Python sigmoid示例

示例#1

文件： vae.py 项目： peaceiris/deep-learning-from-scratch-3

 def __call__(self, x):
     x = F.relu(self.linear(x))
     x = F.reshape(x, (-1,) + self.to_shape)  # reshape to (-1, C, H, W)
     x = F.relu(self.deconv(x))
     x = self.conv(x)
     x = F.sigmoid(x)
     return x

示例#2

文件： layers.py 项目： leynard007/deep-learning-from-scratch-3

    def __call__(self, x):
        if self.h is None:
            N, D = x.shape
            H, H = self.h2f.W.shape
            self.h = np.zeros((N, H), np.float32)
            self.c = np.zeros((N, H), np.float32)

        f = F.sigmoid(self.x2f(x) + self.h2f(self.h))
        i = F.sigmoid(self.x2i(x) + self.h2i(self.h))
        o = F.sigmoid(self.x2o(x) + self.h2o(self.h))
        u = F.tanh(self.x2u(x) + self.h2u(self.h))

        c = (f * self.c) + (i * u)
        h = o * F.tanh(c)

        self.h, self.c = h, c
        return h

示例#3

def predict(x):
    y = F.linear(x, W1, b1)
    y = F.sigmoid(y)
    y = F.linear(y, W2, b2)
    #y = F.matmul(x, W1) + b1
    #y = F.sigmoid_simple(y)
    #y = F.matmul(y, W2) + b2
    return y

示例#4

    def test_sigmoid(self):

        x = Variable(np.array([[1], [2]]))
        y = sigmoid(x)
        y.backward()
        assert_array_equal(
            y.data, np.array([[1 / (1 + np.exp(-1))], [1 / (1 + np.exp(-2))]]))
        assert_array_equal(
            x.grad.data,
            np.array([[np.exp(-1) / (1 + np.exp(-1))**2],
                      [np.exp(-2) / (1 + np.exp(-2))**2]]))

示例#5

    def forward(self, x):
        if self.h is None:
            f = F.sigmoid(self.x2f(x))
            i = F.sigmoid(self.x2i(x))
            o = F.sigmoid(self.x2o(x))
            u = F.sigmoid(self.x2u(x))
        else:
            f = F.sigmoid(self.x2f(x) + self.h2f(self.h))
            i = F.sigmoid(self.x2i(x) + self.h2i(self.h))
            o = F.sigmoid(self.x2u(x) + self.h2u(self.h))
            u = F.tanh(self.x2u(x) + self.h2u(self.h))

        if self.c is None:
            c_new = (i * u)
        else:
            c_new = (f * self.c) + (i * u)

        h_new = o * F.tanh(c_new)

        self.h, self.c = h_new, c_new
        return h_new

示例#6

文件： layers.py 项目： timwuu/deep-learning-from-scratch-3

    def __call__(self, x):
        if self.h is None:
            f = F.sigmoid(self.x2f(x))
            i = F.sigmoid(self.x2i(x))
            o = F.sigmoid(self.x2o(x))
            u = F.tanh(self.x2u(x))
        else:
            f = F.sigmoid(self.x2f(x) + self.h2f(self.h))
            i = F.sigmoid(self.x2i(x) + self.h2i(self.h))
            o = F.sigmoid(self.x2o(x) + self.h2o(self.h))
            u = F.tanh(self.x2u(x) + self.h2u(self.h))

        if self.c is None:
            c = (i * u)
        else:
            c = (f * self.c) + (i * u)

        h = o * F.tanh(c)

        self.h, self.c = h, c
        return h

示例#7

def predict(x):
  y = l1(x)
  y = F.sigmoid(y)
  y = l2(y)
  return y

示例#8

文件： test_sigmoid.py 项目： leynard007/deep-learning-from-scratch-3

 def test_forward1(self):
     x = np.array([[0, 1, 2], [0, 2, 4]], np.float32)
     y2 = CF.sigmoid(x)
     y = F.sigmoid(Variable(x))
     res = np.allclose(y.data, y2.data)
     self.assertTrue(res)

示例#9

 def forward(self, x):
     y = F.sigmoid(self.l1(x))
     y = self.l2(y)
     return y

示例#10

 def __call__(self, x):
     y = F.sigmoid(self.l1(x))
     y = self.l2(y)
     return y

示例#11

文件： test_sigmoid.py 项目： leynard007/deep-learning-from-scratch-3

 def test_forward2(self):
     x = np.random.randn(10, 10).astype(np.float32)
     y2 = CF.sigmoid(x)
     y = F.sigmoid(Variable(x))
     res = np.allclose(y.data, y2.data)
     self.assertTrue(res)

示例#12

文件： step44.py 项目： aha-oretama/deep-learning-from-scratch-3

def predict(x):
    y = l1.forward(x)
    y = F.sigmoid(y)
    y = l2.forward(y)
    return y

示例#13

文件： step43.py 项目： peaceiris/deep-learning-from-scratch-3

def predict(x):
    y = F.linear(x, W1, b1)
    y = F.sigmoid(y)
    y = F.linear(y, W2, b2)
    return y

示例#14

文件： sample_neural_network.py 项目： 6315481/DeZero

def predict(x):
    x2 = F.sigmoid(F.linear(x, W1, b1))
    x2 = F.linear(x2, W2, b2)
    return x2

示例#15

文件： test_functions.py 项目： Hagihara-A/dl-scratch-3

 def test_backward(self):
     out = 0.88079707
     x = Variable(np.array([2.0, 2.0]))
     y = sigmoid(x)
     y.backward()
     assert_almost_equal(x.grad.data, [(1 - out) * out] * 2)

示例#16

文件： test_functions.py 项目： Hagihara-A/dl-scratch-3

 def test_forward(self):
     x = Variable(np.array([2.0, 2.0]))
     y = sigmoid(x)
     assert_almost_equal(y.data, np.array([0.88079707, 0.88079707]))

示例#17

文件： test_regression.py 项目： centertong/dezero

 def predict(model, x):
     y = model.l1(x)
     y = F.sigmoid(y)
     y = model.l2(y)
     return y

示例#18

def predict(x):
    y = F.matmul(x, w1) + b1
    y = F.sigmoid(y)
    y = F.matmul(y, w2) + b2
    return y