Python keras.activations.serialize() Examples
The following are 30
code examples of keras.activations.serialize().
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Example #1
| Source File: PhasedLSTM.py From PhasedLSTM-Keras with MIT License | 6 votes |
|
def get_config(self):
config = {'units': self.units,
'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),
'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}
base_config = super(PhasedLSTM, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #2
| 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()))
Example #3
| 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 #4
| 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 #5
| 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 #6
| 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 #7
| 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 #8
| 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 #9
| 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 #10
| 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 #11
| 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 #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: 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 #14
| Source File: TTRNN.py From TT_RNN with MIT License | 6 votes |
|
def get_config(self):
config = {'units': self.units,
'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),
'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}
base_config = super(TT_LSTM, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #15
| 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 #16
| Source File: TTRNN.py From TT_RNN with MIT License | 6 votes |
|
def get_config(self):
config = {'units': self.units,
'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),
'dropout': self.dropout,
'recurrent_dropout': self.recurrent_dropout}
base_config = super(TT_GRU, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #17
| 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 #18
| 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 #19
| Source File: TTRNN.py From TT_RNN with MIT License | 6 votes |
|
def get_config(self):
config = {'units': self.units,
'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}
base_config = super(TT_RNN, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #20
| Source File: DenseMoE.py From mixture-of-experts with GNU General Public License v3.0 | 6 votes |
|
def get_config(self):
config = {
'units': self.units,
'n_experts':self.n_experts,
'expert_activation': activations.serialize(self.expert_activation),
'gating_activation': activations.serialize(self.gating_activation),
'use_expert_bias': self.use_expert_bias,
'use_gating_bias': self.use_gating_bias,
'expert_kernel_initializer_scale': self.expert_kernel_initializer_scale,
'gating_kernel_initializer_scale': self.gating_kernel_initializer_scale,
'expert_bias_initializer': initializers.serialize(self.expert_bias_initializer),
'gating_bias_initializer': initializers.serialize(self.gating_bias_initializer),
'expert_kernel_regularizer': regularizers.serialize(self.expert_kernel_regularizer),
'gating_kernel_regularizer': regularizers.serialize(self.gating_kernel_regularizer),
'expert_bias_regularizer': regularizers.serialize(self.expert_bias_regularizer),
'gating_bias_regularizer': regularizers.serialize(self.gating_bias_regularizer),
'expert_kernel_constraint': constraints.serialize(self.expert_kernel_constraint),
'gating_kernel_constraint': constraints.serialize(self.gating_kernel_constraint),
'expert_bias_constraint': constraints.serialize(self.expert_bias_constraint),
'gating_bias_constraint': constraints.serialize(self.gating_bias_constraint),
'activity_regularizer': regularizers.serialize(self.activity_regularizer)
}
base_config = super(DenseMoE, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #21
| Source File: hadamard.py From landmark-recognition-challenge with GNU General Public License v3.0 | 5 votes |
|
def get_config(self):
config = {
'output_dim': self.output_dim,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'l2_normalize': self.l2_normalize,
'output_raw_logits' : self.output_raw_logits,
}
base_config = super(HadamardClassifier, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #22
| Source File: custom.py From graph-representation-learning with MIT License | 5 votes |
|
def get_config(self):
config = {
'units': self.units,
'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(DenseTied, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #23
| Source File: ConvolutionalMoE.py From mixture-of-experts with GNU General Public License v3.0 | 5 votes |
|
def get_config(self):
config = {
'rank': self.rank,
'n_filters': self.n_filters,
'n_experts_per_filter':n_experts_per_filter,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'expert_activation': activations.serialize(self.expert_activation),
'gating_activation': activations.serialize(self.gating_activation),
'use_expert_bias': self.use_expert_bias,
'use_gating_bias': self.use_gating_bias,
'expert_kernel_initializer_scale':self.expert_kernel_initializer_scale,
'gating_kernel_initializer_scale':self.gating_kernel_initializer_scale,
'expert_bias_initializer': initializers.serialize(self.expert_bias_initializer),
'gating_bias_initializer': initializers.serialize(self.gating_bias_initializer),
'expert_kernel_regularizer': regularizers.serialize(self.expert_kernel_regularizer),
'gating_kernel_regularizer': regularizers.serialize(self.gating_kernel_regularizer),
'expert_bias_regularizer': regularizers.serialize(self.expert_bias_regularizer),
'gating_bias_regularizer': regularizers.serialize(self.gating_bias_regularizer),
'expert_kernel_constraint': constraints.serialize(self.expert_kernel_constraint),
'gating_kernel_constraint': constraints.serialize(self.gating_kernel_constraint),
'expert_bias_constraint': constraints.serialize(self.expert_bias_constraint),
'gating_bias_constraint': constraints.serialize(self.gating_bias_constraint),
'activity_regularizer': regularizers.serialize(self.activity_regularizer)
}
base_config = super(_ConvMoE, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #24
| Source File: activations_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_serialization():
all_activations = ['softmax', 'relu', 'elu', 'tanh',
'sigmoid', 'hard_sigmoid', 'linear',
'softplus', 'softsign', 'selu']
for name in all_activations:
fn = activations.get(name)
ref_fn = getattr(activations, name)
assert fn == ref_fn
config = activations.serialize(fn)
fn = activations.deserialize(config)
assert fn == ref_fn
Example #25
| Source File: activations_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_serialization():
all_activations = ['softmax', 'relu', 'elu', 'tanh',
'sigmoid', 'hard_sigmoid', 'linear',
'softplus', 'softsign', 'selu']
for name in all_activations:
fn = activations.get(name)
ref_fn = getattr(activations, name)
assert fn == ref_fn
config = activations.serialize(fn)
fn = activations.deserialize(config)
assert fn == ref_fn
Example #26
| Source File: attention.py From MusiteDeep with GNU General Public License v2.0 | 5 votes |
|
def get_config(self):
config = {
'hidden':self.hidden,
'activation': activations.serialize(self.activation),
'init': initializers.serialize(self.init),
'W_regularizer': regularizers.serialize(self.W_regularizer),
'W0_regularizer': regularizers.serialize(self.W0_regularizer),
'W_constraint':constraints.serialize(self.W_constraint),
'W0_constraint':constraints.serialize(self.W0_constraint)
}
base_config = super(Attention, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #27
| Source File: activations_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_serialization():
all_activations = ['softmax', 'relu', 'elu', 'tanh',
'sigmoid', 'hard_sigmoid', 'linear',
'softplus', 'softsign', 'selu']
for name in all_activations:
fn = activations.get(name)
ref_fn = getattr(activations, name)
assert fn == ref_fn
config = activations.serialize(fn)
fn = activations.deserialize(config)
assert fn == ref_fn
Example #28
| Source File: mmoe.py From keras-mmoe with MIT License | 5 votes |
|
def get_config(self):
"""
Method for returning the configuration of the MMoE layer.
:return: Config dictionary
"""
config = {
'units': self.units,
'num_experts': self.num_experts,
'num_tasks': self.num_tasks,
'use_expert_bias': self.use_expert_bias,
'use_gate_bias': self.use_gate_bias,
'expert_activation': activations.serialize(self.expert_activation),
'gate_activation': activations.serialize(self.gate_activation),
'expert_bias_initializer': initializers.serialize(self.expert_bias_initializer),
'gate_bias_initializer': initializers.serialize(self.gate_bias_initializer),
'expert_bias_regularizer': regularizers.serialize(self.expert_bias_regularizer),
'gate_bias_regularizer': regularizers.serialize(self.gate_bias_regularizer),
'expert_bias_constraint': constraints.serialize(self.expert_bias_constraint),
'gate_bias_constraint': constraints.serialize(self.gate_bias_constraint),
'expert_kernel_initializer': initializers.serialize(self.expert_kernel_initializer),
'gate_kernel_initializer': initializers.serialize(self.gate_kernel_initializer),
'expert_kernel_regularizer': regularizers.serialize(self.expert_kernel_regularizer),
'gate_kernel_regularizer': regularizers.serialize(self.gate_kernel_regularizer),
'expert_kernel_constraint': constraints.serialize(self.expert_kernel_constraint),
'gate_kernel_constraint': constraints.serialize(self.gate_kernel_constraint),
'activity_regularizer': regularizers.serialize(self.activity_regularizer)
}
base_config = super(MMoE, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
Example #29
| Source File: activations_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_serialization():
all_activations = ['softmax', 'relu', 'elu', 'tanh',
'sigmoid', 'hard_sigmoid', 'linear',
'softplus', 'softsign', 'selu']
for name in all_activations:
fn = activations.get(name)
ref_fn = getattr(activations, name)
assert fn == ref_fn
config = activations.serialize(fn)
fn = activations.deserialize(config)
assert fn == ref_fn
Example #30
| Source File: core.py From keras-contrib with MIT License | 5 votes |
|
def get_config(self):
config = {
'units': self.units,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'activation': activations.serialize(self.activation),
'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(CosineDense, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
