Python keras.constraints() Examples
The following are 6 code examples for showing how to use keras.constraints(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
| Project: fancy-cnn Author: textclf File: timedistributed.py License: MIT License | 6 votes |
|
def build(self):
try:
self.input_ndim = len(self.previous.input_shape)
except AttributeError:
self.input_ndim = len(self.input_shape)
self.layer.set_input_shape((None, ) + self.input_shape[2:])
if hasattr(self.layer, 'regularizers'):
self.regularizers = self.layer.regularizers
if hasattr(self.layer, 'constraints'):
self.constraints = self.layer.constraints
if hasattr(self.layer, 'trainable_weights'):
self.trainable_weights = self.layer.trainable_weights
if self.initial_weights is not None:
self.layer.set_weights(self.initial_weights)
del self.initial_weights
Example 2
| Project: deep_complex_networks Author: ChihebTrabelsi File: bn.py License: MIT License | 6 votes |
|
def get_config(self):
config = {
'axis': self.axis,
'momentum': self.momentum,
'epsilon': self.epsilon,
'center': self.center,
'scale': self.scale,
'beta_initializer': sanitizedInitSer(self.beta_initializer),
'gamma_diag_initializer': sanitizedInitSer(self.gamma_diag_initializer),
'gamma_off_initializer': sanitizedInitSer(self.gamma_off_initializer),
'moving_mean_initializer': sanitizedInitSer(self.moving_mean_initializer),
'moving_variance_initializer': sanitizedInitSer(self.moving_variance_initializer),
'moving_covariance_initializer': sanitizedInitSer(self.moving_covariance_initializer),
'beta_regularizer': regularizers.serialize(self.beta_regularizer),
'gamma_diag_regularizer': regularizers.serialize(self.gamma_diag_regularizer),
'gamma_off_regularizer': regularizers.serialize(self.gamma_off_regularizer),
'beta_constraint': constraints .serialize(self.beta_constraint),
'gamma_diag_constraint': constraints .serialize(self.gamma_diag_constraint),
'gamma_off_constraint': constraints .serialize(self.gamma_off_constraint),
}
base_config = super(ComplexBatchNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example 3
| Project: deeplearning4nlp-tutorial Author: UKPLab File: FixedEmbedding.py License: Apache License 2.0 | 6 votes |
|
def __init__(self, input_dim, output_dim, init='uniform', input_length=None,
W_regularizer=None, activity_regularizer=None, W_constraint=None,
mask_zero=False, weights=None, **kwargs):
self.input_dim = input_dim
self.output_dim = output_dim
self.init = initializations.get(init)
self.input_length = input_length
self.mask_zero = mask_zero
self.W_constraint = constraints.get(W_constraint)
self.constraints = [self.W_constraint]
self.W_regularizer = regularizers.get(W_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.initial_weights = weights
kwargs['input_shape'] = (self.input_dim,)
super(FixedEmbedding, self).__init__(**kwargs)
Example 4
| Project: deeplearning4nlp-tutorial Author: UKPLab File: ConvolutionalMaxOverTime.py License: Apache License 2.0 | 6 votes |
|
def __init__(self, output_dim, init='glorot_uniform', activation='linear', weights=None,
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None, input_dim=None, **kwargs):
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.output_dim = output_dim
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.constraints = [self.W_constraint, self.b_constraint]
self.initial_weights = weights
self.input_dim = input_dim
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(ConvolutionalMaxOverTime, self).__init__(**kwargs)
Example 5
| Project: deeplearning4nlp-tutorial Author: UKPLab File: ConvolutionalMaxOverTime.py License: Apache License 2.0 | 6 votes |
|
def __init__(self, output_dim, init='glorot_uniform', activation='linear', weights=None,
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None, input_dim=None, **kwargs):
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.output_dim = output_dim
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.constraints = [self.W_constraint, self.b_constraint]
self.initial_weights = weights
self.input_dim = input_dim
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(ConvolutionalMaxOverTime, self).__init__(**kwargs)
Example 6
| Project: deep_complex_networks Author: ChihebTrabelsi File: bn.py License: MIT License | 5 votes |
|
def __init__(self,
axis=-1,
momentum=0.9,
epsilon=1e-4,
center=True,
scale=True,
beta_initializer='zeros',
gamma_diag_initializer='sqrt_init',
gamma_off_initializer='zeros',
moving_mean_initializer='zeros',
moving_variance_initializer='sqrt_init',
moving_covariance_initializer='zeros',
beta_regularizer=None,
gamma_diag_regularizer=None,
gamma_off_regularizer=None,
beta_constraint=None,
gamma_diag_constraint=None,
gamma_off_constraint=None,
**kwargs):
super(ComplexBatchNormalization, self).__init__(**kwargs)
self.supports_masking = True
self.axis = axis
self.momentum = momentum
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = sanitizedInitGet(beta_initializer)
self.gamma_diag_initializer = sanitizedInitGet(gamma_diag_initializer)
self.gamma_off_initializer = sanitizedInitGet(gamma_off_initializer)
self.moving_mean_initializer = sanitizedInitGet(moving_mean_initializer)
self.moving_variance_initializer = sanitizedInitGet(moving_variance_initializer)
self.moving_covariance_initializer = sanitizedInitGet(moving_covariance_initializer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.gamma_diag_regularizer = regularizers.get(gamma_diag_regularizer)
self.gamma_off_regularizer = regularizers.get(gamma_off_regularizer)
self.beta_constraint = constraints .get(beta_constraint)
self.gamma_diag_constraint = constraints .get(gamma_diag_constraint)
self.gamma_off_constraint = constraints .get(gamma_off_constraint)
