我尝试使用 torch 将掉落率放入我的神经网络(NN)中,最后出现了一个奇怪的错误。我该如何修复它?
所以我的想法是在函数内部编写一个神经网络以使其更容易调用。函数如下: (我个人认为问题出在神经网络的类内部,但为了有一个有效的例子,我把所有的东西都放在了里面)。
def train_neural_network(data_train_X, data_train_Y, batch_size, learning_rate, graph = True, dropout = 0.0 ):
input_size = len(data_test_X.columns)
hidden_size = 200
num_classes = 4
num_epochs = 120
batch_size = batch_size
learning_rate = learning_rate
# The class of NN
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes, p = dropout):
super(NeuralNet, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 = nn.Linear(hidden_size, num_classes)
def forward(self, x, p = dropout):
out = F.relu(self.fc1(x))
out = F.relu(self.fc2(out))
out = nn.Dropout(out, p) #drop
out = self.fc3(out)
return out
# Prepare data
X_train = torch.from_numpy(data_train_X.values).float()
Y_train = torch.from_numpy(data_train_Y.values).float()
# Loading data
train = torch.utils.data.TensorDataset(X_train, Y_train)
train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size)
net = NeuralNet(input_size, hidden_size, num_classes)
# Loss
criterion = nn.CrossEntropyLoss()
# Optimiser
optimiser = torch.optim.SGD(net.parameters(), lr=learning_rate)
# Proper training
total_step = len(train_loader)
loss_values = []
for epoch in range(num_epochs+1):
net.train()
train_loss = 0.0
for i, (predictors, results) in enumerate(train_loader, 0):
# Forward pass
outputs = net(predictors)
results = results.long()
results = results.squeeze_()
loss = criterion(outputs, results)
# Backward and optimise
optimiser.zero_grad()
loss.backward()
optimiser.step()
# Update loss
train_loss += loss.item()
loss_values.append(train_loss / batch_size )
print('Finished Training')
return net
当我调用该函数时:
net = train_neural_network(data_train_X = data_train_X, data_train_Y = data_train_Y, batch_size = batch_size, learning_rate = learning_rate, dropout = 0.1)
错误如下:
net = train_neural_network(data_train_X = data_train_X, data_train_Y = data_train_Y, batch_size = batch_size, learning_rate = learning_rate, dropout = 0.1)
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/dropout.py in __init__(self, p, inplace)
8 def __init__(self, p=0.5, inplace=False):
9 super(_DropoutNd, self).__init__()
---> 10 if p < 0 or p > 1:
11 raise ValueError("dropout probability has to be between 0 and 1, "
12 "but got {}".format(p))
RuntimeError: bool value of Tensor with more than one value is ambiguous
您认为为什么会出现错误?
在设置掉落率之前,一切正常。如果您知道如何的话,可以为您额外加分 在我的网络中实现偏见!例如,在隐藏层上。我在网上找不到任何示例。
最佳答案
为此更改您的架构:
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes, p=dropout):
super(NeuralNet, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 = nn.Linear(hidden_size, num_classes)
self.dropout = nn.Dropout(p=p)
def forward(self, x):
out = F.relu(self.fc1(x))
out = F.relu(self.fc2(out))
out = self.dropout(self.fc3(out))
return out
让我知道它是否有效。
关于python - 调试神经网络丢失问题的概率不在 [0,1] 内,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/59008098/