如何将MLP的神经网络从tensorflow转换为pytorch

Question

我使用"Tensorflow"建立了一个MLP神经网络，如下所示：

model_mlp=Sequential()
model_mlp.add(Dense(units=35, input_dim=train_X.shape[1], kernel_initializer='normal', activation='relu'))
model_mlp.add(Dense(units=86, kernel_initializer='normal', activation='relu'))
model_mlp.add(Dense(units=86, kernel_initializer='normal', activation='relu'))
model_mlp.add(Dense(units=10, kernel_initializer='normal', activation='relu'))
model_mlp.add(Dense(units=1))

我想用pytorch转换上面的MLP代码。怎么做？我试着这样做：

class MLP(nn.Module):
def __init__(self):
super(MLP, self).__init__()
self.fc1 = nn.Linear(train_X.shape[1],35)
self.fc2 = nn.Linear(35, 86)
self.fc3 = nn.Linear(86, 86)
self.fc4 = nn.Linear(86, 10)
self.fc5 = nn.Linear(10, 1)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = F.relu(self.fc4(x))
x = self.fc5(x)
return x
def predict(self, x_test):
x_test = torch.from_numpy(x_test).float()
x_test = self.forward(x_test)
return x_test.view(-1).data.numpy()
model = MLP()

我使用相同的数据集，但这两个代码给出了两个不同的答案。使用Tensorflow编写的代码总是比使用Pytorch编写的代码产生更好的结果。我想知道我在pytorch中的代码是否不正确。如果我在PyTorch中编写的代码是正确的，我想知道如何解释其中的差异。我期待着任何答复。

Answer 1

欢迎使用pytorch！

我想问题出在你的网络初始化上。我就是这么做的：

def init_weights(m):
if type(m) == nn.Linear:
torch.nn.init.xavier_normal(m.weight)  # initialize with xaver normal (called gorot in tensorflow)
m.bias.data.fill_(0.01) # initialize bias with a constant
class MLP(nn.Module):
def __init__(self, input_dim):
super(MLP, self).__init__()
self.mlp = nn.Sequential(nn.Linear(input_dim ,35), nn.ReLU(),
nn.Linear(35, 86), nn.ReLU(),
nn.Linear(86, 86), nn.ReLU(), 
nn.Linear(86, 10), nn.ReLU(),
nn.Linear(10, 1), nn.ReLU())
def forward(self, x):
y =self.mlp(x)
return y
model = MLP(input_dim)
model.apply(init_weights)
optimizer = Adam(model.parameters())
loss_func = BCEWithLogistLoss()
# training loop
for data, label in dataloader:
optimizer.zero_grad()

pred = model(data)
loss = loss_func(pred, lable)
loss.backward()
optimizer.step()

请注意，在pytorch中，我们不调用model.forward(x)，而是调用model(x)。这是因为nn.Module在.__call__()中应用在后向通路中使用的钩子

您可以在此处查看重量初始化的文档：https://pytorch.org/docs/stable/nn.init.html