tf.compat.v1.nn.quantized_relu_x

Computes Quantized Rectified Linear X: min(max(features, 0), max_value)

tf.compat.v1.nn.quantized_relu_x(
    features: Annotated[Any, TV_QuantizedReluX_Tinput],
    max_value: Annotated[Any, _atypes.Float32],
    min_features: Annotated[Any, _atypes.Float32],
    max_features: Annotated[Any, _atypes.Float32],
    out_type: TV_QuantizedReluX_out_type = tf.dtypes.quint8,
    name=None
)

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).

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.