CODEWITHSUNDEEP
numpy
marlon
marlon

Reputation: 7683

Is the numpy sum method superfluous in this code?

I am reading a book, and found an error as below:

def relu(x):
    return (x>0)*x

def relu2dev(x):
    return (x>0)

street_lights = np.array([[1,0,1],[0,1,1],[0,0,1],[1,1,1]])

walk_stop = np.array([[1,1,0,0]]).T

alpha = 0.2
hidden_size = 4

weights_0_1 = 2*np.random.random((3,hidden_size))-1
weights_1_2 = 2*np.random.random((hidden_size,1))-1

for it in range(60):
    layer_2_error = 0;

    for i in range(len(street_lights)):
        layer_0 = street_lights[i:i+1]
        layer_1 = relu(np.dot(layer_0,weights_0_1))
        layer_2 = np.dot(layer_1,weights_1_2)

        layer_2_delta = (layer_2-walk_stop[i:i+1])

        # -> layer_2_delta's shape is (1,1), so why np.sum?
        layer_2_error += np.sum((layer_2_delta)**2)

        layer_1_delta = layer_2_delta.dot(weights_1_2.T) * relu2dev(layer_1)

        weights_1_2 -= alpha * layer_1.T.dot(layer_2_delta)
        weights_0_1 -= alpha * layer_0.T.dot(layer_1_delta)

    if(it % 10 == 9):
        print("Error: " + str(layer_2_error))

The error place is commented with # ->:

layer_2_delta's shape is (1,1), so why would one use np.sum? I think np.sum can be removed, but not quite sure, since it comes from a book.

Upvotes: 2

Views: 30

Answers (1)

nick
nick

Reputation: 1350

As you say, layer_2_delta has a shape of (1,1). This means it is a 2 dimensional array with one element: layer_2_delta = np.array([[X]]). However, layer_2_error is a scalar. So you can get the scalar from the array by either selecting the value at the first index (layer_2_delta[0,0]) or by summing all the elements (which in this case is just the one). As the book seems to use "sum of square errors", it seems natural to keep the notation which is square each element in array and then add all of these up (for instruction purposes): this would be more general (e.g., to cases where the layer has more than one element) than the index approach. But you're right, there could be other ways to do this :).

Upvotes: 1

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