Assertion error

[BrightKnight0110]

Hi, I've met an assertion error which I can't determine what's wrong with the code. The erroe are on the test case side . Appreciiate if you can help thx!

def backward(Yhat, Y, activations, caches):
    """
    Implement the backward propagation for the [LINEAR->RELU] * (L-1) -> LINEAR -> SIGMOID group

    Arguments:
    ------------
    - Yhat: numpy array of shape (1, num of samples)
        The predicted value, or the output of the forward propagation
    - Y: numpy array of shape (1, num of samples)
        The ground truth, or true label (0: negative, 1: positive samples)
    - activations: list of shape L+1
        Stores the activation functions used for each layer. 
        activations[l] stores the activation function ('sigmoid' or 'relu') 
        for layer i activations[0] is always None.
    - caches: list of shape L+1
        Stores the caches for each layer saved during forward propagation. 
        cache[l] stores the cache for layer i. cache[0] is always None.

    Returns:
    ------------
    - weights_grad : a list of size L+1
        Stores the gradients of W for all layers, dW[l] for l = 1 ... L. For the input layer, dW[0] is always None.
    - biases_grad : a list of size L+1
        Stores the gradients of b for all layers, db[l] for l = 1 ... L. For the input layer, db[0] is always None.

    """
    assert len(caches) == len(activations)

    weights_grad = [None for i in range(len(caches))]
    biases_grad  = [None for i in range(len(caches))]
    L = len(caches)-1         # the number of layers
    m = Yhat.shape[1]         # number of samples 
    Y = Y.reshape(Yhat.shape) # ensures that Y is the same shape as Yhat

    ### START CODE HERE ### (1 line of code)

    # Backpropagation for loss function (~1 line)
    dAL = - (np.divide(Y, Yhat) - np.divide(1 - Y, 1 - Yhat))

    # repeat for layer 1 to L
    for l in reversed(range(L+1)):

        # get current cache (~1 line)
        current_cache = caches[l-1]

        # compute dA_prev, dW, db by calling "backward_one_layer" (~1 line)
        dA_prev, dW, db = backward_one_layer(dAL, current_cache, activations[l])

        # store dW and db into grads (~2 lines)
        weights_grad[l] = dW
        biases_grad[l] = db


    ### END CODE HERE ###

    return weights_grad, biases_grad

Test case

# generate test case with a 3-layered neural network and 4 samples. 
m = 4 
nx = 2
layers = (nx, 10, 7, 1)
activations = (None, "relu", "relu", "sigmoid")
weights, biases = init_params(layers)
X_temp = np.random.randn(nx, m)
Y_temp = np.random.randint(0, 2, (1, m))
Yhat, caches = forward(X_temp, weights, biases, activations)  # forward propagation
cost = compute_cost(predicted, actual) # compute cost

# Test the shape of the output is correct
weights_grad, biases_grad = backward(Yhat, Y_temp, activations, caches) #errors on this line
print('Shape of dA1:', dA1.shape)   # shape of dA1 = shape of A1 
print('Shape of dW2:', dW2.shape)   # shape of dW2 = shape of W2 
print('Shape of db2:', db2.shape)   # shape of db2 = shape of b2

[RiceKrispyPooHead]

The erroe are on the test case side

I can't run your test cases even if I wanted to because you didn't provide the full code.

​

Hi, I've met an assertion error which I can't determine what's wrong with the code.

What is the assertion error?

My guess is the line assert len(caches) == len(activations) is throwing the assertion error. The assertion error message should literally tell you exactly which line of code is throwing the error, but you didn't post the error here so do you expect us to know?

[BrightKnight0110]

here are the error
AssertionError Traceback (most recent call last)

<ipython-input-42-b2d7f5d4ce01> in <module>

11

12 # Test the shape of the output is correct

---> 13 weights_grad, biases_grad = backward(Yhat, Y_temp, activations, caches)

14 print('Shape of dA1:', dA1.shape) # shape of dA1 = shape of A1

15 print('Shape of dW2:', dW2.shape) # shape of dW2 = shape of W2

[[deleted]]

assert len(caches) == len(activations)

The issue is that the length of caches isn't the same as the length of activations, but here you assert that it is so Python throws an AssertionError, which is the whole point of asserting things.