构建具有基本事实的非图像分类器

Question

我有一个如下所示的数据集：

标签基本上是我得到的项目列表（比方说，停车场里的汽车），总共有 10 个，标记为 0 到 10。我有 14 个 class es（比方说，14 个不同的汽车品牌）。每个浮点值只是特定项目所属 class 的百分比。例如，项目 2 可能 class 2 的概率为 0.995275:

print(set(list(df['label'])))
> {0, 1, 2, 3, 4, 5, 6, 7, 9}

我的目标是训练一个 class 输出一个从 0 到 14 的整数来预测 class 标签 x 属于什么。

我正在尝试构建一个具有 3 个隐藏层（+ 输入和输出层）的前馈 NN，并采用 15 个输入并输出从 0 到 14 的预测。这就是我到目前为止的设计：

class NNO(nn.Module):
  def __init__(self):
    super(NNO, self).__init__()
    h= [2,1]
    self.hidden = nn.Linear(h[0], h[1])
    self.hidden = nn.Linear(2,20)
    self.hidden = nn.Linear(20,20)
    self.output = nn.Linear(20,15)

    self.sigmoid = nn.Sigmoid()
    self.softmax = nn.Softmax(dim = 1)
  
  def forward(self, y):
    x = self.hidden(x)
    x = self.sigmoid(x)
    x = self.output(x)
    x = self.softmax(x)

我的问题是这样的。如何将数据集提供给我的神经网络以开始训练时代？我找不到与此类数据集相关的任何资源。

Answer 1

答案如下：

# First I create some dummy data
label = np.random.randint(0, 14, 1000)
random = np.random.random((1000, 14))
total = pd.DataFrame(data=random, columns=[f'{i}_col' for i in range(14)])
total['label'] = label

'''
From what I understood you need 1 class in output that has the highest probability and hence this is a multi-class classification problem. In my case, I will just use the highest value from `random` as the target class. 
'''
class TDataset(torch.utils.data.Dataset):
    def __init__(self, df):
        self.inputs = df[[f'{i}_col' for i in range(14)] + ['label']].values
        self.outputs = df[[f'{i}_col' for i in range(14)]].values
    
    def __len__(self):
        return len(self.inputs)
    
    def __getitem__(self, idx):
        x = torch.tensor(self.inputs[idx], dtype=torch.float)
        y = torch.tensor(np.argmax(self.outputs[idx]))
        return x, y

ds = TDataset(total)
dl = torch.utils.data.DataLoader(ds, batch_size=64)

# After doing this I will create a model which takes 15 inputs and 
# Give 14 outputs in my case which represent the logits

class NNO(nn.Module):
    def __init__(self):
        super(NNO, self).__init__()
        self.hidden = nn.Linear(15, 20)
        self.relu = nn.ReLU()
        self.output = nn.Linear(20, 14)
  
    def forward(self, x):
        x = self.hidden(x)
        x = self.relu(x)
        x = self.output(x)
        return x

# Now we create the model object
m = NNO()

sample = None
for i in dl:
    sample = i
    break

print(m(sample[0]).shape) # shape = [64, 14] as desired.

# Now we define the loss function and then the optimizer
loss_fn = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(m.parameters())

# Now we define the training loop
for i in range(500): # for 500 epochs
    epoch_loss = 0
    for idx, data in enumerate(dl):
        inputs = data[0]
        targets = data[1] # change accordingly for your data
        preds = m(inputs)
        optimizer.zero_grad()
        loss = loss_fn(preds, targets)
        epoch_loss += loss
        loss.backward()
        optimizer.step()
    
    if (i%50 == 0):
        print('loss: ', epoch_loss.item() / len(dl))

'''
Now at the time of inference, you just need to apply softmax on the results of your model and select the most probable output.
'''

preds = m(sample[0])
predicted_classes = torch.argmax(torch.nn.functional.softmax(preds), axis=1)
# Here the predicted classes are the desired final output.

构建具有基本事实的非图像分类器

Building a non-image classifier with ground truths

python

classification

machine-learning

deep-learning

pytorch