kerasadf.layers.convolutional.Conv

class kerasadf.layers.convolutional.Conv(rank, filters, kernel_size, strides=1, padding='valid', data_format=None, dilation_rate=1, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs)

Bases: ADFLayer

Abstract nD convolution layer (private, used as implementation base).

Assumed Density Filtering (ADF) version of the abstract Keras Conv layer.

Parameters:

rankint

Rank of the convolution, e.g. “2” for 2D convolution.

filtersint

Dimensionality of the output space (i.e. the number of filters in the convolution).

kernel_sizeint or tuple of int or list of int

An integer or tuple/list of n integers, specifying the length of the convolution window.

stridesint or tuple of int or list of int, optional

An integer or tuple/list of n integers, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1. Default is 1.

padding{“valid”, “same”}, optional

The padding method. Case-insensitive. Default is “valid”.

data_format{“channels_last”, “channels_first”}, optional

The ordering of the dimensions in the inputs. “channels_last” corresponds to inputs with shape (batch, ..., channels) while “channels_first” corresponds to inputs with shape (batch, channels, ...). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be “channels_last”.

dilation_rateint or tuple of int or list of int, optional

An integer or tuple/list of n integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any strides value != 1. Default is 1.

activationcallable() or str, optional

Activation function to use. Default is no activation (ie. “linear” activation: a(x) = x).

use_biasbool

Whether the layer uses a bias.

kernel_initializerInitializer or str, optional

Initializer for the convolution kernel. Default is “glorot_uniform” initialization.

bias_initializerInitializer or str, optional

Initializer for the bias vector. Default is None.

kernel_regularizerRegularizer or str, optional

Regularizer function applied to the convolution kernel. Default is None.

bias_regularizerRegularizer or str, optional

Regularizer function applied to the bias vector. Default is None.

activity_regularizerRegularizer or str, optional

Regularizer function applied to the output of the layer. Default is None.

kernel_constraintConstraint or str, optional

Constraint function applied to the convolution kernel. Default is None.

bias_constraintConstraint or str, optional

Constraint function applied to the bias vector. Default is None.

build(input_shape)

call(inputs)

compute_output_shape(input_shape)

get_config()