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- # -*- coding: utf-8 -*-
- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
- #
- # Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
- #
- # Unless required by applicable law or agreed to in writing,
- # software distributed under the License is distributed on an
- # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- import numpy as np
-
- import megengine.functional as F
- from megengine import tensor
-
-
- def test_cross_entropy_with_logits():
- data = tensor([1, 100]).astype(np.float32).reshape((1, 2))
- label = tensor([1]).astype(np.int32)
- loss = F.nn.cross_entropy(data, label)
- np.testing.assert_allclose(loss.numpy(), 0.0)
- label = tensor([0]).astype(np.int32)
- loss = F.nn.cross_entropy(data, label)
- np.testing.assert_allclose(loss.numpy(), 100 - 1)
-
- label = np.array([1])
- loss = F.nn.cross_entropy(data, label)
- np.testing.assert_allclose(loss.numpy(), 0.0)
-
-
- def test_cross_entropy():
- def softmax(x):
- x = np.exp(x)
- x /= x.sum(1, keepdims=True)
- return x
-
- def ref(x, y):
- return np.mean([-np.log(x[i, y[i]]) for i in range(len(y))])
-
- x = (np.random.rand(5, 10) - 0.5) * 4
- y = np.random.randint(10, size=(5,))
- for i in range(len(x)):
- x[i, y[i]] += np.random.rand() * 2
- x = softmax(x)
- l_ref = ref(x, y)
- l = F.nn.cross_entropy(tensor(x, "float32"), tensor(y, "int32"), with_logits=False)
- np.testing.assert_allclose(l.numpy(), l_ref)
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