pyml.neural_network.layer.activation.tanh.Tanh#

Bases: _Activation

Tanh activation function

The tanh function is defined as:

\(\tanh x = {\frac {\sinh x}{\cosh x}} = {\frac {\mathrm {e} ^{x}-\mathrm {e} ^{-x}}{\mathrm {e} ^{x}+\mathrm {e} ^{-x}}} = {\frac {\mathrm {e} ^{2x}-1}{\mathrm {e} ^{2x}+1}}=1-{\frac {2}{\mathrm {e} ^{2x}+1}}\).

Methods

`__init__`
`backward`	Computes the backward step
`forward`	Computes a forward pass
`predictions`	Converts the calculated output into actual predictions
`set_adjacent_layers`	Set adjacent layers which are needed for the model to iterate through the layers.

backward(dvalues)[source]#

Computes the backward step

The derivative of the tanh function will be calculated as follows:

\(\dfrac{d\tanh}{dx}=1-\tanh^2=\dfrac{1}{\cosh^2 x}\).

Return type:: None
Parameters:: dvalues (numpy.ndarray) – Derived gradient from the previous layers (reversed order).

forward(inputs)[source]#

Computes a forward pass

Return type:: None
Parameters:: inputs (numpy.ndarray) – Input values from previous neural layer.

abstract predictions()#: Converts the calculated output into actual predictions

set_adjacent_layers(previous_layer, next_layer)#

Set adjacent layers which are needed for the model to iterate through the layers.

Parameters:

previous_layer (_Layer) – Layer that is previous to this layer.
next_layer (_Layer) – Layer that is subsequent to this layer.