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.

You may check out the related API usage on the sidebar.

You may also want to check out all available functions/classes of the module keras , or try the search function .

Example 1
Project: fancy-cnn   Author: textclf   File: timedistributed.py    License: MIT License 6 votes vote down vote up
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 vote down vote up
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 vote down vote up
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 vote down vote up
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 vote down vote up
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 vote down vote up
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)