使用LeNet在MNIST数据集实现图像分类

作者: PaddlePaddle

日期: 2021.01

摘要: 本示例教程演示如何在MNIST数据集上用LeNet进行图像分类。

一、环境配置

本教程基于Paddle 2.0 编写,如果您的环境不是本版本,请先参考官网安装 Paddle 2.0 。

import paddle
print(paddle.__version__)
2.0.0

二、数据加载

手写数字的MNIST数据集,包含60,000个用于训练的示例和10,000个用于测试的示例。这些数字已经过尺寸标准化并位于图像中心,图像是固定大小(28x28像素),其值为0到1。该数据集的官方地址为:http://yann.lecun.com/exdb/mnist

我们使用飞桨框架自带的 paddle.vision.datasets.MNIST 完成mnist数据集的加载。

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')
download training data and load training data
load finished

取训练集中的一条数据看一下。

import numpy as np
import matplotlib.pyplot as plt
train_data0, train_label_0 = train_dataset[0][0],train_dataset[0][1]
train_data0 = train_data0.reshape([28,28])
plt.figure(figsize=(2,2))
plt.imshow(train_data0, cmap=plt.cm.binary)
print('train_data0 label is: ' + str(train_label_0))
train_data0 label is: [5]
../../../_images/image_classification_6_1.png

三、组网

用paddle.nn下的API,如Conv2DMaxPool2DLinear完成LeNet的构建。

import paddle
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 = x.reshape((-1, 1, 28, 28))
        x = self.conv1(x)
        x = F.relu(x)
        x = self.max_pool1(x)
        x = F.relu(x)
        x = self.conv2(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

四、方式1:基于高层API,完成模型的训练与预测

通过paddle提供的Model 构建实例,使用封装好的训练与测试接口,快速完成模型训练与测试。

4.1 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
        )
The loss value printed in the log is the current step, and the metric is the average value of previous step.
Epoch 1/2
step 938/938 [==============================] - loss: 0.0159 - acc: 0.9521 - 19ms/step
Epoch 2/2
step 938/938 [==============================] - loss: 0.0029 - acc: 0.9834 - 19ms/step

4.2 使用 Model.evaluate 来预测模型

model.evaluate(test_dataset, batch_size=64, verbose=1)
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 157/157 [==============================] - loss: 7.3696e-04 - acc: 0.9493 - 7ms/step
Eval samples: 10000
{'loss': [0.00073696], 'acc': 0.9493}

方式一结束

以上就是方式一,可以快速、高效的完成网络模型训练与预测。

五、方式2:基于基础API,完成模型的训练与预测

5.1 模型训练

组网后,开始对模型进行训练,先构建train_loader,加载训练数据,然后定义train函数,设置好损失函数后,按batch加载数据,完成模型的训练。

import paddle.nn.functional as F
train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True)
# 加载训练集 batch_size 设为 64
def train(model):
    model.train()
    epochs = 2
    optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
    # 用Adam作为优化函数
    for epoch in range(epochs):
        for batch_id, data in enumerate(train_loader()):
            x_data = data[0]
            y_data = data[1]
            predicts = model(x_data)
            loss = F.cross_entropy(predicts, y_data)
            # 计算损失
            acc = paddle.metric.accuracy(predicts, y_data)
            loss.backward()
            if batch_id % 300 == 0:
                print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), acc.numpy()))
            optim.step()
            optim.clear_grad()
model = LeNet()
train(model)
epoch: 0, batch_id: 0, loss is: [3.292166], acc is: [0.046875]
epoch: 0, batch_id: 300, loss is: [0.05979356], acc is: [0.984375]
epoch: 0, batch_id: 600, loss is: [0.04557724], acc is: [0.984375]
epoch: 0, batch_id: 900, loss is: [0.09153229], acc is: [0.96875]
epoch: 1, batch_id: 0, loss is: [0.01268834], acc is: [1.]
epoch: 1, batch_id: 300, loss is: [0.22756869], acc is: [0.921875]
epoch: 1, batch_id: 600, loss is: [0.00377245], acc is: [1.]
epoch: 1, batch_id: 900, loss is: [0.00929211], acc is: [1.]

5.2 模型验证

训练完成后,需要验证模型的效果,此时,加载测试数据集,然后用训练好的模对测试集进行预测,计算损失与精度。

test_loader = paddle.io.DataLoader(test_dataset, places=paddle.CPUPlace(), batch_size=64)
# 加载测试数据集
def test(model):
    model.eval()
    batch_size = 64
    for batch_id, data in enumerate(test_loader()):
        x_data = data[0]
        y_data = data[1]
        predicts = model(x_data)
        # 获取预测结果
        loss = F.cross_entropy(predicts, y_data)
        acc = paddle.metric.accuracy(predicts, y_data)
        if batch_id % 20 == 0:
            print("batch_id: {}, loss is: {}, acc is: {}".format(batch_id, loss.numpy(), acc.numpy()))
test(model)
batch_id: 0, loss is: [0.01291558], acc is: [1.]
batch_id: 20, loss is: [0.07833393], acc is: [0.96875]
batch_id: 40, loss is: [0.04836973], acc is: [0.984375]
batch_id: 60, loss is: [0.11191542], acc is: [0.984375]
batch_id: 80, loss is: [0.04298809], acc is: [0.984375]
batch_id: 100, loss is: [0.00484229], acc is: [1.]
batch_id: 120, loss is: [0.00393359], acc is: [1.]
batch_id: 140, loss is: [0.02711521], acc is: [0.984375]

方式二结束

以上就是方式二,通过底层API,可以清楚的看到训练和测试中的每一步过程。但是,这种方式比较复杂。因此,我们提供了训练方式一,使用高层API来完成模型的训练与预测。对比底层API,高层API能够更加快速、高效的完成模型的训练与测试。

六、总结

以上就是用LeNet对手写数字数据及MNIST进行分类。本示例提供了两种训练模型的方式,一种可以快速完成模型的组建与预测,非常适合新手用户上手。另一种则需要多个步骤来完成模型的训练,适合进阶用户使用。