Python keras.constraints() Examples
The following are code examples for showing how to use keras.constraints(). They are from open source Python projects. You can vote up the examples you like or vote down the ones you don't like.
Example 1
| Project: fancy-cnn Author: textclf File: timedistributed.py 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: deeplearning4nlp-tutorial Author: UKPLab File: FixedEmbedding.py 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 3
| Project: deeplearning4nlp-tutorial Author: UKPLab File: ConvolutionalMaxOverTime.py 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 4
| Project: deeplearning4nlp-tutorial Author: UKPLab File: ConvolutionalMaxOverTime.py 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: TemporalActionParsing-FineGrained Author: yz-cnsdqz File: RP_Bilinear_Pooling.py MIT License | 5 votes |
|
def build(self, input_shape):
self.shape=input_shape
in_dim = input_shape[-1]
if self.n_basis > in_dim:
print('[ERROR]: n_basis must not be larger than in_dim! Program terminates')
sys.exit()
self.E_layer = keras.layers.Conv1D(self.n_basis,
kernel_size=1,
activation=self.activation,
use_bias=self.use_bias
# kernel_constraint= non_neg_unit_norm(axis=1)
# kernel_constraint=keras.constraints.NonNeg()
# kernel_regularizer=regularizers.l1(100.0)
)
self.F_layer = keras.layers.Conv1D(self.n_basis,
kernel_size=1,
activation=self.activation,
use_bias=self.use_bias
# kernel_constraint= non_neg_unit_norm(axis=1)
# kernel_constraint=keras.constraints.NonNeg()
# kernel_regularizer=regularizers.l1(100.0)
)
super(TensorRelaxationPooling2, self).build(input_shape)
Example 6
| Project: TemporalActionParsing-FineGrained Author: yz-cnsdqz File: RP_Bilinear_Pooling.py MIT License | 4 votes |
|
def build(self, input_shape):
self.shape=input_shape
in_dim = input_shape[-1]
if self.n_basis > in_dim:
print('[ERROR]: n_basis must not be larger than in_dim! Program terminates')
sys.exit()
## define the two matrix with orthogonal columns
self.E_list = []
self.F_list = []
if not self.init_sigma:
init_sigma = np.sqrt(in_dim)
else:
init_sigma = self.init_sigma
for n in range(self.n_components):
E0 = self.add_weight(name='E_{}'.format(n),
shape=[in_dim, self.n_basis],
initializer=keras.initializers.Orthogonal(),
trainable=False)
sigma_e = self.add_weight(name='sE_{}'.format(n),
shape=[1],
initializer=keras.initializers.Constant(init_sigma),
constraint=keras.constraints.NonNeg(),
trainable=self.learnable_radius)
self.E_list.append( np.sqrt(in_dim) / (K.epsilon() + sigma_e) * E0 )
F0 = self.add_weight(name='F_{}'.format(n),
shape=[in_dim, self.n_basis],
initializer=keras.initializers.Orthogonal(),
trainable=False)
sigma_f = self.add_weight(name='sF_{}'.format(n),
shape=[1],
initializer=keras.initializers.Constant(init_sigma),
constraint=keras.constraints.NonNeg(),
trainable=self.learnable_radius)
self.F_list.append( np.sqrt(in_dim) / (K.epsilon() + sigma_f) * F0 )
super(RPGaussianPooling, self).build(input_shape)
Example 7
| Project: TemporalActionParsing-FineGrained Author: yz-cnsdqz File: RP_Bilinear_Pooling.py MIT License | 4 votes |
|
def build(self, input_shape):
self.shape=input_shape
in_dim = input_shape[-1]
if self.n_basis > in_dim:
print('[ERROR]: n_basis must not be larger than in_dim! Program terminates')
sys.exit()
## define the two matrix with orthogonal columns
self.E_list = []
self.F_list = []
if not self.init_sigma:
init_sigma = np.sqrt(in_dim)
else:
init_sigma = self.init_sigma
for n in range(self.n_components):
E0 = self.add_weight(name='E_{}'.format(n),
shape=[in_dim, self.n_basis],
initializer=keras.initializers.Orthogonal(),
trainable=False)
sigma_e = self.add_weight(name='sE_{}'.format(n),
shape=[1],
initializer=keras.initializers.Constant(init_sigma),
constraint=keras.constraints.NonNeg(),
trainable=self.learnable_radius)
self.E_list.append( np.sqrt(in_dim) / (K.epsilon() + sigma_e) * E0 )
F0 = self.add_weight(name='F_{}'.format(n),
shape=[in_dim, self.n_basis],
initializer=keras.initializers.Orthogonal(),
trainable=False)
sigma_f = self.add_weight(name='sF_{}'.format(n),
shape=[1],
initializer=keras.initializers.Constant(init_sigma),
constraint=keras.constraints.NonNeg(),
trainable=self.learnable_radius)
self.F_list.append( np.sqrt(in_dim) / (K.epsilon() + sigma_f) * F0 )
super(RPGaussianPooling2, self).build(input_shape)
