kerasadf.layers.convolutional.Conv2D

class kerasadf.layers.convolutional.Conv2D(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: Conv

2D convolution layer (for example spatial convolution).

Assumed Density Filtering (ADF) version of keras.layers.Conv2D.

Parameters:

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 two integers, specifying the width and height of the convolution window.

stridesint or tuple of int or list of int, optional

An integer or tuple/list of two integers, specifying the stride lengths 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, height, width, channels) while “channels_first” corresponds to inputs with shape (batch, channels, height, width). 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 two integers, specifying the dilation rates 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_constraint: Constraint or string, optional

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

bias_constraint: Constraint or string, optional

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

Notes

Input shape

4D tensor with shape (samples, channels, height, width) if data_format is “channels_first” or shape (samples, height, width, channels) if data_format is “channels_last”.

Output shape

4D tensor with shape (samples, filters, new_height, new_width) if data_format is “channels_first” or shape (samples, new_height, new_width, filters) if data_format is “channels_last”. new_height and new_width values might be different from height and width due to padding.