Forward pass for all the layers.

forward.py

def forward_pass(train_X, params_w, params_b, layers=[4, 5, 1], activate=['R', 'S']):

    num_layers = len(layers) - 1

    activation_dict = {}
    output_dict = {}

    curr_act = train_X

    for index in range(num_layers):

        layer_index = index + 1
        prev_act = curr_act      

        curr_weight = params_w["weight" + str(layer_index)]
        curr_bias = params_b["bias" + str(layer_index)]

        curr_act, curr_out = one_layer_forward_pass(prev_act, curr_weight, curr_bias, activate[index])

        activation_dict["act" + str(index)] = prev_act
        output_dict["out" + str(layer_index)] = curr_out

    return curr_act, activation_dict, output_dict