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# 加载数据集
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import paddle
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from paddle.vision.transforms import Compose, Normalize
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transform = Compose([Normalize(mean=[127.5],
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std=[127.5],
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data_format='CHW')])
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# 使用transform对数据集做归一化
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print('download training data and load training data')
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train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
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test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)
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print('load finished')
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# 定义网络结构
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import paddle.nn.functional as F
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class LeNet(paddle.nn.Layer):
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def __init__(self):
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super(LeNet, self).__init__()
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self.conv1 = paddle.nn.Conv2D(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)
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self.max_pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
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self.conv2 = paddle.nn.Conv2D(in_channels=6, out_channels=16, kernel_size=5, stride=1)
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self.max_pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
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self.linear1 = paddle.nn.Linear(in_features=16*5*5, out_features=120)
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self.linear2 = paddle.nn.Linear(in_features=120, out_features=84)
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self.linear3 = paddle.nn.Linear(in_features=84, out_features=10)
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def forward(self, x):
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x = self.conv1(x)
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x = F.relu(x)
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x = self.max_pool1(x)
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x = self.conv2(x)
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x = F.relu(x)
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x = self.max_pool2(x)
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x = paddle.flatten(x, start_axis=1,stop_axis=-1)
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x = self.linear1(x)
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x = F.relu(x)
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x = self.linear2(x)
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x = F.relu(x)
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x = self.linear3(x)
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return x
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# 使用 Model.fit来训练模型
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from paddle.metric import Accuracy
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model = paddle.Model(LeNet()) # 用Model封装模型
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optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
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# 配置模型
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model.prepare(
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optim,
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paddle.nn.CrossEntropyLoss(),
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Accuracy()
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)
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# 训练模型
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model.fit(train_dataset,
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epochs=2,
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batch_size=64,
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verbose=1
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)
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# 使用 Model.evaluate 来预测模型
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model.evaluate(test_dataset, batch_size=64, verbose=1)
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