MaxPoolWithArgmax

tensorflow C++ API

tensorflow::ops::MaxPoolWithArgmax

Performs max pooling on the input and outputs both max values and indices.

Summary

The indices inargmaxare flattened, so that a maximum value at position[b, y, x, c]becomes flattened index((b * height + y) * width + x) * channels + c.

The indices returned are always in[0, height) x [0, width)before flattening, even if padding is involved and the mathematically correct answer is outside (either negative or too large). This is a bug, but fixing it is difficult to do in a safe backwards compatible way, especially due to flattening.

Arguments:

• scope: A Scope object
• input: 4-D with shape [batch, height, width, channels].Input to pool over.
• ksize: The size of the window for each dimension of the input tensor.
• strides: The stride of the sliding window for each dimension of the input tensor.
• padding: The type of padding algorithm to use.

Returns:

• Outputoutput: The max pooled output tensor.
• Outputargmax: 4-D. The flattened indices of the max values chosen for each output.

MaxPoolWithArgmax 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 input: 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

Return:

• Output output: Output object of MaxPoolWithArgmax class object.
• Output argmax: Output object of MaxPoolWithArgmax class object.

Result:

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

Using Method