Activation Layers
- class gwu_nn.activation_layers.ActivationLayer(activation)
The ActivationLayer class acts as a connector between a layer and an activation function. It ensures that the activation function is used correctly during forward and backward propogation.
- backward_propagation(output_error, learning_rate)
Applies the classes activation function to the provided input
- Args:
output_error (np.array): output_error calculated backwards to this layer
- Returns:
np.array(float): backwards pass (output_error) up to this layer
- forward_propagation(input)
Applies the classes activation function to the provided input
- Args:
input (np.array): output calculated forward up to this layer
- Returns:
np.array(float): forward pass (output) up to this layer
- class gwu_nn.activation_layers.Sigmoid
Layer that applies the Sigmoid activation function. Inheirits forward_propagation and backward_prop from ActivationLayer
- class gwu_nn.activation_layers.RELU
Layer that applies the ReLU activation function. Inheirits forward_propagation and backward_prop from ActivationLayer
- class gwu_nn.activation_layers.Softmax
Layer that applies the Softmax activation function. Inheirits forward_propagation and backward_prop from ActivationLayer