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)
