# max_unpool2d¶

paddle.nn.functional. max_unpool2d ( x, indices, kernel_size, stride=None, padding=0, data_format='NCHW', output_size=None, name=None ) [source]

This API implements max unpooling 2d opereation. See more details in api_nn_pooling_MaxUnPool2D .

Parameters
• x (Tensor) – The input tensor of unpooling operator which is a 4-D tensor with shape [N, C, H, W]. The format of input tensor is “NCHW”, where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature. The data type if float32 or float64.

• indices (Tensor) – The indices given out by maxpooling2d which is a 4-D tensor with shape [N, C, H, W]. The format of input tensor is “NCHW” , where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature. The data type if float32 or float64.

• kernel_size (int|tuple) – The unpool kernel size. If unpool kernel size is a tuple or list, it must contain an integer.

• stride (int|list|tuple) – The unpool stride size. If unpool stride size is a tuple or list, it must contain an integer.

• kernel_size – Size of the max unpooling window.

• output_size (list|tuple, optional) – The target output size. If output_size is not specified, the actual output shape will be automatically calculated by (input_shape, kernel_size, padding).

• name (str, optional) – For detailed information, please refer to Name. Usually name is no need to set and None by default.

• Input (-) – $$(N, C, H_{in}, W_{in})$$

• Output (-) –

$$(N, C, H_{out}, W_{out})$$, where

$H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}$
$W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}$

or as given by output_size in the call operator

• Returns – Tensor: The output tensor of unpooling result.

• Raises – ValueError: If the input is not a 4-D tensor. ValueError: If indeces shape is not equal input shape.

• Examples