Python keras.initializers.serialize() Examples
The following are 30
code examples of keras.initializers.serialize().
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Example #1
| Source File: dense.py From Quaternion-Convolutional-Neural-Networks-for-End-to-End-Automatic-Speech-Recognition with GNU General Public License v3.0 | 6 votes |
|
def get_config(self):
if self.kernel_initializer == 'quaternion':
ki = self.kernel_init
else:
ki = initializers.serialize(self.kernel_initializer)
config = {
'units': self.units,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'init_criterion': self.init_criterion,
'kernel_initializer': ki,
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'seed': self.seed,
}
base_config = super(QuaternionDense, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
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: abn.py From ddan 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': initializers.serialize(self.beta_initializer),
'gamma_initializer': initializers.serialize(self.gamma_initializer),
'moving_mean_initializer': initializers.serialize(self.moving_mean_initializer),
'moving_variance_initializer': initializers.serialize(self.moving_variance_initializer),
'beta_regularizer': regularizers.serialize(self.beta_regularizer),
'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
'beta_constraint': constraints.serialize(self.beta_constraint),
'gamma_constraint': constraints.serialize(self.gamma_constraint)
}
base_config = super(BatchNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #4
| Source File: layers.py From indic_tagger with Apache License 2.0 | 6 votes |
|
def get_config(self):
config = {'units': self.units,
'learn_mode': self.learn_mode,
'test_mode': self.test_mode,
'use_boundary': self.use_boundary,
'use_bias': self.use_bias,
'sparse_target': self.sparse_target,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'chain_initializer': initializers.serialize(self.chain_initializer),
'boundary_initializer': initializers.serialize(self.boundary_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'activation': activations.serialize(self.activation),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'chain_regularizer': regularizers.serialize(self.chain_regularizer),
'boundary_regularizer': regularizers.serialize(self.boundary_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'chain_constraint': constraints.serialize(self.chain_constraint),
'boundary_constraint': constraints.serialize(self.boundary_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'input_dim': self.input_dim,
'unroll': self.unroll}
base_config = super(CRF, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #5
| Source File: capslayers.py From deepcaps with MIT License | 6 votes |
|
def get_config(self):
config = {
'ch_j': self.ch_j,
'n_j': self.n_j,
'kernel_size': self.kernel_size,
'strides': self.strides,
'b_alphas': self.b_alphas,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint)
}
base_config = super(Conv2DCaps, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #6
| Source File: group_norm.py From keras-global-context-networks with MIT License | 6 votes |
|
def get_config(self):
config = {
'groups': self.groups,
'axis': self.axis,
'epsilon': self.epsilon,
'center': self.center,
'scale': self.scale,
'beta_initializer': initializers.serialize(self.beta_initializer),
'gamma_initializer': initializers.serialize(self.gamma_initializer),
'beta_regularizer': regularizers.serialize(self.beta_regularizer),
'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
'beta_constraint': constraints.serialize(self.beta_constraint),
'gamma_constraint': constraints.serialize(self.gamma_constraint)
}
base_config = super(GroupNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #7
| Source File: nascell.py From neural-architecture-search with MIT License | 6 votes |
|
def get_config(self):
config = {'units': self.units,
'projection_units': self.projection_units,
'activation': activations.serialize(self.activation),
'recurrent_activation': activations.serialize(self.recurrent_activation),
'projection_activation': activations.serialize(self.projection_activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'recurrent_initializer': initializers.serialize(self.recurrent_initializer),
'projection_initializer': initializers.serialize(self.projection_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'unit_forget_bias': self.unit_forget_bias,
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer),
'projection_regularizer': regularizers.serialize(self.projection_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'recurrent_constraint': constraints.serialize(self.recurrent_constraint),
'projection_constraint': constraints.serialize(self.projection_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'dropout': self.dropout,
'recurrent_dropout': self.recurrent_dropout,
'implementation': self.implementation}
base_config = super(NASCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #8
| Source File: dense.py From deep_complex_networks with MIT License | 6 votes |
|
def get_config(self):
if self.kernel_initializer in {'complex'}:
ki = self.kernel_initializer
else:
ki = initializers.serialize(self.kernel_initializer)
config = {
'units': self.units,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'init_criterion': self.init_criterion,
'kernel_initializer': ki,
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'seed': self.seed,
}
base_config = super(ComplexDense, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #9
| Source File: sn.py From Coloring-greyscale-images with MIT License | 6 votes |
|
def get_config(self):
config = {
'rank': self.rank,
'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(_Conv, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #10
| Source File: nalu.py From keras-neural-alu with MIT License | 6 votes |
|
def get_config(self):
config = {
'units': self.units,
'use_gating': self.use_gating,
'kernel_W_initializer': initializers.serialize(self.kernel_W_initializer),
'kernel_M_initializer': initializers.serialize(self.kernel_M_initializer),
'gate_initializer': initializers.serialize(self.gate_initializer),
'kernel_W_regularizer': regularizers.serialize(self.kernel_W_regularizer),
'kernel_M_regularizer': regularizers.serialize(self.kernel_M_regularizer),
'gate_regularizer': regularizers.serialize(self.gate_regularizer),
'kernel_W_constraint': constraints.serialize(self.kernel_W_constraint),
'kernel_M_constraint': constraints.serialize(self.kernel_M_constraint),
'gate_constraint': constraints.serialize(self.gate_constraint),
'epsilon': self.epsilon
}
base_config = super(NALU, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #11
| Source File: utils.py From face_landmark_dnn with MIT License | 6 votes |
|
def get_config(self):
config = super(DepthwiseConv2D, self).get_config()
config.pop('filters')
config.pop('kernel_initializer')
config.pop('kernel_regularizer')
config.pop('kernel_constraint')
config['depth_multiplier'] = self.depth_multiplier
config['depthwise_initializer'] = initializers.serialize(
self.depthwise_initializer)
config['depthwise_regularizer'] = regularizers.serialize(
self.depthwise_regularizer)
config['depthwise_constraint'] = constraints.serialize(
self.depthwise_constraint)
return config
# Tracker
Example #12
| Source File: layers.py From anago with MIT License | 6 votes |
|
def get_config(self):
config = {'units': self.units,
'learn_mode': self.learn_mode,
'test_mode': self.test_mode,
'use_boundary': self.use_boundary,
'use_bias': self.use_bias,
'sparse_target': self.sparse_target,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'chain_initializer': initializers.serialize(self.chain_initializer),
'boundary_initializer': initializers.serialize(self.boundary_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'activation': activations.serialize(self.activation),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'chain_regularizer': regularizers.serialize(self.chain_regularizer),
'boundary_regularizer': regularizers.serialize(self.boundary_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'chain_constraint': constraints.serialize(self.chain_constraint),
'boundary_constraint': constraints.serialize(self.boundary_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'input_dim': self.input_dim,
'unroll': self.unroll}
base_config = super(CRF, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #13
| Source File: norm.py From deep_complex_networks with MIT License | 6 votes |
|
def get_config(self):
config = {
'axis': self.axis,
'epsilon': self.epsilon,
'center': self.center,
'scale': self.scale,
'beta_initializer': initializers.serialize(self.beta_initializer),
'gamma_diag_initializer': initializers.serialize(self.gamma_diag_initializer),
'gamma_off_initializer': initializers.serialize(self.gamma_off_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(ComplexLayerNorm, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #14
| Source File: normalizations.py From se_relativisticgan with MIT License | 6 votes |
|
def get_config(self):
config = {'epsilon': self.epsilon,
'axis': self.axis,
'center': self.center,
'scale': self.scale,
'momentum': self.momentum,
'gamma_regularizer': initializers.serialize(self.gamma_regularizer),
'beta_regularizer': initializers.serialize(self.beta_regularizer),
'moving_mean_initializer': initializers.serialize(self.moving_mean_initializer),
'moving_variance_initializer': initializers.serialize(self.moving_variance_initializer),
'beta_constraint': constraints.serialize(self.beta_constraint),
'gamma_constraint': constraints.serialize(self.gamma_constraint),
'r_max_value': self.r_max_value,
'd_max_value': self.d_max_value,
't_delta': self.t_delta}
base_config = super(BatchRenormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #15
| Source File: normalizations.py From se_relativisticgan with MIT License | 6 votes |
|
def get_config(self):
config = {
'groups': self.groups,
'axis': self.axis,
'epsilon': self.epsilon,
'center': self.center,
'scale': self.scale,
'beta_initializer': initializers.serialize(self.beta_initializer),
'gamma_initializer': initializers.serialize(self.gamma_initializer),
'beta_regularizer': regularizers.serialize(self.beta_regularizer),
'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
'beta_constraint': constraints.serialize(self.beta_constraint),
'gamma_constraint': constraints.serialize(self.gamma_constraint)
}
base_config = super(GroupNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #16
| Source File: group_norm.py From Keras-Group-Normalization with MIT License | 6 votes |
|
def get_config(self):
config = {
'groups': self.groups,
'axis': self.axis,
'epsilon': self.epsilon,
'center': self.center,
'scale': self.scale,
'beta_initializer': initializers.serialize(self.beta_initializer),
'gamma_initializer': initializers.serialize(self.gamma_initializer),
'beta_regularizer': regularizers.serialize(self.beta_regularizer),
'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
'beta_constraint': constraints.serialize(self.beta_constraint),
'gamma_constraint': constraints.serialize(self.gamma_constraint)
}
base_config = super(GroupNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #17
| Source File: layers.py From sequence-tagging-ner with Apache License 2.0 | 6 votes |
|
def get_config(self):
config = {'units': self.units,
'learn_mode': self.learn_mode,
'test_mode': self.test_mode,
'use_boundary': self.use_boundary,
'use_bias': self.use_bias,
'sparse_target': self.sparse_target,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'chain_initializer': initializers.serialize(self.chain_initializer),
'boundary_initializer': initializers.serialize(self.boundary_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'activation': activations.serialize(self.activation),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'chain_regularizer': regularizers.serialize(self.chain_regularizer),
'boundary_regularizer': regularizers.serialize(self.boundary_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'chain_constraint': constraints.serialize(self.chain_constraint),
'boundary_constraint': constraints.serialize(self.boundary_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'input_dim': self.input_dim,
'unroll': self.unroll}
base_config = super(CRF, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #18
| Source File: gcnn.py From nn_playground with MIT License | 6 votes |
|
def get_config(self):
config = {'output_dim': self.output_dim,
'window_size': self.window_size,
'init': self.init.get_config(),
'stride': self.strides[0],
'activation': activations.serialize(self.activation),
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activy_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'use_bias': self.use_bias,
'input_dim': self.input_dim,
'input_length': self.input_length}
base_config = super(GCNN, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #19
| Source File: qrnn.py From nn_playground with MIT License | 6 votes |
|
def get_config(self):
config = {'units': self.units,
'window_size': self.window_size,
'stride': self.strides[0],
'return_sequences': self.return_sequences,
'go_backwards': self.go_backwards,
'stateful': self.stateful,
'unroll': self.unroll,
'use_bias': self.use_bias,
'dropout': self.dropout,
'activation': activations.serialize(self.activation),
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'input_dim': self.input_dim,
'input_length': self.input_length}
base_config = super(QRNN, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #20
| Source File: normalization.py From faceswap with GNU General Public License v3.0 | 6 votes |
|
def get_config(self):
config = {
"axis": self.axis,
"epsilon": self.epsilon,
"center": self.center,
"scale": self.scale,
"beta_initializer": initializers.serialize(self.beta_initializer),
"gamma_initializer": initializers.serialize(self.gamma_initializer),
"beta_regularizer": regularizers.serialize(self.beta_regularizer),
"gamma_regularizer": regularizers.serialize(self.gamma_regularizer),
"beta_constraint": constraints.serialize(self.beta_constraint),
"gamma_constraint": constraints.serialize(self.gamma_constraint)
}
base_config = super(InstanceNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
# Update normalization into Keras custom objects
Example #21
| Source File: switchnorm.py From keras-switchnorm with MIT License | 6 votes |
|
def get_config(self):
config = {
'axis': self.axis,
'epsilon': self.epsilon,
'momentum': self.momentum,
'center': self.center,
'scale': self.scale,
'beta_initializer': initializers.serialize(self.beta_initializer),
'gamma_initializer': initializers.serialize(self.gamma_initializer),
'mean_weights_initializer': initializers.serialize(self.mean_weights_initializer),
'variance_weights_initializer': initializers.serialize(self.variance_weights_initializer),
'moving_mean_initializer': initializers.serialize(self.moving_mean_initializer),
'moving_variance_initializer': initializers.serialize(self.moving_variance_initializer),
'beta_regularizer': regularizers.serialize(self.beta_regularizer),
'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
'mean_weights_regularizer': regularizers.serialize(self.mean_weights_regularizer),
'variance_weights_regularizer': regularizers.serialize(self.variance_weights_regularizer),
'beta_constraint': constraints.serialize(self.beta_constraint),
'gamma_constraint': constraints.serialize(self.gamma_constraint),
'mean_weights_constraints': constraints.serialize(self.mean_weights_constraints),
'variance_weights_constraints': constraints.serialize(self.variance_weights_constraints),
}
base_config = super(SwitchNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #22
| Source File: ind_rnn.py From Keras-IndRNN with MIT License | 6 votes |
|
def get_config(self):
config = {'units': self.units,
'recurrent_clip_min': self.recurrent_clip_min,
'recurrent_clip_max': self.recurrent_clip_max,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'recurrent_initializer': initializers.serialize(self.recurrent_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'recurrent_constraint': constraints.serialize(self.recurrent_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'dropout': self.dropout,
'recurrent_dropout': self.recurrent_dropout,
'implementation': self.implementation}
base_config = super(IndRNNCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #23
| Source File: ind_rnn.py From Keras-IndRNN with MIT License | 6 votes |
|
def get_config(self):
config = {'units': self.units,
'recurrent_clip_min': self.recurrent_clip_min,
'recurrent_clip_max': self.recurrent_clip_max,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'recurrent_initializer': initializers.serialize(self.recurrent_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'recurrent_constraint': constraints.serialize(self.recurrent_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'dropout': self.dropout,
'recurrent_dropout': self.recurrent_dropout,
'implementation': self.implementation}
base_config = super(IndRNN, self).get_config()
del base_config['cell']
return dict(list(base_config.items()) + list(config.items()))
Example #24
| Source File: nested_lstm.py From Nested-LSTM with MIT License | 6 votes |
|
def get_config(self):
config = {'units': self.units,
'depth': self.depth,
'activation': activations.serialize(self.activation),
'recurrent_activation': activations.serialize(self.recurrent_activation),
'cell_activation': activations.serialize(self.cell_activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'recurrent_initializer': initializers.serialize(self.recurrent_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'unit_forget_bias': self.unit_forget_bias,
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'recurrent_constraint': constraints.serialize(self.recurrent_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'dropout': self.dropout,
'recurrent_dropout': self.recurrent_dropout,
'implementation': self.implementation}
base_config = super(NestedLSTMCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #25
| Source File: nested_lstm.py From Nested-LSTM with MIT License | 6 votes |
|
def get_config(self):
config = {'units': self.units,
'depth': self.depth,
'activation': activations.serialize(self.activation),
'recurrent_activation': activations.serialize(self.recurrent_activation),
'cell_activation': activations.serialize(self.cell_activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'recurrent_initializer': initializers.serialize(self.recurrent_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'unit_forget_bias': self.unit_forget_bias,
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'recurrent_constraint': constraints.serialize(self.recurrent_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'dropout': self.dropout,
'recurrent_dropout': self.recurrent_dropout,
'implementation': self.implementation}
base_config = super(NestedLSTM, self).get_config()
del base_config['cell']
return dict(list(base_config.items()) + list(config.items()))
Example #26
| Source File: qrnn.py From embedding-as-service with MIT License | 6 votes |
|
def get_config(self):
config = {'units': self.units,
'window_size': self.window_size,
'stride': self.strides[0],
'return_sequences': self.return_sequences,
'go_backwards': self.go_backwards,
'stateful': self.stateful,
'unroll': self.unroll,
'use_bias': self.use_bias,
'dropout': self.dropout,
'activation': activations.serialize(self.activation),
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'input_dim': self.input_dim,
'input_length': self.input_length}
base_config = super(QRNN, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #27
| Source File: batch_renorm.py From BatchRenormalization with MIT License | 6 votes |
|
def get_config(self):
config = {
'epsilon': self.epsilon,
'axis': self.axis,
'center': self.center,
'scale': self.scale,
'momentum': self.momentum,
'gamma_regularizer': initializers.serialize(self.gamma_regularizer),
'beta_regularizer': initializers.serialize(self.beta_regularizer),
'moving_mean_initializer': initializers.serialize(
self.moving_mean_initializer),
'moving_variance_initializer': initializers.serialize(
self.moving_variance_initializer),
'beta_constraint': constraints.serialize(self.beta_constraint),
'gamma_constraint': constraints.serialize(self.gamma_constraint),
'r_max_value': self.r_max_value,
'd_max_value': self.d_max_value,
't_delta': self.t_delta
}
base_config = super(BatchRenormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #28
| Source File: cells.py From recurrentshop with MIT License | 6 votes |
|
def get_config(self):
config = {
'activation': activations.serialize(self.activation),
'recurrent_activation': activations.serialize(self.recurrent_activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'recurrent_initializer': initializers.serialize(self.recurrent_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'recurrent_constraint': constraints.serialize(self.recurrent_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(ExtendedRNNCell, self).get_config()
config.update(base_config)
return config
Example #29
| Source File: groupnormalization.py From keras-contrib with MIT License | 6 votes |
|
def get_config(self):
config = {
'groups': self.groups,
'axis': self.axis,
'epsilon': self.epsilon,
'center': self.center,
'scale': self.scale,
'beta_initializer': initializers.serialize(self.beta_initializer),
'gamma_initializer': initializers.serialize(self.gamma_initializer),
'beta_regularizer': regularizers.serialize(self.beta_regularizer),
'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
'beta_constraint': constraints.serialize(self.beta_constraint),
'gamma_constraint': constraints.serialize(self.gamma_constraint)
}
base_config = super(GroupNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #30
| Source File: cosineconvolution2d.py From keras-contrib with MIT License | 6 votes |
|
def get_config(self):
config = {
'filters': self.filters,
'kernel_size': self.kernel_size,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'activation': activations.serialize(self.activation),
'padding': self.padding,
'strides': self.strides,
'data_format': self.data_format,
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'use_bias': self.use_bias}
base_config = super(CosineConvolution2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
