coding_nn_fwd_pass.py

# Define helper functions that will be used in L-model forward prop
def linear_forward(A_prev, W, b):
    Z = np.dot(W, A_prev) + b
    cache = (A_prev, W, b)
    return Z, cache


def linear_activation_forward(A_prev, W, b, activation_fn):
    assert activation_fn == "sigmoid" or activation_fn == "tanh" or \
        activation_fn == "relu"

    if activation_fn == "sigmoid":
        Z, linear_cache = linear_forward(A_prev, W, b)
        A, activation_cache = sigmoid(Z)

    elif activation_fn == "tanh":
        Z, linear_cache = linear_forward(A_prev, W, b)
        A, activation_cache = tanh(Z)

    elif activation_fn == "relu":
        Z, linear_cache = linear_forward(A_prev, W, b)
        A, activation_cache = relu(Z)

    assert A.shape == (W.shape[0], A_prev.shape[1])

    cache = (linear_cache, activation_cache)
    return A, cache


def L_model_forward(X, parameters, hidden_layers_activation_fn="relu"):
    A = X                           
    caches = []                     
    L = len(parameters) // 2        

    for l in range(1, L):
        A_prev = A
        A, cache = linear_activation_forward(
            A_prev, parameters["W" + str(l)], parameters["b" + str(l)],
            activation_fn=hidden_layers_activation_fn)
        caches.append(cache)

    AL, cache = linear_activation_forward(
        A, parameters["W" + str(L)], parameters["b" + str(L)],
        activation_fn="sigmoid")
    caches.append(cache)

    assert AL.shape == (1, X.shape[1])
    return AL, caches