pytorch 如何编辑线上代码 pytorch attention代码怎么写

现在AIGC很火，chatGPT的功能也是非常的强大，作为一个学计算机的，我想chatGPT必须要玩起来，哈哈哈哈～～～
突发奇想，虽然学了几年的计算机，但是编程能力还是不行，既然chatGPT那么滴niubility，那么我来试一下让他帮我学代码吧！

我：机器学习中常见的神经网络有哪些，并作出深刻的理解与说明

chatGPT答：

我：用python来实现一个简单的前馈神经网络（Feedforward Neural Network，FNN）

chatGPT答： 实现一个简单的前馈神经网络（Feedforward Neural Network，FNN）可以遵循以下步骤

总结： 生成的代码直接复制粘贴到jupyter中，发现一个错误：

然后我又把它的错误返回给chatGPT，他就解决了：

== self.output_error = y.reshape(-1, 1) - output ==

把这一句改变之后，程序代码确实正确了，但是准确率很低，才50%左右。

问：用pytorch实现前馈神经网络（Feedforward Neural Network，FNN）

chatGPT答：

用pytorch实现的代码也是有错误的，用同样的方法来解决

完整的代码如下：

import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

X, y = make_classification(n_samples=1000, n_features=10, n_informative=8, n_redundant=2, random_state=42)
X = torch.tensor(X).float()
y = torch.tensor(y).float()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

class FNN(nn.Module):
    def __init__(self, n_inputs, n_hidden, n_outputs):
        super(FNN, self).__init__()
        self.fc1 = nn.Linear(n_inputs, n_hidden)
        self.fc2 = nn.Linear(n_hidden, n_outputs)
        self.sigmoid = nn.Sigmoid()
        
    def forward(self, x):
        x = self.fc1(x)
        x = self.sigmoid(x)
        x = self.fc2(x)
        x = self.sigmoid(x)
        return x

fnn = FNN(n_inputs=X_train.shape[1], n_hidden=4, n_outputs=1)
criterion = nn.BCELoss()
optimizer = optim.SGD(fnn.parameters(), lr=0.01)

for i in range(1000):
    optimizer.zero_grad()
    output = fnn(X_train)
#     loss = criterion(output, y_train)
    loss = criterion(output, y_train.unsqueeze(1))
    loss.backward()
    optimizer.step()

with torch.no_grad():
    y_pred = fnn(X_test)
    y_pred = torch.round(y_pred)
    accuracy = torch.mean((y_pred == y_test).float())
    print(f"Accuracy: {accuracy}")

实验结果：依旧效果很差

问用python和pytorch实现卷积神经网络CNN

chatGPT答：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms

# 设置超参数
batch_size = 64
learning_rate = 0.001
num_epochs = 5

# 加载 MNIST 数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())

# 创建数据加载器
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)

# 定义 CNN 模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, padding=2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.fc = nn.Linear(7*7*32, 10)
    
    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

# 实例化模型和损失函数
cnn = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(cnn.parameters(), lr=learning_rate)

# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        outputs = cnn(images)
        loss = criterion(outputs, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        outputs = cnn(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))

这次没有错误，结果也还行：

问用tensorflow实现卷积神经网络CNN

chatGPT答

import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical

# 超参数
num_classes = 10
batch_size = 128
epochs = 10

# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 将输入数据转换为float类型，并将像素值归一化到0到1之间
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# 对标签进行one-hot编码
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)

# 将输入数据转换为4D张量
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)

# 构建卷积神经网络模型
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

# 编译模型
model.compile(loss=tf.keras.losses.categorical_crossentropy,
              optimizer=tf.keras.optimizers.Adadelta(),
              metrics=['accuracy'])

# 训练模型
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))

# 测试模型
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

运行结果如下：

总结：本人学疏才浅，也不一定全能判断出它生成的到底对不对，如果有什么不对的地方还请大家留言or私聊指出，谢谢大家！！让我们一起进步吧！

总的来说写的还行，我不会写的他都会，我要失业了，呜呜～
准确率比较低，等有时间改进一下。