class Momentum(MinibatchGradientDescent):
"""
Momentum algorithm.
Parameters
----------
momentum : float
Control previous gradient ratio. Defaults to ``0.9``.
nesterov : bool
Instead of classic momentum computes Nesterov momentum.
Defaults to ``False``.
{MinibatchGradientDescent.Parameters}
Attributes
----------
{MinibatchGradientDescent.Attributes}
Methods
-------
{MinibatchGradientDescent.Methods}
Examples
--------
>>> import numpy as np
>>> from neupy import algorithms
>>>
>>> x_train = np.array([[1, 2], [3, 4]])
>>> y_train = np.array([[1], [0]])
>>>
>>> mnet = algorithms.Momentum((2, 3, 1))
>>> mnet.train(x_train, y_train)
See Also
--------
:network:`GradientDescent` : GradientDescent algorithm.
"""
momentum = ProperFractionProperty(default=0.9)
nesterov = Property(default=False, expected_type=bool)
def init_param_updates(self, layer, parameter):
step = self.variables.step
parameter_shape = parameter.get_value().shape
previous_velocity = theano.shared(
name="{}/previous-velocity".format(parameter.name),
value=asfloat(np.zeros(parameter_shape)),
)
gradient = T.grad(self.variables.error_func, wrt=parameter)
velocity = self.momentum * previous_velocity - step * gradient
if self.nesterov:
velocity = self.momentum * velocity - step * gradient
return [
(parameter, parameter + velocity),
(previous_velocity, velocity),
]
class Momentum(GradientDescent):
"""
Momentum algorithm.
Parameters
----------
momentum : float
Control previous gradient ratio. Defaults to ``0.9``.
nesterov : bool
Instead of classic momentum computes Nesterov momentum.
Defaults to ``False``.
{GradientDescent.Parameters}
Attributes
----------
{GradientDescent.Attributes}
Methods
-------
{GradientDescent.Methods}
Examples
--------
>>> import numpy as np
>>> from neupy import algorithms
>>> from neupy.layers import *
>>>
>>> x_train = np.array([[1, 2], [3, 4]])
>>> y_train = np.array([[1], [0]])
>>>
>>> network = Input(2) >> Sigmoid(3) >> Sigmoid(1)
>>> optimizer = algorithms.Momentum(network)
>>> optimizer.train(x_train, y_train)
See Also
--------
:network:`GradientDescent` : GradientDescent algorithm.
"""
momentum = ProperFractionProperty(default=0.9)
nesterov = Property(default=False, expected_type=bool)
def init_train_updates(self):
optimizer = tf.train.MomentumOptimizer(
use_nesterov=self.nesterov,
momentum=self.momentum,
learning_rate=self.step,
)
self.functions.optimizer = optimizer
return [optimizer.minimize(self.variables.loss)]
class GlobalPooling(BaseLayer):
"""
Global pooling layer.
Parameters
----------
function : callable
Function that aggregates over dimensions.
Defaults to ``theano.tensor.mean``.
.. code-block:: python
def agg_func(x, axis=None):
pass
{BaseLayer.Parameters}
Methods
-------
{BaseLayer.Methods}
Attributes
----------
{BaseLayer.Attributes}
Examples
--------
>>> from neupy import layers
>>>
>>> network = layers.join(
... layers.Input((16, 4, 4)),
... layers.GlobalPooling(),
... )
>>> network.output_shape
(16,)
"""
function = Property(default=T.mean)
@property
def output_shape(self):
if self.input_shape is not None:
return as_tuple(self.input_shape[0])
def output(self, input_value):
if input_value.ndim in (1, 2):
return input_value
agg_axis = range(2, input_value.ndim)
return self.function(input_value, axis=list(agg_axis))
class BaseRNNLayer(BaseLayer):
"""
Base class for the recurrent layers
Parameters
----------
n_units : int
Number of hidden units in the layer.
only_return_final : bool
If ``True``, only return the final sequential output
(e.g. for tasks where a single target value for the entire
sequence is desired). In this case, Tensorfow makes an
optimization which saves memory. Defaults to ``True``.
{BaseLayer.name}
"""
n_units = IntProperty(minval=1)
only_return_final = Property(expected_type=bool)
def __init__(self, n_units, only_return_final=True, name=None):
super(BaseRNNLayer, self).__init__(name=name)
self.only_return_final = only_return_final
self.n_units = n_units
def fail_if_shape_invalid(self, input_shape):
if input_shape and input_shape.ndims != 3:
clsname = self.__class__.__name__
raise LayerConnectionError(
"{} layer was expected input with three dimensions, "
"but got input with {} dimensions instead. Layer: {}"
"".format(clsname, input_shape.ndims, self))
def get_output_shape(self, input_shape):
input_shape = tf.TensorShape(input_shape)
n_samples = input_shape[0]
self.fail_if_shape_invalid(input_shape)
if self.only_return_final:
return tf.TensorShape((n_samples, self.n_units))
n_time_steps = input_shape[1]
return tf.TensorShape((n_samples, n_time_steps, self.n_units))
class BaseRNNLayer(BaseLayer):
"""
Base class for the recurrent layers
Parameters
----------
size : int
Number of hidden units in the layer.
only_return_final : bool
If ``True``, only return the final sequential output
(e.g. for tasks where a single target value for the entire
sequence is desired). In this case, Theano makes an
optimization which saves memory. Defaults to ``True``.
{BaseLayer.Parameters}
"""
size = IntProperty(minval=1)
only_return_final = Property(default=True, expected_type=bool)
def __init__(self, size, **kwargs):
super(BaseRNNLayer, self).__init__(size=size, **kwargs)
def validate(self, input_shape):
n_input_dims = len(input_shape) + 1 # +1 for batch dimension
clsname = self.__class__.__name__
if n_input_dims < 3:
raise LayerConnectionError(
"{} layer was expected input with at least three "
"dimensions, got input with {} dimensions instead"
"".format(clsname, n_input_dims))
@property
def output_shape(self):
if self.only_return_final:
return as_tuple(self.size)
n_time_steps = self.input_shape[0]
return as_tuple(n_time_steps, self.size)
class A(Configurable):
int_property = Property(expected_type=int)
class BaseNetwork(BaseSkeleton):
"""
Base class for Neural Network algorithms.
Parameters
----------
step : float
Learning rate, defaults to ``0.1``.
show_epoch : int or str
This property controls how often the network will
display information about training. There are two
main syntaxes for this property.
- You can define it as a positive integer number. It
defines how offen would you like to see summary
output in terminal. For instance, number `100` mean
that network shows summary at 100th, 200th,
300th ... epochs.
- String defines number of times you want to see output in
terminal. For instance, value ``'2 times'`` mean that
the network will show output twice with approximately
equal period of epochs and one additional output would
be after the finall epoch.
Defaults to ``1``.
shuffle_data : bool
If it's ``True`` class shuffles all your training data before
training your network, defaults to ``True``.
epoch_end_signal : function
Calls this function when train epoch finishes.
train_end_signal : function
Calls this function when train process finishes.
{Verbose.Parameters}
Attributes
----------
errors : ErrorHistoryList
Contains list of training errors. This object has the same
properties as list and in addition there are three additional
useful methods: `last`, `previous` and `normalized`.
train_errors : ErrorHistoryList
Alias to the ``errors`` attribute.
validation_errors : ErrorHistoryList
The same as `errors` attribute, but it contains only validation
errors.
last_epoch : int
Value equals to the last trained epoch. After initialization
it is equal to ``0``.
"""
step = NumberProperty(default=0.1, minval=0)
show_epoch = ShowEpochProperty(minval=1, default=1)
shuffle_data = Property(default=False, expected_type=bool)
epoch_end_signal = Property(expected_type=types.FunctionType)
train_end_signal = Property(expected_type=types.FunctionType)
def __init__(self, *args, **options):
self.errors = self.train_errors = ErrorHistoryList()
self.validation_errors = ErrorHistoryList()
self.training = AttributeKeyDict()
self.last_epoch = 0
super(BaseNetwork, self).__init__(*args, **options)
if self.verbose:
show_network_options(self, highlight_options=options)
def predict(self, input_data):
"""
Return prediction results for the input data.
Parameters
----------
input_data : array-like
Returns
-------
array-like
"""
raise NotImplementedError
def on_epoch_start_update(self, epoch):
"""
Function would be trigger before run all training procedure
related to the current epoch.
Parameters
----------
epoch : int
Current epoch number.
"""
self.last_epoch = epoch
def train_epoch(self, input_train, target_train=None):
raise NotImplementedError()
def prediction_error(self, input_test, target_test):
raise NotImplementedError()
def train(self, input_train, target_train=None, input_test=None,
target_test=None, epochs=100, epsilon=None,
summary='table'):
"""
Method train neural network.
Parameters
----------
input_train : array-like
target_train : array-like or None
input_test : array-like or None
target_test : array-like or None
epochs : int
Defaults to `100`.
epsilon : float or None
Defaults to ``None``.
"""
show_epoch = self.show_epoch
logs = self.logs
training = self.training = AttributeKeyDict()
if epochs <= 0:
raise ValueError("Number of epochs needs to be greater than 0.")
if epsilon is not None and epochs <= 2:
raise ValueError("Network should train at teast 3 epochs before "
"check the difference between errors")
logging_info_about_the_data(self, input_train, input_test)
logging_info_about_training(self, epochs, epsilon)
logs.newline()
if summary == 'table':
summary = SummaryTable(
table_builder=table.TableBuilder(
table.Column(name="Epoch #"),
table.NumberColumn(name="Train err", places=4),
table.NumberColumn(name="Valid err", places=4),
table.TimeColumn(name="Time", width=10),
stdout=logs.write
),
network=self,
delay_limit=1.,
delay_history_length=10,
)
elif summary == 'inline':
summary = InlineSummary(network=self)
else:
raise ValueError("`{}` is unknown summary type"
"".format(summary))
iterepochs = create_training_epochs_iterator(self, epochs, epsilon)
show_epoch = parse_show_epoch_property(self, epochs, epsilon)
training.show_epoch = show_epoch
# Storring attributes and methods in local variables we prevent
# useless __getattr__ call a lot of times in each loop.
# This variables speed up loop in case on huge amount of
# iterations.
training_errors = self.errors
validation_errors = self.validation_errors
shuffle_data = self.shuffle_data
train_epoch = self.train_epoch
epoch_end_signal = self.epoch_end_signal
train_end_signal = self.train_end_signal
on_epoch_start_update = self.on_epoch_start_update
is_first_iteration = True
can_compute_validation_error = (input_test is not None)
last_epoch_shown = 0
#############################################
symMatrix = tt.dmatrix("symMatrix")
symEigenvalues, eigenvectors = tt.nlinalg.eig(symMatrix)
get_Eigen = theano.function([symMatrix], [symEigenvalues, eigenvectors])
#############################################
with logs.disable_user_input():
for epoch in iterepochs:
validation_error = None
epoch_start_time = time.time()
on_epoch_start_update(epoch)
if shuffle_data:
data = shuffle(*as_tuple(input_train, target_train))
input_train, target_train = data[:-1], data[-1]
try:
train_error = train_epoch(input_train, target_train)
print epoch
name=str(self)
if(name.split('(')[0]=='Hessian'):
H=self.variables.hessian.get_value()
ev,_=get_Eigen(H)
print "positive EV ",np.sum(ev>0)
print "Just zero EV", np.sum(ev==0)
print "Zero EV ", np.sum(ev==0)+np.sum((ev < 0) & (ev > (np.min(ev)/2.0)))
print "Neg EV ", np.sum(ev<0)
print "Max EV ",np.max(ev)
print "Min EV ",np.min(ev)
s=str(self.itr)+'.npy'
np.save(s,ev)
if can_compute_validation_error:
validation_error = self.prediction_error(input_test,
target_test)
training_errors.append(train_error)
validation_errors.append(validation_error)
epoch_finish_time = time.time()
training.epoch_time = epoch_finish_time - epoch_start_time
if epoch % training.show_epoch == 0 or is_first_iteration:
summary.show_last()
last_epoch_shown = epoch
if epoch_end_signal is not None:
epoch_end_signal(self)
is_first_iteration = False
except StopTraining as err:
# TODO: This notification breaks table view in terminal.
# I need to show it in a different way.
logs.message("TRAIN", "Epoch #{} stopped. {}"
"".format(epoch, str(err)))
break
if epoch != last_epoch_shown:
summary.show_last()
if train_end_signal is not None:
train_end_signal(self)
summary.finish()
logs.newline()
class D(A):
property_d = Property()
class A(object):
# Doesn't have Configurable as a parent class
property_a = Property()
class BaseNetwork(BaseSkeleton):
"""
Base class for Neural Network algorithms.
Parameters
----------
step : float
Learning rate, defaults to ``0.1``.
show_epoch : int
This property controls how often the network will display
information about training. It has to be defined as positive
integer. For instance, number ``100`` mean that network shows
summary at 1st, 100th, 200th, 300th ... and last epochs.
Defaults to ``1``.
shuffle_data : bool
If it's ``True`` than training data will be shuffled before
the training. Defaults to ``True``.
signals : dict, list or function
Function that will be triggered after certain events during
the training.
{Verbose.Parameters}
Methods
-------
{BaseSkeleton.fit}
predict(X)
Propagates input ``X`` through the network and
returns produced output.
plot_errors(logx=False, show=True, **figkwargs)
Using errors collected during the training this method
generates plot that can give additional insight into the
performance reached during the training.
Attributes
----------
errors : list
Information about errors. It has two main attributes, namely
``train`` and ``valid``. These attributes provide access to
the training and validation errors respectively.
last_epoch : int
Value equals to the last trained epoch. After initialization
it is equal to ``0``.
n_updates_made : int
Number of training updates applied to the network.
"""
step = NumberProperty(default=0.1, minval=0)
show_epoch = IntProperty(minval=1, default=1)
shuffle_data = Property(default=False, expected_type=bool)
signals = Property(expected_type=object)
def __init__(self, *args, **options):
super(BaseNetwork, self).__init__(*args, **options)
self.last_epoch = 0
self.n_updates_made = 0
self.errors = base_signals.ErrorCollector()
signals = list(
as_tuple(
base_signals.ProgressbarSignal(),
base_signals.PrintLastErrorSignal(),
self.errors,
self.signals,
))
for i, signal in enumerate(signals):
if inspect.isfunction(signal):
signals[i] = base_signals.EpochEndSignal(signal)
elif inspect.isclass(signal):
signals[i] = signal()
self.events = Events(network=self, signals=signals)
def one_training_update(self, X_train, y_train=None):
"""
Function would be trigger before run all training procedure
related to the current epoch.
Parameters
----------
epoch : int
Current epoch number.
"""
raise NotImplementedError()
def score(self, X, y):
raise NotImplementedError()
def plot_errors(self, logx=False, show=True, **figkwargs):
return plot_optimizer_errors(optimizer=self,
logx=logx,
show=show,
**figkwargs)
def train(self,
X_train,
y_train=None,
X_test=None,
y_test=None,
epochs=100,
batch_size=None):
"""
Method train neural network.
Parameters
----------
X_train : array-like
y_train : array-like or None
X_test : array-like or None
y_test : array-like or None
epochs : int
Defaults to ``100``.
epsilon : float or None
Defaults to ``None``.
"""
if epochs <= 0:
raise ValueError("Number of epochs needs to be a positive number")
epochs = int(epochs)
first_epoch = self.last_epoch + 1
batch_size = batch_size or getattr(self, 'batch_size', None)
self.events.trigger(
name='train_start',
X_train=X_train,
y_train=y_train,
epochs=epochs,
batch_size=batch_size,
store_data=False,
)
try:
for epoch in range(first_epoch, first_epoch + epochs):
self.events.trigger('epoch_start')
self.last_epoch = epoch
iterator = iters.minibatches(
(X_train, y_train),
batch_size,
self.shuffle_data,
)
for X_batch, y_batch in iterator:
self.events.trigger('update_start')
update_start_time = time.time()
train_error = self.one_training_update(X_batch, y_batch)
self.n_updates_made += 1
self.events.trigger(
name='train_error',
value=train_error,
eta=time.time() - update_start_time,
epoch=epoch,
n_updates=self.n_updates_made,
n_samples=iters.count_samples(X_batch),
store_data=True,
)
self.events.trigger('update_end')
if X_test is not None:
test_start_time = time.time()
validation_error = self.score(X_test, y_test)
self.events.trigger(
name='valid_error',
value=validation_error,
eta=time.time() - test_start_time,
epoch=epoch,
n_updates=self.n_updates_made,
n_samples=iters.count_samples(X_test),
store_data=True,
)
self.events.trigger('epoch_end')
except StopTraining as err:
self.logs.message(
"TRAIN",
"Epoch #{} was stopped. Message: {}".format(epoch, str(err)))
self.events.trigger('train_end')
class B(A):
property_b = Property()
def test_property_repr_with_name(self):
prop = Property(default=3)
prop.name = 'test'
self.assertEqual('Property(name="test")', repr(prop))
class DiscreteHopfieldNetwork(DiscreteMemory):
"""
Discrete Hopfield Network. It can memorize binary samples
and reconstruct them from corrupted samples.
Notes
-----
- Works only with binary data. Input matrix should
contain only zeros and ones.
Parameters
----------
{DiscreteMemory.mode}
{DiscreteMemory.n_times}
check_limit : bool
Option enable a limit of patterns control for the
network using logarithmically proportion rule.
Defaults to ``True``.
.. math::
\\frac{{n_{{features}}}}{{2 \\cdot log_{{e}}(n_{{features}})}}
Methods
-------
energy(input_data)
Compute Discrete Hopfield Energy.
train(input_data)
Save input data pattern into the network memory.
predict(input_data, n_times=None)
Recover data from the memory using input pattern.
For the prediction procedure you can control number
of iterations. If you set up this value equal to ``None``
then the value would be equal to the value that you
set up for the property with the same name - ``n_times``.
Examples
--------
>>> import numpy as np
>>> from neupy import algorithms
>>>
>>> def draw_bin_image(image_matrix):
... for row in image_matrix.tolist():
... print('| ' + ' '.join(' *'[val] for val in row))
...
>>> zero = np.matrix([
... 0, 1, 1, 1, 0,
... 1, 0, 0, 0, 1,
... 1, 0, 0, 0, 1,
... 1, 0, 0, 0, 1,
... 1, 0, 0, 0, 1,
... 0, 1, 1, 1, 0
... ])
>>>
>>> one = np.matrix([
... 0, 1, 1, 0, 0,
... 0, 0, 1, 0, 0,
... 0, 0, 1, 0, 0,
... 0, 0, 1, 0, 0,
... 0, 0, 1, 0, 0,
... 0, 0, 1, 0, 0
... ])
>>>
>>> two = np.matrix([
... 1, 1, 1, 0, 0,
... 0, 0, 0, 1, 0,
... 0, 0, 0, 1, 0,
... 0, 1, 1, 0, 0,
... 1, 0, 0, 0, 0,
... 1, 1, 1, 1, 1,
... ])
>>>
>>> half_zero = np.matrix([
... 0, 1, 1, 1, 0,
... 1, 0, 0, 0, 1,
... 1, 0, 0, 0, 1,
... 0, 0, 0, 0, 0,
... 0, 0, 0, 0, 0,
... 0, 0, 0, 0, 0,
... ])
>>>
>>> draw_bin_image(zero.reshape((6, 5)))
| * * *
| * *
| * *
| * *
| * *
| * * *
>>> draw_bin_image(half_zero.reshape((6, 5)))
| * * *
| * *
| * *
|
|
|
>>> data = np.concatenate([zero, one, two], axis=0)
>>>
>>> dhnet = algorithms.DiscreteHopfieldNetwork()
>>> dhnet.train(data)
>>>
>>> result = dhnet.predict(half_zero)
>>> draw_bin_image(result.reshape((6, 5)))
| * * *
| * *
| * *
| * *
| * *
| * * *
See Also
--------
:ref:`password-recovery`: Password recovery with Discrete Hopfield Network.
:ref:`discrete-hopfield-network`: Discrete Hopfield Network article.
"""
check_limit = Property(default=True, expected_type=bool)
def __init__(self, **options):
super(DiscreteHopfieldNetwork, self).__init__(**options)
self.n_memorized_samples = 0
def train(self, input_data):
self.discrete_validation(input_data)
input_data = bin2sign(input_data)
input_data = format_data(input_data, is_feature1d=False)
n_rows, n_features = input_data.shape
n_rows_after_update = self.n_memorized_samples + n_rows
if self.check_limit:
memory_limit = math.ceil(n_features / (2 * math.log(n_features)))
if n_rows_after_update > memory_limit:
raise ValueError("You can't memorize more than {0} "
"samples".format(memory_limit))
weight_shape = (n_features, n_features)
if self.weight is None:
self.weight = np.zeros(weight_shape, dtype=int)
if self.weight.shape != weight_shape:
n_features_expected = self.weight.shape[1]
raise ValueError("Input data has invalid number of features. "
"Got {} features instead of {}."
"".format(n_features, n_features_expected))
self.weight = input_data.T.dot(input_data)
np.fill_diagonal(self.weight, np.zeros(len(self.weight)))
self.n_memorized_samples = n_rows_after_update
def predict(self, input_data, n_times=None):
self.discrete_validation(input_data)
input_data = format_data(bin2sign(input_data), is_feature1d=False)
if self.mode == 'async':
if n_times is None:
n_times = self.n_times
_, n_features = input_data.shape
output_data = input_data
for _ in range(n_times):
position = np.random.randint(0, n_features - 1)
raw_new_value = output_data.dot(self.weight[:, position])
output_data[:, position] = np.sign(raw_new_value)
else:
output_data = input_data.dot(self.weight)
return step_function(output_data).astype(int)
def energy(self, input_data):
self.discrete_validation(input_data)
input_data = bin2sign(input_data)
input_data = format_data(input_data, is_feature1d=False)
n_rows, n_features = input_data.shape
if n_rows == 1:
return hopfield_energy(self.weight, input_data, input_data)
output = np.zeros(n_rows)
for i, row in enumerate(input_data):
output[i] = hopfield_energy(self.weight, row, row)
return output
def test_property_get_method(self):
prop = Property(default=3)
self.assertEqual(None, prop.__get__(None, None))
def test_property_repr(self):
prop = Property(default=3)
self.assertEqual('Property()', repr(prop))
class A(Configurable):
required_prop = Property(required=True)
class B(Configurable):
int_property = Property(expected_type=(str, set))
class A(Configurable):
prop = Property(default=3)
class BaseLayer(BaseConnection, Configurable):
"""
Base class for all layers.
Parameters
----------
name : str or None
Layer's identifier. If name is equal to ``None`` than name
will be generated automatically. Defaults to ``None``.
Methods
-------
disable_training_state()
Swith off trainig state.
initialize()
Set up important configurations related to the layer.
Attributes
----------
input_shape : tuple
Layer's input shape.
output_shape : tuple
Layer's output shape.
training_state : bool
Defines whether layer in training state or not.
parameters : dict
Trainable parameters.
graph : LayerGraph instance
Graphs that stores all relations between layers.
"""
name = Property(expected_type=six.string_types)
# Stores global identifier index for each layer class
global_identifiers_map = {}
def __new__(cls, *args, **kwargs):
if cls not in cls.global_identifiers_map:
cls.global_identifiers_map[cls] = 1
return super(BaseLayer, cls).__new__(cls)
def __init__(self, *args, **options):
super(BaseLayer, self).__init__(*args)
self.updates = []
self.parameters = OrderedDict()
self.name = generate_layer_name(layer=self)
self.input_shape_ = None
self.graph.add_layer(self)
Configurable.__init__(self, **options)
def validate(self, input_shape):
"""
Validate input shape value before assigning it.
Parameters
----------
input_shape : tuple with int
"""
@property
def input_shape(self):
return self.input_shape_
@input_shape.setter
def input_shape(self, shape):
self.validate(shape)
self.input_shape_ = shape
@property
def output_shape(self):
return self.input_shape
def output(self, input_value):
return input_value
def add_parameter(self, value, name, shape=None, trainable=True):
theano_name = 'layer:{layer_name}/{parameter_name}'.format(
layer_name=self.name,
parameter_name=name.replace('_', '-'))
parameter = create_shared_parameter(value, theano_name, shape)
parameter.trainable = trainable
self.parameters[name] = parameter
setattr(self, name, parameter)
return parameter
def __repr__(self):
classname = self.__class__.__name__
return '{name}()'.format(name=classname)
class LSTM(BaseRNNLayer):
"""
Long Short Term Memory (LSTM) Layer.
Parameters
----------
{BaseRNNLayer.size}
input_weights : Initializer, ndarray
Weight parameters for input connection.
Defaults to :class:`HeNormal() <neupy.init.HeNormal>`.
hidden_weights : Initializer, ndarray
Weight parameters for hidden connection.
Defaults to :class:`HeNormal() <neupy.init.HeNormal>`.
cell_weights : Initializer, ndarray
Weight parameters for cell connection. Require only when
``peepholes=True`` otherwise it will be ignored.
Defaults to :class:`HeNormal() <neupy.init.HeNormal>`.
bias : Initializer, ndarray
Bias parameters for all gates.
Defaults to :class:`Constant(0) <neupy.init.Constant>`.
activation_functions : dict, callable
Activation functions for different gates. Defaults to:
.. code-block:: python
# import tensorflow as tf
dict(
ingate=tf.nn.sigmoid,
forgetgate=tf.nn.sigmoid,
outgate=tf.nn.sigmoid,
cell=tf.tanh,
)
If application requires modification to only one parameter
then it's better to specify the one that you need to modify
and ignore other parameters
.. code-block:: python
dict(ingate=tf.tanh)
Other parameters like ``forgetgate`` or ``outgate`` will be
equal to their default values.
learn_init : bool
If ``True``, make ``cell_init`` and ``hidden_init`` trainable
variables. Defaults to ``False``.
cell_init : array-like, Tensorfow variable, scalar or Initializer
Initializer for initial cell state (:math:`c_0`).
Defaults to :class:`Constant(0) <neupy.init.Constant>`.
hidden_init : array-like, Tensorfow variable, scalar or Initializer
Initializer for initial hidden state (:math:`h_0`).
Defaults to :class:`Constant(0) <neupy.init.Constant>`.
backwards : bool
If ``True``, process the sequence backwards and then reverse the
output again such that the output from the layer is always
from :math:`x_1` to :math:`x_n`. Defaults to ``False``
{BaseRNNLayer.only_return_final}
peepholes : bool
If ``True``, the LSTM uses peephole connections.
When ``False``, cell parameters are ignored.
Defaults to ``False``.
unroll_scan : bool
If ``True`` the recursion is unrolled instead of using scan.
For some graphs this gives a significant speed up but it
might also consume more memory. When ``unroll_scan=True``,
backpropagation always includes the full sequence, so
``n_gradient_steps`` must be set to ``-1`` and the input
sequence length must be known at compile time (i.e.,
cannot be given as ``None``). Defaults to ``False``.
gradient_clipping : float or int
If nonzero, the gradient messages are clipped to the
given value during the backward pass. Defaults to ``0``.
{BaseLayer.Parameters}
Notes
-----
Code was adapted from the
`Lasagne <https://github.com/Lasagne/Lasagne>`_ library.
Examples
--------
Sequence classification
.. code-block:: python
from neupy import layers, algorithms
n_time_steps = 40
n_categories = 20
embedded_size = 10
network = algorithms.RMSProp(
[
layers.Input(n_time_steps),
layers.Embedding(n_categories, embedded_size),
layers.LSTM(20),
layers.Sigmoid(1),
]
)
"""
input_weights = ParameterProperty(default=init.HeNormal())
hidden_weights = ParameterProperty(default=init.HeNormal())
cell_weights = ParameterProperty(default=init.HeNormal())
biases = ParameterProperty(default=init.Constant(0))
activation_functions = MultiCallableProperty(default=dict(
ingate=tf.nn.sigmoid,
forgetgate=tf.nn.sigmoid,
outgate=tf.nn.sigmoid,
cell=tf.tanh,
))
learn_init = Property(default=False, expected_type=bool)
cell_init = ParameterProperty(default=init.Constant(0))
hidden_init = ParameterProperty(default=init.Constant(0))
unroll_scan = Property(default=False, expected_type=bool)
backwards = Property(default=False, expected_type=bool)
peepholes = Property(default=False, expected_type=bool)
gradient_clipping = NumberProperty(default=0, minval=0)
def initialize(self):
super(LSTM, self).initialize()
n_inputs = np.prod(self.input_shape[1:])
# If peephole (cell to gate) connections were enabled, initialize
# peephole connections. These are elementwise products with the cell
# state, so they are represented as vectors.
if self.peepholes:
self.weight_cell_to_ingate = self.add_parameter(
value=self.cell_weights,
name='weight_cell_to_ingate',
shape=(self.size, ))
self.weight_cell_to_forgetgate = self.add_parameter(
value=self.cell_weights,
name='weight_cell_to_forgetgate',
shape=(self.size, ))
self.weight_cell_to_outgate = self.add_parameter(
value=self.cell_weights,
name='weight_cell_to_outgate',
shape=(self.size, ))
self.input_weights = self.add_parameter(
value=self.input_weights,
name='input_weights',
shape=(n_inputs, 4 * self.size),
)
self.hidden_weights = self.add_parameter(
value=self.hidden_weights,
name='hidden_weights',
shape=(self.size, 4 * self.size),
)
self.biases = self.add_parameter(
value=self.biases,
name='biases',
shape=(4 * self.size, ),
)
# Initialization parameters
self.add_parameter(
value=self.cell_init,
shape=(1, self.size),
name="cell_init",
trainable=self.learn_init,
)
self.add_parameter(
value=self.hidden_init,
shape=(1, self.size),
name="hidden_init",
trainable=self.learn_init,
)
def output(self, input_value):
# Because scan iterates over the first dimension we
# dimshuffle to (n_time_steps, n_batch, n_features)
input_value = tf.transpose(input_value, [1, 0, 2])
input_shape = tf.shape(input_value)
n_batch = input_shape[1]
def one_lstm_step(states, input_n):
with tf.name_scope('lstm-cell'):
cell_previous, hid_previous = states
input_n = tf.matmul(input_n, self.input_weights) + self.biases
# Calculate gates pre-activations and slice
gates = input_n + tf.matmul(hid_previous, self.hidden_weights)
# Clip gradients
if self.gradient_clipping != 0:
gates = clip_gradient(gates, self.gradient_clipping)
# Extract the pre-activation gate values
ingate, forgetgate, cell_input, outgate = tf.split(gates,
4,
axis=1)
if self.peepholes:
# Compute peephole connections
ingate += cell_previous * self.weight_cell_to_ingate
forgetgate += (cell_previous *
self.weight_cell_to_forgetgate)
# Apply nonlinearities
ingate = self.activation_functions.ingate(ingate)
forgetgate = self.activation_functions.forgetgate(forgetgate)
cell_input = self.activation_functions.cell(cell_input)
# Compute new cell value
cell = forgetgate * cell_previous + ingate * cell_input
if self.peepholes:
outgate += cell * self.weight_cell_to_outgate
outgate = self.activation_functions.outgate(outgate)
# Compute new hidden unit activation
hid = outgate * tf.tanh(cell)
return [cell, hid]
cell_init = tf.tile(self.cell_init, (n_batch, 1))
hidden_init = tf.tile(self.hidden_init, (n_batch, 1))
sequence = input_value
if self.backwards:
sequence = tf.reverse(sequence, axis=[0])
if self.unroll_scan:
# Explicitly unroll the recurrence instead of using scan
hid_out = unroll_scan(
fn=one_lstm_step,
sequence=sequence,
outputs_info=[cell_init, hidden_init],
)
else:
_, hid_out = tf.scan(
fn=one_lstm_step,
elems=input_value,
initializer=[cell_init, hidden_init],
name='lstm-scan',
)
# When it is requested that we only return the final sequence step,
# we need to slice it out immediately after scan is applied
if self.only_return_final:
return hid_out[-1]
# if scan is backward reverse the output
if self.backwards:
hid_out = tf.reverse(hid_out, axis=[0])
# dimshuffle back to (n_batch, n_time_steps, n_features))
hid_out = tf.transpose(hid_out, [1, 0, 2])
return hid_out
class LVQ(BaseNetwork):
"""
Learning Vector Quantization (LVQ) algorithm.
Notes
-----
- Input data needs to be normalized, because LVQ uses
Euclidian distance to find clusters.
- Training error is just a ratio of miscassified
samples
Parameters
----------
n_inputs : int
Number of input units. It should be equal to the
number of features in the input data set.
n_subclasses : int, None
Defines total number of subclasses. Values should be greater
or equal to the number of classes. ``None`` will set up number
of subclasses equal to the number of classes. Defaults to ``None``
(or the same as ``n_classes``).
n_classes : int
Number of classes in the data set.
prototypes_per_class : list, None
Defines number of prototypes per each class. For instance,
if ``n_classes=3`` and ``n_subclasses=8`` then there are
can be 3 subclasses for the first class, 3 for the second one
and 2 for the third one (3 + 3 + 2 == 8). The following example
can be specified as ``prototypes_per_class=[3, 3, 2]``.
There are two rules that apply to this parameter:
1. ``sum(prototypes_per_class) == n_subclasses``
2. ``len(prototypes_per_class) == n_classes``
The ``None`` value will distribute approximately equal
number of subclasses per each class. It's approximately,
because in casses when ``n_subclasses % n_classes != 0``
there is no way to distribute equal number of subclasses
per each class.
Defaults to ``None``.
{BaseNetwork.step}
n_updates_to_stepdrop : int or None
If this options is not equal to ``None`` then after every
update LVQ reduces step size and do it until number of
applied updates would reach the ``n_updates_to_stepdrop``
value. The minimum possible step size defined in the
``minstep`` parameter.
Be aware that number of updates is not the same as number
of epochs. LVQ applies update after each propagated sample
through the network. Relations between this parameter and
maximum number of epochs is following
.. code-block:: python
n_updates_to_stepdrop = n_samples * n_max_epochs
If parameter equal to ``None`` then step size wouldn't be
reduced after each update.
Defaults to ``None``.
minstep : float
Step size would never be lower than this value. This
property useful only in case if ``n_updates_to_stepdrop``
is not ``None``. Defaults to ``1e-5``.
{BaseNetwork.show_epoch}
{BaseNetwork.shuffle_data}
{BaseNetwork.epoch_end_signal}
{BaseNetwork.train_end_signal}
{Verbose.verbose}
Methods
-------
{BaseSkeleton.predict}
{BaseSkeleton.fit}
"""
n_inputs = IntProperty(minval=1)
n_subclasses = IntProperty(minval=2, default=None, allow_none=True)
n_classes = IntProperty(minval=2)
prototypes_per_class = TypedListProperty(allow_none=True, default=None)
weight = Property(expected_type=(np.ndarray, init.Initializer),
allow_none=True, default=None)
n_updates_to_stepdrop = IntProperty(default=None, allow_none=True,
minval=1)
minstep = NumberProperty(minval=0, default=1e-5)
def __init__(self, **options):
self.initialized = False
super(LVQ, self).__init__(**options)
self.n_updates = 0
if self.n_subclasses is None:
self.n_subclasses = self.n_classes
if isinstance(self.weight, init.Initializer):
weight_shape = (self.n_inputs, self.n_subclasses)
self.weight = self.weight.sample(weight_shape)
if self.weight is not None:
self.initialized = True
if self.n_subclasses < self.n_classes:
raise ValueError("Number of subclasses should be greater "
"or equal to the number of classes. Network "
"was defined with {} subclasses and {} classes"
"".format(self.n_subclasses, self.n_classes))
if self.prototypes_per_class is None:
whole, reminder = divmod(self.n_subclasses, self.n_classes)
self.prototypes_per_class = [whole] * self.n_classes
if reminder:
# Since we have reminder left, it means that we cannot
# have an equal number of subclasses per each class,
# therefor we will add +1 to randomly selected class.
class_indeces = np.random.choice(self.n_classes, reminder,
replace=False)
for class_index in class_indeces:
self.prototypes_per_class[class_index] += 1
if len(self.prototypes_per_class) != self.n_classes:
raise ValueError("LVQ defined for classification problem that has "
"{} classes, but the `prototypes_per_class` "
"variable has defined data for {} classes."
"".format(self.n_classes,
len(self.prototypes_per_class)))
if sum(self.prototypes_per_class) != self.n_subclasses:
raise ValueError("Invalid distribution of subclasses for the "
"`prototypes_per_class` variable. Got total "
"of {} subclasses ({}) instead of {} expected"
"".format(sum(self.prototypes_per_class),
self.prototypes_per_class,
self.n_subclasses))
self.subclass_to_class = []
for class_id, n_prototypes in enumerate(self.prototypes_per_class):
self.subclass_to_class.extend([class_id] * n_prototypes)
@property
def training_step(self):
if self.n_updates_to_stepdrop is None:
return self.step
updates_ratio = (1 - self.n_updates / self.n_updates_to_stepdrop)
return self.minstep + (self.step - self.minstep) * updates_ratio
def predict(self, input_data):
if not self.initialized:
raise NotTrained("LVQ network hasn't been trained yet")
input_data = format_data(input_data)
subclass_to_class = self.subclass_to_class
weight = self.weight
predictions = []
for input_row in input_data:
output = euclid_distance(input_row, weight)
winner_subclass = int(output.argmin(axis=1))
predicted_class = subclass_to_class[winner_subclass]
predictions.append(predicted_class)
return np.array(predictions)
def train(self, input_train, target_train, *args, **kwargs):
input_train = format_data(input_train)
target_train = format_data(target_train)
n_input_samples = len(input_train)
if n_input_samples <= self.n_subclasses:
raise ValueError("Number of training input samples should be "
"greater than number of sublcasses. Training "
"method recived {} input samples."
"".format(n_input_samples))
if not self.initialized:
target_classes = sorted(np.unique(target_train).astype(np.int))
expected_classes = list(range(self.n_classes))
if target_classes != expected_classes:
raise ValueError("All classes should be integers from the "
"range [0, {}], but got the following "
"classes instead {}".format(
self.n_classes - 1, target_classes))
weights = []
iterator = zip(target_classes, self.prototypes_per_class)
for target_class, n_prototypes in iterator:
is_valid_class = (target_train[:, 0] == target_class)
is_valid_class = is_valid_class.astype('float64')
n_samples_per_class = sum(is_valid_class)
is_valid_class /= n_samples_per_class
if n_samples_per_class <= n_prototypes:
raise ValueError("Input data has {0} samples for class-{1}"
". Number of samples per specified "
"class-{1} should be greater than {2}."
"".format(n_samples_per_class,
target_class, n_prototypes))
class_weight_indeces = np.random.choice(
np.arange(n_input_samples), n_prototypes,
replace=False, p=is_valid_class)
class_weight = input_train[class_weight_indeces]
weights.extend(class_weight)
self.weight = np.array(weights)
self.initialized = True
super(LVQ, self).train(input_train, target_train, *args, **kwargs)
def train_epoch(self, input_train, target_train):
weight = self.weight
subclass_to_class = self.subclass_to_class
n_correct_predictions = 0
for input_row, target in zip(input_train, target_train):
step = self.training_step
output = euclid_distance(input_row, weight)
winner_subclass = int(output.argmin())
predicted_class = subclass_to_class[winner_subclass]
weight_update = input_row - weight[winner_subclass, :]
is_correct_prediction = (predicted_class == target)
if is_correct_prediction:
weight[winner_subclass, :] += step * weight_update
else:
weight[winner_subclass, :] -= step * weight_update
n_correct_predictions += is_correct_prediction
self.n_updates += 1
n_samples = len(input_train)
return 1 - n_correct_predictions / n_samples
class GRU(BaseRNNLayer):
"""
Gated Recurrent Unit (GRU) Layer.
Parameters
----------
{BaseRNNLayer.size}
input_weights : Initializer, ndarray
Weight parameters for input connection.
Defaults to :class:`HeNormal() <neupy.init.HeNormal>`.
hidden_weights : Initializer, ndarray
Weight parameters for hidden connection.
Defaults to :class:`HeNormal() <neupy.init.HeNormal>`.
bias : Initializer, ndarray
Bias parameters for all gates.
Defaults to :class:`Constant(0) <neupy.init.Constant>`.
activation_functions : dict, callable
Activation functions for different gates. Defaults to:
.. code-block:: python
# import tensorflow as tf
dict(
resetgate=tf.nn.sigmoid,
updategate=tf.nn.sigmoid,
hidden_update=tf.tanh,
)
If application requires modification to only one parameter
then it's better to specify the one that you need to modify
and ignore other parameters
.. code-block:: python
dict(resetgate=tf.tanh)
Other parameters like ``updategate`` or ``hidden_update``
will be equal to their default values.
learn_init : bool
If ``True``, make ``hidden_init`` trainable variable.
Defaults to ``False``.
hidden_init : array-like, Tensorfow variable, scalar or Initializer
Initializer for initial hidden state (:math:`h_0`).
Defaults to :class:`Constant(0) <neupy.init.Constant>`.
{BaseRNNLayer.only_return_final}
backwards : bool
If ``True``, process the sequence backwards and then reverse the
output again such that the output from the layer is always
from :math:`x_1` to :math:`x_n`. Defaults to ``False``.
unroll_scan : bool
If ``True`` the recursion is unrolled instead of using scan.
For some graphs this gives a significant speed up but it
might also consume more memory. When ``unroll_scan=True``,
backpropagation always includes the full sequence, so
``n_gradient_steps`` must be set to ``-1`` and the input
sequence length must be known at compile time (i.e.,
cannot be given as ``None``). Defaults to ``False``.
{BaseLayer.Parameters}
Notes
-----
Code was adapted from the
`Lasagne <https://github.com/Lasagne/Lasagne>`_ library.
Examples
--------
Sequence classification
.. code-block:: python
from neupy import layers, algorithms
n_time_steps = 40
n_categories = 20
embedded_size = 10
network = algorithms.RMSProp(
[
layers.Input(n_time_steps),
layers.Embedding(n_categories, embedded_size),
layers.GRU(20),
layers.Sigmoid(1),
]
)
"""
input_weights = ParameterProperty(default=init.HeNormal())
hidden_weights = ParameterProperty(default=init.HeNormal())
biases = ParameterProperty(default=init.Constant(0))
activation_functions = MultiCallableProperty(default=dict(
resetgate=tf.nn.sigmoid,
updategate=tf.nn.sigmoid,
hidden_update=tf.tanh,
))
learn_init = Property(default=False, expected_type=bool)
hidden_init = ParameterProperty(default=init.Constant(0))
backwards = Property(default=False, expected_type=bool)
unroll_scan = Property(default=False, expected_type=bool)
gradient_clipping = NumberProperty(default=0, minval=0)
def initialize(self):
super(GRU, self).initialize()
n_inputs = np.prod(self.input_shape[1:])
self.input_weights = self.add_parameter(
value=self.input_weights,
name='input_weights',
shape=(n_inputs, 3 * self.size),
)
self.hidden_weights = self.add_parameter(
value=self.hidden_weights,
name='hidden_weights',
shape=(self.size, 3 * self.size),
)
self.biases = self.add_parameter(
value=self.biases,
name='biases',
shape=(3 * self.size, ),
)
self.add_parameter(value=self.hidden_init,
shape=(1, self.size),
name="hidden_init",
trainable=self.learn_init)
def output(self, input_value):
# Because scan iterates over the first dimension we
# dimshuffle to (n_time_steps, n_batch, n_features)
input_value = tf.transpose(input_value, [1, 0, 2])
input_shape = tf.shape(input_value)
n_batch = input_shape[1]
# Create single recurrent computation step function
# input_n is the n'th vector of the input
def one_gru_step(states, input_n):
with tf.name_scope('gru-cell'):
hid_previous, = states
input_n = tf.matmul(input_n, self.input_weights) + self.biases
# Compute W_{hr} h_{t - 1}, W_{hu} h_{t - 1},
# and W_{hc} h_{t - 1}
hid_input = tf.matmul(hid_previous, self.hidden_weights)
if self.gradient_clipping != 0:
input_n = clip_gradient(input_n, self.gradient_clipping)
hid_input = clip_gradient(hid_input,
self.gradient_clipping)
hid_resetgate, hid_updategate, hid_hidden = tf.split(hid_input,
3,
axis=1)
in_resetgate, in_updategate, in_hidden = tf.split(input_n,
3,
axis=1)
# Reset and update gates
resetgate = self.activation_functions.resetgate(hid_resetgate +
in_resetgate)
updategate = self.activation_functions.updategate(
hid_updategate + in_updategate)
# Compute W_{xc}x_t + r_t \odot (W_{hc} h_{t - 1})
hidden_update = in_hidden + resetgate * hid_hidden
if self.gradient_clipping != 0:
hidden_update = clip_gradient(hidden_update,
self.gradient_clipping)
hidden_update = self.activation_functions.hidden_update(
hidden_update)
# Compute (1 - u_t)h_{t - 1} + u_t c_t
return [
hid_previous - updategate * (hid_previous - hidden_update)
]
hidden_init = tf.tile(self.hidden_init, (n_batch, 1))
sequence = input_value
if self.backwards:
sequence = tf.reverse(sequence, axis=[0])
if self.unroll_scan:
# Explicitly unroll the recurrence instead of using scan
hid_out = unroll_scan(fn=one_gru_step,
sequence=sequence,
outputs_info=[hidden_init])
else:
hid_out, = tf.scan(
fn=one_gru_step,
elems=input_value,
initializer=[hidden_init],
name='gru-scan',
)
# When it is requested that we only return the final sequence step,
# we need to slice it out immediately after scan is applied
if self.only_return_final:
return hid_out[-1]
# if scan is backward reverse the output
if self.backwards:
hid_out = tf.reverse(hid_out, axis=[0])
# dimshuffle back to (n_batch, n_time_steps, n_features))
hid_out = tf.transpose(hid_out, [1, 0, 2])
return hid_out
class BaseOptimizer(BaseNetwork):
"""
Gradient descent algorithm.
Parameters
----------
network : list, tuple or LayerConnection instance
Network's architecture. There are a few ways
to define it.
- List of layers.
For instance, ``[Input(2), Tanh(4), Relu(1)]``.
- Constructed layers.
For instance, ``Input(2) >> Tanh(4) >> Relu(1)``.
regularizer : function or None
Network's regularizer.
loss : str or function
Error/loss function. Defaults to ``mse``.
- ``mae`` - Mean Absolute Error.
- ``mse`` - Mean Squared Error.
- ``rmse`` - Root Mean Squared Error.
- ``msle`` - Mean Squared Logarithmic Error.
- ``rmsle`` - Root Mean Squared Logarithmic Error.
- ``categorical_crossentropy`` - Categorical cross entropy.
- ``binary_crossentropy`` - Binary cross entropy.
- ``binary_hinge`` - Binary hinge entropy.
- ``categorical_hinge`` - Categorical hinge entropy.
- Custom function which accepts two mandatory arguments.
The first one is expected value and the second one is
predicted value. Example:
.. code-block:: python
def custom_func(expected, predicted):
return expected - predicted
step : float, Variable
Learning rate, defaults to ``0.1``.
{BaseNetwork.show_epoch}
{BaseNetwork.shuffle_data}
{BaseNetwork.signals}
{BaseNetwork.verbose}
Attributes
----------
{BaseNetwork.Attributes}
Methods
-------
{BaseSkeleton.predict}
train(X_train, y_train, X_test=None, y_test=None, epochs=100)
Train network. You can control network's training procedure
with ``epochs`` parameter. The ``X_test`` and ``y_test`` should
be presented both in case network's validation required
after each training epoch.
{BaseSkeleton.fit}
"""
step = ScalarVariableProperty(default=0.1)
target = Property(default=None, allow_none=True)
regularizer = Property(default=None, allow_none=True)
loss = FunctionWithOptionsProperty(default='mse',
choices={
'mae':
objectives.mae,
'mse':
objectives.mse,
'rmse':
objectives.rmse,
'msle':
objectives.msle,
'rmsle':
objectives.rmsle,
'binary_crossentropy':
objectives.binary_crossentropy,
'categorical_crossentropy':
objectives.categorical_crossentropy,
'binary_hinge':
objectives.binary_hinge,
'categorical_hinge':
objectives.categorical_hinge,
})
def __init__(self, network, options=None, **kwargs):
options = options or kwargs
if isinstance(network, (list, tuple)):
network = layers.join(*network)
self.network = network
if len(self.network.output_layers) != 1:
n_outputs = len(network.output_layers)
raise InvalidConnection("Connection should have one output "
"layer, got {}".format(n_outputs))
target = options.get('target')
if target is not None and isinstance(target, (list, tuple)):
options['target'] = tf.placeholder(tf.float32, shape=target)
self.target = self.network.targets
super(BaseOptimizer, self).__init__(**options)
start_init_time = time.time()
self.logs.message("TENSORFLOW",
"Initializing Tensorflow variables and functions.")
self.variables = AttributeKeyDict()
self.functions = AttributeKeyDict()
self.network.outputs
self.init_functions()
self.logs.message(
"TENSORFLOW",
"Initialization finished successfully. It took {:.2f} seconds"
"".format(time.time() - start_init_time))
def init_train_updates(self):
raise NotImplementedError()
def init_functions(self):
loss = self.loss(self.target, self.network.outputs)
val_loss = self.loss(self.target, self.network.training_outputs)
if self.regularizer is not None:
loss += self.regularizer(self.network)
self.variables.update(
step=self.step,
loss=loss,
val_loss=val_loss,
)
with tf.name_scope('training-updates'):
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
training_updates = self.init_train_updates()
training_updates.extend(update_ops)
tf_utils.initialize_uninitialized_variables()
self.functions.update(
predict=tf_utils.function(inputs=as_tuple(self.network.inputs),
outputs=self.network.outputs,
name='optimizer/predict'),
one_training_update=tf_utils.function(
inputs=as_tuple(self.network.inputs, self.target),
outputs=loss,
updates=training_updates,
name='optimizer/one-update-step'),
score=tf_utils.function(inputs=as_tuple(self.network.inputs,
self.target),
outputs=val_loss,
name='optimizer/score'))
def format_input(self, X):
X = as_tuple(X)
X_formatted = []
if len(X) != len(self.network.input_layers):
raise ValueError("Number of inputs doesn't match number "
"of input layers in the network.")
for input, input_layer in zip(X, self.network.input_layers):
input_shape = tf.TensorShape(input_layer.input_shape)
is_feature1d = (input_shape.ndims == 2 and input_shape[1] == 1)
formatted_input = format_data(input, is_feature1d=is_feature1d)
if (formatted_input.ndim + 1) == input_shape.ndims:
# We assume that when one dimension was missed than user
# wants to propagate single sample through the network
formatted_input = np.expand_dims(formatted_input, axis=0)
X_formatted.append(formatted_input)
return X_formatted
def format_target(self, y):
output_shape = tf.TensorShape(self.network.output_shape)
is_feature1d = (output_shape.ndims == 2 and output_shape[1] == 1)
formatted_target = format_data(y, is_feature1d=is_feature1d)
if (formatted_target.ndim + 1) == len(output_shape):
# We assume that when one dimension was missed than user
# wants to propagate single sample through the network
formatted_target = np.expand_dims(formatted_target, axis=0)
return formatted_target
def score(self, X, y):
"""
Calculate prediction accuracy for input data.
Parameters
----------
X : array-like
y : array-like
Returns
-------
float
Prediction error.
"""
X = self.format_input(X)
y = self.format_target(y)
return self.functions.score(*as_tuple(X, y))
def predict(self, *X, **kwargs):
"""
Makes a raw prediction.
Parameters
----------
X : array-like
Returns
-------
array-like
"""
default_batch_size = getattr(self, 'batch_size', None)
predict_kwargs = dict(
batch_size=kwargs.pop('batch_size', default_batch_size),
verbose=self.verbose,
)
# We require do to this check for python 2 compatibility
if kwargs:
raise TypeError("Unknown arguments: {}".format(kwargs))
return self.network.predict(*self.format_input(X), **predict_kwargs)
def train(self,
X_train,
y_train,
X_test=None,
y_test=None,
*args,
**kwargs):
is_test_data_partialy_missing = (
(X_test is None and y_test is not None)
or (X_test is not None and y_test is None))
if is_test_data_partialy_missing:
raise ValueError("Input or target test samples are missed. They "
"must be defined together or none of them.")
X_train = self.format_input(X_train)
y_train = self.format_target(y_train)
if X_test is not None:
X_test = self.format_input(X_test)
y_test = self.format_target(y_test)
return super(BaseOptimizer, self).train(X_train=X_train,
y_train=y_train,
X_test=X_test,
y_test=y_test,
*args,
**kwargs)
def one_training_update(self, X_train, y_train):
return self.functions.one_training_update(*as_tuple(X_train, y_train))
def get_params(self, deep=False, with_network=True):
params = super(BaseOptimizer, self).get_params()
if with_network:
params['network'] = self.network
return params
def __reduce__(self):
parameters = self.get_params(with_network=False)
# We only need to know placeholders shape
# in order to be able to reconstruct it
parameters['target'] = tf_utils.shape_to_tuple(
parameters['target'].shape)
args = (self.network, parameters)
return (self.__class__, args)
def __repr__(self):
return "{}({}, {})".format(self.__class__.__name__, self.network,
self.repr_options())
class BaseNetwork(BaseSkeleton):
""" Base class for Neural Network algorithms.
Parameters
----------
step : float
Learning rate, defaults to ``0.1``.
show_epoch : int or str
This property controls how often the network will display information
about training. There are two main syntaxes for this property.
You can describe it as positive integer number and it
will describe how offen would you like to see summary output in
terminal. For instance, number `100` mean that network will show you
summary in 100, 200, 300 ... epochs. String value should be in a
specific format. It should contain the number of times that the output
will be displayed in the terminal. The second part is just
a syntax word ``time`` or ``times`` just to make text readable.
For instance, value ``'2 times'`` mean that the network will show
output twice with approximately equal period of epochs and one
additional output would be after the finall epoch.
Defaults to ``1``.
shuffle_data : bool
If it's ``True`` class shuffles all your training data before
training your network, defaults to ``True``.
epoch_end_signal : function
Calls this function when train epoch finishes.
train_end_signal : function
Calls this function when train process finishes.
{Verbose.verbose}
Attributes
----------
errors : ErrorHistoryList
Contains list of training errors. This object has the same
properties as list and in addition there are three additional
useful methods: `last`, `previous` and `normalized`.
train_errors : ErrorHistoryList
Alias to `errors` attribute.
validation_errors : ErrorHistoryList
The same as `errors` attribute, but it contains only validation
errors.
last_epoch : int
Value equals to the last trained epoch. After initialization
it is equal to ``0``.
"""
step = NumberProperty(default=0.1, minval=0)
show_epoch = ShowEpochProperty(minval=1, default=1)
shuffle_data = Property(default=False, expected_type=bool)
epoch_end_signal = Property(expected_type=types.FunctionType)
train_end_signal = Property(expected_type=types.FunctionType)
def __init__(self, *args, **options):
self.errors = self.train_errors = ErrorHistoryList()
self.validation_errors = ErrorHistoryList()
self.training = AttributeKeyDict()
self.last_epoch = 0
super(BaseNetwork, self).__init__(*args, **options)
self.init_properties()
if self.verbose:
show_network_options(self, highlight_options=options)
def init_properties(self):
""" Setup default values before populate the options.
"""
def predict(self, input_data):
""" Return prediction results for the input data. Output result
includes post-processing step related to the final layer that
transforms output to convenient format for end-use.
Parameters
----------
input_data : array-like
Returns
-------
array-like
"""
def on_epoch_start_update(self, epoch):
""" Function would be trigger before run all training procedure
related to the current epoch.
Parameters
----------
epoch : int
Current epoch number.
"""
self.last_epoch = epoch
def train_epoch(self, input_train, target_train=None):
raise NotImplementedError()
def prediction_error(self, input_test, target_test):
raise NotImplementedError()
def train(self, input_train, target_train=None, input_test=None,
target_test=None, epochs=100, epsilon=None,
summary_type='table'):
""" Method train neural network.
Parameters
----------
input_train : array-like
target_train : array-like or Npne
input_test : array-like or None
target_test : array-like or None
epochs : int
Defaults to `100`.
epsilon : float or None
Defaults to ``None``.
"""
show_epoch = self.show_epoch
logs = self.logs
training = self.training = AttributeKeyDict()
if epochs <= 0:
raise ValueError("Number of epochs needs to be greater than 0.")
if epsilon is not None and epochs <= 2:
raise ValueError("Network should train at teast 3 epochs before "
"check the difference between errors")
if summary_type == 'table':
logging_info_about_the_data(self, input_train, input_test)
logging_info_about_training(self, epochs, epsilon)
logs.newline()
summary = SummaryTable(
table_builder=table.TableBuilder(
table.Column(name="Epoch #"),
table.NumberColumn(name="Train err"),
table.NumberColumn(name="Valid err"),
table.TimeColumn(name="Time", width=10),
stdout=logs.write
),
network=self,
delay_limit=1.,
delay_history_length=10,
)
elif summary_type == 'inline':
summary = InlineSummary(network=self)
else:
raise ValueError("`{}` is unknown summary type"
"".format(summary_type))
iterepochs = create_training_epochs_iterator(self, epochs, epsilon)
show_epoch = parse_show_epoch_property(self, epochs, epsilon)
training.show_epoch = show_epoch
# Storring attributes and methods in local variables we prevent
# useless __getattr__ call a lot of times in each loop.
# This variables speed up loop in case on huge amount of
# iterations.
training_errors = self.errors
validation_errors = self.validation_errors
shuffle_data = self.shuffle_data
train_epoch = self.train_epoch
epoch_end_signal = self.epoch_end_signal
train_end_signal = self.train_end_signal
on_epoch_start_update = self.on_epoch_start_update
is_first_iteration = True
can_compute_validation_error = (input_test is not None)
last_epoch_shown = 0
with logs.disable_user_input():
for epoch in iterepochs:
validation_error = np.nan
epoch_start_time = time.time()
on_epoch_start_update(epoch)
if shuffle_data:
input_train, target_train = shuffle(input_train,
target_train)
try:
train_error = train_epoch(input_train, target_train)
if can_compute_validation_error:
validation_error = self.prediction_error(input_test,
target_test)
training_errors.append(train_error)
validation_errors.append(validation_error)
epoch_finish_time = time.time()
training.epoch_time = epoch_finish_time - epoch_start_time
if epoch % training.show_epoch == 0 or is_first_iteration:
summary.show_last()
last_epoch_shown = epoch
if epoch_end_signal is not None:
epoch_end_signal(self)
is_first_iteration = False
except StopNetworkTraining as err:
# TODO: This notification breaks table view in terminal.
# I need to show it in a different way.
logs.message("TRAIN", "Epoch #{} stopped. {}"
"".format(epoch, str(err)))
break
if epoch != last_epoch_shown:
summary.show_last()
if train_end_signal is not None:
train_end_signal(self)
summary.finish()
logs.newline()
logs.message("TRAIN", "Trainig finished")
class A(Configurable):
property_a = Property()
def test_property_get_method(self):
prop = Property(default=3)
self.assertEqual(None, prop.__get__(None, None))
class C(B):
property_c = Property()
class LSTM(BaseRNNLayer):
"""
Long Short Term Memory (LSTM) Layer.
Parameters
----------
{BaseRNNLayer.size}
weights : dict or Initializer
Weight parameters for different gates.
Defaults to :class:`XavierUniform() <neupy.init.XavierUniform>`.
- In case if application requires the same initialization method
for all weights, then it's possible to specify initialization
method that would be automaticaly applied to all weight
parameters in the LSTM layer.
.. code-block:: python
layers.LSTM(2, weights=init.Normal(0.1))
- In case if application requires different initialization
values for different weights then it's possible to specify
an exact weight by name.
.. code-block:: python
dict(
weight_in_to_ingate=init.XavierUniform(),
weight_hid_to_ingate=init.XavierUniform(),
weight_cell_to_ingate=init.XavierUniform(),
weight_in_to_forgetgate=init.XavierUniform(),
weight_hid_to_forgetgate=init.XavierUniform(),
weight_cell_to_forgetgate=init.XavierUniform(),
weight_in_to_outgate=init.XavierUniform(),
weight_hid_to_outgate=init.XavierUniform(),
weight_cell_to_outgate=init.XavierUniform(),
weight_in_to_cell=init.XavierUniform(),
weight_hid_to_cell=init.XavierUniform(),
)
If application requires modification to only one (or multiple)
parameter then it's better to specify the one that you need to
modify and ignore other parameters
.. code-block:: python
dict(weight_in_to_ingate=init.Normal(0.1))
Other parameters like ``weight_cell_to_outgate`` will be
equal to their default values.
biases : dict or Initializer
Bias parameters for different gates.
Defaults to :class:`Constant(0) <neupy.init.Constant>`.
- In case if application requires the same initialization method
for all biases, then it's possible to specify initialization
method that would be automaticaly applied to all bias parameters
in the LSTM layer.
.. code-block:: python
layers.LSTM(2, biases=init.Constant(1))
- In case if application requires different initialization
values for different weights then it's possible to specify
an exact weight by name.
.. code-block:: python
dict(
bias_ingate=init.Constant(0),
bias_forgetgate=init.Constant(0),
bias_cell=init.Constant(0),
bias_outgate=init.Constant(0),
)
If application requires modification to only one (or multiple)
parameter then it's better to specify the one that you need to
modify and ignore other parameters
.. code-block:: python
dict(bias_ingate=init.Constant(1))
Other parameters like ``bias_cell`` will be
equal to their default values.
activation_functions : dict, callable
Activation functions for different gates. Defaults to:
.. code-block:: python
# import theano.tensor as T
dict(
ingate=T.nnet.sigmoid,
forgetgate=T.nnet.sigmoid,
outgate=T.nnet.sigmoid,
cell=T.tanh,
)
If application requires modification to only one parameter
then it's better to specify the one that you need to modify
and ignore other parameters
.. code-block:: python
dict(ingate=T.tanh)
Other parameters like ``forgetgate`` or ``outgate`` will be
equal to their default values.
learn_init : bool
If ``True``, make ``cell_init`` and ``hid_init`` trainable
variables. Defaults to ``False``.
cell_init : array-like, Theano variable, scalar or Initializer
Initializer for initial cell state (:math:`c_0`).
Defaults to :class:`Constant(0) <neupy.init.Constant>`.
hid_init : array-like, Theano variable, scalar or Initializer
Initializer for initial hidden state (:math:`h_0`).
Defaults to :class:`Constant(0) <neupy.init.Constant>`.
backwards : bool
If ``True``, process the sequence backwards and then reverse the
output again such that the output from the layer is always
from :math:`x_1` to :math:`x_n`. Defaults to ``False``
{BaseRNNLayer.only_return_final}
precompute_input : bool
if ``True``, precompute ``input_to_hid`` before iterating
through the sequence. This can result in a speed up at the
expense of an increase in memory usage.
Defaults to ``True``.
peepholes : bool
If ``True``, the LSTM uses peephole connections.
When ``False``, cell parameters are ignored.
Defaults to ``False``.
unroll_scan : bool
If ``True`` the recursion is unrolled instead of using scan.
For some graphs this gives a significant speed up but it
might also consume more memory. When ``unroll_scan=True``,
backpropagation always includes the full sequence, so
``n_gradient_steps`` must be set to ``-1`` and the input
sequence length must be known at compile time (i.e.,
cannot be given as ``None``). Defaults to ``False``.
gradient_clipping : flaot or int
If nonzero, the gradient messages are clipped to the
given value during the backward pass. Defaults to ``0``.
n_gradient_steps : int
Number of timesteps to include in the backpropagated gradient.
If ``-1``, backpropagate through the entire sequence.
Defaults to ``-1``.
{BaseLayer.Parameters}
Notes
-----
Code was adapted from the
`Lasagne <https://github.com/Lasagne/Lasagne>`_ library.
Examples
--------
Sequence classification
.. code-block:: python
from neupy import layers, algorithms
n_time_steps = 40
n_categories = 20
embedded_size = 10
network = algorithms.RMSProp(
[
layers.Input(n_time_steps),
layers.Embedding(n_categories, embedded_size),
layers.LSTM(20),
layers.Sigmoid(1),
]
)
"""
weights = MultiParameterProperty(
default=dict(
weight_in_to_ingate=init.XavierUniform(),
weight_hid_to_ingate=init.XavierUniform(),
weight_cell_to_ingate=init.XavierUniform(),
weight_in_to_forgetgate=init.XavierUniform(),
weight_hid_to_forgetgate=init.XavierUniform(),
weight_cell_to_forgetgate=init.XavierUniform(),
weight_in_to_outgate=init.XavierUniform(),
weight_hid_to_outgate=init.XavierUniform(),
weight_cell_to_outgate=init.XavierUniform(),
weight_in_to_cell=init.XavierUniform(),
weight_hid_to_cell=init.XavierUniform(),
))
biases = MultiParameterProperty(
default=dict(
bias_ingate=init.Constant(0),
bias_forgetgate=init.Constant(0),
bias_cell=init.Constant(0),
bias_outgate=init.Constant(0),
))
activation_functions = MultiCallableProperty(
default=dict(
ingate=T.nnet.sigmoid,
forgetgate=T.nnet.sigmoid,
outgate=T.nnet.sigmoid,
cell=T.tanh,
))
learn_init = Property(default=False, expected_type=bool)
cell_init = ParameterProperty(default=init.Constant(0))
hid_init = ParameterProperty(default=init.Constant(0))
unroll_scan = Property(default=False, expected_type=bool)
backwards = Property(default=False, expected_type=bool)
precompute_input = Property(default=True, expected_type=bool)
peepholes = Property(default=False, expected_type=bool)
n_gradient_steps = IntProperty(default=-1)
gradient_clipping = NumberProperty(default=0, minval=0)
def initialize(self):
super(LSTM, self).initialize()
n_inputs = np.prod(self.input_shape[1:])
weights = self.weights
biases = self.biases
# Input gate parameters
self.weight_in_to_ingate = self.add_parameter(
value=weights.weight_in_to_ingate,
name='weight_in_to_ingate',
shape=(n_inputs, self.size))
self.weight_hid_to_ingate = self.add_parameter(
value=weights.weight_hid_to_ingate,
name='weight_hid_to_ingate',
shape=(self.size, self.size))
self.bias_ingate = self.add_parameter(
value=biases.bias_ingate, name='bias_ingate',
shape=(self.size,))
# Forget gate parameters
self.weight_in_to_forgetgate = self.add_parameter(
value=weights.weight_in_to_forgetgate,
name='weight_in_to_forgetgate',
shape=(n_inputs, self.size))
self.weight_hid_to_forgetgate = self.add_parameter(
value=weights.weight_hid_to_forgetgate,
name='weight_hid_to_forgetgate',
shape=(self.size, self.size))
self.bias_forgetgate = self.add_parameter(
value=biases.bias_forgetgate, name='bias_forgetgate',
shape=(self.size,))
# Cell parameters
self.weight_in_to_cell = self.add_parameter(
value=weights.weight_in_to_cell,
name='weight_in_to_cell',
shape=(n_inputs, self.size))
self.weight_hid_to_cell = self.add_parameter(
value=weights.weight_hid_to_cell,
name='weight_hid_to_cell',
shape=(self.size, self.size))
self.bias_cell = self.add_parameter(
value=biases.bias_cell, name='bias_cell',
shape=(self.size,))
# If peephole (cell to gate) connections were enabled, initialize
# peephole connections. These are elementwise products with the cell
# state, so they are represented as vectors.
if self.peepholes:
self.weight_cell_to_ingate = self.add_parameter(
value=weights.weight_cell_to_ingate,
name='weight_cell_to_ingate',
shape=(self.size,))
self.weight_cell_to_forgetgate = self.add_parameter(
value=weights.weight_cell_to_forgetgate,
name='weight_cell_to_forgetgate',
shape=(self.size,))
self.weight_cell_to_outgate = self.add_parameter(
value=weights.weight_cell_to_outgate,
name='weight_cell_to_outgate',
shape=(self.size,))
# Output gate parameters
self.weight_in_to_outgate = self.add_parameter(
value=weights.weight_in_to_outgate,
name='weight_in_to_outgate',
shape=(n_inputs, self.size))
self.weight_hid_to_outgate = self.add_parameter(
value=weights.weight_hid_to_outgate,
name='weight_hid_to_outgate',
shape=(self.size, self.size))
self.bias_outgate = self.add_parameter(
value=biases.bias_outgate, name='bias_outgate',
shape=(self.size,))
# Initialization parameters
self.add_parameter(value=self.cell_init, shape=(1, self.size),
name="cell_init", trainable=self.learn_init)
self.add_parameter(value=self.hid_init, shape=(1, self.size),
name="hid_init", trainable=self.learn_init)
def output(self, input_value):
# Treat all dimensions after the second as flattened
# feature dimensions
if input_value.ndim > 3:
input_value = T.flatten(input_value, 3)
# Because scan iterates over the first dimension we
# dimshuffle to (n_time_steps, n_batch, n_features)
input_value = input_value.dimshuffle(1, 0, 2)
seq_len, n_batch, _ = input_value.shape
# Stack input weight matrices into a (num_inputs, 4 * num_units)
# matrix, which speeds up computation
weight_in_stacked = T.concatenate([
self.weight_in_to_ingate,
self.weight_in_to_forgetgate,
self.weight_in_to_cell,
self.weight_in_to_outgate], axis=1)
# Same for hidden weight matrices
weight_hid_stacked = T.concatenate([
self.weight_hid_to_ingate,
self.weight_hid_to_forgetgate,
self.weight_hid_to_cell,
self.weight_hid_to_outgate], axis=1)
# Stack biases into a (4 * num_units) vector
bias_stacked = T.concatenate([
self.bias_ingate,
self.bias_forgetgate,
self.bias_cell,
self.bias_outgate], axis=0)
if self.precompute_input:
# Because the input is given for all time steps, we can
# precompute_input the inputs dot weight matrices before scanning.
# weight_in_stacked is (n_features, 4 * num_units).
# Input: (n_time_steps, n_batch, 4 * num_units).
input_value = T.dot(input_value, weight_in_stacked) + bias_stacked
# When theano.scan calls step, input_n will be
# (n_batch, 4 * num_units). We define a slicing function
# that extract the input to each LSTM gate
def slice_w(x, n):
return x[:, n * self.size:(n + 1) * self.size]
def one_lstm_step(input_n, cell_previous, hid_previous, *args):
if not self.precompute_input:
input_n = T.dot(input_n, weight_in_stacked) + bias_stacked
# Calculate gates pre-activations and slice
gates = input_n + T.dot(hid_previous, weight_hid_stacked)
# Clip gradients
if self.gradient_clipping:
gates = theano.gradient.grad_clip(
gates, -self.gradient_clipping, self.gradient_clipping)
# Extract the pre-activation gate values
ingate = slice_w(gates, 0)
forgetgate = slice_w(gates, 1)
cell_input = slice_w(gates, 2)
outgate = slice_w(gates, 3)
if self.peepholes:
# Compute peephole connections
ingate += cell_previous * self.weight_cell_to_ingate
forgetgate += cell_previous * self.weight_cell_to_forgetgate
# Apply nonlinearities
ingate = self.activation_functions.ingate(ingate)
forgetgate = self.activation_functions.forgetgate(forgetgate)
cell_input = self.activation_functions.cell(cell_input)
# Compute new cell value
cell = forgetgate * cell_previous + ingate * cell_input
if self.peepholes:
outgate += cell * self.weight_cell_to_outgate
outgate = self.activation_functions.outgate(outgate)
# Compute new hidden unit activation
hid = outgate * T.tanh(cell)
return [cell, hid]
ones = T.ones((n_batch, 1))
cell_init = T.dot(ones, self.cell_init)
hid_init = T.dot(ones, self.hid_init)
non_sequences = [weight_hid_stacked]
# When we aren't precomputing the input outside of scan, we need to
# provide the input weights and biases to the step function
if not self.precompute_input:
non_sequences += [weight_in_stacked, bias_stacked]
# The "peephole" weight matrices are only used
# when self.peepholes=True
if self.peepholes:
non_sequences += [self.weight_cell_to_ingate,
self.weight_cell_to_forgetgate,
self.weight_cell_to_outgate]
if self.unroll_scan:
# Retrieve the dimensionality of the incoming layer
n_time_steps = self.input_shape[0]
# Explicitly unroll the recurrence instead of using scan
_, hid_out = unroll_scan(
fn=one_lstm_step,
sequences=[input_value],
outputs_info=[cell_init, hid_init],
go_backwards=self.backwards,
non_sequences=non_sequences,
n_steps=n_time_steps)
else:
(_, hid_out), _ = theano.scan(
fn=one_lstm_step,
sequences=input_value,
outputs_info=[cell_init, hid_init],
go_backwards=self.backwards,
truncate_gradient=self.n_gradient_steps,
non_sequences=non_sequences,
strict=True)
# When it is requested that we only return the final sequence step,
# we need to slice it out immediately after scan is applied
if self.only_return_final:
return hid_out[-1]
# dimshuffle back to (n_batch, n_time_steps, n_features))
hid_out = hid_out.dimshuffle(1, 0, 2)
# if scan is backward reverse the output
if self.backwards:
hid_out = hid_out[:, ::-1]
return hid_out
class A(Configurable):
correct_property = Property()
class GRU(BaseRNNLayer):
"""
Gated Recurrent Unit (GRU) Layer.
Parameters
----------
{BaseRNNLayer.size}
weights : dict or Initializer
Weight parameters for different gates.
Defaults to :class:`XavierUniform() <neupy.init.XavierUniform>`.
- In case if application requires the same initialization method
for all weights, then it's possible to specify initialization
method that would be automaticaly applied to all weight
parameters in the GRU layer.
.. code-block:: python
layers.GRU(2, weights=init.Normal(0.1))
- In case if application requires different initialization
values for different weights then it's possible to specify
an exact weight by name.
.. code-block:: python
dict(
weight_in_to_updategate=init.XavierUniform(),
weight_hid_to_updategate=init.XavierUniform(),
weight_in_to_resetgate=init.XavierUniform(),
weight_hid_to_resetgate=init.XavierUniform(),
weight_in_to_hidden_update=init.XavierUniform(),
weight_hid_to_hidden_update=init.XavierUniform(),
)
If application requires modification to only one (or multiple)
parameter then it's better to specify the one that you need to
modify and ignore other parameters
.. code-block:: python
dict(weight_in_to_updategate=init.Normal(0.1))
Other parameters like ``weight_in_to_resetgate`` will be
equal to their default values.
biases : dict or Initializer
Bias parameters for different gates.
Defaults to :class:`Constant(0) <neupy.init.Constant>`.
- In case if application requires the same initialization method
for all biases, then it's possible to specify initialization
method that would be automaticaly applied to all bias parameters
in the GRU layer.
.. code-block:: python
layers.GRU(2, biases=init.Constant(1))
- In case if application requires different initialization
values for different weights then it's possible to specify
an exact weight by name.
.. code-block:: python
dict(
bias_updategate=init.Constant(0),
bias_resetgate=init.Constant(0),
bias_hidden_update=init.Constant(0),
)
If application requires modification to only one (or multiple)
parameter then it's better to specify the one that you need to
modify and ignore other parameters
.. code-block:: python
dict(bias_resetgate=init.Constant(1))
Other parameters like ``bias_updategate`` will be
equal to their default values.
activation_functions : dict, callable
Activation functions for different gates. Defaults to:
.. code-block:: python
# import theano.tensor as T
dict(
resetgate=T.nnet.sigmoid,
updategate=T.nnet.sigmoid,
hidden_update=T.tanh,
)
If application requires modification to only one parameter
then it's better to specify the one that you need to modify
and ignore other parameters
.. code-block:: python
dict(resetgate=T.tanh)
Other parameters like ``updategate`` or ``hidden_update``
will be equal to their default values.
learn_init : bool
If ``True``, make ``hid_init`` trainable variable.
Defaults to ``False``.
hid_init : array-like, Theano variable, scalar or Initializer
Initializer for initial hidden state (:math:`h_0`).
Defaults to :class:`Constant(0) <neupy.init.Constant>`.
{BaseRNNLayer.only_return_final}
backwards : bool
If ``True``, process the sequence backwards and then reverse the
output again such that the output from the layer is always
from :math:`x_1` to :math:`x_n`. Defaults to ``False``.
precompute_input : bool
if ``True``, precompute ``input_to_hid`` before iterating
through the sequence. This can result in a speed up at the
expense of an increase in memory usage.
Defaults to ``True``.
unroll_scan : bool
If ``True`` the recursion is unrolled instead of using scan.
For some graphs this gives a significant speed up but it
might also consume more memory. When ``unroll_scan=True``,
backpropagation always includes the full sequence, so
``n_gradient_steps`` must be set to ``-1`` and the input
sequence length must be known at compile time (i.e.,
cannot be given as ``None``). Defaults to ``False``.
{BaseLayer.Parameters}
Notes
-----
Code was adapted from the
`Lasagne <https://github.com/Lasagne/Lasagne>`_ library.
Examples
--------
Sequence classification
.. code-block:: python
from neupy import layers, algorithms
n_time_steps = 40
n_categories = 20
embedded_size = 10
network = algorithms.RMSProp(
[
layers.Input(n_time_steps),
layers.Embedding(n_categories, embedded_size),
layers.GRU(20),
layers.Sigmoid(1),
]
)
"""
weights = MultiParameterProperty(
default=dict(
weight_in_to_updategate=init.XavierUniform(),
weight_hid_to_updategate=init.XavierUniform(),
weight_in_to_resetgate=init.XavierUniform(),
weight_hid_to_resetgate=init.XavierUniform(),
weight_in_to_hidden_update=init.XavierUniform(),
weight_hid_to_hidden_update=init.XavierUniform(),
))
biases = MultiParameterProperty(
default=dict(
bias_updategate=init.Constant(0),
bias_resetgate=init.Constant(0),
bias_hidden_update=init.Constant(0),
))
activation_functions = MultiCallableProperty(
default=dict(
resetgate=T.nnet.sigmoid,
updategate=T.nnet.sigmoid,
hidden_update=T.tanh,
))
learn_init = Property(default=False, expected_type=bool)
hid_init = ParameterProperty(default=init.Constant(0))
backwards = Property(default=False, expected_type=bool)
unroll_scan = Property(default=False, expected_type=bool)
precompute_input = Property(default=True, expected_type=bool)
n_gradient_steps = IntProperty(default=-1)
gradient_clipping = NumberProperty(default=0, minval=0)
def initialize(self):
super(GRU, self).initialize()
n_inputs = np.prod(self.input_shape[1:])
weights = self.weights
biases = self.biases
# Update gate parameters
self.weight_in_to_updategate = self.add_parameter(
value=weights.weight_in_to_updategate,
name='weight_in_to_updategate',
shape=(n_inputs, self.size))
self.weight_hid_to_updategate = self.add_parameter(
value=weights.weight_hid_to_updategate,
name='weight_hid_to_updategate',
shape=(self.size, self.size))
self.bias_updategate = self.add_parameter(
value=biases.bias_updategate, name='bias_updategate',
shape=(self.size,))
# Reset gate parameters
self.weight_in_to_resetgate = self.add_parameter(
value=weights.weight_in_to_resetgate,
name='weight_in_to_resetgate',
shape=(n_inputs, self.size))
self.weight_hid_to_resetgate = self.add_parameter(
value=weights.weight_hid_to_resetgate,
name='weight_hid_to_resetgate',
shape=(self.size, self.size))
self.bias_resetgate = self.add_parameter(
value=biases.bias_resetgate, name='bias_forgetgate',
shape=(self.size,))
# Hidden update gate parameters
self.weight_in_to_hidden_update = self.add_parameter(
value=weights.weight_in_to_hidden_update,
name='weight_in_to_hidden_update',
shape=(n_inputs, self.size))
self.weight_hid_to_hidden_update = self.add_parameter(
value=weights.weight_hid_to_hidden_update,
name='weight_hid_to_hidden_update',
shape=(self.size, self.size))
self.bias_hidden_update = self.add_parameter(
value=biases.bias_hidden_update, name='bias_hidden_update',
shape=(self.size,))
self.add_parameter(value=self.hid_init, shape=(1, self.size),
name="hid_init", trainable=self.learn_init)
def output(self, input_value):
# Treat all dimensions after the second as flattened
# feature dimensions
if input_value.ndim > 3:
input_value = T.flatten(input_value, 3)
# Because scan iterates over the first dimension we
# dimshuffle to (n_time_steps, n_batch, n_features)
input_value = input_value.dimshuffle(1, 0, 2)
seq_len, n_batch, _ = input_value.shape
# Stack input weight matrices into a (num_inputs, 3 * num_units)
# matrix, which speeds up computation
weight_in_stacked = T.concatenate([
self.weight_in_to_updategate,
self.weight_in_to_resetgate,
self.weight_in_to_hidden_update], axis=1)
# Same for hidden weight matrices
weight_hid_stacked = T.concatenate([
self.weight_hid_to_updategate,
self.weight_hid_to_resetgate,
self.weight_hid_to_hidden_update], axis=1)
# Stack biases into a (3 * num_units) vector
bias_stacked = T.concatenate([
self.bias_updategate,
self.bias_resetgate,
self.bias_hidden_update], axis=0)
if self.precompute_input:
# Because the input is given for all time steps, we can
# precompute_input the inputs dot weight matrices before scanning.
# weight_in_stacked is (n_features, 3 * num_units).
# Input: (n_time_steps, n_batch, 3 * num_units).
input_value = T.dot(input_value, weight_in_stacked) + bias_stacked
# When theano.scan calls step, input_n will be
# (n_batch, 3 * num_units). We define a slicing function
# that extract the input to each GRU gate
def slice_w(x, n):
s = x[:, n * self.size:(n + 1) * self.size]
if self.size == 1:
s = T.addbroadcast(s, 1) # Theano cannot infer this by itself
return s
# Create single recurrent computation step function
# input_n is the n'th vector of the input
def one_gru_step(input_n, hid_previous, *args):
# Compute W_{hr} h_{t - 1}, W_{hu} h_{t - 1},
# and W_{hc} h_{t - 1}
hid_input = T.dot(hid_previous, weight_hid_stacked)
if self.gradient_clipping:
input_n = theano.gradient.grad_clip(
input_n,
-self.gradient_clipping,
self.gradient_clipping)
hid_input = theano.gradient.grad_clip(
hid_input,
-self.gradient_clipping,
self.gradient_clipping)
if not self.precompute_input:
# Compute W_{xr}x_t + b_r, W_{xu}x_t + b_u,
# and W_{xc}x_t + b_c
input_n = T.dot(input_n, weight_in_stacked) + bias_stacked
# Reset and update gates
resetgate = slice_w(hid_input, 0) + slice_w(input_n, 0)
resetgate = self.activation_functions.resetgate(resetgate)
updategate = slice_w(hid_input, 1) + slice_w(input_n, 1)
updategate = self.activation_functions.updategate(updategate)
# Compute W_{xc}x_t + r_t \odot (W_{hc} h_{t - 1})
hidden_update_in = slice_w(input_n, 2)
hidden_update_hid = slice_w(hid_input, 2)
hidden_update = hidden_update_in + resetgate * hidden_update_hid
if self.gradient_clipping:
hidden_update = theano.gradient.grad_clip(
hidden_update,
-self.gradient_clipping,
self.gradient_clipping)
hidden_update = self.activation_functions.hidden_update(
hidden_update)
# Compute (1 - u_t)h_{t - 1} + u_t c_t
hid = (1 - updategate) * hid_previous + updategate * hidden_update
return hid
hid_init = T.dot(T.ones((n_batch, 1)), self.hid_init)
# The hidden-to-hidden weight matrix is always used in step
non_sequences = [weight_hid_stacked]
# When we aren't precomputing the input outside of scan, we need to
# provide the input weights and biases to the step function
if not self.precompute_input:
non_sequences += [weight_in_stacked, bias_stacked]
if self.unroll_scan:
# Retrieve the dimensionality of the incoming layer
n_time_steps = self.input_shape[0]
# Explicitly unroll the recurrence instead of using scan
hid_out, = unroll_scan(
fn=one_gru_step,
sequences=[input_value],
outputs_info=[hid_init],
go_backwards=self.backwards,
non_sequences=non_sequences,
n_steps=n_time_steps)
else:
# Scan op iterates over first dimension of input and
# repeatedly applies the step function
hid_out, _ = theano.scan(
fn=one_gru_step,
sequences=[input_value],
outputs_info=[hid_init],
go_backwards=self.backwards,
non_sequences=non_sequences,
truncate_gradient=self.n_gradient_steps,
strict=True)
# When it is requested that we only return the final sequence step,
# we need to slice it out immediately after scan is applied
if self.only_return_final:
return hid_out[-1]
# dimshuffle back to (n_batch, n_time_steps, n_features))
hid_out = hid_out.dimshuffle(1, 0, 2)
# if scan is backward reverse the output
if self.backwards:
hid_out = hid_out[:, ::-1]
return hid_out
class B(Configurable):
property_b = Property()
def test_property_repr_with_name(self):
prop = Property(default=3)
prop.name = 'test'
self.assertEqual('Property(name="test")', repr(prop))