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()))
python类serialize()的实例源码
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()))
multiplicative_lstm.py 文件源码
项目：Keras-Multiplicative-LSTM
作者: titu1994
项目源码
文件源码
阅读 26
收藏 0
点赞 0
评论 0
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(MultiplicativeLSTM, 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,
'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()))
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 = {
'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 = {
'filters': self.filters,
'sum_axes': self.sum_axes,
'filter_axes': self.filter_axes,
'activation': activations.serialize(self.activation),
'kernel_activation': activations.serialize(self.kernel_activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.kernel_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(FilterDims, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'filters_simple': self.filters_simple,
'filters_complex': self.filters_complex,
'sum_axes': self.sum_axes,
'filter_axes': self.filter_axes,
'activation': activations.serialize(self.activation),
'kernel_activation': activations.serialize(self.kernel_activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.kernel_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(FilterDimsV1, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
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()))
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
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()))
def get_config(self):
config = {
'ratio': self.ratio,
'data_format': self.data_format,
'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),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(SE, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
config = {
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_initializer': initializers.serialize(self.bias_initializer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'bias_constraint': constraints.serialize(self.bias_constraint),
'context_initializer': initializers.serialize(self.context_initializer),
'context_regularizer': regularizers.serialize(self.context_regularizer),
'context_constraint': constraints.serialize(self.context_constraint)
}
base_config = super(AttentionLayer, 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 = 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 = 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 = {
'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 = {
'units': self.units,
'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(WeightedMean, 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 = {
'init': initializers.serialize(self.init),
'U_regularizer': regularizers.serialize(self.U_regularizer),
'b_start_regularizer': regularizers.serialize(self.b_start_regularizer),
'b_end_regularizer': regularizers.serialize(self.b_end_regularizer),
'U_constraint': constraints.serialize(self.U_constraint),
'b_start_constraint': constraints.serialize(self.b_start_constraint),
'b_end_constraint': constraints.serialize(self.b_end_constraint)
}
base_config = super(ChainCRF, 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 = {'W_regularizer': regularizers.serialize(self.W_regularizer),
'u_regularizer': regularizers.serialize(self.u_regularizer),
'b_regularizer': regularizers.serialize(self.b_regularizer),
'W_constraint': constraints.serialize(self.W_constraint),
'u_constraint': constraints.serialize(self.u_constraint),
'b_constraint': constraints.serialize(self.b_constraint),
'W_dropout': self.W_dropout,
'u_dropout': self.u_dropout,
'bias': self.bias}
base_config = super(AttentionWithContext, 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
