# tf.nn.quantized_avg_pool(input, min_input, max_input, ksize, strides, padding, name=None)

### tf.nn.quantized_avg_pool(input, min_input, max_input, ksize, strides, padding, name=None)

See the guide: Neural Network > Candidate Sampling

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

#### Args:

• input: A Tensor. Must be one of the following types: qint8, quint8, qint16, quint16, qint32. 4-D with shape [batch, height, width, channels].
• min_input: A Tensor of type float32. The float value that the lowest quantized input value represents.
• max_input: A Tensor of type float32. The float value that the highest quantized input value represents.
• ksize: A list of ints. 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: A list of ints. 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: A string from: "SAME", "VALID". The type of padding algorithm to use.
• name: A name for the operation (optional).

#### Returns:

A tuple of Tensor objects (output, min_output, max_output). output: A Tensor. Has the same type as input. min_output: A Tensor of type float32. The float value that the lowest quantized output value represents. * max_output: A Tensor of type float32. The float value that the highest quantized output value represents.

Defined in tensorflow/python/ops/gen_nn_ops.py.