Function test_Embedding

numpy_ml/tests/test_nn.py:699–745

(N=15)

def test_Embedding(N=15):
    from numpy_ml.neural_nets.layers import Embedding

    np.random.seed(12345)

    N = np.inf if N is None else N

    i = 1
    while i < N + 1:
        vocab_size = np.random.randint(1, 2000)
        n_ex = np.random.randint(1, 100)
        n_in = np.random.randint(1, 100)
        emb_dim = np.random.randint(1, 100)

        X = np.random.randint(0, vocab_size, (n_ex, n_in))

        # initialize Embedding layer
        L1 = Embedding(n_out=emb_dim, vocab_size=vocab_size)

        # forward prop
        y_pred = L1.forward(X)

        # backprop
        dLdy = np.ones_like(y_pred)
        #  dLdX = L1.backward(dLdy)
        L1.backward(dLdy)

        # get gold standard gradients
        gold_mod = TorchEmbeddingLayer(vocab_size, emb_dim, L1.parameters)
        golds = gold_mod.extract_grads(X)

        params = [
            (L1.X[0], "X"),
            (y_pred, "y"),
            (L1.parameters["W"], "W"),
            (dLdy, "dLdy"),
            (L1.gradients["W"], "dLdW"),
            #  (dLdX, "dLdX"),
        ]

        print("\nTrial {}".format(i))
        for ix, (mine, label) in enumerate(params):
            assert_almost_equal(
                mine, golds[label], err_msg=err_fmt(params, golds, ix), decimal=3
            )
            print("\tPASSED {}".format(label))
        i += 1


def test_BatchNorm1D(N=15):