A random number generator that is used when adding noise.
I recommend using Theano's sandbox.rng_mrg.MRG_RandomStreams.
weights_init : str
Determines the method for initializing model weights. See opendeep.utils.nnet for options.
weights_interval : str or float
If Uniform `weights_init`, the +- interval to use. See opendeep.utils.nnet for options.
weights_mean : float
If Gaussian `weights_init`, the mean value to use.
weights_std : float
If Gaussian `weights_init`, the standard deviation to use.
bias_init : float
The initial value to use for the bias parameter. Most often, the default of 0.0 is preferred.
r_weights_init : str
Determines the method for initializing recurrent model weights. See opendeep.utils.nnet for options.
r_weights_interval : str or float
If Uniform `r_weights_init`, the +- interval to use. See opendeep.utils.nnet for options.
r_weights_mean : float
If Gaussian `r_weights_init`, the mean value to use.
r_weights_std : float
If Gaussian `r_weights_init`, the standard deviation to use.
r_bias_init : float
The initial value to use for the recurrent bias parameter. Most often, the default of 0.0 is preferred.
cost_function : str or callable
The function to use when calculating the output cost of the model.
See opendeep.utils.cost for options. You can also specify your own function, which needs to be callable.
cost_args : dict
Any additional named keyword arguments to pass to the specified `cost_function`.
noise : str
What type of noise to use for the hidden layers and outputs. See opendeep.utils.noise
for options. This should be appropriate for the unit activation, i.e. Gaussian for tanh or other
real-valued activations, etc.
noise_level : float
The amount of noise to use for the noise function specified by `hidden_noise`. This could be the
standard deviation for gaussian noise, the interval for uniform noise, the dropout amount, etc.
noise_decay : str or False
Whether to use `noise` scheduling (decay `noise_level` during the course of training),
and if so, the string input specifies what type of decay to use. See opendeep.utils.decay for options.
Noise decay (known as noise scheduling) effectively helps the model learn larger variance features first,
and then smaller ones later (almost as a kind of curriculum learning). May help it converge faster.
noise_decay_amount : float
The amount to reduce the `noise_level` after each training epoch based on the decay function specified
in `noise_decay`.
direction : str
The direction this recurrent model should go over its inputs. Can be 'forward', 'backward', or
'bidirectional'. In the case of 'bidirectional', it will make two passes over the sequence,
computing two sets of hiddens and merging them before running through the final decoder.
clip_recurrent_grads : False or float, optional
Whether to clip the gradients for the parameters that unroll over timesteps (such as the weights
connecting previous hidden states to the current hidden state, and not the weights from current
input to hiddens). If it is a float, the gradients for the weights will be hard clipped to the range
`+-clip_recurrent_grads`.
Raises
------
AssertionError
When asserting various properties of input parameters. See error messages.
"""
initial_parameters = locals().copy()
initial_parameters.pop('self')
super(RNN, self).__init__(**initial_parameters)
##################
# specifications #
##################
self.direction = direction
self.bidirectional = (direction == "bidirectional")
self.backward = (direction == "backward")
self.layers = layers
self.noise = noise
self.weights_init = weights_init
self.weights_mean = weights_mean
self.weights_std = weights_std
self.weights_interval = weights_interval
self.r_weights_init = r_weights_init
self.r_weights_mean = r_weights_mean
self.r_weights_std = r_weights_std
self.r_weights_interval = r_weights_interval
self.bias_init = bias_init
self.r_bias_init = r_bias_init
#########################################
# activation, cost, and noise functions #
#########################################
# recurrent hidden activation function!
self.hidden_activation_func = get_activation_function(
hidden_activation)
# output activation function!
self.activation_func = get_activation_function(activation)
# Cost function
self.cost_function = get_cost_function(cost_function)
self.cost_args = cost_args or dict()
# Now deal with noise if we added it:
if self.noise:
log.debug('Adding %s noise switch.' % str(noise))
if noise_level is not None:
noise_level = sharedX(value=noise_level)
self.noise_func = get_noise(noise,
noise_level=noise_level,
mrg=mrg)
else:
self.noise_func = get_noise(noise, mrg=mrg)
# apply the noise as a switch!
# default to apply noise. this is for the cost and gradient functions to be computed later
# (not sure if the above statement is accurate such that gradient depends on initial value of switch)
self.noise_switch = sharedX(value=1,
name="basiclayer_noise_switch")
# noise scheduling
if noise_decay and noise_level is not None:
self.noise_schedule = get_decay_function(
noise_decay, noise_level, noise_level.get_value(),
noise_decay_amount)
###############
# inputs hook #
###############
# grab info from the inputs_hook
# in the case of an inputs_hook, recurrent will always work with the leading tensor dimension
# being the temporal dimension.
# input is 3D tensor of (timesteps, batch_size, data_dim)
# if input is 2D tensor, assume it is of the form (timesteps, data_dim) i.e. batch_size is 1. Convert to 3D.
# if input is > 3D tensor, assume it is of form (timesteps, batch_size, data...) and flatten to 3D.
if self.inputs_hook is not None:
self.input = self.inputs_hook[1]
if self.input.ndim == 1:
self.input = T.unbroadcast(self.input.dimshuffle(0, 'x', 'x'),
[1, 2])
self.input_size = 1
elif self.input.ndim == 2:
self.input = T.unbroadcast(self.input.dimshuffle(0, 'x', 1), 1)
elif self.input.ndim == 3:
pass
elif self.input.ndim > 3:
self.input = self.input.flatten(3)
self.input_size = sum(self.input_size)
else:
raise NotImplementedError(
"Recurrent input with %d dimensions not supported!" %
self.input.ndim)
else:
# Assume input coming from optimizer is (batches, timesteps, data)
# so, we need to reshape to (timesteps, batches, data)
xs = T.tensor3("Xs")
xs = xs.dimshuffle(1, 0, 2)
self.input = xs
# The target outputs for supervised training - in the form of (batches, timesteps, output) which is
# the same dimension ordering as the expected input from optimizer.
# therefore, we need to swap it like we did to input xs.
ys = T.tensor3("Ys")
ys = ys.dimshuffle(1, 0, 2)
self.target = ys
################
# hiddens hook #
################
# set an initial value for the recurrent hiddens from hook
if self.hiddens_hook is not None:
self.h_init = self.hiddens_hook[1]
self.hidden_size = self.hiddens_hook[0]
else:
# deal with h_init after parameters are made (have to make the same size as hiddens that are computed)
self.hidden_size = hidden_size
##################
# for generating #
##################
# symbolic scalar for how many recurrent steps to use during generation from the model
self.n_steps = T.iscalar("generate_n_steps")
self.output, self.hiddens, self.updates, self.cost, self.params = self.build_computation_graph(
)
def __init__(
self,
inputs_hook=None,
params_hook=None,
outdir="outputs/basic",
input_size=None,
output_size=None,
activation="rectifier",
cost="mse",
cost_args=None,
weights_init="uniform",
weights_mean=0,
weights_std=5e-3,
weights_interval="montreal",
bias_init=0.0,
noise=None,
noise_level=None,
mrg=RNG_MRG.MRG_RandomStreams(1),
**kwargs
):
"""
Initialize a basic layer.
Parameters
----------
inputs_hook : Tuple of (shape, variable)
Routing information for the model to accept inputs from elsewhere. This is used for linking
different models together. For now, it needs to include the shape information (normally the
dimensionality of the input i.e. input_size).
params_hook : List(theano shared variable)
A list of model parameters (shared theano variables) that you should use when constructing
this model (instead of initializing your own shared variables). This parameter is useful when you want to
have two versions of the model that use the same parameters - such as a training model with dropout applied
to layers and one without for testing, where the parameters are shared between the two.
outdir : str
The directory you want outputs (parameters, images, etc.) to save to. If None, nothing will
be saved.
input_size : int
The size (dimensionality) of the input to the layer. If shape is provided in `inputs_hook`,
this is optional.
output_size : int
The size (dimensionality) of the output from the layer.
activation : str or callable
The activation function to use after the dot product going from input -> output. This can be a string
representing an option from opendeep.utils.activation, or your own function as long as it is callable.
cost : str or callable
The cost function to use when training the layer. This should be appropriate for the output type, i.e.
mse for real-valued outputs, binary cross-entropy for binary outputs, etc.
cost_args : dict
Any additional named keyword arguments to pass to the specified `cost_function`.
weights_init : str
Determines the method for initializing input -> output weights. See opendeep.utils.nnet for options.
weights_interval : str or float
If Uniform `weights_init`, the +- interval to use. See opendeep.utils.nnet for options.
weights_mean : float
If Gaussian `weights_init`, the mean value to use.
weights_std : float
If Gaussian `weights_init`, the standard deviation to use.
bias_init : float
The initial value to use for the bias parameter. Most often, the default of 0.0 is preferred.
noise : str
What type of noise to use for corrupting the output (if not None). See opendeep.utils.noise
for options. This should be appropriate for the output activation, i.e. Gaussian for tanh or other
real-valued activations, etc. Often, you will use 'dropout' here as a regularization in BasicLayers.
noise_level : float
The amount of noise to use for the noise function specified by `noise`. This could be the
standard deviation for gaussian noise, the interval for uniform noise, the dropout amount, etc.
mrg : random
A random number generator that is used when adding noise.
I recommend using Theano's sandbox.rng_mrg.MRG_RandomStreams.
"""
# init Model to combine the defaults and config dictionaries with the initial parameters.
initial_parameters = locals().copy()
initial_parameters.pop("self")
super(Dense, self).__init__(**initial_parameters)
##################
# specifications #
##################
# grab info from the inputs_hook, or from parameters
if inputs_hook is not None: # inputs_hook is a tuple of (Shape, Input)
assert len(inputs_hook) == 2, "Expected inputs_hook to be tuple!" # make sure inputs_hook is a tuple
self.input = inputs_hook[1]
else:
# make the input a symbolic matrix
self.input = T.matrix("X")
# now that we have the input specs, define the output 'target' variable to be used in supervised training!
if kwargs.get("out_as_probs") == False:
self.target = T.vector("Y", dtype="int64")
else:
self.target = T.matrix("Y")
# either grab the output's desired size from the parameter directly, or copy input_size
self.output_size = self.output_size or self.input_size
# other specifications
# activation function!
activation_func = get_activation_function(activation)
# cost function!
cost_func = get_cost_function(cost)
cost_args = cost_args or dict()
####################################################
# parameters - make sure to deal with params_hook! #
####################################################
if params_hook is not None:
# make sure the params_hook has W (weights matrix) and b (bias vector)
assert len(params_hook) == 2, "Expected 2 params (W and b) for Dense, found {0!s}!".format(len(params_hook))
W, b = params_hook
else:
W = get_weights(
weights_init=weights_init,
shape=(self.input_size, self.output_size),
name="W",
rng=mrg,
# if gaussian
mean=weights_mean,
std=weights_std,
# if uniform
interval=weights_interval,
)
# grab the bias vector
b = get_bias(shape=output_size, name="b", init_values=bias_init)
# Finally have the two parameters - weights matrix W and bias vector b. That is all!
self.params = [W, b]
###############
# computation #
###############
# Here is the meat of the computation transforming input -> output
# It simply involves a matrix multiplication of inputs*weights, adding the bias vector, and then passing
# the result through our activation function (normally something nonlinear such as: max(0, output))
self.output = activation_func(T.dot(self.input, W) + b)
# Now deal with noise if we added it:
if noise:
log.debug("Adding noise switch.")
if noise_level is not None:
noise_func = get_noise(noise, noise_level=noise_level, mrg=mrg)
else:
noise_func = get_noise(noise, mrg=mrg)
# apply the noise as a switch!
# default to apply noise. this is for the cost and gradient functions to be computed later
# (not sure if the above statement is accurate such that gradient depends on initial value of switch)
self.switch = sharedX(value=1, name="basiclayer_noise_switch")
self.output = T.switch(self.switch, noise_func(input=self.output), self.output)
# now to define the cost of the model - use the cost function to compare our output with the target value.
self.cost = cost_func(output=self.output, target=self.target, **cost_args)
log.debug(
"Initialized a basic fully-connected layer with shape %s and activation: %s",
str((self.input_size, self.output_size)),
str(activation),
)
def __init__(self,
inputs_hook=None,
params_hook=None,
outdir='outputs/basic',
input_size=None,
output_size=None,
activation='rectifier',
cost='mse',
cost_args=None,
weights_init='uniform',
weights_mean=0,
weights_std=5e-3,
weights_interval='montreal',
bias_init=0.0,
noise=None,
noise_level=None,
mrg=RNG_MRG.MRG_RandomStreams(1),
**kwargs):
"""
Initialize a basic layer.
Parameters
----------
inputs_hook : Tuple of (shape, variable)
Routing information for the model to accept inputs from elsewhere. This is used for linking
different models together. For now, it needs to include the shape information (normally the
dimensionality of the input i.e. input_size).
params_hook : List(theano shared variable)
A list of model parameters (shared theano variables) that you should use when constructing
this model (instead of initializing your own shared variables). This parameter is useful when you want to
have two versions of the model that use the same parameters - such as a training model with dropout applied
to layers and one without for testing, where the parameters are shared between the two.
outdir : str
The directory you want outputs (parameters, images, etc.) to save to. If None, nothing will
be saved.
input_size : int
The size (dimensionality) of the input to the layer. If shape is provided in `inputs_hook`,
this is optional.
output_size : int
The size (dimensionality) of the output from the layer.
activation : str or callable
The activation function to use after the dot product going from input -> output. This can be a string
representing an option from opendeep.utils.activation, or your own function as long as it is callable.
cost : str or callable
The cost function to use when training the layer. This should be appropriate for the output type, i.e.
mse for real-valued outputs, binary cross-entropy for binary outputs, etc.
cost_args : dict
Any additional named keyword arguments to pass to the specified `cost_function`.
weights_init : str
Determines the method for initializing input -> output weights. See opendeep.utils.nnet for options.
weights_interval : str or float
If Uniform `weights_init`, the +- interval to use. See opendeep.utils.nnet for options.
weights_mean : float
If Gaussian `weights_init`, the mean value to use.
weights_std : float
If Gaussian `weights_init`, the standard deviation to use.
bias_init : float
The initial value to use for the bias parameter. Most often, the default of 0.0 is preferred.
noise : str
What type of noise to use for corrupting the output (if not None). See opendeep.utils.noise
for options. This should be appropriate for the output activation, i.e. Gaussian for tanh or other
real-valued activations, etc. Often, you will use 'dropout' here as a regularization in BasicLayers.
noise_level : float
The amount of noise to use for the noise function specified by `noise`. This could be the
standard deviation for gaussian noise, the interval for uniform noise, the dropout amount, etc.
mrg : random
A random number generator that is used when adding noise.
I recommend using Theano's sandbox.rng_mrg.MRG_RandomStreams.
"""
# init Model to combine the defaults and config dictionaries with the initial parameters.
initial_parameters = locals().copy()
initial_parameters.pop('self')
super(Dense, self).__init__(**initial_parameters)
##################
# specifications #
##################
# grab info from the inputs_hook, or from parameters
if inputs_hook is not None: # inputs_hook is a tuple of (Shape, Input)
assert len(
inputs_hook
) == 2, 'Expected inputs_hook to be tuple!' # make sure inputs_hook is a tuple
self.input = inputs_hook[1]
else:
# make the input a symbolic matrix
self.input = T.matrix('X')
# now that we have the input specs, define the output 'target' variable to be used in supervised training!
if kwargs.get('out_as_probs') == False:
self.target = T.vector('Y', dtype='int64')
else:
self.target = T.matrix('Y')
# either grab the output's desired size from the parameter directly, or copy input_size
self.output_size = self.output_size or self.input_size
# other specifications
# activation function!
activation_func = get_activation_function(activation)
# cost function!
cost_func = get_cost_function(cost)
cost_args = cost_args or dict()
####################################################
# parameters - make sure to deal with params_hook! #
####################################################
if params_hook is not None:
# make sure the params_hook has W (weights matrix) and b (bias vector)
assert len(params_hook) == 2, \
"Expected 2 params (W and b) for Dense, found {0!s}!".format(len(params_hook))
W, b = params_hook
else:
W = get_weights(
weights_init=weights_init,
shape=(self.input_size, self.output_size),
name="W",
rng=mrg,
# if gaussian
mean=weights_mean,
std=weights_std,
# if uniform
interval=weights_interval)
# grab the bias vector
b = get_bias(shape=output_size, name="b", init_values=bias_init)
# Finally have the two parameters - weights matrix W and bias vector b. That is all!
self.params = [W, b]
###############
# computation #
###############
# Here is the meat of the computation transforming input -> output
# It simply involves a matrix multiplication of inputs*weights, adding the bias vector, and then passing
# the result through our activation function (normally something nonlinear such as: max(0, output))
self.output = activation_func(T.dot(self.input, W) + b)
# Now deal with noise if we added it:
if noise:
log.debug('Adding noise switch.')
if noise_level is not None:
noise_func = get_noise(noise, noise_level=noise_level, mrg=mrg)
else:
noise_func = get_noise(noise, mrg=mrg)
# apply the noise as a switch!
# default to apply noise. this is for the cost and gradient functions to be computed later
# (not sure if the above statement is accurate such that gradient depends on initial value of switch)
self.switch = sharedX(value=1, name="basiclayer_noise_switch")
self.output = T.switch(self.switch, noise_func(input=self.output),
self.output)
# now to define the cost of the model - use the cost function to compare our output with the target value.
self.cost = cost_func(output=self.output,
target=self.target,
**cost_args)
log.debug(
"Initialized a basic fully-connected layer with shape %s and activation: %s",
str((self.input_size, self.output_size)), str(activation))
def get_updates(self, gradients):
"""
Compute AdaSecant updates (see the paper for details).
Parameters
----------
gradients : dict
A dictionary mapping from the model's parameters to their gradients.
Returns
-------
OrderdDict
A dictionary mapping from the old model parameters
to their new values after a single iteration of the learning rule.
"""
updates = OrderedDict({})
eps = self.damping
step = sharedX(0., name="step")
if self.skip_nan_inf:
#If norm of the gradients of a parameter is inf or nan don't update that parameter
#That might be useful for RNNs.
gradients = OrderedDict({
p:
T.switch(T.or_(T.isinf(gradients[p]), T.isnan(gradients[p])),
0, gradients[p])
for p in gradients.keys()
})
#Block-normalize gradients:
gradients = OrderedDict({
p: gradients[p] / (gradients[p].norm(2) + eps)
for p in gradients.keys()
})
# nparams = len(gradients.keys())
#
# #Apply the gradient clipping, this is only necessary for RNNs and sometimes for very deep
# #networks
# if self.grad_clip:
# gnorm = sum([g.norm(2) for g in gradients.values()])
#
# gradients = OrderedDict({p: T.switch(gnorm/nparams > self.grad_clip,
# g * self.grad_clip * nparams / gnorm , g)\
# for p, g in gradients.items()})
for param in gradients.keys():
gradients[param].name = "grad_%s" % param.name
mean_grad = sharedX(param.get_value() * 0. + eps,
name="mean_grad_%s" % param.name)
# mean_corrected_grad = sharedX(param.get_value() * 0 + eps, name="mean_corrected_grad_%s" % param.name)
slow_constant = 2.1
if self.use_adagrad:
# sum_square_grad := \sum_i g_i^2
sum_square_grad = sharedX(param.get_value(borrow=True) * 0.,
name="sum_square_grad_%s" %
param.name)
"""
Initialization of accumulators
"""
taus_x_t = sharedX(
(numpy.ones_like(param.get_value()) + eps) * slow_constant,
name="taus_x_t_" + param.name)
self.taus_x_t = taus_x_t
#Variance reduction parameters
#Numerator of the gamma:
gamma_nume_sqr = sharedX(numpy.zeros_like(param.get_value()) + eps,
name="gamma_nume_sqr_" + param.name)
#Denominator of the gamma:
gamma_deno_sqr = sharedX(numpy.zeros_like(param.get_value()) + eps,
name="gamma_deno_sqr_" + param.name)
#For the covariance parameter := E[\gamma \alpha]_{t-1}
cov_num_t = sharedX(numpy.zeros_like(param.get_value()) + eps,
name="cov_num_t_" + param.name)
# mean_squared_grad := E[g^2]_{t-1}
mean_square_grad = sharedX(numpy.zeros_like(param.get_value()) +
eps,
name="msg_" + param.name)
# mean_square_dx := E[(\Delta x)^2]_{t-1}
mean_square_dx = sharedX(param.get_value() * 0.,
name="msd_" + param.name)
if self.use_corrected_grad:
old_grad = sharedX(param.get_value() * 0. + eps)
#The uncorrected gradient of previous of the previous update:
old_plain_grad = sharedX(param.get_value() * 0. + eps)
mean_curvature = sharedX(param.get_value() * 0. + eps)
mean_curvature_sqr = sharedX(param.get_value() * 0. + eps)
# Initialize the E[\Delta]_{t-1}
mean_dx = sharedX(param.get_value() * 0.)
# Block-wise normalize the gradient:
norm_grad = gradients[param]
#For the first time-step, assume that delta_x_t := norm_grad
cond = T.eq(step, 0)
msdx = cond * norm_grad**2 + (1 - cond) * mean_square_dx
mdx = cond * norm_grad + (1 - cond) * mean_dx
"""
Compute the new updated values.
"""
# E[g_i^2]_t
new_mean_squared_grad = (mean_square_grad * self.decay +
T.sqr(norm_grad) * (1 - self.decay))
new_mean_squared_grad.name = "msg_" + param.name
# E[g_i]_t
new_mean_grad = (mean_grad * self.decay + norm_grad *
(1 - self.decay))
new_mean_grad.name = "nmg_" + param.name
mg = new_mean_grad
mgsq = new_mean_squared_grad
# Keep the rms for numerator and denominator of gamma.
new_gamma_nume_sqr = (gamma_nume_sqr * (1 - 1 / taus_x_t) + T.sqr(
(norm_grad - old_grad) * (old_grad - mg)) / taus_x_t)
new_gamma_nume_sqr.name = "ngammasqr_num_" + param.name
new_gamma_deno_sqr = (gamma_deno_sqr * (1 - 1 / taus_x_t) + T.sqr(
(mg - norm_grad) * (old_grad - mg)) / taus_x_t)
new_gamma_deno_sqr.name = "ngammasqr_den_" + param.name
gamma = T.sqrt(gamma_nume_sqr) / T.sqrt(gamma_deno_sqr + eps)
gamma.name = "gamma_" + param.name
if self.gamma_clip:
gamma = T.minimum(gamma, self.gamma_clip)
momentum_step = gamma * mg
corrected_grad_cand = (norm_grad + momentum_step) / (1 + gamma)
#For starting the variance reduction.
if self.start_var_reduction > -1:
cond = T.le(self.start_var_reduction, step)
corrected_grad = cond * corrected_grad_cand + (
1 - cond) * norm_grad
else:
corrected_grad = norm_grad
new_sum_squared_grad = None
if self.use_adagrad:
g = corrected_grad
# Accumulate gradient
new_sum_squared_grad = (sum_square_grad + T.sqr(g))
rms_g_t = T.sqrt(new_sum_squared_grad)
rms_g_t = T.maximum(rms_g_t, 1.0)
# Use the gradients from the previous update
# to compute the \nabla f(x_t) - \nabla f(x_{t-1})
cur_curvature = norm_grad - old_plain_grad
cur_curvature_sqr = T.sqr(cur_curvature)
new_curvature_ave = (mean_curvature * (1 - 1 / taus_x_t) +
(cur_curvature / taus_x_t))
new_curvature_ave.name = "ncurve_ave_" + param.name
#Average average curvature
nc_ave = new_curvature_ave
new_curvature_sqr_ave = (mean_curvature_sqr * (1 - 1 / taus_x_t) +
(cur_curvature_sqr / taus_x_t))
new_curvature_sqr_ave.name = "ncurve_sqr_ave_" + param.name
#Unbiased average squared curvature
nc_sq_ave = new_curvature_sqr_ave
epsilon = self.lr_scalers.get(param, 1.) * self.learning_rate
scaled_lr = self.lr_scalers.get(param, 1.) * sharedX(1.0)
rms_dx_tm1 = T.sqrt(msdx + epsilon)
rms_curve_t = T.sqrt(new_curvature_sqr_ave + epsilon)
#This is where the update step is being defined
delta_x_t = -scaled_lr * (rms_dx_tm1 / rms_curve_t - cov_num_t /
(new_curvature_sqr_ave + epsilon))
delta_x_t.name = "delta_x_t_" + param.name
# This part seems to be necessary for only RNNs
# For feedforward networks this does not seem to be important.
if self.delta_clip:
log.info("Clipping will be applied on the adaptive step size.")
delta_x_t = delta_x_t.clip(-self.delta_clip, self.delta_clip)
if self.use_adagrad:
delta_x_t = delta_x_t * corrected_grad / rms_g_t
else:
log.info("Clipped adagrad is disabled.")
delta_x_t = delta_x_t * corrected_grad
else:
log.info(
"Clipping will not be applied on the adaptive step size.")
if self.use_adagrad:
delta_x_t = delta_x_t * corrected_grad / rms_g_t
else:
log.info("Clipped adagrad will not be used.")
delta_x_t = delta_x_t * corrected_grad
new_taus_t = (1 - T.sqr(mdx) / (msdx + eps)) * taus_x_t + sharedX(
1 + eps, "stabilized")
#To compute the E[\Delta^2]_t
new_mean_square_dx = (msdx * (1 - 1 / taus_x_t) +
(T.sqr(delta_x_t) / taus_x_t))
#To compute the E[\Delta]_t
new_mean_dx = (mean_dx * (1 - 1 / taus_x_t) + (delta_x_t /
(taus_x_t)))
#Perform the outlier detection:
#This outlier detection is slightly different:
new_taus_t = T.switch(
T.or_(
abs(norm_grad - mg) > (2 * T.sqrt(mgsq - mg**2)),
abs(cur_curvature - nc_ave) >
(2 * T.sqrt(nc_sq_ave - nc_ave**2))), sharedX(2.2),
new_taus_t)
#Apply the bound constraints on tau:
new_taus_t = T.maximum(self.lower_bound_tau, new_taus_t)
new_taus_t = T.minimum(self.upper_bound_tau, new_taus_t)
new_cov_num_t = (cov_num_t * (1 - 1 / taus_x_t) +
(delta_x_t * cur_curvature) * (1 / taus_x_t))
update_step = delta_x_t
# Apply updates
updates[mean_square_grad] = new_mean_squared_grad
updates[mean_square_dx] = new_mean_square_dx
updates[mean_dx] = new_mean_dx
updates[gamma_nume_sqr] = new_gamma_nume_sqr
updates[gamma_deno_sqr] = new_gamma_deno_sqr
updates[taus_x_t] = new_taus_t
updates[cov_num_t] = new_cov_num_t
updates[mean_grad] = new_mean_grad
updates[old_plain_grad] = norm_grad
updates[mean_curvature] = new_curvature_ave
updates[mean_curvature_sqr] = new_curvature_sqr_ave
updates[param] = param + update_step
updates[step] = step + 1
if self.use_adagrad:
updates[sum_square_grad] = new_sum_squared_grad
if self.use_corrected_grad:
updates[old_grad] = corrected_grad
return updates
def get_updates(self, gradients):
"""
Compute AdaSecant updates (see the paper for details).
Parameters
----------
gradients : dict
A dictionary mapping from the model's parameters to their gradients.
Returns
-------
OrderdDict
A dictionary mapping from the old model parameters
to their new values after a single iteration of the learning rule.
"""
updates = OrderedDict({})
eps = self.damping
step = sharedX(0., name="step")
if self.skip_nan_inf:
#If norm of the gradients of a parameter is inf or nan don't update that parameter
#That might be useful for RNNs.
gradients = OrderedDict({p: T.switch(T.or_(T.isinf(gradients[p]),
T.isnan(gradients[p])), 0, gradients[p]) for
p in gradients.keys()})
#Block-normalize gradients:
gradients = OrderedDict({p: gradients[p] / (gradients[p].norm(2) + eps) for p in gradients.keys()})
# nparams = len(gradients.keys())
#
# #Apply the gradient clipping, this is only necessary for RNNs and sometimes for very deep
# #networks
# if self.grad_clip:
# gnorm = sum([g.norm(2) for g in gradients.values()])
#
# gradients = OrderedDict({p: T.switch(gnorm/nparams > self.grad_clip,
# g * self.grad_clip * nparams / gnorm , g)\
# for p, g in gradients.items()})
for param in gradients.keys():
gradients[param].name = "grad_%s" % param.name
mean_grad = sharedX(param.get_value() * 0. + eps, name="mean_grad_%s" % param.name)
# mean_corrected_grad = sharedX(param.get_value() * 0 + eps, name="mean_corrected_grad_%s" % param.name)
slow_constant = 2.1
if self.use_adagrad:
# sum_square_grad := \sum_i g_i^2
sum_square_grad = sharedX(param.get_value(borrow=True) * 0., name="sum_square_grad_%s" % param.name)
"""
Initialization of accumulators
"""
taus_x_t = sharedX((numpy.ones_like(param.get_value()) + eps) * slow_constant,
name="taus_x_t_" + param.name)
self.taus_x_t = taus_x_t
#Variance reduction parameters
#Numerator of the gamma:
gamma_nume_sqr = sharedX(numpy.zeros_like(param.get_value()) + eps,
name="gamma_nume_sqr_" + param.name)
#Denominator of the gamma:
gamma_deno_sqr = sharedX(numpy.zeros_like(param.get_value()) + eps,
name="gamma_deno_sqr_" + param.name)
#For the covariance parameter := E[\gamma \alpha]_{t-1}
cov_num_t = sharedX(numpy.zeros_like(param.get_value()) + eps,
name="cov_num_t_" + param.name)
# mean_squared_grad := E[g^2]_{t-1}
mean_square_grad = sharedX(numpy.zeros_like(param.get_value()) + eps,
name="msg_" + param.name)
# mean_square_dx := E[(\Delta x)^2]_{t-1}
mean_square_dx = sharedX(param.get_value() * 0., name="msd_" + param.name)
if self.use_corrected_grad:
old_grad = sharedX(param.get_value() * 0. + eps)
#The uncorrected gradient of previous of the previous update:
old_plain_grad = sharedX(param.get_value() * 0. + eps)
mean_curvature = sharedX(param.get_value() * 0. + eps)
mean_curvature_sqr = sharedX(param.get_value() * 0. + eps)
# Initialize the E[\Delta]_{t-1}
mean_dx = sharedX(param.get_value() * 0.)
# Block-wise normalize the gradient:
norm_grad = gradients[param]
#For the first time-step, assume that delta_x_t := norm_grad
cond = T.eq(step, 0)
msdx = cond * norm_grad**2 + (1 - cond) * mean_square_dx
mdx = cond * norm_grad + (1 - cond) * mean_dx
"""
Compute the new updated values.
"""
# E[g_i^2]_t
new_mean_squared_grad = (mean_square_grad * self.decay + T.sqr(norm_grad) * (1 - self.decay))
new_mean_squared_grad.name = "msg_" + param.name
# E[g_i]_t
new_mean_grad = (mean_grad * self.decay + norm_grad * (1 - self.decay))
new_mean_grad.name = "nmg_" + param.name
mg = new_mean_grad
mgsq = new_mean_squared_grad
# Keep the rms for numerator and denominator of gamma.
new_gamma_nume_sqr = (
gamma_nume_sqr * (1 - 1 / taus_x_t) + T.sqr((norm_grad - old_grad) * (old_grad - mg)) / taus_x_t
)
new_gamma_nume_sqr.name = "ngammasqr_num_" + param.name
new_gamma_deno_sqr = (
gamma_deno_sqr * (1 - 1 / taus_x_t) + T.sqr((mg - norm_grad) * (old_grad - mg)) / taus_x_t
)
new_gamma_deno_sqr.name = "ngammasqr_den_" + param.name
gamma = T.sqrt(gamma_nume_sqr) / T.sqrt(gamma_deno_sqr + eps)
gamma.name = "gamma_" + param.name
if self.gamma_clip:
gamma = T.minimum(gamma, self.gamma_clip)
momentum_step = gamma * mg
corrected_grad_cand = (norm_grad + momentum_step) / (1 + gamma)
#For starting the variance reduction.
if self.start_var_reduction > -1:
cond = T.le(self.start_var_reduction, step)
corrected_grad = cond * corrected_grad_cand + (1 - cond) * norm_grad
else:
corrected_grad = norm_grad
new_sum_squared_grad = None
if self.use_adagrad:
g = corrected_grad
# Accumulate gradient
new_sum_squared_grad = (sum_square_grad + T.sqr(g))
rms_g_t = T.sqrt(new_sum_squared_grad)
rms_g_t = T.maximum(rms_g_t, 1.0)
# Use the gradients from the previous update
# to compute the \nabla f(x_t) - \nabla f(x_{t-1})
cur_curvature = norm_grad - old_plain_grad
cur_curvature_sqr = T.sqr(cur_curvature)
new_curvature_ave = (mean_curvature * (1 - 1 / taus_x_t) + (cur_curvature / taus_x_t))
new_curvature_ave.name = "ncurve_ave_" + param.name
#Average average curvature
nc_ave = new_curvature_ave
new_curvature_sqr_ave = (mean_curvature_sqr * (1 - 1 / taus_x_t) + (cur_curvature_sqr / taus_x_t))
new_curvature_sqr_ave.name = "ncurve_sqr_ave_" + param.name
#Unbiased average squared curvature
nc_sq_ave = new_curvature_sqr_ave
epsilon = self.lr_scalers.get(param, 1.) * self.learning_rate
scaled_lr = self.lr_scalers.get(param, 1.) * sharedX(1.0)
rms_dx_tm1 = T.sqrt(msdx + epsilon)
rms_curve_t = T.sqrt(new_curvature_sqr_ave + epsilon)
#This is where the update step is being defined
delta_x_t = -scaled_lr * (rms_dx_tm1 / rms_curve_t - cov_num_t / (new_curvature_sqr_ave + epsilon))
delta_x_t.name = "delta_x_t_" + param.name
# This part seems to be necessary for only RNNs
# For feedforward networks this does not seem to be important.
if self.delta_clip:
log.info("Clipping will be applied on the adaptive step size.")
delta_x_t = delta_x_t.clip(-self.delta_clip, self.delta_clip)
if self.use_adagrad:
delta_x_t = delta_x_t * corrected_grad / rms_g_t
else:
log.info("Clipped adagrad is disabled.")
delta_x_t = delta_x_t * corrected_grad
else:
log.info("Clipping will not be applied on the adaptive step size.")
if self.use_adagrad:
delta_x_t = delta_x_t * corrected_grad / rms_g_t
else:
log.info("Clipped adagrad will not be used.")
delta_x_t = delta_x_t * corrected_grad
new_taus_t = (1 - T.sqr(mdx) / (msdx + eps)) * taus_x_t + sharedX(1 + eps, "stabilized")
#To compute the E[\Delta^2]_t
new_mean_square_dx = (msdx * (1 - 1 / taus_x_t) + (T.sqr(delta_x_t) / taus_x_t))
#To compute the E[\Delta]_t
new_mean_dx = (mean_dx * (1 - 1 / taus_x_t) + (delta_x_t / (taus_x_t)))
#Perform the outlier detection:
#This outlier detection is slightly different:
new_taus_t = T.switch(T.or_(abs(norm_grad - mg) > (2 * T.sqrt(mgsq - mg**2)),
abs(cur_curvature - nc_ave) > (2 * T.sqrt(nc_sq_ave - nc_ave**2))),
sharedX(2.2), new_taus_t)
#Apply the bound constraints on tau:
new_taus_t = T.maximum(self.lower_bound_tau, new_taus_t)
new_taus_t = T.minimum(self.upper_bound_tau, new_taus_t)
new_cov_num_t = (cov_num_t * (1 - 1 / taus_x_t) + (delta_x_t * cur_curvature) * (1 / taus_x_t))
update_step = delta_x_t
# Apply updates
updates[mean_square_grad] = new_mean_squared_grad
updates[mean_square_dx] = new_mean_square_dx
updates[mean_dx] = new_mean_dx
updates[gamma_nume_sqr] = new_gamma_nume_sqr
updates[gamma_deno_sqr] = new_gamma_deno_sqr
updates[taus_x_t] = new_taus_t
updates[cov_num_t] = new_cov_num_t
updates[mean_grad] = new_mean_grad
updates[old_plain_grad] = norm_grad
updates[mean_curvature] = new_curvature_ave
updates[mean_curvature_sqr] = new_curvature_sqr_ave
updates[param] = param + update_step
updates[step] = step + 1
if self.use_adagrad:
updates[sum_square_grad] = new_sum_squared_grad
if self.use_corrected_grad:
updates[old_grad] = corrected_grad
return updates
def __init__(self,
dataset,
model=None,
epochs=10,
batch_size=100,
min_batch_size=1,
save_freq=None,
stop_threshold=None,
stop_patience=None,
learning_rate=1e-6,
lr_decay=None,
lr_decay_factor=None,
decay=0.95,
gamma_clip=1.8,
damping=1e-7,
grad_clip=None,
hard_clip=False,
start_var_reduction=0,
delta_clip=None,
use_adagrad=False,
skip_nan_inf=False,
upper_bound_tau=1e8,
lower_bound_tau=1.5,
use_corrected_grad=True):
"""
Initialize AdaSecant.
Parameters
----------
dataset : Dataset
The Dataset to use when training the Model.
model : Model
The Model to train. Needed if the Optimizer isn't being passed to a Model's .train() method.
epochs : int
how many training iterations over the dataset to go.
batch_size : int
How many examples from the training dataset to use in parallel.
min_batch_size : int
The minimum number of examples required at a time (for things like time series, this would be > 1).
save_freq : int
How many epochs to train between each new save of the Model's parameters.
stop_threshold : float
The factor by how much the best validation training score needs to improve to determine early stopping.
stop_patience : int
The patience or number of epochs to wait after the stop_threshold has been reached before stopping.
learning_rate : float
The multiplicative amount to adjust parameters based on their gradient values.
lr_decay : str
The type of decay function to use for changing the learning rate over epochs. See
`opendeep.utils.decay` for options.
lr_decay_factor : float
The amount to use for the decay function when changing the learning rate over epochs. See
`opendeep.utils.decay` for its effect for given decay functions.
decay : float, optional
Decay rate :math:`\\rho` in Algorithm 1 of the aforementioned
paper. Decay 0.95 seems to work fine for several tasks.
gamma_clip : float, optional
The clipping threshold for the gamma. In general 1.8 seems to
work fine for several tasks.
start_var_reduction: float, optional,
How many updates later should the variance reduction start from?
delta_clip: float, optional,
The threshold to clip the deltas after.
grad_clip: float, optional,
Apply gradient clipping for RNNs (not necessary for feedforward networks). But this is
a constraint on the norm of the gradient per layer.
hard_clip : bool
Whether to use a hard cutoff or rescaling for clipping gradients.
use_adagrad: bool, optional
Either to use clipped adagrad or not.
use_corrected_grad: bool, optional
Either to use correction for gradients (referred as variance
reduction in the workshop paper).
"""
# get everything together with the Optimizer class
initial_parameters = locals().copy()
initial_parameters.pop('self')
super(AdaSecant, self).__init__(**initial_parameters)
assert decay >= 0., "Decay needs to be >=0."
assert decay < 1., "Decay needs to be <1."
self.decay = sharedX(decay, "decay")
self.damping = damping
self.skip_nan_inf = skip_nan_inf
# if grad_clip:
# assert grad_clip > 0.
# assert grad_clip <= 1., "Norm of the gradients per layer can not be larger than 1."
# self.grad_clip = grad_clip
self.use_adagrad = use_adagrad
self.use_corrected_grad = use_corrected_grad
self.gamma_clip = gamma_clip
self.start_var_reduction = start_var_reduction
self.delta_clip = delta_clip
# We have to bound the tau to prevent it to
# grow to an arbitrarily large number, oftenwise
# that causes numerical instabilities for very deep
# networks. Note that once tau become very large, it will keep,
# increasing indefinitely.
self.lower_bound_tau = lower_bound_tau
self.upper_bound_tau = upper_bound_tau
def __init__(self, dataset, loss=None, model=None,
epochs=1000, batch_size=100, min_batch_size=1,
save_freq=10, stop_threshold=None, stop_patience=50,
learning_rate=1e-3, lr_decay=None, lr_decay_factor=None,
grad_clip=None, hard_clip=False,
**kwargs):
"""
Initialize the Optimizer.
Parameters
----------
dataset : Dataset
The :class:`opendeep.data.Dataset` to use when training the Model.
loss : Loss
The :class:`opendeep.optimization.loss.Loss` function to compare the model to a 'target' result.
model : Model
The :class:`opendeep.models.Model` to train. Needed if the Optimizer isn't being passed to a
Model's .train() method.
epochs : int
How many training iterations over the dataset to go.
batch_size : int
How many examples from the training dataset to use in parallel.
min_batch_size : int
The minimum number of examples required at a time (for things like time series, this would be > 1).
save_freq : int, optional
How many epochs to train between each new save of the Model's parameters.
stop_threshold : float, optional
The factor by how much the best validation training score needs to improve to determine early stopping.
stop_patience : int, optional
The patience or number of epochs to wait after the stop_threshold has been reached before stopping.
learning_rate : float
The multiplicative amount to adjust parameters based on their gradient values.
lr_decay : str
The decay function to use for changing the learning rate over epochs. See
`opendeep.utils.decay` for classes of decay and documentation.
lr_decay_factor : float
The amount of decay to use for the ``lr_decay`` type of decay.
grad_clip : float, optional
Whether to clip gradients. This will clip the norm of the gradients either with a hard cutoff or rescaling.
hard_clip : bool
Whether to use a hard cutoff or rescaling for clipping gradients.
"""
log.info("Initializing optimizer %s", str(self.__class__.__name__))
# Deal with early stopping None initializations (no early stopping).
if not stop_threshold:
stop_threshold = numpy.inf
if not save_freq:
save_freq = 1000000
if not stop_patience:
stop_patience = 1
# Put all init parameters in self.args so we can log the initial configuration.
self.args = locals().copy()
self.args.pop('self')
kwargs = self.args.pop('kwargs')
self.args = add_kwargs_to_dict(kwargs, self.args)
# log the arguments
log.info("Optimizer config args: %s", str(self.args))
# if the optimizer wasn't initialized with a Model (train() being called from the model class itself),
# just return. (This seems kinda hacky but hey, people wanted .train() to happen from Model and there
# wasn't really a better way unless the epoch looping logic was in that method for Model. That wasn't
# the best option because other methods besides stochastic ones can exist for optimizers in the future.
# TODO: fix this up - feels like a hack just to make model.train() work...
if not model:
return
# Otherwise, things are proceeding as normal. Carry on...
assert isinstance(model, Model), "Optimizer input model needs to be a Model class! " \
"Found %s" % str(model.__class__.__name__)
assert isinstance(dataset, Dataset), "Optimizer input dataset needs to be a Dataset class! " \
"Found %s" % str(dataset.__class__.__name__)
# deal with loss expression/targets
if loss is not None:
assert isinstance(loss, Loss), "Optimizer input loss needs to be a Loss class! " \
"Found %s" % str(loss.__class__.__name__)
if isinstance(loss, Loss):
self.loss_targets = loss.get_targets()
self.loss_expression = loss.get_loss()
else:
assert model.get_loss() is not None, "No Loss specified, and the model does not have one implemented."
if isinstance(model.get_loss(), tuple):
self.loss_targets = raise_to_list(model.get_loss()[0])
self.loss_expression = model.get_loss()[1]
else:
self.loss_targets = None
self.loss_expression = model.get_loss()
model_inputs = raise_to_list(model.get_inputs())
n_model_inputs = len(model_inputs)
model_targets = self.loss_targets or []
for input in model_inputs:
if input in model_targets:
model_targets.remove(input)
n_model_targets = len(model_targets)
self.unsupervised = (n_model_targets is 0)
# make sure the number of inputs/targets matches up with the dataset properties
# train
assert n_model_inputs == len(raise_to_list(dataset.train_inputs)), \
"Dataset has %d train inputs, while model expects %d" % \
(len(raise_to_list(dataset.train_inputs)), n_model_inputs)
if not self.unsupervised:
assert n_model_targets == len(raise_to_list(dataset.train_targets) or []), \
"Dataset has %d train targets, while model expects %d" % \
(len(raise_to_list(dataset.train_targets) or []), n_model_targets)
# valid
if dataset.valid_inputs is not None:
assert n_model_inputs == len(raise_to_list(dataset.valid_inputs)), \
"Dataset has %d valid inputs, while model expects %d" % \
(len(raise_to_list(dataset.valid_inputs)), n_model_inputs)
if not self.unsupervised:
assert n_model_targets == len(raise_to_list(dataset.valid_targets) or []), \
"Dataset has %d valid targets, while model expects %d" % \
(len(raise_to_list(dataset.valid_targets) or []), n_model_targets)
# test
if dataset.test_inputs is not None:
assert n_model_inputs == len(raise_to_list(dataset.test_inputs)), \
"Dataset has %d test inputs, while model expects %d" % \
(len(raise_to_list(dataset.test_inputs)), n_model_inputs)
if not self.unsupervised:
assert n_model_targets == len(raise_to_list(dataset.test_targets) or []), \
"Dataset has %d test targets, while model expects %d" % \
(len(raise_to_list(dataset.test_targets) or []), n_model_targets)
# now we are happy, we can add them to `self`
self.model = model
self.dataset = dataset
self.loss = loss
# Learning rate - how drastic of a step do the parameters change
self.learning_rate = sharedX(learning_rate, 'learning_rate')
# whether to scale individual model parameters' learning rates.
self.lr_scalers = self.model.get_lr_scalers()
# whether to decay
if lr_decay:
self.learning_rate_decay = get_decay_function(lr_decay,
self.learning_rate,
learning_rate,
lr_decay_factor)
else:
self.learning_rate_decay = False
# rest of initial parameters needed for training.
self.batch_size = batch_size
self.min_batch_size = min_batch_size
self.n_epoch = epochs
self.save_frequency = save_freq
self.early_stop_threshold = stop_threshold
self.early_stop_length = stop_patience
self.grad_clip = grad_clip
self.hard_clip = hard_clip
def __init__(self, inputs_hook=None, hiddens_hook=None, params_hook=None, outdir='outputs/lstm/',
input_size=None, hidden_size=None, output_size=None,
activation='sigmoid', hidden_activation='relu', inner_hidden_activation='sigmoid',
mrg=RNG_MRG.MRG_RandomStreams(1),
weights_init='uniform', weights_interval='montreal', weights_mean=0, weights_std=5e-3,
bias_init=0.0,
r_weights_init='identity', r_weights_interval='montreal', r_weights_mean=0, r_weights_std=5e-3,
r_bias_init=0.0,
cost_function='mse', cost_args=None,
noise='dropout', noise_level=None, noise_decay=False, noise_decay_amount=.99,
direction='forward',
clip_recurrent_grads=False):
"""
Initialize a simple recurrent network.
Parameters
----------
inputs_hook : Tuple of (shape, variable)
Routing information for the model to accept inputs from elsewhere. This is used for linking
different models together (e.g. setting the Softmax model's input layer to the DAE's hidden layer gives a
newly supervised classification model). For now, it needs to include the shape information (normally the
dimensionality of the input i.e. n_in).
hiddens_hook : Tuple of (shape, variable)
Routing information for the model to accept its hidden representation from elsewhere. For recurrent nets,
this will be the initial starting value for hidden layers.
params_hook : List(theano shared variable)
A list of model parameters (shared theano variables) that you should use when constructing
this model (instead of initializing your own shared variables). This parameter is useful when you want to
have two versions of the model that use the same parameters.
outdir : str
The location to produce outputs from training or running the :class:`RNN`. If None, nothing will be saved.
input_size : int
The size (dimensionality) of the input. If shape is provided in `inputs_hook`, this is optional.
hidden_size : int
The size (dimensionality) of the hidden layers. If shape is provided in `hiddens_hook`, this is optional.
output_size : int
The size (dimensionality) of the output.
activation : str or callable
The nonlinear (or linear) activation to perform after the dot product from hiddens -> output layer.
This activation function should be appropriate for the output unit types, i.e. 'sigmoid' for binary.
See opendeep.utils.activation for a list of available activation functions. Alternatively, you can pass
your own function to be used as long as it is callable.
hidden_activation : str or callable
The activation to perform for the hidden units.
See opendeep.utils.activation for a list of available activation functions. Alternatively, you can pass
your own function to be used as long as it is callable.
inner_hidden_activation : str or callable
The activation to perform for the hidden gates.
See opendeep.utils.activation for a list of available activation functions. Alternatively, you can pass
your own function to be used as long as it is callable.
mrg : random
A random number generator that is used when adding noise.
I recommend using Theano's sandbox.rng_mrg.MRG_RandomStreams.
weights_init : str
Determines the method for initializing model weights. See opendeep.utils.nnet for options.
weights_interval : str or float
If Uniform `weights_init`, the +- interval to use. See opendeep.utils.nnet for options.
weights_mean : float
If Gaussian `weights_init`, the mean value to use.
weights_std : float
If Gaussian `weights_init`, the standard deviation to use.
bias_init : float
The initial value to use for the bias parameter. Most often, the default of 0.0 is preferred.
r_weights_init : str
Determines the method for initializing recurrent model weights. See opendeep.utils.nnet for options.
r_weights_interval : str or float
If Uniform `r_weights_init`, the +- interval to use. See opendeep.utils.nnet for options.
r_weights_mean : float
If Gaussian `r_weights_init`, the mean value to use.
r_weights_std : float
If Gaussian `r_weights_init`, the standard deviation to use.
r_bias_init : float
The initial value to use for the recurrent bias parameter. Most often, the default of 0.0 is preferred.
cost_function : str or callable
The function to use when calculating the output cost of the model.
See opendeep.utils.cost for options. You can also specify your own function, which needs to be callable.
cost_args : dict
Any additional named keyword arguments to pass to the specified `cost_function`.
noise : str
What type of noise to use for the hidden layers and outputs. See opendeep.utils.noise
for options. This should be appropriate for the unit activation, i.e. Gaussian for tanh or other
real-valued activations, etc.
noise_level : float
The amount of noise to use for the noise function specified by `hidden_noise`. This could be the
standard deviation for gaussian noise, the interval for uniform noise, the dropout amount, etc.
noise_decay : str or False
Whether to use `noise` scheduling (decay `noise_level` during the course of training),
and if so, the string input specifies what type of decay to use. See opendeep.utils.decay for options.
Noise decay (known as noise scheduling) effectively helps the model learn larger variance features first,
and then smaller ones later (almost as a kind of curriculum learning). May help it converge faster.
noise_decay_amount : float
The amount to reduce the `noise_level` after each training epoch based on the decay function specified
in `noise_decay`.
direction : str
The direction this recurrent model should go over its inputs.
Can be 'forward', 'backward', or 'bidirectional'.
clip_recurrent_grads : False or float, optional
Whether to clip the gradients for the parameters that unroll over timesteps (such as the weights
connecting previous hidden states to the current hidden state, and not the weights from current
input to hiddens). If it is a float, the gradients for the weights will be hard clipped to the range
`+-clip_recurrent_grads`.
"""
initial_parameters = locals().copy()
initial_parameters.pop('self')
super(LSTM, self).__init__(**initial_parameters)
##################
# specifications #
##################
backward = direction.lower() == 'backward'
bidirectional = direction.lower() == 'bidirectional'
#########################################
# activation, cost, and noise functions #
#########################################
# recurrent hidden activation function!
self.hidden_activation_func = get_activation_function(hidden_activation)
self.inner_hidden_activation_func = get_activation_function(inner_hidden_activation)
# output activation function!
activation_func = get_activation_function(activation)
# Cost function
cost_function = get_cost_function(cost_function)
cost_args = cost_args or dict()
# Now deal with noise if we added it:
if noise:
log.debug('Adding %s noise switch.' % str(noise))
if noise_level is not None:
noise_level = sharedX(value=noise_level)
noise_func = get_noise(noise, noise_level=noise_level, mrg=mrg)
else:
noise_func = get_noise(noise, mrg=mrg)
# apply the noise as a switch!
# default to apply noise. this is for the cost and gradient functions to be computed later
# (not sure if the above statement is accurate such that gradient depends on initial value of switch)
self.noise_switch = sharedX(value=1, name="basiclayer_noise_switch")
# noise scheduling
if noise_decay and noise_level is not None:
self.noise_schedule = get_decay_function(noise_decay,
noise_level,
noise_level.get_value(),
noise_decay_amount)
###############
# inputs hook #
###############
# grab info from the inputs_hook
# in the case of an inputs_hook, recurrent will always work with the leading tensor dimension
# being the temporal dimension.
# input is 3D tensor of (timesteps, batch_size, data_dim)
# if input is 2D tensor, assume it is of the form (timesteps, data_dim) i.e. batch_size is 1. Convert to 3D.
# if input is > 3D tensor, assume it is of form (timesteps, batch_size, data...) and flatten to 3D.
if self.inputs_hook is not None:
self.input = self.inputs_hook[1]
if self.input.ndim == 1:
self.input = T.unbroadcast(self.input.dimshuffle(0, 'x', 'x'), [1, 2])
self.input_size = 1
elif self.input.ndim == 2:
self.input = T.unbroadcast(self.input.dimshuffle(0, 'x', 1), 1)
elif self.input.ndim > 3:
self.input = self.input.flatten(3)
self.input_size = sum(self.input_size)
else:
raise NotImplementedError("Recurrent input with %d dimensions not supported!" % self.input.ndim)
xs = self.input
else:
# Assume input coming from optimizer is (batches, timesteps, data)
# so, we need to reshape to (timesteps, batches, data)
self.input = T.tensor3("Xs")
xs = self.input.dimshuffle(1, 0, 2)
# The target outputs for supervised training - in the form of (batches, timesteps, output) which is
# the same dimension ordering as the expected input from optimizer.
# therefore, we need to swap it like we did to input xs.
self.target = T.tensor3("Ys")
ys = self.target.dimshuffle(1, 0, 2)
################
# hiddens hook #
################
# set an initial value for the recurrent hiddens from hook
if self.hiddens_hook is not None:
h_init = self.hiddens_hook[1]
self.hidden_size = self.hiddens_hook[0]
else:
# deal with h_init after parameters are made (have to make the same size as hiddens that are computed)
self.hidden_size = hidden_size
##################
# for generating #
##################
# symbolic scalar for how many recurrent steps to use during generation from the model
self.n_steps = T.iscalar("generate_n_steps")
####################################################
# parameters - make sure to deal with params_hook! #
####################################################
if self.params_hook is not None:
if not bidirectional:
(W_x_c, W_x_i, W_x_f, W_x_o,
U_h_c, U_h_i, U_h_f, U_h_o,
W_h_y, b_c, b_i, b_f, b_o,
b_y) = self.params_hook
recurrent_params = [U_h_c, U_h_i, U_h_f, U_h_o]
else:
(W_x_c, W_x_i, W_x_f, W_x_o,
U_h_c, U_h_i, U_h_f, U_h_o,
U_h_c_b, U_h_i_b, U_h_f_b, U_h_o_b,
W_h_y, b_c, b_i, b_f, b_o,
b_y) = self.params_hook
recurrent_params = [U_h_c, U_h_i, U_h_f, U_h_o, U_h_c_b, U_h_i_b, U_h_f_b, U_h_o_b]
# otherwise, construct our params
else:
# all input-to-hidden weights
W_x_c, W_x_i, W_x_f, W_x_o = [
get_weights(weights_init=weights_init,
shape=(self.input_size, self.hidden_size),
name="W_x_%s" % sub,
# if gaussian
mean=weights_mean,
std=weights_std,
# if uniform
interval=weights_interval)
for sub in ['c', 'i', 'f', 'o']
]
# all hidden-to-hidden weights
U_h_c, U_h_i, U_h_f, U_h_o = [
get_weights(weights_init=r_weights_init,
shape=(self.hidden_size, self.hidden_size),
name="U_h_%s" % sub,
# if gaussian
mean=r_weights_mean,
std=r_weights_std,
# if uniform
interval=r_weights_interval)
for sub in ['c', 'i', 'f', 'o']
]
# hidden-to-output weights
W_h_y = get_weights(weights_init=weights_init,
shape=(self.hidden_size, self.output_size),
name="W_h_y",
# if gaussian
mean=weights_mean,
std=weights_std,
# if uniform
interval=weights_interval)
# biases
b_c, b_i, b_f, b_o = [
get_bias(shape=(self.hidden_size,),
name="b_%s" % sub,
init_values=r_bias_init)
for sub in ['c', 'i', 'f', 'o']
]
# output bias
b_y = get_bias(shape=(self.output_size,),
name="b_y",
init_values=bias_init)
# clip gradients if we are doing that
recurrent_params = [U_h_c, U_h_i, U_h_f, U_h_o]
if clip_recurrent_grads:
clip = abs(clip_recurrent_grads)
U_h_c, U_h_i, U_h_f, U_h_o = [theano.gradient.grad_clip(p, -clip, clip) for p in recurrent_params]
# bidirectional params
if bidirectional:
# all hidden-to-hidden weights
U_h_c_b, U_h_i_b, U_h_f_b, U_h_o_b = [
get_weights(weights_init=r_weights_init,
shape=(self.hidden_size, self.hidden_size),
name="U_h_%s_b" % sub,
# if gaussian
mean=r_weights_mean,
std=r_weights_std,
# if uniform
interval=r_weights_interval)
for sub in ['c', 'i', 'f', 'o']
]
recurrent_params += [U_h_c_b, U_h_i_b, U_h_f_b, U_h_o_b]
if clip_recurrent_grads:
clip = abs(clip_recurrent_grads)
U_h_c_b, U_h_i_b, U_h_f_b, U_h_o_b = [theano.gradient.grad_clip(p, -clip, clip) for p in
[U_h_c_b, U_h_i_b, U_h_f_b, U_h_o_b]]
# put all the parameters into our list, and make sure it is in the same order as when we try to load
# them from a params_hook!!!
self.params = [W_x_c, W_x_i, W_x_f, W_x_o] + recurrent_params + [W_h_y, b_c, b_i, b_f, b_o, b_y]
# make h_init the right sized tensor
if not self.hiddens_hook:
h_init = T.zeros_like(T.dot(xs[0], W_x_c))
c_init = T.zeros_like(T.dot(xs[0], W_x_c))
###############
# computation #
###############
# move some computation outside of scan to speed it up!
x_c = T.dot(xs, W_x_c) + b_c
x_i = T.dot(xs, W_x_i) + b_i
x_f = T.dot(xs, W_x_f) + b_f
x_o = T.dot(xs, W_x_o) + b_o
# now do the recurrent stuff
(self.hiddens, _), self.updates = theano.scan(
fn=self.recurrent_step,
sequences=[x_c, x_i, x_f, x_o],
outputs_info=[h_init, c_init],
non_sequences=[U_h_c, U_h_i, U_h_f, U_h_o],
go_backwards=backward,
name="lstm_scan",
strict=True
)
# if bidirectional, do the same in reverse!
if bidirectional:
(hiddens_b, _), updates_b = theano.scan(
fn=self.recurrent_step,
sequences=[x_c, x_i, x_f, x_o],
outputs_info=[h_init, c_init],
non_sequences=[U_h_c_b, U_h_i_b, U_h_f_b, U_h_o_b],
go_backwards=not backward,
name="lstm_scan_back",
strict=True
)
# flip the hiddens to be the right direction
hiddens_b = hiddens_b[::-1]
# update stuff
self.updates.update(updates_b)
self.hiddens += hiddens_b
# add noise (like dropout) if we wanted it!
if noise:
self.hiddens = T.switch(self.noise_switch,
noise_func(input=self.hiddens),
self.hiddens)
# now compute the outputs from the leftover (top level) hiddens
self.output = activation_func(
T.dot(self.hiddens, W_h_y) + b_y
)
# now to define the cost of the model - use the cost function to compare our output with the target value.
self.cost = cost_function(output=self.output, target=ys, **cost_args)
log.info("Initialized an LSTM!")
def __init__(self, inputs=None,
noise='dropout', noise_level=0.5, noise_decay=False, noise_decay_amount=0.99,
mrg=RNG_MRG.MRG_RandomStreams(1), switch=True):
"""
Parameters
----------
inputs : tuple(shape, `Theano.TensorType`)
tuple(shape, `Theano.TensorType`) describing the inputs to use for this layer.
`shape` will be a monad tuple representing known sizes for each dimension in the `Theano.TensorType`.
The length of `shape` should be equal to number of dimensions in `Theano.TensorType`, where the shape
element is an integer representing the size for its dimension, or None if the shape isn't known.
For example, if you have a matrix with unknown batch size but fixed feature size of 784, `shape` would
be: (None, 784). The full form of `inputs` would be:
[((None, 784), <TensorType(float32, matrix)>)].
noise : str
What type of noise to use for the output. See opendeep.utils.noise
for options. This should be appropriate for the unit activation, i.e. Gaussian for tanh or other
real-valued activations, etc.
noise_level : float
The amount of noise to use for the noise function specified by `noise`. This could be the
standard deviation for gaussian noise, the interval for uniform noise, the dropout amount, etc.
noise_decay : str or False
Whether to use `noise` scheduling (decay `noise_level` during the course of training),
and if so, the string input specifies what type of decay to use. See opendeep.utils.decay for options.
Noise decay (known as noise scheduling) effectively helps the model learn larger variance features first,
and then smaller ones later (almost as a kind of curriculum learning). May help it converge faster.
noise_decay_amount : float
The amount to reduce the `noise_level` after each training epoch based on the decay function specified
in `noise_decay`.
mrg : random
A random number generator that is used when adding noise.
I recommend using Theano's sandbox.rng_mrg.MRG_RandomStreams.
switch : boolean
Whether to create a switch to turn noise on during training and off during testing (True). If False,
noise will be applied at both training and testing times.
"""
super(Noise, self).__init__(inputs=inputs,
noise=noise, noise_level=noise_level,
noise_decay=noise_decay, noise_decay_amount=noise_decay_amount,
mrg=mrg, switch=switch)
# self.inputs is a list from superclass initialization, grab the first element
self.output_size, self.inputs = self.inputs[0]
log.debug('Adding %s noise switch.' % str(noise))
if noise_level is not None:
noise_level = sharedX(value=noise_level)
noise_func = get_noise(noise, noise_level=noise_level, mrg=mrg)
else:
noise_func = get_noise(noise, mrg=mrg)
# apply the noise as a switch!
# default to apply noise. this is for the cost and gradient functions to be computed later
# (not sure if the above statement is accurate such that gradient depends on initial value of switch)
if switch:
self.noise_switch = sharedX(value=1, name="noise_switch")
# noise scheduling
if noise_decay and noise_level is not None:
self.noise_schedule = get_decay_function(noise_decay,
noise_level,
noise_level.get_value(),
noise_decay_amount)
# apply noise to the inputs!
if switch:
self.outputs = Tswitch(self.noise_switch,
noise_func(input=self.inputs),
self.inputs)
else:
self.outputs = noise_func(input=self.inputs)
def __init__(self,
dataset,
loss,
model=None,
epochs=10,
batch_size=100,
min_batch_size=1,
save_freq=None,
stop_threshold=None,
stop_patience=None,
learning_rate=.1,
lr_decay="exponential",
lr_decay_factor=.995,
momentum=0.5,
momentum_decay="linear",
momentum_factor=0,
nesterov_momentum=True,
grad_clip=None,
hard_clip=False):
"""
Initialize SGD.
Parameters
----------
dataset : Dataset
The :class:`opendeep.data.Dataset` to use when training the Model.
loss : Loss
The :class:`opendeep.optimization.loss.Loss` function to compare the model to a 'target' result.
model : Model
The :class:`opendeep.models.Model` to train. Needed if the Optimizer isn't being passed to a
Model's .train() method.
epochs : int
how many training iterations over the dataset to go.
batch_size : int
How many examples from the training dataset to use in parallel.
min_batch_size : int
The minimum number of examples required at a time (for things like time series, this would be > 1).
save_freq : int
How many epochs to train between each new save of the Model's parameters.
stop_threshold : float
The factor by how much the best validation training score needs to improve to determine early stopping.
stop_patience : int
The patience or number of epochs to wait after the stop_threshold has been reached before stopping.
learning_rate : float
The multiplicative amount to adjust parameters based on their gradient values.
lr_decay : str
The type of decay function to use for changing the learning rate over epochs. See
`opendeep.utils.decay` for options.
lr_decay_factor : float
The amount to use for the decay function when changing the learning rate over epochs. See
`opendeep.utils.decay` for its effect for given decay functions.
momentum : float
The momentum to use during gradient updates.
momentum_decay : str
The type of decay function to use for changing the momentum over epochs. See
`opendeep.utils.decay` for options.
momentum_factor : float
The amount to use for the decay function when changing the momentum over epochs. See
`opendeep.utils.decay` for its effect for given decay functions.
nesterov_momentum : bool
Whether or not to use Nesterov momentum.
grad_clip : float, optional
Whether to clip gradients. This will clip with a maximum of grad_clip or the parameter norm.
hard_clip : bool
Whether to use a hard cutoff or rescaling for clipping gradients.
"""
# superclass init
initial_parameters = locals().copy()
initial_parameters.pop('self')
super(SGD, self).__init__(**initial_parameters)
# Momentum - smoothing over the parameter changes (see Hinton)
if momentum:
self.momentum = sharedX(momentum, 'momentum')
if momentum_decay is not None and \
momentum_decay is not False and \
momentum_factor is not None:
self.momentum_decay = get_decay_function(
momentum_decay, self.momentum, self.momentum.get_value(),
momentum_factor)
else:
self.momentum_decay = False
else:
self.momentum = 0
self.momentum_decay = False
self.nesterov_momentum = nesterov_momentum
def __init__(self, inputs_hook=None, hiddens_hook=None, params_hook=None, outdir='outputs/gsn/',
input_size=None, hidden_size=1000,
layers=2, walkbacks=4,
visible_activation='sigmoid', hidden_activation='tanh',
input_sampling=True, mrg=RNG_MRG.MRG_RandomStreams(1),
tied_weights=True,
weights_init='uniform', weights_interval='montreal', weights_mean=0, weights_std=5e-3,
bias_init=0.0,
cost_function='binary_crossentropy', cost_args=None,
add_noise=True, noiseless_h1=True,
hidden_noise='gaussian', hidden_noise_level=2, input_noise='salt_and_pepper', input_noise_level=0.4,
noise_decay='exponential', noise_annealing=1,
image_width=None, image_height=None,
**kwargs):
"""
Initialize a GSN.
Parameters
----------
inputs_hook : Tuple of (shape, variable)
Routing information for the model to accept inputs from elsewhere. This is used for linking
different models together (e.g. setting the Softmax model's input layer to the DAE's hidden layer gives a
newly supervised classification model). For now, it needs to include the shape information (normally the
dimensionality of the input i.e. n_in).
hiddens_hook : Tuple of (shape, variable)
Routing information for the model to accept its hidden representation from elsewhere.
This is used for linking different models together (e.g. setting the DAE model's hidden layers to the RNN's
output layer gives a generative recurrent model.) For now, it needs to include the shape
information (normally the dimensionality of the hiddens i.e. n_hidden).
params_hook : List(theano shared variable)
A list of model parameters (shared theano variables) that you should use when constructing
this model (instead of initializing your own shared variables). This parameter is useful when you want to
have two versions of the model that use the same parameters - such as a training model with dropout applied
to layers and one without for testing, where the parameters are shared between the two.
outdir : str
The directory you want outputs (parameters, images, etc.) to save to. If None, nothing will
be saved.
input_size : int
The size (dimensionality) of the input to the DAE. If shape is provided in `inputs_hook`, this is optional.
The :class:`Model` requires an `output_size`, which gets set to this value because the DAE is an
unsupervised model. The output is a reconstruction of the input.
hidden_size : int
The size (dimensionality) of the hidden layer for the DAE. Generally, you want it to be larger than
`input_size`, which is known as *overcomplete*.
visible_activation : str or callable
The nonlinear (or linear) visible activation to perform after the dot product from hiddens -> visible layer.
This activation function should be appropriate for the input unit types, i.e. 'sigmoid' for binary inputs.
See opendeep.utils.activation for a list of available activation functions. Alternatively, you can pass
your own function to be used as long as it is callable.
hidden_activation : str or callable
The nonlinear (or linear) hidden activation to perform after the dot product from visible -> hiddens layer.
See opendeep.utils.activation for a list of available activation functions. Alternatively, you can pass
your own function to be used as long as it is callable.
layers : int
The number of hidden layers to use.
walkbacks : int
The number of walkbacks to perform (the variable K in Bengio's paper above). A walkback is a Gibbs sample
from the DAE, which means the model generates inputs in sequence, where each generated input is compared
to the original input to create the reconstruction cost for training. For running the model, the very last
generated input in the Gibbs chain is used as the output.
input_sampling : bool
During walkbacks, whether to sample from the generated input to create a new starting point for the next
walkback (next step in the Gibbs chain). This generally makes walkbacks more effective by making the
process more stochastic - more likely to find spurious modes in the model's representation.
mrg : random
A random number generator that is used when adding noise into the network and for sampling from the input.
I recommend using Theano's sandbox.rng_mrg.MRG_RandomStreams.
tied_weights : bool
DAE has two weight matrices - W from input -> hiddens and V from hiddens -> input. This boolean
determines if V = W.T, which 'ties' V to W and reduces the number of parameters necessary during training.
weights_init : str
Determines the method for initializing model weights. See opendeep.utils.nnet for options.
weights_interval : str or float
If Uniform `weights_init`, the +- interval to use. See opendeep.utils.nnet for options.
weights_mean : float
If Gaussian `weights_init`, the mean value to use.
weights_std : float
If Gaussian `weights_init`, the standard deviation to use.
bias_init : float
The initial value to use for the bias parameter. Most often, the default of 0.0 is preferred.
cost_function : str or callable
The function to use when calculating the reconstruction cost of the model. This should be appropriate
for the type of input, i.e. use 'binary_crossentropy' for binary inputs, or 'mse' for real-valued inputs.
See opendeep.utils.cost for options. You can also specify your own function, which needs to be callable.
cost_args : dict
Any additional named keyword arguments to pass to the specified `cost_function`.
add_noise : bool
Whether to add noise (corrupt) the input before passing it through the computation graph during training.
This should most likely be set to the default of True, because this is a *denoising* autoencoder after all.
noiseless_h1 : bool
Whether to not add noise (corrupt) the hidden layer during computation.
hidden_noise : str
What type of noise to use for corrupting the hidden layer (if not `noiseless_h1`). See opendeep.utils.noise
for options. This should be appropriate for the hidden unit activation, i.e. Gaussian for tanh or other
real-valued activations, etc.
hidden_noise_level : float
The amount of noise to use for the noise function specified by `hidden_noise`. This could be the
standard deviation for gaussian noise, the interval for uniform noise, the dropout amount, etc.
input_noise : str
What type of noise to use for corrupting the input before computation (if `add_noise`).
See opendeep.utils.noise for options. This should be appropriate for the input units, i.e. salt-and-pepper
for binary units, etc.
input_noise_level : float
The amount of noise used to corrupt the input. This could be the masking probability for salt-and-pepper,
standard deviation for Gaussian, interval for Uniform, etc.
noise_decay : str or False
Whether to use `input_noise` scheduling (decay `input_noise_level` during the course of training),
and if so, the string input specifies what type of decay to use. See opendeep.utils.decay for options.
Noise decay (known as noise scheduling) effectively helps the DAE learn larger variance features first,
and then smaller ones later (almost as a kind of curriculum learning). May help it converge faster.
noise_annealing : float
The amount to reduce the `input_noise_level` after each training epoch based on the decay function specified
in `noise_decay`.
image_width : int
If the input should be represented as an image, the width of the input image. If not specified, it will be
close to the square factor of the `input_size`.
image_height : int
If the input should be represented as an image, the height of the input image. If not specified, it will be
close to the square factor of the `input_size`.
"""
# init Model to combine the defaults and config dictionaries with the initial parameters.
initial_parameters = locals().copy()
initial_parameters.pop('self')
super(GSN, self).__init__(**initial_parameters)
self.input_size = input_size
# when the input should be thought of as an image, either use the specified width and height,
# or try to make as square as possible.
if image_height is None and image_width is None:
(_h, _w) = closest_to_square_factors(self.input_size)
self.image_width = _w
self.image_height = _h
else:
self.image_height = image_height
self.image_width = image_width
############################
# Theano variables and RNG #
############################
if self.inputs_hook is None:
self.X = T.matrix('X')
else:
# inputs_hook is a (shape, input) tuple
self.X = self.inputs_hook[1]
##########################
# Network specifications #
##########################
# generally, walkbacks should be at least 2*layers
if layers % 2 == 0:
if walkbacks < 2*layers:
log.warning('Not enough walkbacks for the layers! Layers is %s and walkbacks is %s. '
'Generaly want 2X walkbacks to layers',
str(layers), str(walkbacks))
else:
if walkbacks < 2*layers-1:
log.warning('Not enough walkbacks for the layers! Layers is %s and walkbacks is %s. '
'Generaly want 2X walkbacks to layers',
str(layers), str(walkbacks))
self.add_noise = add_noise
self.noise_annealing = as_floatX(noise_annealing) # noise schedule parameter
self.hidden_noise_level = sharedX(hidden_noise_level, dtype=theano.config.floatX)
self.hidden_noise = get_noise(name=hidden_noise, noise_level=self.hidden_noise_level, mrg=mrg)
self.input_noise_level = sharedX(input_noise_level, dtype=theano.config.floatX)
self.input_noise = get_noise(name=input_noise, noise_level=self.input_noise_level, mrg=mrg)
self.walkbacks = walkbacks
self.tied_weights = tied_weights
self.layers = layers
self.noiseless_h1 = noiseless_h1
self.input_sampling = input_sampling
self.noise_decay = noise_decay
# if there was a hiddens_hook, unpack the hidden layers in the tensor
if self.hiddens_hook is not None:
hidden_size = self.hiddens_hook[0]
self.hiddens_flag = True
else:
self.hiddens_flag = False
# determine the sizes of each layer in a list.
# layer sizes, from h0 to hK (h0 is the visible layer)
hidden_size = list(raise_to_list(hidden_size))
if len(hidden_size) == 1:
self.layer_sizes = [self.input_size] + hidden_size * self.layers
else:
assert len(hidden_size) == self.layers, "Hiddens sizes and number of hidden layers mismatch." + \
"Hiddens %d and layers %d" % (len(hidden_size), self.layers)
self.layer_sizes = [self.input_size] + hidden_size
if self.hiddens_hook is not None:
self.hiddens = self.unpack_hiddens(self.hiddens_hook[1])
#########################
# Activation functions! #
#########################
# hidden unit activation
self.hidden_activation = get_activation_function(hidden_activation)
# Visible layer activation
self.visible_activation = get_activation_function(visible_activation)
# make sure the sampling functions are appropriate for the activation functions.
if is_binary(self.visible_activation):
self.visible_sampling = mrg.binomial
else:
# TODO: implement non-binary activation
log.error("Non-binary visible activation not supported yet!")
raise NotImplementedError("Non-binary visible activation not supported yet!")
# Cost function
self.cost_function = get_cost_function(cost_function)
self.cost_args = cost_args or dict()
###############
# Parameters! #
###############
# make sure to deal with params_hook!
if self.params_hook is not None:
# if tied weights, expect layers*2 + 1 params
if self.tied_weights:
assert len(self.params_hook) == 2*layers + 1, \
"Tied weights: expected {0!s} params, found {1!s}!".format(2*layers+1, len(self.params_hook))
self.weights_list = self.params_hook[:layers]
self.bias_list = self.params_hook[layers:]
# if untied weights, expect layers*3 + 1 params
else:
assert len(self.params_hook) == 3*layers + 1, \
"Untied weights: expected {0!s} params, found {1!s}!".format(3*layers+1, len(self.params_hook))
self.weights_list = self.params_hook[:2*layers]
self.bias_list = self.params_hook[2*layers:]
# otherwise, construct our params
else:
# initialize a list of weights and biases based on layer_sizes for the GSN
self.weights_list = [get_weights(weights_init=weights_init,
shape=(self.layer_sizes[i], self.layer_sizes[i+1]),
name="W_{0!s}_{1!s}".format(i, i+1),
rng=mrg,
# if gaussian
mean=weights_mean,
std=weights_std,
# if uniform
interval=weights_interval)
for i in range(layers)]
# add more weights if we aren't tying weights between layers (need to add for higher-lower layers now)
if not tied_weights:
self.weights_list.extend(
[get_weights(weights_init=weights_init,
shape=(self.layer_sizes[i+1], self.layer_sizes[i]),
name="W_{0!s}_{1!s}".format(i+1, i),
rng=mrg,
# if gaussian
mean=weights_mean,
std=weights_std,
# if uniform
interval=weights_interval)
for i in reversed(range(layers))]
)
# initialize each layer bias to 0's.
self.bias_list = [get_bias(shape=(self.layer_sizes[i],),
name='b_' + str(i),
init_values=bias_init)
for i in range(layers+1)]
# build the params of the model into a list
self.params = self.weights_list + self.bias_list
log.debug("gsn params: %s", str(self.params))
# using the properties, build the computational graph
self.cost, self.monitors, self.output, self.hiddens = self.build_computation_graph()
