【Paddle】实践作业——建立模型并测试100张图片

课程中以1张图片为例，测试了预测效果，请从原始mnist数据集中，随机抽取出100张图片，测试下模型的分类准确率？

【作业内容】

✓代码跑通 请大家根据课上所学内容，补全代码，保证程序跑通。

【评分标准】

✓代码运行成功且有结果（打印100张图片的分类准确率），100分

感谢大佬的博客讲解

如果notebook的代码自动提示能力可以和本地的pycharm自动提示能力差不多就好了

1. 添加库文件

import os
import random
import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
import numpy as np
from PIL import Image

import gzip
import json

2.利用飞桨的dataset.mnist中的api 获取训练数据和测试数据

• 首先获得训练数据和测试数据
• 接着对训练数据和测试数据进行乱序操作
• 最后对读取数据的reader进行装饰，得到批处理数据，batch_size = 100（方便测试时打印100张图片的分类准确度）

# 首先获得训练数据和测试数据
train_data = paddle.dataset.mnist.train()
test_data = paddle.dataset.mnist.test()

# 接着对训练数据和测试数据进行乱序操作,缓冲区大小设置为100
train_data = fluid.io.shuffle(train_data,100)
test_data = fluid.io.shuffle(test_data,100)

# 最后对读取数据的reader进行装饰，得到批处理数据，batch_size = 100（方便测试时打印100张图片的分类准确度
train_data = fluid.io.batch(train_data,100)
test_data = fluid.io.batch(test_data,100)

3. 按照项目9的网络定义不变

# 定义模型结构
class MNIST(fluid.dygraph.Layer):
     def __init__(self):
         super(MNIST, self).__init__()
         # 定义一个卷积层，使用relu激活函数
         self.conv1 = Conv2D(num_channels=1, num_filters=20, filter_size=5, stride=1, padding=2, act='relu')
         # 定义一个池化层，池化核为2，步长为2，使用最大池化方式
         self.pool1 = Pool2D(pool_size=2, pool_stride=2, pool_type='max')
         # 定义一个卷积层，使用relu激活函数
         self.conv2 = Conv2D(num_channels=20, num_filters=20, filter_size=5, stride=1, padding=2, act='relu')
         # 定义一个池化层，池化核为2，步长为2，使用最大池化方式
         self.pool2 = Pool2D(pool_size=2, pool_stride=2, pool_type='max')
         # 定义一个全连接层，输出节点数为10 
         self.fc = Linear(input_dim=980, output_dim=10, act='softmax')
    # 定义网络的前向计算过程
     def forward(self, inputs, label):
         x = self.conv1(inputs)
         x = self.pool1(x)
         x = self.conv2(x)
         x = self.pool2(x)
         x = fluid.layers.reshape(x, [x.shape[0], 980])
         x = self.fc(x)
         if label is not None:
             acc = fluid.layers.accuracy(input=x, label=label)
             return x, acc
         else:
             return x

4.按照项目9的训练配置，训练过程不作改变

#在使用GPU机器时，可以将use_gpu变量设置成True
use_gpu = False
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()


with fluid.dygraph.guard(place):
    model = MNIST()
    model.train() 
    
    EPOCH_NUM = 5
    BATCH_SIZE = 100
    # 定义学习率，并加载优化器参数到模型中
    total_steps = (int(60000//BATCH_SIZE) + 1) * EPOCH_NUM
    lr = fluid.dygraph.PolynomialDecay(0.01, total_steps, 0.001)
    
    # 使用Adam优化器
    optimizer = fluid.optimizer.AdamOptimizer(learning_rate=lr, parameter_list=model.parameters())
    
    for epoch_id in range(EPOCH_NUM):
        for batch_id, data in enumerate(train_data()):
            # 准备数据，变得更加简洁
            image_data = np.array([x[0] for x in data]).astype('float32').reshape(-1, 1, 28, 28)
            # 获得图像标签数据，并转为float32类型的数组
            label_data = np.array([x[1] for x in data]).astype('int64').reshape(-1, 1)
            image = fluid.dygraph.to_variable(image_data)
            label = fluid.dygraph.to_variable(label_data)
            
            #前向计算的过程，同时拿到模型输出值和分类准确率
            predict, acc = model(image, label)
            avg_acc = fluid.layers.mean(acc)
            
            #计算损失，取一个批次样本损失的平均值
            loss = fluid.layers.cross_entropy(predict, label)
            avg_loss = fluid.layers.mean(loss)
            
            #每训练了200批次的数据，打印下当前Loss的情况
            if batch_id % 200 == 0:
                print("epoch: {}, batch: {}, loss is: {}, acc is {}".format(epoch_id, batch_id, avg_loss.numpy(),avg_acc.numpy()))
            
            #后向传播，更新参数的过程
            avg_loss.backward()
            optimizer.minimize(avg_loss)
            model.clear_gradients()
            
        # 保存模型参数和优化器的参数
        fluid.save_dygraph(model.state_dict(), './checkpoint/mnist_epoch{}'.format(epoch_id))
        fluid.save_dygraph(optimizer.state_dict(), './checkpoint/mnist_epoch{}'.format(epoch_id))
    fluid.save_dygraph(model.state_dict(),'mnist')

epoch: 0, batch: 0, loss is: [2.9192822], acc is [0.18]
epoch: 0, batch: 200, loss is: [0.33062753], acc is [0.92]
epoch: 0, batch: 400, loss is: [0.09664985], acc is [0.95]
epoch: 1, batch: 0, loss is: [0.07815135], acc is [0.98]
epoch: 1, batch: 200, loss is: [0.07338614], acc is [0.98]
epoch: 1, batch: 400, loss is: [0.02472404], acc is [1.]
epoch: 2, batch: 0, loss is: [0.04026771], acc is [0.98]
epoch: 2, batch: 200, loss is: [0.07566174], acc is [0.98]
epoch: 2, batch: 400, loss is: [0.01209071], acc is [1.]
epoch: 3, batch: 0, loss is: [0.03427401], acc is [0.99]
epoch: 3, batch: 200, loss is: [0.03426887], acc is [0.98]
epoch: 3, batch: 400, loss is: [0.00716644], acc is [1.]
epoch: 4, batch: 0, loss is: [0.0203767], acc is [0.99]
epoch: 4, batch: 200, loss is: [0.00760919], acc is [1.]
epoch: 4, batch: 400, loss is: [0.00716131], acc is [1.]

5.测试过程，从随机的测试数据中选择前100个图片进行测试

• 首先加载已经训练好的模型参数
• 读取test_data的第一个批次，因为test_data之前已经进行了乱序操作，并且batch_size = 100，因此保证得到是随机得到的100张图片
• 因为每次运行整个项目，训练集和测试集的又会重新乱序，因此得到的正确率也会不同，但变化不大

with fluid.dygraph.guard():
    print('start testing ......')
    # 加载模型参数
    model = MNIST()
    model_state_dict, _ = fluid.load_dygraph('mnist')
    # 这里选择使用set_dict,因为load_dict即将过时
    model.set_dict(model_state_dict)

    model.eval()
    for batch_id, data in enumerate(test_data()):
        image_data = np.array([x[0] for x in data]).astype('float32').reshape(-1, 1, 28, 28)
        label_data = np.array([x[1] for x in data]).astype('int64').reshape(-1, 1)
        image = fluid.dygraph.to_variable(image_data)
        label = fluid.dygraph.to_variable(label_data)
        prediction, acc = model(image, label)
        avg_acc = fluid.layers.mean(acc)
        print('随机抽取100张图片，测试平均准确率为{}'.format(avg_acc.numpy()))
        break

start testing ......
随机抽取100张图片，测试平均准确率为[0.98]

6. 增加测试样本数量

经过多次的测试发现，由于测试数据量小，并且样本随机，因此测试的平均正确率会出现较大误差
为了增加测试的可信性
这里测试100批的数据，batch_size = 100, 统计不同批次的平均正确率，并计算总的平均正确率

import matplotlib.pyplot as plt

with fluid.dygraph.guard():
    print('start testing ......')
    # 加载模型参数
    model = MNIST()
    model_state_dict, _ = fluid.load_dygraph('mnist')
    # 这里选择使用set_dict,因为load_dict即将过时
    model.set_dict(model_state_dict)

    model.eval()
    avgs = []
    id = []
    for batch_id, data in enumerate(test_data()):
        image_data = np.array([x[0] for x in data]).astype('float32').reshape(-1, 1, 28, 28)
        label_data = np.array([x[1] for x in data]).astype('int64').reshape(-1, 1)
        image = fluid.dygraph.to_variable(image_data)
        label = fluid.dygraph.to_variable(label_data)
        prediction, acc = model(image, label)
        avg_acc = fluid.layers.mean(acc)
        id.append(batch_id)
        avgs.append(avg_acc.numpy())
    plt.title("Average accuracy of multiple batches of sample testing")
    plt.plot(id,avgs)
    plt.xlabel("batch_id") 
    plt.ylabel("average accuracy") 
    plt.show()

    # 10000个测试样本的平均正确率
    avg_all_acc = np.array(avgs).mean()
label("batch_id") 
    plt.ylabel("average accuracy") 
    plt.show()

    # 10000个测试样本的平均正确率
    avg_all_acc = np.array(avgs).mean()
    print("the average accuracy of the 10000 testing samples is {}".format(avg_all_acc))

start testing ......

the average accuracy of the 10000 testing samples is 0.9869000315666199