Python torch 模块，rsqrt() 实例源码

def test_rsqrt(self):
        self._testMath(torch.rsqrt, lambda x: 1 / math.sqrt(x) if x > 0 else float('nan'))

def test_sigmoid(self):
        # TODO: why not simulate math.sigmoid like with rsqrt?
        inputValues = [-1000,-1,0,0.5,1,2,1000]
        expectedOutput = [0.0000, 0.2689, 0.5, 0.6225, 0.7311, 0.8808, 1.000]
        precision_4dps = 0.0002

        def checkType(tensor):
            self.assertEqual(tensor(inputValues).sigmoid(), tensor(expectedOutput), precision_4dps)

        checkType(torch.FloatTensor)
        checkType(torch.DoubleTensor)

def test_rsqrt(self):
        self._testMath(torch.rsqrt, lambda x: 1 / math.sqrt(x) if x > 0 else float('nan'))

def test_sigmoid(self):
        # TODO: why not simulate math.sigmoid like with rsqrt?
        inputValues = [-1000, -1, 0, 0.5, 1, 2, 1000]
        expectedOutput = [0.0000, 0.2689, 0.5, 0.6225, 0.7311, 0.8808, 1.000]
        precision_4dps = 0.0002

        def checkType(tensor):
            self.assertEqual(tensor(inputValues).sigmoid(), tensor(expectedOutput), precision_4dps)

        checkType(torch.FloatTensor)
        checkType(torch.DoubleTensor)

def test_rsqrt(self):
        self._testMath(torch.rsqrt, lambda x: 1 / math.sqrt(x) if x > 0 else float('nan'))

def test_sigmoid(self):
        # TODO: why not simulate math.sigmoid like with rsqrt?
        inputValues = [-1000, -1, 0, 0.5, 1, 2, 1000]
        expectedOutput = [0.0000, 0.2689, 0.5, 0.6225, 0.7311, 0.8808, 1.000]
        precision_4dps = 0.0002

        def checkType(tensor):
            self.assertEqual(tensor(inputValues).sigmoid(), tensor(expectedOutput), precision_4dps)

        checkType(torch.FloatTensor)
        checkType(torch.DoubleTensor)

def test_rsqrt(self):
        self._testMath(torch.rsqrt, lambda x: 1 / math.sqrt(x) if x > 0 else float('nan'))

def test_sigmoid(self):
        # TODO: why not simulate math.sigmoid like with rsqrt?
        inputValues = [-1000, -1, 0, 0.5, 1, 2, 1000]
        expectedOutput = [0.0000, 0.2689, 0.5, 0.6225, 0.7311, 0.8808, 1.000]
        precision_4dps = 0.0002

        def checkType(tensor):
            self.assertEqual(tensor(inputValues).sigmoid(), tensor(expectedOutput), precision_4dps)

        checkType(torch.FloatTensor)
        checkType(torch.DoubleTensor)

def test_rsqrt(self):
        self._testMath(torch.rsqrt, lambda x: 1 / math.sqrt(x) if x > 0 else float('nan'))

def test_sigmoid(self):
        # TODO: why not simulate math.sigmoid like with rsqrt?
        inputValues = [-1000, -1, 0, 0.5, 1, 2, 1000]
        expectedOutput = [0.0000, 0.2689, 0.5, 0.6225, 0.7311, 0.8808, 1.000]
        precision_4dps = 0.0002

        def checkType(tensor):
            self.assertEqual(tensor(inputValues).sigmoid(), tensor(expectedOutput), precision_4dps)

        checkType(torch.FloatTensor)
        checkType(torch.DoubleTensor)