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)