ReLU layer

• 원본 링크 : https://keras.io/api/layers/activation_layers/relu/
• 최종 확인 : 2024-11-25

source

ReLU class

keras.layers.ReLU(max_value=None, negative_slope=0.0, threshold=0.0, **kwargs)

Rectified Linear Unit activation function layer.

Formula:

f(x) = max(x,0)
f(x) = max_value if x >= max_value
f(x) = x if threshold <= x < max_value
f(x) = negative_slope * (x - threshold) otherwise

Example

relu_layer = keras.layers.activations.ReLU(
    max_value=10,
    negative_slope=0.5,
    threshold=0,
)
input = np.array([-10, -5, 0.0, 5, 10])
result = relu_layer(input)

Arguments

• max_value: Float >= 0. Maximum activation value. None means unlimited. Defaults to None.
• negative_slope: Float >= 0. Negative slope coefficient. Defaults to 0.0.
• threshold: Float >= 0. Threshold value for thresholded activation. Defaults to 0.0.
• **kwargs: Base layer keyword arguments, such as name and dtype.