问由于矩阵形状/大小不兼容而无法进行预测

EN

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, TensorDataset

import numpy as np
import pandas as pd

from LSR import ListSearchReplace as LSR

csv = pd.read_csv("main.csv")
partialInputs = csv[["age", "bmi", "children"]]
smoker, sex = list(csv["smoker"]), list(csv["sex"])

L1 = LSR(smoker)
L1.replace("yes", 1, True)
L1.replace("no", 0, True)

L2 = LSR(sex)
L2.replace("female", 1, True)
L2.replace("male", 0, True)

pdReadySmoker = pd.DataFrame({"smoker": smoker})
pdReadySex = pd.DataFrame({"sex": sex})

SmokerAndSex = pd.merge(pdReadySmoker, pdReadySex, how="outer", left_index=True, right_index=True)

INPUTS = pd.merge(partialInputs, SmokerAndSex, how="outer", left_index=True, right_index=True)
TARGETS = csv["charges"]

INPUTS = torch.from_numpy(np.array(INPUTS, dtype='float32'))
TARGETS = torch.from_numpy(np.array(TARGETS, dtype='float32'))

print(INPUTS.shape, TARGETS.shape)

loss_fn = F.mse_loss
model = nn.Linear(5, 3) # <-- changing this, changes the error message.
opt = torch.optim.SGD(model.parameters(), lr=1e-5)

trainDataset = TensorDataset(INPUTS, TARGETS)
BATCH_SIZE = 5
trainDataloader = DataLoader(trainDataset, BATCH_SIZE, shuffle=True)

def fit(numEpochs, model, loss_fn, opt, trainDataloader):
    for epochs in range(numEpochs):
        for inputBatch, targetBatch in trainDataloader:
            preds = model(inputBatch)
            loss = loss_fn(preds, targetBatch)
            loss.backward()
            
            opt.step()
            
            opt.zero_grad()
        
        e = epoch + 1
        if e % 10 == 0:
            print(f"Epoch: {e/numEpochs}, loss: {loss.item():.4f}")

fit(100, model, loss_fn, opt, trainDataloader) <-- error

<ipython-input-7-b7028a3d94fd>:5: UserWarning: Using a target size (torch.Size([5])) that is different to the input size (torch.Size([5, 3])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
  loss = loss_fn(preds, targetBatch)

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-20-d8f5bcdc847d> in <module>
----> 1 fit(100, model, loss_fn, opt, trainDataloader)

<ipython-input-7-b7028a3d94fd> in fit(numEpochs, model, loss_fn, opt, trainDataloader)
      3         for inputBatch, targetBatch in trainDataloader:
      4             preds = model(inputBatch)
----> 5             loss = loss_fn(preds, targetBatch)
      6             loss.backward()
      7 

D:\coding\machine-learning\env-ml\lib\site-packages\torch\nn\functional.py in mse_loss(input, target, size_average, reduce, reduction)
   2657         reduction = _Reduction.legacy_get_string(size_average, reduce)
   2658 
-> 2659     expanded_input, expanded_target = torch.broadcast_tensors(input, target)
   2660     return torch._C._nn.mse_loss(expanded_input, expanded_target, _Reduction.get_enum(reduction))
   2661 

D:\coding\machine-learning\env-ml\lib\site-packages\torch\functional.py in broadcast_tensors(*tensors)
     69         if any(type(t) is not Tensor for t in tensors) and has_torch_function(tensors):
     70             return handle_torch_function(broadcast_tensors, tensors, *tensors)
---> 71     return _VF.broadcast_tensors(tensors)  # type: ignore
     72 
     73 

RuntimeError: The size of tensor a (3) must match the size of tensor b (5) at non-singleton dimension 1

model = nn.Linear(5, 1338)

Error:
RuntimeError: The size of tensor a (1338) must match the size of tensor b (5) at non-singleton dimension 1

model = nn.Linear(1338, 1338)

Error:
RuntimeError: mat1 and mat2 shapes cannot be multiplied (5x5 and 1338x1338)