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
def forward(self, x):
x = F.relu(self.conv1(x)) # (OH, OW)=(28, 28)
x = F.pooling(x, 2, 2) # (OH, OW)=(14, 14)
#x = F.relu(self.conv2(x))
#x = F.pooling(x, 2, 2)
x = F.reshape(x, (x.shape[0], -1)) # (14, 14)->(196, )
x = F.dropout(F.relu(self.fc3(x)))
#x = F.dropout(F.relu(self.fc4(x)))
x = self.fc5(x)
return x
def __call__(self, x):
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = F.relu(self.conv3(x))
x = F.relu(self.conv4(x))
x = F.flatten(x)
x = F.relu(self.linear1(x))
z_mean = self.linear2(x)
z_log_var = self.linear3(x)
return z_mean, z_log_var
def forward(self, x):
x = F.relu(self.conv1_1(x)) # (OH, OW)=(28, 28)
x = F.relu(self.conv1_2(x)) # (OH, OW)=(28, 28)
x = F.pooling(x, 2, 2) # (OH, OW)=(14, 14)
x = F.relu(self.conv2_1(x)) # (OH, OW)=(14, 14)
x = F.relu(self.conv2_2(x)) # (OH, OW)=(14, 14)
x = F.pooling(x, 2, 2) # (OH, OW)=(7, 7)
x = F.reshape(x, (x.shape[0], -1)) # (7, 7)->(49, )
x = F.dropout(F.relu(self.fc3(x)))
x = self.fc4(x)
return x
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.pooling(x, kernel_size=3, stride=2)
x = self.res2(x)
x = self.res3(x)
x = self.res4(x)
x = self.res5(x)
x = _global_average_pooling_2d(x)
x = self.fc6(x)
return x
def forward(self, x):
x = F.relu(self.l1(x))
x = F.relu(self.l2(x))
x = self.l3(x)
return x
def __call__(self, x):
x = F.relu(self.conv1_1(x))
x = F.relu(self.conv1_2(x))
x = F.pooling(x, 2, 2)
x = F.relu(self.conv2_1(x))
x = F.relu(self.conv2_2(x))
x = F.pooling(x, 2, 2)
x = F.relu(self.conv3_1(x))
x = F.relu(self.conv3_2(x))
x = F.relu(self.conv3_3(x))
x = F.pooling(x, 2, 2)
x = F.relu(self.conv4_1(x))
x = F.relu(self.conv4_2(x))
x = F.relu(self.conv4_3(x))
x = F.pooling(x, 2, 2)
x = F.relu(self.conv5_1(x))
x = F.relu(self.conv5_2(x))
x = F.relu(self.conv5_3(x))
x = F.pooling(x, 2, 2)
x = F.reshape(x, (x.shape[0], -1))
x = F.dropout(F.relu(self.fc6(x)))
x = F.dropout(F.relu(self.fc7(x)))
x = self.fc8(x)
return x
def forward(self, x):
h = F.relu(self.bn1(self.conv1(x)))
h = F.relu(self.bn2(self.conv2(h)))
h = self.bn3(self.conv3(h))
return F.relu(h + x)
def forward(self, x):
h1 = F.relu(self.bn1(self.conv1(x)))
h1 = F.relu(self.bn2(self.conv2(h1)))
h1 = self.bn3(self.conv3(h1))
h2 = self.bn4(self.conv4(x))
return F.relu(h1 + h2)
def forward(self, x):
x = F.relu(self.l1(x))
x = F.softmax(self.l2(x))
return x
def test_backward(self):
x = Variable(np.array([[1, 0, -1, 5], [-1, -2, 5, 0]]))
y = F.relu(x)
y.backward()
assert_equal(x.grad.data, [[1, 0, 0, 1], [0, 0, 1, 0]])
def test_forward(self):
x = np.array([[1, 0, -1, 5], [-1, -2, 5, 0]])
y = F.relu(x)
assert_equal(y.data, [[1, 0, 0, 5], [0, 0, 5, 0]])
def forward(self, x):
x = F.relu(self.conv1_1)
x = F.relu(self.conv1_2)
x = F.pooling(x, 2, 2)
x = F.relu(self.conv2_1)
x = F.relu(self.conv2_2)
x = F.pooling(x, 2, 2)
x = F.relu(self.conv3_1)
x = F.relu(self.conv3_2)
x = F.relu(self.conv3_3)
x = F.pooling(x, 2, 2)
x = F.relu(self.conv4_1)
x = F.relu(self.conv4_2)
x = F.relu(self.conv4_3)
x = F.pooling(x, 2, 2)
x = F.relu(self.conv5_1)
x = F.relu(self.conv5_2)
x = F.relu(self.conv5_3)
x = F.pooling(x, 2, 2)
x = F.reshape(x, (x.shape[0], -1))
x = F.dropout(F.relu(self.fc6(x)))
x = F.dropout(F.relu(self.fc7(x)))
x = self.fc8(x)
return x
def test_forward1(self):
x = np.array([[-1, 0], [2, -3], [-2, 1]], np.float32)
res = F.relu(x)
ans = np.array([[0, 0], [2, 0], [0, 1]], np.float32)
self.assertTrue(array_allclose(res, ans))
def extract(self, x):
c1_1 = F.relu(self.conv1_1(x))
c1_2 = F.relu(self.conv1_2(c1_1))
p1 = F.average_pooling(c1_2, 2, 2)
c2_1 = F.relu(self.conv2_1(p1))
c2_2 = F.relu(self.conv2_2(c2_1))
p2 = F.average_pooling(c2_2, 2, 2)
c3_1 = F.relu(self.conv3_1(p2))
c3_2 = F.relu(self.conv3_2(c3_1))
c3_3 = F.relu(self.conv3_3(c3_2))
p3 = F.average_pooling(c3_3, 2, 2)
c4_1 = F.relu(self.conv4_1(p3))
c4_2 = F.relu(self.conv4_2(c4_1))
c4_3 = F.relu(self.conv4_3(c4_2))
p4 = F.average_pooling(c4_3, 2, 2)
c5_1 = F.relu(self.conv5_1(p4))
c5_2 = F.relu(self.conv5_2(c5_1))
c5_3 = F.relu(self.conv5_3(c5_2))
return {
'conv1_1': c1_1,
'conv1_2': c1_2,
'conv2_1': c2_1,
'conv2_2': c2_2,
'conv3_1': c3_1,
'conv3_2': c3_2,
'conv3_3': c3_3,
'conv4_1': c4_1,
'conv5_1': c5_1,
'conv5_2': c5_2,
'conv5_3': c5_3
}
