给出一个利用VGG模型解决手写数字识别的paddle代码
时间: 2024-01-20 15:04:35 浏览: 39
好的,以下是一个利用 VGG 模型解决手写数字识别的 Paddle 代码示例:
```python
import paddle
import paddle.nn as nn
class VGGBlock(nn.Layer):
def __init__(self, in_channels, out_channels, num_convs):
super(VGGBlock, self).__init__()
self.conv1 = nn.Conv2D(in_channels, out_channels, kernel_size=3, padding=1)
self.relu = nn.ReLU()
self.convs = nn.Sequential()
for i in range(num_convs - 1):
conv_name = 'conv{}'.format(i + 2)
setattr(self.convs, conv_name, nn.Conv2D(out_channels, out_channels, kernel_size=3, padding=1))
self.pool = nn.MaxPool2D(kernel_size=2, stride=2)
def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.convs(x)
x = self.pool(x)
return x
class VGGNet(nn.Layer):
def __init__(self):
super(VGGNet, self).__init__()
self.block1 = VGGBlock(in_channels=1, out_channels=64, num_convs=2)
self.block2 = VGGBlock(in_channels=64, out_channels=128, num_convs=2)
self.block3 = VGGBlock(in_channels=128, out_channels=256, num_convs=3)
self.block4 = VGGBlock(in_channels=256, out_channels=512, num_convs=3)
self.block5 = VGGBlock(in_channels=512, out_channels=512, num_convs=3)
self.flatten = nn.Flatten()
self.fc1 = nn.Linear(in_features=512*7*7, out_features=4096)
self.fc2 = nn.Linear(in_features=4096, out_features=4096)
self.fc3 = nn.Linear(in_features=4096, out_features=10)
def forward(self, x):
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.block4(x)
x = self.block5(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
return x
# 加载数据集
train_dataset = paddle.vision.datasets.MNIST(mode='train')
test_dataset = paddle.vision.datasets.MNIST(mode='test')
# 定义数据预处理函数
transform = paddle.vision.transforms.Compose([
paddle.vision.transforms.Resize((32, 32)),
paddle.vision.transforms.ToTensor(),
paddle.vision.transforms.Normalize([0.5], [0.5])
])
# 定义数据加载器
train_loader = paddle.io.DataLoader(train_dataset.transform(transform),
batch_size=64,
shuffle=True)
test_loader = paddle.io.DataLoader(test_dataset.transform(transform),
batch_size=64,
shuffle=False)
# 定义模型、优化器和损失函数
model = VGGNet()
optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
criterion = nn.CrossEntropyLoss()
# 训练模型
epochs = 10
for epoch in range(epochs):
for batch_id, data in enumerate(train_loader()):
x_data = data[0]
y_data = paddle.to_tensor(data[1])
logits = model(x_data)
loss = criterion(logits, y_data)
loss.backward()
optimizer.step()
optimizer.clear_grad()
if batch_id % 100 == 0:
print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, loss.numpy()))
# 评估模型
model.eval()
accs = []
for batch_id, data in enumerate(test_loader()):
x_data = data[0]
y_data = paddle.to_tensor(data[1])
logits = model(x_data)
pred = paddle.argmax(logits, axis=-1).numpy()
acc = np.mean((pred == y_data.numpy()).astype(np.float32))
accs.append(acc)
print("accuracy: ", np.mean(accs))
```
这份代码使用了 VGGNet 作为模型,并使用了 MNIST 数据集进行训练和测试。在训练过程中,采用了 Adam 优化器和交叉熵损失函数,训练了 10 个 epoch,最终在测试集上达到了较好的准确率。
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