def hard_sigmoid(x): """Hard sigmoid activation function. A faster approximation of the sigmoid activation. Piecewise linear approximation of the sigmoid function. Ref: 'https://en.wikipedia.org/wiki/Hard_sigmoid' For example: >>> a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32) >>> b = tf.keras.activations.hard_sigmoid(a) >>> b.numpy() array([0. , 0.3, 0.5, 0.7, 1. ], dtype=float32) Args: x: Input tensor. Returns: The hard sigmoid activation, defined as: - `if x < -2.5: return 0` - `if x > 2.5: return 1` - `if -2.5 <= x <= 2.5: return 0.2 * x + 0.5` """ return backend.hard_sigmoid(x)
def hard_sigmoid(x): """Hard sigmoid activation function. Faster to compute than sigmoid activation. Arguments: x: Input tensor. Returns: Hard sigmoid activation: - `0` if `x < -2.5` - `1` if `x > 2.5` - `0.2 * x + 0.5` if `-2.5 <= x <= 2.5`. """ return K.hard_sigmoid(x)
def hard_sigmoid(x): """Hard sigmoid activation function. Faster to compute than sigmoid activation. Arguments: x: Input tensor. Returns: Hard sigmoid activation: - `0` if `x < -2.5` - `1` if `x > 2.5` - `0.2 * x + 0.5` if `-2.5 <= x <= 2.5`. """ return K.hard_sigmoid(x)
def hard_sigmoid(x): """Hard sigmoid activation function. Faster to compute than sigmoid activation. For example: >>> a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32) >>> b = tf.keras.activations.hard_sigmoid(a) >>> b.numpy() array([0. , 0.3, 0.5, 0.7, 1. ], dtype=float32) Arguments: x: Input tensor. Returns: The hard sigmoid activation: - `0` if `x < -2.5` - `1` if `x > 2.5` - `0.2 * x + 0.5` if `-2.5 <= x <= 2.5`. """ return K.hard_sigmoid(x)
def hard_sigmoid(x): """Hard sigmoid activation function. A faster approximation of the sigmoid activation. For example: >>> a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32) >>> b = tf.keras.activations.hard_sigmoid(a) >>> b.numpy() array([0. , 0.3, 0.5, 0.7, 1. ], dtype=float32) Arguments: x: Input tensor. Returns: The hard sigmoid activation, defined as: - `if x < -2.5: return 0` - `if x > 2.5: return 1` - `if -2.5 <= x <= 2.5: return 0.2 * x + 0.5` """ return K.hard_sigmoid(x)
def hard_sigmoid(x): return K.hard_sigmoid(x)
def hard_sigmoid(x): return K.hard_sigmoid(x)