上传文件至 'MNIST_Example/PaddlePaddle'

MNIST_Example/PaddlePaddle/train.py

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