tf.nn.quantized_avg_pool
tf.nn.quantized_avg_pool
tf.nn.quantized_avg_pool
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.
Args:
-
input
: ATensor
. Must be one of the following types:qint8
,quint8
,qint16
,quint16
,qint32
. 4-D with shape[batch, height, width, channels]
. -
min_input
: ATensor
of typefloat32
. The float value that the lowest quantized input value represents. -
max_input
: ATensor
of typefloat32
. The float value that the highest quantized input value represents. -
ksize
: A list ofints
. 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 ofints
. 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
: Astring
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
: ATensor
. Has the same type asinput
. -
min_output
: ATensor
of typefloat32
. The float value that the lowest quantized output value represents. -
max_output
: ATensor
of typefloat32
. The float value that the highest quantized output value represents.
© 2017 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