# conv_transpose¶

dragon.nn.conv_transpose(
inputs,
kernel_shape=(3, 3),
strides=1,
pads=0,
dilations=1,
group=1,
padding='VALID',
output_padding=None,
output_shape=None,
data_format='NCHW',
**kwargs
)[source]

Apply the n-dimension deconvolution.

• If data_format is 'NCHW', excepts input shape $$(N, C_{\text{in}}, D1, D2, ...)$$, weight shape $$(C_{\text{in}}, C_{\text{out}}, D1_{\text{k}}, D2_{\text{k}}, ...)$$, and output shape is $$(N, C_{\text{out}}, D1_{\text{out}}, D2_{\text{out}}, ...)$$.
• If data_format is 'NHWC', excepts input shape $$(N, D1, D2, ..., C_{\text{in}})$$, weight shape $$(C_{\text{in}}, D1_{\text{k}}, D2_{\text{k}}, ..., C_{\text{out}})$$, and output shape is $$(N, D1_{\text{out}}, D2_{\text{out}}, ..., C_{\text{out}})$$.
• If padding is 'VALID', pads controls the explicit padding size. Otherwise, size are computed automatically use the given method.

Examples:

for i in range(3):
ndim = i + 1
x = dragon.ones((1, 2) + (2,) * ndim)
w = dragon.ones((3, 2) + (1,) * ndim)
y = dragon.nn.conv_transpose(
[x, w],
kernel_shape=(1,) * ndim,
output_shape=(3,) * ndim,
output_padding=(1,) * ndim)
assert y.shape == (1, 3) + (3,) * ndim

Parameters:
• inputs (Sequence[dragon.Tensor]) – The tensor x, weight and optional bias.
• kernel_shape (Sequence[int], optional, default=(3, 3)) – The shape of convolution window.
• strides (Union[int, Sequence[int]], optional, default=1) – The stride of convolution window.
• pads (Union[int, Sequence[int]], optional, default=0) – The zero padding size.
• dilations (Union[int, Sequence[int]], optional, default=1) – The rate of dilated convolution.
• group (int, optional, default=1) – The number of groups to split channels into.
• padding (str, optional, default='VALID') – 'VALID', 'SAME', 'SAME_UPPER' or 'SAME_LOWER'.
• output_padding (Union[Sequence[int], dragon.Tensor], optional) – The additional size added to the output shape.
• output_shape (Union[Sequence[int], dragon.Tensor], optional) – The output shape for automatic padding.
• data_format (str, optional, default='NCHW') – 'NCHW' or 'NHWC'.
Returns:

dragon.Tensor – The output tensor.