def __init__(self,
shared_value,
final_value,
epochs_to_saturation):
assert_is_instance(shared_value,
theano.tensor.sharedvar.SharedVariable)
assert_is_subdtype(shared_value.dtype, numpy.floating)
assert_equal(shared_value.ndim == 0, numpy.isscalar(final_value))
if numpy.isscalar(final_value):
assert_floating(final_value)
else:
assert_is_subdtype(final_value.dtype, numpy.floating)
assert_equal(final_value.shape,
shared_value.get_value().shape)
assert_integer(epochs_to_saturation)
assert_greater(epochs_to_saturation, 0)
self.shared_value = shared_value
cast = numpy.cast[shared_value.dtype]
self._final_value = cast(final_value)
self._epochs_to_saturation = epochs_to_saturation
self._num_epochs_seen = None
self._initial_value = None
def limit_param_norms(parameter_updater, param, max_norm, input_axes):
'''
Modifies the update of an SgdParameterUpdater to limit param L2 norms.
Parameter norms are computed by summing over the input_axes, provided.
These are so named because you typically want to sum over the axes
that get dotted with the input to the node (e.g. input_axes=[0] for Linear,
input_axes=[1, 2, 3] for Conv2D).
Parameters
----------
parameter_updater: simplelearn.training.ParameterUpdater
The parameter updater whose updates this will modify.
param: theano shared variable
The parameter being updated by parameter_updater.
(No way to get this from SgdParameterUpdater at present; it updates the
parameter and its velocity, and there's no way to safely distinguish them
in parameter_updates.update_pairs)
max_norm: floating-point scalar
The maximum L2 norm to be permitted for the parameters.
input_axes: Sequence
A Sequence of ints. The indices to sum over when computing the
L2 norm of the updated params.
'''
assert_is_instance(parameter_updater, ParameterUpdater)
assert_in(param, parameter_updater.update_pairs)
assert_floating(max_norm)
assert_greater(max_norm, 0.0)
assert_greater(len(input_axes), 0)
assert_all_integer(input_axes)
assert_all_greater_equal(input_axes, 0)
assert_all_less(input_axes, param.ndim)
input_axes = numpy.asarray(input_axes)
updated_param = parameter_updater.update_pairs[param]
norms = T.sqrt(T.sum(T.sqr(updated_param),
axis=input_axes,
keepdims=True))
desired_norms = T.clip(norms, 0, max_norm)
broadcast_mask = numpy.zeros(param.ndim, dtype=bool)
broadcast_mask[input_axes] = True
scales = T.patternbroadcast(desired_norms / (1e-7 + norms),
broadcast_mask)
parameter_updater.update_pairs[param] = updated_param * scales
def normal_distribution_init(rng, params, stddev):
'''
Fills params with values uniformly sampled from
[-init_range, init_range]
'''
assert_floating(stddev)
assert_greater_equal(stddev, 0)
values = params.get_value()
values[...] = rng.standard_normal(values.shape) * stddev
params.set_value(values)
def uniform_init(rng, params, init_range):
"""
Fills params with values uniformly sampled from
[-init_range, init_range]
"""
assert_floating(init_range)
assert_greater_equal(init_range, 0)
values = params.get_value()
values[...] = rng.uniform(low=-init_range, high=init_range, size=values.shape)
params.set_value(values)
def __init__(self,
shared_value,
final_scale,
epochs_to_saturation):
assert_is_instance(shared_value,
theano.tensor.sharedvar.SharedVariable)
assert_floating(final_scale)
assert_greater_equal(final_scale, 0.0)
assert_less_equal(final_scale, 1.0)
assert_integer(epochs_to_saturation)
assert_greater(epochs_to_saturation, 0)
self.shared_value = shared_value
self._final_scale = final_scale
self._epochs_to_saturation = epochs_to_saturation
# initialized in on_start_training()
self._initial_value = None
self._num_epochs_seen = None
