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'.
|