tf.nn.quantized_relu_x
tf.nn.quantized_relu_x
tf.nn.quantized_relu_x
quantized_relu_x( features, max_value, min_features, max_features, out_type=None, name=None )
Defined in tensorflow/python/ops/gen_nn_ops.py
.
See the guide: Neural Network > Candidate Sampling
Computes Quantized Rectified Linear X: min(max(features, 0), max_value)
Args:
-
features
: ATensor
. Must be one of the following types:qint8
,quint8
,qint16
,quint16
,qint32
. -
max_value
: ATensor
of typefloat32
. -
min_features
: ATensor
of typefloat32
. The float value that the lowest quantized value represents. -
max_features
: ATensor
of typefloat32
. The float value that the highest quantized value represents. -
out_type
: An optionaltf.DType
from:tf.qint8, tf.quint8, tf.qint16, tf.quint16, tf.qint32
. Defaults totf.quint8
. -
name
: A name for the operation (optional).
Returns:
A tuple of Tensor
objects (activations, min_activations, max_activations).
-
activations
: ATensor
of typeout_type
. Has the same output shape as "features". -
min_activations
: ATensor
of typefloat32
. The float value that the lowest quantized value represents. -
max_activations
: ATensor
of typefloat32
. The float value that the highest quantized 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_relu_x