AvgPool3DGrad

tensorflow C++ API

tensorflow::ops::AvgPool3DGrad

Computes gradients of average pooling function.

Summary

Arguments:

• scope: A Scope object
• orig_input_shape: The original input dimensions.
• grad:Output backprop of shape[batch, depth, rows, cols, channels].
• ksize: 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have ksize[0] = ksize[4] = 1.
• strides: 1-D tensor of length 5. The stride of the sliding window for each dimension ofinput. Must have strides[0] = strides[4] = 1.
• padding: The type of padding algorithm to use.

Optional attributes (seeAttrs):

• data_format: The data format of the input and output data. With the default format “NDHWC”, the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be “NCDHW”, the data storage order is: [batch, in_channels, in_depth, in_height, in_width].

Returns:

• Output: The backprop for input.

AvgPool3DGrad block

Source link : https://github.com/EXPNUNI/enuSpaceTensorflow/blob/master/enuSpaceTensorflow/tf_nn.cpp

Argument:

• Scope scope : A Scope object (A scope is generated automatically each page. A scope is not connected.)
• Input orig_input_shape: connect Input node.
• Input grad: connect Input node.
• ArraySlice< int> ksize: input ksize in values. ex)1,2,2,2,1
• ArraySlice< int> strides: input ksize in values. ex)1,4,3,2,1
• stringpiece padding: input padding in value. ex)SAME
• AvgPool3DGrad::Attrs attrs: input attrs in values )data_format_ = NHWC;

Return:

• Output output: Output object of AvgPool3DGrad class object.

Result:

• std::vector(Tensor) result_output : Returned object of executed result by calling session.

Using Method