NumPy-neural-net-6.py

def activation(input_, act_func):
    if act_func == 'relu':
        return np.maximum(input_, np.zeros(input_.shape))
    elif act_func == 'linear':
        return input_
    else:
        raise Exception('Activation function is not defined.')
        
  
def forward_prop(input_vec, layers_dim=layers_dim, neural_net=neural_net):
    neural_net[0].A = input_vec # Define A in input layer for for-loop convenience
    for layer_index in range(1,len(layers_dim)): # W,b,Z,A are undefined in input layer
        neural_net[layer_index].Z = np.add(np.dot(neural_net[layer_index].W, neural_net[layer_index-1].A), neural_net[layer_index].b)
        neural_net[layer_index].A = activation(neural_net[layer_index].Z, neural_net[layer_index].activation)
    return neural_net[layer_index].A