tf.nn.quantized_avg_pool( input, min_input, max_input, ksize, strides, padding, name=None )
Defined in tensorflow/python/ops/gen_nn_ops.py
.
See the guide: Neural Network > Candidate Sampling
Produces the average pool of the input tensor for quantized types.
input
: A Tensor
. Must be one of the following types: qint8
, quint8
, qint32
, qint16
, quint16
. 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).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
.max_output
: A Tensor
of type float32
.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/quantized_avg_pool