tf.nn.quantized_relu_x

tf.nn.quantized_relu_x(
    features,
    max_value,
    min_features,
    max_features,
    out_type=tf.quint8,
    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: A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.
  • max_value: A Tensor of type float32.
  • min_features: A Tensor of type float32. The float value that the lowest quantized value represents.
  • max_features: A Tensor of type float32. The float value that the highest quantized value represents.
  • out_type: An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.quint8.
  • name: A name for the operation (optional).

Returns:

A tuple of Tensor objects (activations, min_activations, max_activations).

  • activations: A Tensor of type out_type.
  • min_activations: A Tensor of type float32.
  • max_activations: A Tensor of type float32.