I am trying to use fastai with a very basic vanilla pytorch model. But despite my definition of metrics, it is now showing the metrics while training and I don’t understand why.

Here is the code:

```
#fastai.__version__ 1.0.25.dev0
import torch
import fastai
from fastai import *
from fastai.vision import *
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original')
X, y = mnist["data"], mnist["target"]
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42)
# Normalization
mean = X_train.mean()
std = X_train.std()
X_train = (X_train-mean)/std
X_valid = (X_valid-mean)/std
# Numpy to Torch Tensor
X_train = torch.from_numpy(np.float32(X_train))
y_train = torch.from_numpy(y_train.astype(np.long))
X_valid = torch.from_numpy(np.float32(X_valid))
y_valid = torch.from_numpy(y_valid.astype(np.long))
train = torch.utils.data.TensorDataset(X_train, y_train)
valid = torch.utils.data.TensorDataset(X_valid, y_valid)
data = ImageDataBunch.create(train_ds = train, valid_ds=valid)
import torch.nn as nn
net = nn.Sequential(
nn.Linear(28*28, 16),
nn.ReLU(),
nn.Linear(16, 16),
nn.ReLU(),
nn.Linear(16, 10),
nn.LogSoftmax(dim=1)
).cuda()
loss_function=nn.NLLLoss()
metrics=[accuracy]
opt=optim.SGD(net.parameters(), lr=1e-1)
fit(model=net, data=data, epochs=5, loss_func=loss_function, opt=opt, metrics=metrics)
```

Can anyone hint why?