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test_softmax_cross_entropy.py
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test_softmax_cross_entropy.py
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import unittest
import numpy as np
from dezero import Variable
import dezero.functions as F
from dezero.utils import gradient_check, array_allclose
import chainer.functions as CF
class TestSoftmaxCrossEntropy(unittest.TestCase):
def test_forward1(self):
x = np.array([[-1, 0, 1, 2], [2, 0, 1, -1]], np.float32)
t = np.array([3, 0]).astype(np.int32)
y = F.softmax_cross_entropy(x, t)
y2 = CF.softmax_cross_entropy(x, t)
res = array_allclose(y.data, y2.data)
self.assertTrue(res)
def test_backward1(self):
x = np.array([[-1, 0, 1, 2], [2, 0, 1, -1]], np.float32)
t = np.array([3, 0]).astype(np.int32)
f = lambda x: F.softmax_cross_entropy(x, Variable(t))
self.assertTrue(gradient_check(f, x))
def test_backward2(self):
N, CLS_NUM = 10, 10
x = np.random.randn(N, CLS_NUM)
t = np.random.randint(0, CLS_NUM, (N,))
f = lambda x: F.softmax_cross_entropy(x, t)
self.assertTrue(gradient_check(f, x))
def test_backward3(self):
N, CLS_NUM = 100, 10
x = np.random.randn(N, CLS_NUM)
t = np.random.randint(0, CLS_NUM, (N,))
f = lambda x: F.softmax_cross_entropy(x, t)
self.assertTrue(gradient_check(f, x))
class TestSoftmaxCrossEntropy_simple(unittest.TestCase):
def test_forward1(self):
x = np.array([[-1, 0, 1, 2], [2, 0, 1, -1]], np.float32)
t = np.array([3, 0]).astype(np.int32)
y = F.softmax_cross_entropy_simple(x, t)
y2 = CF.softmax_cross_entropy(x, t)
res = array_allclose(y.data, y2.data)
self.assertTrue(res)
def test_backward1(self):
x = np.array([[-1, 0, 1, 2], [2, 0, 1, -1]], np.float32)
t = np.array([3, 0]).astype(np.int32)
f = lambda x: F.softmax_cross_entropy_simple(x, Variable(t))
self.assertTrue(gradient_check(f, x))
def test_backward2(self):
N, CLS_NUM = 10, 10
x = np.random.randn(N, CLS_NUM)
t = np.random.randint(0, CLS_NUM, (N,))
f = lambda x: F.softmax_cross_entropy_simple(x, t)
self.assertTrue(gradient_check(f, x))
def test_backward3(self):
N, CLS_NUM = 100, 10
x = np.random.randn(N, CLS_NUM)
t = np.random.randint(0, CLS_NUM, (N,))
f = lambda x: F.softmax_cross_entropy_simple(x, t)
self.assertTrue(gradient_check(f, x))