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}.