Python keras.constraints() Examples
The following are 6
code examples of keras.constraints().
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Example #1
| Source File: timedistributed.py From fancy-cnn with 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
| Source File: bn.py From deep_complex_networks with 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
| Source File: FixedEmbedding.py From deeplearning4nlp-tutorial with 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
| Source File: ConvolutionalMaxOverTime.py From deeplearning4nlp-tutorial with 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
| Source File: ConvolutionalMaxOverTime.py From deeplearning4nlp-tutorial with 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
| Source File: bn.py From deep_complex_networks with 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)
