Apply 2D conv with un-shared weights.
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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'. |