把chatgpt神經網路模型當成linux終端機然後在裡面再訓練一個神經網路

I want you to act as a Linux terminal, 
I will type commands and you will reply with what the terminal should show. 
I want you to reply with the terminal output inside a unique code block and nothing else.
do not write explanations.
do not type commands unless I instruct you to do so.
When I need to tell you something in English I will do so by putting text inside curly brackets {something like this}.
my first command is pwd.

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable

class MnistModel(nn.Module):
    def __init__(self):
        super(MnistModel, self).__init__()
        # input is 28x28
        # padding=2 for same padding
        self.conv1 = nn.Conv2d(1, 32, 5, padding=2)
        # feature map size is 14*14 by pooling
        # padding=2 for same padding
        self.conv2 = nn.Conv2d(32, 64, 5, padding=2)
        # feature map size is 7*7 by pooling
        self.fc1 = nn.Linear(64*7*7, 1024)
        self.fc2 = nn.Linear(1024, 10)
        
    def forward(self, x):
        x = F.max_pool2d(F.relu(self.conv1(x)), 2)
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, 64*7*7)   # reshape Variable
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x)
    
model = MnistModel()
model

batch_size = 50
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('data', train=True, download=True, transform=transforms.ToTensor()),
    batch_size=batch_size, shuffle=True)

test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('data', train=False, transform=transforms.ToTensor()),
    batch_size=1000)

for p in model.parameters():
    print(p.size())

optimizer = optim.Adam(model.parameters(), lr=0.0001)
model.train()
train_loss = []
train_accu = []
i = 0
for epoch in range(15):
    for data, target in train_loader:
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()    # calc gradients
        train_loss.append(loss.item())
        optimizer.step()   # update gradients
        prediction = output.data.max(1)[1]   # first column has actual prob.
        accuracy = prediction.eq(target.data).sum()/batch_size*100
        train_accu.append(accuracy)
        if i % 10 == 0:
            print('Train Step: {}\tLoss: {:.3f}\tAccuracy: {:.3f}'.format(i, loss.item(), accuracy))
        i += 1

1. chatgpt具有模擬終端機的能力,而且幾乎以假亂真
2. chatgpt也有模擬python3環境的能力,但當運算變得複雜

When I need to tell you something in English I will do so by putting text inside curly brackets {something like this}.

putting text inside curly brackets {something like this}.