[toc]

实践总体过程和步骤如下图：

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#导入需要的包
import os
import zipfile
import random
import json
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import paddle
from paddle.fluid.dygraph import Linear

Python 依赖库

numpy---------->python 第三方库，用于进行科学计算

PIL------------> Python Image Library,python 第三方图像处理库

matplotlib----->python 的绘图库 pyplot:matplotlib 的绘图框架

os------------->提供了丰富的方法来处理文件和目录

数据准备

数据集介绍

MNIST 数据集包含 60000 个训练集和 10000 测试数据集。分为图片和标签，图片是 28*28 的像素矩阵，标签为 0~9 共 10 个数字。

train_reader 和 test_reader

paddle.dataset.mnist.train()和 test()分别用于获取 mnist 训练集和测试集

使用 paddle.io.DataLoader()进行 batch 训练

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!mkdir -p /home/aistudio/.cache/paddle/dataset/mnist/
!cp -r /home/aistudio/data/data65/*  /home/aistudio/.cache/paddle/dataset/mnist/
!ls /home/aistudio/.cache/paddle/dataset/mnist/
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t10k-images-idx3-ubyte.gz  train-images-idx3-ubyte.gz
t10k-labels-idx1-ubyte.gz  train-labels-idx1-ubyte.gz
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BUF_SIZE = 512
BATCH_SIZE = 128
#用于训练的数据提供器，每次从缓存的数据项中随机读取批次大小的数据
train_reader = paddle.batch(
    paddle.reader.shuffle(paddle.dataset.mnist.train(),
                          buf_size=BUF_SIZE),
    batch_size=BATCH_SIZE)
#用于训练的数据提供器，每次从缓存的数据项中随机读取批次大小的数据
test_reader = paddle.batch(
    paddle.reader.shuffle(paddle.dataset.mnist.test(),
                          buf_size=BUF_SIZE),
    batch_size=BATCH_SIZE)
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# 用于打印，查看mnist数据
train_data = paddle.dataset.mnist.train();
sampledata = next(train_data())
print(sampledata)
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       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        , -1.        ,
       -1.        , -1.        , -1.        , -1.        ], dtype=float32), 5)

可以看出 数值为-1 表示灰度为 0，其余数值范围为[-1, 1]对应灰度 0~255

网络配置

以下的代码判断就是定义一个简单的多层感知器，一共有三层，两个大小为 100 的隐层和一个大小为 10 的输出层，因为 MNIST 数据集是手写 0 到 9 的灰度图像，类别有 10 个，所以最后的输出大小是 10。最后输出层的激活函数是 Softmax，所以最后的输出层相当于一个分类器。加上一个输入层的话，多层感知器的结构是：输入层–>>隐层–>>隐层–>>输出层。

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# 定义多层感知器
# 动态图定义多层感知器
class multilayer_perceptron(paddle.fluid.dygraph.Layer):
    def __init__(self):
        super(multilayer_perceptron,self).__init__()
        self.fc1 = Linear(input_dim=28*28, output_dim=100, act='relu')
        self.fc2 = Linear(input_dim=100, output_dim=100, act='relu')
        self.fc3 = Linear(input_dim=100, output_dim=10,act="softmax")
    def forward(self, input_):
        x = paddle.fluid.layers.reshape(input_, [input_.shape[0], -1])
        x = self.fc1(x)
        x = self.fc2(x)
        y = self.fc3(x)
        return y
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# 展示模型训练曲线
all_train_iter=0
all_train_iters=[]
all_train_costs=[]
all_train_accs=[]


#绘制训练过程
def draw_train_process(title,iters,costs,accs,label_cost,lable_acc):
    plt.title(title, fontsize=24)
    plt.xlabel("iter", fontsize=20)
    plt.ylabel("cost/acc", fontsize=20)
    plt.plot(iters, costs,color='red',label=label_cost)
    plt.plot(iters, accs,color='green',label=lable_acc)
    plt.legend()
    plt.grid()
    plt.show()


def draw_process(title,color,iters,data,label):
    plt.title(title, fontsize=24)
    plt.xlabel("iter", fontsize=20)
    plt.ylabel(label, fontsize=20)
    plt.plot(iters, data,color=color,label=label)
    plt.legend()
    plt.grid()
    plt.show()
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'''
训练并保存模型
训练需要有一个训练程序和一些必要参数，并构建了一个获取训练过程中测试误差的函数。必要参数有executor,program,reader,feeder,fetch_list。
'''
# 用动态图进行训练
all_train_iter=0
all_train_iters=[]
all_train_costs=[]
all_train_accs=[]

best_test_acc = 0.0


with paddle.fluid.dygraph.guard():
    model = multilayer_perceptron() # 模型实例化
    model.train() # 训练模式
    # ExponentialDecay?
    opt = paddle.fluid.optimizer.Adam(learning_rate=paddle.fluid.dygraph.ExponentialDecay(
              learning_rate=0.001,
              decay_steps=4000,
              decay_rate=0.1,
              staircase=True), parameter_list=model.parameters())

    epochs_num = 10 #迭代次数

    for pass_num in range(epochs_num):
        lr = opt.current_step_lr()
        print("learning-rate:", lr)

        for batch_id,data in enumerate(train_reader()):
            images = np.array([x[0].reshape(1,28,28) for x in data],np.float32)

            labels = np.array([x[1] for x in data]).astype('int64')
            labels = labels[:, np.newaxis]

            image = paddle.fluid.dygraph.to_variable(images)
            label = paddle.fluid.dygraph.to_variable(labels)
            predict = model(image)#预测
            #print(predict)
            loss = paddle.fluid.layers.cross_entropy(predict,label)
            avg_loss = paddle.fluid.layers.mean(loss)#获取loss值

            acc = paddle.fluid.layers.accuracy(predict,label)#计算精度
            avg_loss.backward()
            opt.minimize(avg_loss)
            model.clear_gradients()

            all_train_iter = all_train_iter + 256
            all_train_iters.append(all_train_iter)
            all_train_costs.append(loss.numpy()[0])
            all_train_accs.append(acc.numpy()[0])


            if batch_id!=0 and batch_id%50==0:
                print("epoch:{}, batch_id:{}, train_loss:{}, train_acc:{}".format(pass_num+1, batch_id, avg_loss.numpy(), acc.numpy()))


        with paddle.fluid.dygraph.guard():
            accs = []
            model.eval()#评估模式
            for batch_id,data in enumerate(test_reader()):#测试集
                images = np.array([x[0].reshape(1,28,28) for x in data],np.float32)
                labels = np.array([x[1] for x in data]).astype('int64')
                labels = labels[:, np.newaxis]

                image = paddle.fluid.dygraph.to_variable(images)
                label = paddle.fluid.dygraph.to_variable(labels)

                predict = model(image)#预测
                acc = paddle.fluid.layers.accuracy(predict,label)
                accs.append(acc.numpy()[0])
                avg_acc = np.mean(accs)


            if avg_acc >= best_test_acc:
                best_test_acc = avg_acc
                if pass_num > 10:
                    paddle.fluid.save_dygraph(model.state_dict(), './work/{}'.format(pass_num))#保存模型

            print('Test:%d, Accuracy:%0.5f, Best: %0.5f'%  (pass_num, avg_acc, best_test_acc))


    paddle.fluid.save_dygraph(model.state_dict(),'./work/fashion_mnist_epoch{}'.format(epochs_num))#保存模型


print('训练模型保存完成！')
print("best_test_acc", best_test_acc)
draw_train_process("training",all_train_iters,all_train_costs,all_train_accs,"trainning cost","trainning acc")
draw_process("trainning loss","red",all_train_iters,all_train_costs,"trainning loss")
draw_process("trainning acc","green",all_train_iters,all_train_accs,"trainning acc")
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learning-rate: 0.001
epoch:1, batch_id:50, train_loss:[0.33342597], train_acc:[0.8984375]
epoch:1, batch_id:100, train_loss:[0.6477896], train_acc:[0.78125]
epoch:1, batch_id:150, train_loss:[0.38204402], train_acc:[0.9140625]
epoch:1, batch_id:200, train_loss:[0.29537392], train_acc:[0.90625]
epoch:1, batch_id:250, train_loss:[0.29159826], train_acc:[0.9140625]
epoch:1, batch_id:300, train_loss:[0.39459157], train_acc:[0.8671875]
epoch:1, batch_id:350, train_loss:[0.25907594], train_acc:[0.9296875]
epoch:1, batch_id:400, train_loss:[0.31777298], train_acc:[0.90625]
epoch:1, batch_id:450, train_loss:[0.16258541], train_acc:[0.9375]
Test:0, Accuracy:0.92524, Best: 0.92524
learning-rate: 0.001
epoch:2, batch_id:50, train_loss:[0.14996889], train_acc:[0.9453125]
epoch:2, batch_id:100, train_loss:[0.2086468], train_acc:[0.9375]
epoch:2, batch_id:150, train_loss:[0.13732132], train_acc:[0.953125]
epoch:2, batch_id:200, train_loss:[0.20005819], train_acc:[0.9375]
epoch:2, batch_id:250, train_loss:[0.22621125], train_acc:[0.921875]
epoch:2, batch_id:300, train_loss:[0.23624715], train_acc:[0.9375]
epoch:2, batch_id:350, train_loss:[0.22858979], train_acc:[0.921875]
epoch:2, batch_id:400, train_loss:[0.15868747], train_acc:[0.9453125]
epoch:2, batch_id:450, train_loss:[0.17579108], train_acc:[0.96875]
Test:1, Accuracy:0.95431, Best: 0.95431
learning-rate: 0.001
epoch:3, batch_id:50, train_loss:[0.09384024], train_acc:[0.9765625]
epoch:3, batch_id:100, train_loss:[0.14337152], train_acc:[0.953125]
epoch:3, batch_id:150, train_loss:[0.09826898], train_acc:[0.96875]
epoch:3, batch_id:200, train_loss:[0.12162703], train_acc:[0.953125]
epoch:3, batch_id:250, train_loss:[0.16990048], train_acc:[0.9375]
epoch:3, batch_id:300, train_loss:[0.11993235], train_acc:[0.9765625]
epoch:3, batch_id:350, train_loss:[0.04041685], train_acc:[0.9921875]
epoch:3, batch_id:400, train_loss:[0.10029075], train_acc:[0.9765625]
epoch:3, batch_id:450, train_loss:[0.20086782], train_acc:[0.9453125]
Test:2, Accuracy:0.96034, Best: 0.96034
learning-rate: 0.001
epoch:4, batch_id:50, train_loss:[0.10540008], train_acc:[0.96875]
epoch:4, batch_id:100, train_loss:[0.06458011], train_acc:[0.96875]
epoch:4, batch_id:150, train_loss:[0.0674578], train_acc:[0.96875]
epoch:4, batch_id:200, train_loss:[0.09675008], train_acc:[0.9609375]
epoch:4, batch_id:250, train_loss:[0.15608555], train_acc:[0.9609375]
epoch:4, batch_id:300, train_loss:[0.09341267], train_acc:[0.9609375]
epoch:4, batch_id:350, train_loss:[0.1041307], train_acc:[0.9609375]
epoch:4, batch_id:400, train_loss:[0.07487246], train_acc:[0.9765625]
epoch:4, batch_id:450, train_loss:[0.15261263], train_acc:[0.96875]
Test:3, Accuracy:0.96351, Best: 0.96351
learning-rate: 0.001
epoch:5, batch_id:50, train_loss:[0.07081573], train_acc:[0.984375]
epoch:5, batch_id:100, train_loss:[0.12329036], train_acc:[0.9453125]
epoch:5, batch_id:150, train_loss:[0.11128808], train_acc:[0.96875]
epoch:5, batch_id:200, train_loss:[0.03693299], train_acc:[0.9921875]
epoch:5, batch_id:250, train_loss:[0.06550381], train_acc:[0.9609375]
epoch:5, batch_id:300, train_loss:[0.11091305], train_acc:[0.96875]
epoch:5, batch_id:350, train_loss:[0.05953867], train_acc:[0.9921875]
epoch:5, batch_id:400, train_loss:[0.05256216], train_acc:[0.984375]
epoch:5, batch_id:450, train_loss:[0.04102388], train_acc:[0.984375]
Test:4, Accuracy:0.96381, Best: 0.96381
learning-rate: 0.001
epoch:6, batch_id:50, train_loss:[0.08369304], train_acc:[0.96875]
epoch:6, batch_id:100, train_loss:[0.09292502], train_acc:[0.9609375]
epoch:6, batch_id:150, train_loss:[0.13268939], train_acc:[0.9609375]
epoch:6, batch_id:200, train_loss:[0.08329619], train_acc:[0.96875]
epoch:6, batch_id:250, train_loss:[0.11900125], train_acc:[0.96875]
epoch:6, batch_id:300, train_loss:[0.08534286], train_acc:[0.953125]
epoch:6, batch_id:350, train_loss:[0.11742742], train_acc:[0.953125]
epoch:6, batch_id:400, train_loss:[0.09688846], train_acc:[0.9765625]
epoch:6, batch_id:450, train_loss:[0.02995617], train_acc:[1.]
Test:5, Accuracy:0.96173, Best: 0.96381
learning-rate: 0.001
epoch:7, batch_id:50, train_loss:[0.05730037], train_acc:[0.96875]
epoch:7, batch_id:100, train_loss:[0.02739977], train_acc:[0.9921875]
epoch:7, batch_id:150, train_loss:[0.04557585], train_acc:[0.9765625]
epoch:7, batch_id:200, train_loss:[0.05771943], train_acc:[0.9765625]
epoch:7, batch_id:250, train_loss:[0.06323972], train_acc:[0.9609375]
epoch:7, batch_id:300, train_loss:[0.0729816], train_acc:[0.9765625]
epoch:7, batch_id:350, train_loss:[0.03425251], train_acc:[0.9921875]
epoch:7, batch_id:400, train_loss:[0.13220268], train_acc:[0.9609375]
epoch:7, batch_id:450, train_loss:[0.0768251], train_acc:[0.96875]
Test:6, Accuracy:0.96529, Best: 0.96529
learning-rate: 0.001
epoch:8, batch_id:50, train_loss:[0.02684894], train_acc:[0.9921875]
epoch:8, batch_id:100, train_loss:[0.05457066], train_acc:[0.9921875]
epoch:8, batch_id:150, train_loss:[0.06887776], train_acc:[0.9765625]
epoch:8, batch_id:200, train_loss:[0.01996839], train_acc:[1.]
epoch:8, batch_id:250, train_loss:[0.07040852], train_acc:[0.96875]
epoch:8, batch_id:300, train_loss:[0.02762877], train_acc:[0.9921875]
epoch:8, batch_id:350, train_loss:[0.0307516], train_acc:[0.9921875]
epoch:8, batch_id:400, train_loss:[0.12568305], train_acc:[0.9609375]
epoch:8, batch_id:450, train_loss:[0.03238961], train_acc:[0.9921875]
Test:7, Accuracy:0.96232, Best: 0.96529
learning-rate: 0.001
epoch:9, batch_id:50, train_loss:[0.04035459], train_acc:[0.984375]
epoch:9, batch_id:100, train_loss:[0.04379664], train_acc:[0.9921875]
epoch:9, batch_id:150, train_loss:[0.0402751], train_acc:[0.9921875]
epoch:9, batch_id:200, train_loss:[0.03802398], train_acc:[0.984375]
epoch:9, batch_id:250, train_loss:[0.09821159], train_acc:[0.953125]
epoch:9, batch_id:300, train_loss:[0.03633454], train_acc:[0.9921875]
epoch:9, batch_id:350, train_loss:[0.065966], train_acc:[0.9609375]
epoch:9, batch_id:400, train_loss:[0.1054427], train_acc:[0.984375]
epoch:9, batch_id:450, train_loss:[0.08116379], train_acc:[0.9765625]
Test:8, Accuracy:0.97943, Best: 0.97943
learning-rate: 0.000100000005
epoch:10, batch_id:50, train_loss:[0.02536881], train_acc:[0.9921875]
epoch:10, batch_id:100, train_loss:[0.01205996], train_acc:[1.]
epoch:10, batch_id:150, train_loss:[0.05764459], train_acc:[0.9765625]
epoch:10, batch_id:200, train_loss:[0.04137428], train_acc:[0.984375]
epoch:10, batch_id:250, train_loss:[0.05747751], train_acc:[0.9609375]
epoch:10, batch_id:300, train_loss:[0.05138961], train_acc:[0.984375]
epoch:10, batch_id:350, train_loss:[0.02714467], train_acc:[0.984375]
epoch:10, batch_id:400, train_loss:[0.08042958], train_acc:[0.984375]
epoch:10, batch_id:450, train_loss:[0.02294997], train_acc:[0.9921875]
Test:9, Accuracy:0.97973, Best: 0.97973
训练模型保存完成！
best_test_acc 0.979727

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在这里插入图片描述

在这里插入图片描述

模型预测

图片预处理

在预测之前，要对图像进行预处理。

首先进行灰度化，然后压缩图像大小为 28*28，接着将图像转换成一维向量，最后再对一维向量进行归一化处理。

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def load_image(file):
    im = Image.open(file).convert('L')                        #将RGB转化为灰度图像，L代表灰度图像，像素值在0~255之间
    im = im.resize((28, 28), Image.ANTIALIAS)                 #resize image with high-quality 图像大小为28*28
    im = np.array(im).reshape(1, 1, 28, 28).astype(np.float32)#返回新形状的数组,把它变成一个 numpy 数组以匹配数据馈送格式。
    # print(im)
    im = im / 255.0 * 2.0 - 1.0                               #归一化到【-1~1】之间
    return im

使用 Matplotlib 工具显示这张图像并预测

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infer_path='/home/aistudio/data/data2394/infer_3.png'
img = Image.open(infer_path)
plt.imshow(img)   #根据数组绘制图像
plt.show()        #显示图像
label_list = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"]

'''
模型预测
'''
para_state_dict = paddle.load("work/fashion_mnist_epoch5.pdparams")
model = multilayer_perceptron()
model.set_state_dict(para_state_dict) #加载模型参数
model.eval() #训练模式
infer_img = load_image(infer_path)
infer_img = np.array(infer_img).astype('float32')
infer_img = infer_img[np.newaxis,:, : ,:]
infer_img = paddle.fluid.dygraph.to_variable(infer_img)
result = model(infer_img)


infer_img = np.array(infer_img).astype('float32')
infer_img = infer_img[np.newaxis,:, : ,:]
infer_img = paddle.fluid.dygraph.to_variable(infer_img)
result = model(infer_img)

print("infer results: %s" % label_list[np.argmax(result.numpy())])

在这里插入图片描述

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infer results: 3