Apply 2D conv with un-shared weights.

    Apply 2D conv with un-shared weights.

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