tf.keras.backend.local_conv2d

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Apply 2D conv with un-shared weights.

tf.keras.backend.local_conv2d(
    inputs, kernel, kernel_size, strides, output_shape, data_format=None
)

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 tuple of 2 integers, specifying the width and height of the 2D convolution window.
• strides: a tuple of 2 integers, specifying the strides of the convolution along the width and height.
• output_shape: a tuple with (output_row, output_col).
• data_format: the data format, channels_first or channels_last.

Returns:

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'.