k_local_conv2d: Apply 2D conv with un-shared weights.
In dfalbel/keras: R Interface to 'Keras'

Description Usage Arguments Value Keras Backend

Apply 2D conv with un-shared weights.

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

`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

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'.

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://keras.rstudio.com/articles/backend.html#backend-functions.

dfalbel/keras documentation built on Nov. 27, 2019, 8:16 p.m.