dlpy.layers.Conv1d

class dlpy.layers.Conv1d(n_filters, width=1, stride=1, name=None, padding=None, act='relu', fcmp_act=None, init=None, std=None, mean=None, truncation_factor=None, init_bias=None, dropout=None, include_bias=True, src_layers=None, **kwargs)

Bases: dlpy.layers._Conv

Convolution layer in 1D

Parameters:

n_filters : int

Specifies the number of filters for the layer.

width : int

Specifies the width of the 1D kernel.
Default: 1

stride : int, optional

Specifies the step size for the moving window of the kernel over the input data.
Default: 1

name : string, optional

Specifies the name of the convolution layer.

padding : int, optional

Specifies the padding size, assuming equal padding vertically and horizontally.

act : string, optional

Specifies the activation function.
Valid Values: AUTO, IDENTITY, LOGISTIC, SIGMOID, TANH, RECTIFIER, RELU, SOFPLUS, ELU, LEAKY, FCMP
Default: RELU

fcmp_act : string, optional

Specifies the FCMP activation function for the layer.

init : string, optional

Specifies the initialization scheme for the layer.
Valid Values: XAVIER, UNIFORM, NORMAL, CAUCHY, XAVIER1, XAVIER2, MSRA, MSRA1, MSRA2
Default: XAVIER

std : float, optional

Specifies the standard deviation value when the init parameter is set to NORMAL.

mean : float, optional

Specifies the mean value when the init parameter is set to NORMAL.

truncation_factor : float, optional

Specifies the truncation threshold (truncationFactor x std), when the init parameter is set to NORMAL

init_bias : float, optional

Specifies the initial bias for the layer.

dropout : float, optional

Specifies the dropout rate.
Default: None

include_bias : bool, optional

Includes bias neurons (default).

src_layers : iter-of-Layers, optional

Specifies the layers directed to this layer.

Returns:

Conv1d

__init__(n_filters, width=1, stride=1, name=None, padding=None, act='relu', fcmp_act=None, init=None, std=None, mean=None, truncation_factor=None, init_bias=None, dropout=None, include_bias=True, src_layers=None, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(n_filters[, width, stride, name, …])	Initialize self.
count_instances()
format_name([block_num, local_count])	Format the name of the layer
get_number_of_instances()
to_model_params()	Convert the model configuration to CAS action parameters