tf.keras.layers.ReLU

Rectified Linear Unit activation function layer.

Inherits From: Layer, Operation

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

Used in the notebooks

Used in the guide Used in the tutorials

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) # result = [-5. , -2.5, 0. , 5. , 10.]

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.

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

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@classmethod from_config( config )

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config A Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

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symbolic_call( *args, **kwargs )