QuantizedAvgPool

tensorflow C++ API

tensorflow::ops::QuantizedAvgPool

Produces the average pool of the input tensor for quantized types.

Summary

Arguments:

• scope: A Scope object
• input: 4-D with shape[batch, height, width, channels].
• min_input: The float value that the lowest quantized input value represents.
• max_input: The float value that the highest quantized input value represents.
• ksize: The size of the window for each dimension of the input tensor. The length must be 4 to match the number of dimensions of the input.
• strides: The stride of the sliding window for each dimension of the input tensor. The length must be 4 to match the number of dimensions of the input.
• padding: The type of padding algorithm to use.

Returns:

• Outputoutput
• Outputmin_output: The float value that the lowest quantized output value represents.
• Outputmax_output: The float value that the highest quantized output value represents.

QuantizedAvgPool 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.
• Input min_input: connect Input node.
• Input max_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 QuantizedAvgPool class object.
• Output min_output: Output object of QuantizedAvgPool class object.
• Output max_output: Output object of QuantizedAvgPool class object.

Result:

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

Using Method