完成在Paddle框架下的手写数字识别任务,修改网络结构,采用卷积神经网络,修改网络损失函数为交叉熵损失,观察模型在不同的优化算法和学习率下的损失函数输出,对模型的性能进行可视化分析。具体代码
时间: 2024-11-09 16:25:43 浏览: 5
深度学习-paddle飞桨-图卷积神经网络-手写数字数据集实例-数据导入+注释
在PaddlePaddle(简称Paddle)框架下完成手写数字识别任务,你可以按照以下步骤进行:
1. **导入库**:
```python
import paddle
from paddle.vision import datasets, transforms
import paddle.nn as nn
import paddle.optim as optim
```
2. **数据预处理**:
```python
transform = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5], std=[0.5])
])
train_dataset = datasets.MNIST(root='./data', train=True, transform=transform)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transform)
```
3. **构建CNN网络结构**:
```python
class MnistModel(nn.Layer):
def __init__(self):
super(MnistModel, self).__init__()
self.conv1 = nn.Conv2D(1, 8, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2D(kernel_size=2, stride=2)
self.conv2 = nn.Conv2D(8, 16, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(16 * 4 * 4, 128)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = paddle.reshape(x, shape=(-1, 16*4*4))
x = F.relu(self.fc1(x))
return x
model = MnistModel()
```
4. **设置损失函数和优化器**:
```python
loss_fn = nn.CrossEntropyLoss() # 使用交叉熵损失
optimizer = optim.Adam(model.parameters(), learning_rate=0.001) # Adam优化器
```
5. **训练和验证循环**:
```python
for epoch in range(num_epochs):
for batch_id, data in enumerate(train_loader):
images, labels = data
outputs = model(images)
loss = loss_fn(outputs, labels)
optimizer.clear_grad()
loss.backward()
optimizer.step()
# 每个epoch结束后,评估模型性能
test_loss = evaluate(model, test_dataset)
```
6. **结果可视化**:
可以使用`paddle.metric`库或其他可视化库(如Matplotlib或Seaborn)来绘制训练过程中的损失函数曲线和精度变化。
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