def forward(self, x):
x = self.conv1(x)
x = H.relu(x)
x = H.max_pool2d(x, kernel_size=2, stride=2)
x = self.conv2(x)
# x = self.se(x)
x = H.relu(x)
x = H.max_pool2d(x, kernel_size=2, stride=2)
x = x.view(x.shape[0], -1)
x = self.fc1(x)
x = H.relu(x)
x = self.fc2(x)
x = H.relu(x)
x = self.fc3(x)
return x
def forward(self, x):
z = H.mean(x, axis=(2, 3))
z = self.fc1(z)
z = H.relu(z)
z = self.fc2(z)
s = H.sigmoid(z)
n, c = s.shape
s = s.reshape(n, c, 1, 1)
x = x * s
return x
def test_relu():
a = gtensor(dm('1 -2; -3 4'))
k = gtensor(dm('8 3; 2 1'))
b = H.relu(a)
ret = (b * k + b).sum()
ret.backward()
grad = dm('9 0; 0 2')
np.testing.assert_allclose(a.grad, grad)
def forward(self, x):
x = self.conv1(x)
x = self.se1(x)
x = self.bn1(x)
x = H.relu(x)
x = H.max_pool2d(x, kernel_size=2, stride=2)
x = self.conv2(x)
x = self.se2(x)
x = self.bn2(x)
x = H.relu(x)
x = H.max_pool2d(x, kernel_size=2, stride=2)
x = x.view(x.shape[0], -1)
x = self.fc1(x)
# x = H.dropout(x, p=0.5, training=self.training)
x = H.relu(x)
x = self.fc2(x)
# x = H.dropout(x, p=0.5, training=self.training)
x = H.relu(x)
x = self.fc3(x)
return x
def forward(self, x):
return H.relu(x)