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
)

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`.

tf.compat.v1.nn.quantized_relu_x

Args

Returns