def get_config(self):
config = {'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),
'input_dim': self.input_dim,
'learnedKernel': self.tied_to.get_weights()[0],
'input_length': self.input_length}
config2 = {'layer_inner': {'bias': np.asarray(self.tied_to.get_weights()[1]),
'weights': np.asarray(self.tied_to.get_weights()[0]),
'class_name': self.tied_to.__class__.__name__,
'config': self.tied_to.get_config()}}
base_config = super(Convolution1D_tied, self).get_config()
return dict(list(base_config.items()) + list(config.items()) + list(config2.items()))
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),
'bias_initializer': initializers.serialize(self.bias_initializer),
'projection_initializer': initializers.serialize(self.projection_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),
'projection_regularizer': regularizers.serialize(self.projection_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),
'projection_constraint': constraints.serialize(self.projection_constraint),
'dropout': self.dropout,
'recurrent_dropout': self.recurrent_dropout,
'implementation': self.implementation}
base_config = super(NASRNN, self).get_config()
del base_config['cell']
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {'epsilon': self.epsilon,
'axis': self.axis,
'gamma_init': initializers.serialize(self.gamma_init),
'beta_init': initializers.serialize(self.beta_init),
'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
'beta_regularizer': regularizers.serialize(self.gamma_regularizer),
'group': self.group}
base_config = super(GroupNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {'init': initializers.serialize(self.init),
'activation': activations.serialize(self.activation),
'W_regularizer': regularizers.serialize(self.W_regularizer),
'b_regularizer': regularizers.serialize(self.b_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'W_constraint': constraints.serialize(self.W_constraint),
'b_constraint': constraints.serialize(self.b_constraint),
'bias': self.bias,
'input_dim': self.input_dim}
base_config = super(Highway, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'alpha_initializer': initializers.serialize(self.alpha_initializer),
'alpha_regularizer': regularizers.serialize(self.alpha_regularizer),
'alpha_constraint': constraints.serialize(self.alpha_constraint),
'beta_initializer': initializers.serialize(self.beta_initializer),
'beta_regularizer': regularizers.serialize(self.beta_regularizer),
'beta_constraint': constraints.serialize(self.beta_constraint),
'shared_axes': self.shared_axes
}
base_config = super(ParametricSoftplus, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
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()))
def get_config(self):
config = {'nb_complex': self.nb_complex,
'filter_delays':self.filter_delays,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint)}
base_config = super(SpatioTemporalFilterComplex, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'a_initializer': initializers.serialize(self.a_initializer),
'a_regularizer': regularizers.serialize(self.a_regularizer),
'a_constraint': constraints.serialize(self.a_constraint),
'k_initializer': initializers.serialize(self.k_initializer),
'k_regularizer': regularizers.serialize(self.k_regularizer),
'k_constraint': constraints.serialize(self.k_constraint),
'n_initializer': initializers.serialize(self.n_initializer),
'n_regularizer': regularizers.serialize(self.n_regularizer),
'n_constraint': constraints.serialize(self.n_constraint),
'z_initializer': initializers.serialize(self.z_initializer),
'z_regularizer': regularizers.serialize(self.z_regularizer),
'z_constraint': constraints.serialize(self.z_constraint),
'shared_axes': self.shared_axes
}
base_config = super(Hill, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'vv_theta': self.vv,
'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(RProjFWH_BatchNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'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(LocallyConnected2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
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
def get_config(self):
config = {
'num_units': self.num_units,
'num_heads': self.num_heads,
'residual': self.residual,
'normalize': self.normalize,
'initializer': initializers.serialize(self.initializer),
'regularizer': regularizers.serialize(self.regularizer),
'constraint': constraints.serialize(self.constraint)
}
base_config = super(MultiHeadAttention, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
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()))
def get_config(self):
config = {
'groups': self.groups,
'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(GroupNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
"input_dim": self.input_dim,
"output_dim": self.output_dim,
"embeddings_initializer": initializers.serialize(
self.embeddings_initializer
),
"embeddings_regularizer": regularizers.serialize(
self.embeddings_regularizer
),
"activity_regularizer": regularizers.serialize(
self.activity_regularizer
),
"embeddings_constraint": constraints.serialize(
self.embeddings_constraint
),
"mask_zero": self.mask_zero,
"input_length": self.input_length,
}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
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,
'sc_dropout': self.sc_dropout}
base_config = super(SC_LSTM, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'units': self.units,
'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)
}
base_config = super(CuDNNLSTM, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'input_dim':
self.input_dim,
'output_dim':
self.output_dim,
'embeddings_initializer':
initializers.serialize(self.embeddings_initializer),
'embeddings_regularizer':
regularizers.serialize(self.embeddings_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'embeddings_constraint':
constraints.serialize(self.embeddings_constraint),
'mask_zero':
self.mask_zero,
'input_length':
self.input_length
}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'ch_j':
self.ch_j,
'n_j':
self.n_j,
'r_num':
self.r_num,
'b_alphas':
self.b_alphas,
'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(DenseCaps, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'nb_complex': self.nb_complex,
'filter_delays': self.filter_delays,
'kernel_initializer':
initializers.serialize(self.kernel_initializer),
'kernel_regularizer':
regularizers.serialize(self.kernel_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint)
}
base_config = super(SpatioTemporalFilterComplex, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {'num_capsule': self.num_capsule,
'dim_capsule': self.dim_capsule,
'routings': self.routings,
'share_weights': self.share_weights,
'activation': activations.serialize(self.activation),
'regularizer': regularizers.serialize(self.regularizer),
'initializer': initializers.serialize(self.initializer),
'constraint': constraints.serialize(self.constraint)}
base_config = super(Capsule, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
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(Convolution2D_test, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'output_dim': self.output_dim,
'adjacency_matrix': self.adjacency_matrix,
'num_filters': self.num_filters,
'graph_conv_filters': self.graph_conv_filters,
'num_attention_heads': self.num_attention_heads,
'attention_combine': self.attention_combine,
'attention_dropout': self.attention_dropout,
'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(GraphAttentionCNN, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'matrix_initializer':
initializers.serialize(self.matrix_initializer),
'matrix_regularizer':
regularizers.serialize(self.matrix_regularizer),
'matrix_constraint': constraints.serialize(self.matrix_constraint),
'matrix_scope': self.matrix_scope,
'projected_atom_shape': self.projected_atom_shape
}
super_config = super(PointMatrixPrototype, self).get_config()
return dict(list(super_config.items()) + list(config.items()))
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),
'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
}
config.update(rnn_utils.config_for_enable_caching_device(self))
base_config = super(LSTMCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'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,
'preslice':
self.preslice,
'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),
'attention_function':
self.attention_function,
'mu1_regularizer':
regularizers.serialize(self.mu1_regularizer),
'sig1_regularizer':
regularizers.serialize(self.sig1_regularizer),
'mu2_regularizer':
regularizers.serialize(self.mu2_regularizer),
'sig2_regularizer':
regularizers.serialize(self.sig2_regularizer),
'mu1_constraint':
constraints.serialize(self.mu1_constraint),
'sig1_constraint':
constraints.serialize(self.sig1_constraint),
'mu2_constraint':
constraints.serialize(self.mu2_constraint),
'sig2_constraint':
constraints.serialize(self.sig2_constraint)
}
base_config = super(Target2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'alpha_pos_initializer': initializers.serialize(self.alpha_pos_initializer),
'alpha_neg_initializer': initializers.serialize(self.alpha_neg_initializer),
'beta_pos_initializer': initializers.serialize(self.beta_pos_initializer),
'beta_neg_initializer': initializers.serialize(self.beta_neg_initializer),
'rho_pos_initializer': initializers.serialize(self.rho_pos_initializer),
'rho_neg_initializer': initializers.serialize(self.rho_neg_initializer),
'alpha_pos_constraint': constraints.serialize(self.alpha_pos_constraint),
'alpha_neg_constraint': constraints.serialize(self.alpha_neg_constraint),
'beta_pos_constraint': constraints.serialize(self.beta_pos_constraint),
'beta_neg_constraint': constraints.serialize(self.beta_neg_constraint),
'rho_pos_constraint': constraints.serialize(self.rho_pos_constraint),
'rho_neg_constraint': constraints.serialize(self.rho_neg_constraint),
'alpha_pos_regularizer': regularizers.serialize(self.alpha_pos_regularizer),
'alpha_neg_regularizer': regularizers.serialize(self.alpha_neg_regularizer),
'beta_pos_regularizer': regularizers.serialize(self.beta_pos_regularizer),
'beta_neg_regularizer': regularizers.serialize(self.beta_neg_regularizer),
'rho_pos_regularizer': regularizers.serialize(self.rho_pos_regularizer),
'rho_neg_regularizer': regularizers.serialize(self.rho_neg_regularizer),
}
base_config = super(PowerPReLU, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
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),
"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,
"reset_after":
self.reset_after,
}
config.update(rnn_utils.config_for_enable_caching_device(self))
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'tt_input_shape':
self.tt_input_shape,
'tt_output_shape':
self.tt_output_shape,
'tt_ranks':
self.tt_ranks,
'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()))
def get_config(self):
config = {
"num_heads":
self._num_heads,
"key_dim":
self._key_dim,
"value_dim":
self._value_dim,
"dropout":
self._dropout,
"use_bias":
self._use_bias,
"output_shape":
self._output_shape,
"attention_axes":
self._attention_axes,
"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),
"query_shape":
self._query_shape,
"key_shape":
self._key_shape,
"value_shape":
self._value_shape,
}
base_config = super(MultiHeadAttention, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'filters':
self.filters,
'kernel_size':
self.kernel_size,
'strides':
self.strides,
'padding':
self.padding,
'data_format':
self.data_format,
'activation':
activations.serialize(self.activation),
'use_bias':
self.use_bias,
'depthwise_initializer':
initializers.serialize(self.depthwise_initializer),
'pointwise_initializer':
initializers.serialize(self.pointwise_initializer),
'bias_initializer':
initializers.serialize(self.bias_initializer),
'depthwise_regularizer':
regularizers.serialize(self.depthwise_regularizer),
'pointwise_regularizer':
regularizers.serialize(self.pointwise_regularizer),
'bias_regularizer':
regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'depthwise_constraint':
constraints.serialize(self.depthwise_constraint),
'pointwise_constraint':
constraints.serialize(self.pointwise_constraint),
'bias_constraint':
constraints.serialize(self.bias_constraint)
}
base_config = super(SeparableConv2DKeras, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
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()))
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()))
def get_config(self):
config = {'units': self.units,
'weight_dim': self.weight_dim,
'interval_dim': self.interval_dim,
'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),
'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(BLSTMCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'units':
self.units,
'kernel_initializer':
initializers.serialize(self.kernel_initializer),
'recurrent_initializer':
initializers.serialize(self.recurrent_initializer),
'kernel_regularizer':
regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer':
regularizers.serialize(self.recurrent_regularizer),
'kernel_constraint':
constraints.serialize(self.kernel_constraint),
'recurrent_constraint':
constraints.serialize(self.recurrent_constraint),
'memory_size':
self.memory_size
}
base_config = super(NeuralMap, self).get_config()
del base_config['cell']
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = super(DepthwiseConv2D, self).get_config()
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
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,
'implementation': self.implementation}
base_config = super(Adaptive_SFM, self).get_config()
del base_config['cell']
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {'units': self.units,
'support': self.support,
'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(GraphConvolution, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
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_diag_initializer': initializers.serialize(self.gamma_diag_initializer) if self.gamma_diag_initializer != sqrt_init else 'sqrt_init',
'gamma_off_initializer': initializers.serialize(self.gamma_off_initializer),
'moving_mean_initializer': initializers.serialize(self.moving_mean_initializer),
'moving_variance_initializer': initializers.serialize(self.moving_variance_initializer) if self.moving_variance_initializer != sqrt_init else 'sqrt_init',
'moving_covariance_initializer': initializers.serialize(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(QuaternionBatchNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {'out_dim': self.out_dim,
'drop_rate': self.drop_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(GraphAttention, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
"""Returns the config of the layer.
The Keras configuration for the layer.
Returns
--------
dict
A python dictionary containing the layer configuration
"""
config = {
'epsilon': self.epsilon,
'axis': self.axis,
'gamma_init': initializers.serialize(self.gamma_init),
'beta_init': initializers.serialize(self.beta_init),
'gamma_regularizer':
regularizers.serialize(self.gamma_regularizer),
'beta_regularizer': regularizers.serialize(self.gamma_regularizer),
'group': self.group
}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'output_dim': self.output_dim,
'num_filters': self.num_filters,
'arma_conv_AR': self.arma_conv_AR,
'arma_conv_MA': self.arma_conv_MA,
'input_signal': self.input_signal,
'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(DFNets, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
