k_local_conv2d: Apply 2D conv with un-shared weights.

In keras: R Interface to 'Keras'

Apply 2D conv with un-shared weights.

Description

Apply 2D conv with un-shared weights.

Usage

k_local_conv2d(
  inputs,
  kernel,
  kernel_size,
  strides,
  output_shape,
  data_format = NULL
)

Arguments

inputs	4D tensor with shape: (batch_size, filters, new_rows, new_cols) if data_format='channels_first' or 4D tensor with shape: (batch_size, new_rows, new_cols, filters) if data_format='channels_last'.
kernel	the unshared weight for convolution, with shape (output_items, feature_dim, filters)
kernel_size	a list of 2 integers, specifying the width and height of the 2D convolution window.
strides	a list of 2 integers, specifying the strides of the convolution along the width and height.
output_shape	a list with (output_row, output_col)
data_format	the data format, channels_first or channels_last

Value

A 4d tensor with shape: (batch_size, filters, new_rows, new_cols) if data_format='channels_first' or 4D tensor with shape: (batch_size, new_rows, new_cols, filters) if data_format='channels_last'.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

keras documentation built on May 29, 2024, 3:20 a.m.