1import os
2import numpy
3import paddle # 导入paddle模块
4import paddle.fluid as fluid
5import gzip
6import struct
7work_path = os.getcwd()
8cluster_train_dir = "%s/train_data" % work_path
9def load_data(file_dir, is_train=True):
10    """
11    :param file_dir:
12    :param is_train:
13    :return:
14    """
15    if is_train:
16        image_path = file_dir + '/train-images-idx3-ubyte.gz'
17        label_path = file_dir + '/train-labels-idx1-ubyte.gz'
18    else:
19        image_path = file_dir + '/t10k-images-idx3-ubyte.gz'
20        label_path = file_dir + '/t10k-labels-idx1-ubyte.gz'
21    with open(image_path.replace('.gz', ''), 'wb') as out_f, gzip.GzipFile(image_path) as zip_f:
22        out_f.write(zip_f.read())
23        os.unlink(image_path)
24    with open(label_path.replace('.gz', ''), 'wb') as out_f, gzip.GzipFile(label_path) as zip_f:
25        out_f.write(zip_f.read())
26        os.unlink(label_path)
27    with open(label_path[:-3], 'rb') as lbpath:
28        magic, n = struct.unpack('>II', lbpath.read(8))
29        labels = numpy.fromfile(lbpath, dtype=numpy.uint8)
30    with open(image_path[:-3], 'rb') as imgpath:
31        magic, num, rows, cols = struct.unpack('>IIII', imgpath.read(16))
32        images = numpy.fromfile(imgpath, dtype=numpy.uint8).reshape(len(labels), 784)
33    return images, labels
34def reader_creator(file_dir, is_train=True, buffer_size=100):
35    """
36    :param file_dir:
37    :param is_train:
38    :param buffer_size:
39    :return:
40    """
41    images, labels = load_data(file_dir, is_train)
42    def reader():
43        """
44        :return:
45        """
46        for num in range(int(len(labels) / buffer_size)):
47            for i in range(buffer_size):
48                yield images[num * buffer_size + i, :], int(labels[num * buffer_size + i])
49    return reader
50def softmax_regression():
51    """
52    定义softmax分类器：
53        一个以softmax为激活函数的全连接层
54    Return:
55        predict_image -- 分类的结果
56    """
57    # 输入的原始图像数据，大小为28*28*1
58    img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
59    # 以softmax为激活函数的全连接层，输出层的大小必须为数字的个数10
60    predict = fluid.layers.fc(
61        input=img, size=10, act='softmax')
62    return predict
63def multilayer_perceptron():
64    """
65    定义多层感知机分类器：
66        含有两个隐藏层（全连接层）的多层感知器
67        其中前两个隐藏层的激活函数采用 ReLU，输出层的激活函数用 Softmax
68    Return:
69        predict_image -- 分类的结果
70    """
71    # 输入的原始图像数据，大小为28*28*1
72    img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
73    # 第一个全连接层，激活函数为ReLU
74    hidden = fluid.layers.fc(input=img, size=200, act='relu')
75    # 第二个全连接层，激活函数为ReLU
76    hidden = fluid.layers.fc(input=hidden, size=200, act='relu')
77    # 以softmax为激活函数的全连接输出层，输出层的大小必须为数字的个数10
78    prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
79    return prediction
80def convolutional_neural_network():
81    """
82    定义卷积神经网络分类器：
83        输入的二维图像，经过两个卷积-池化层，使用以softmax为激活函数的全连接层作为输出层
84    Return:
85        predict -- 分类的结果
86    """
87    # 输入的原始图像数据，大小为28*28*1
88    img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
89    # 第一个卷积-池化层
90    # 使用20个5*5的滤波器，池化大小为2，池化步长为2，激活函数为Relu
91    conv_pool_1 = fluid.nets.simple_img_conv_pool(
92        input=img,
93        filter_size=5,
94        num_filters=20,
95        pool_size=2,
96        pool_stride=2,
97        act="relu")
98    conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
99    # 第二个卷积-池化层
100    # 使用20个5*5的滤波器，池化大小为2，池化步长为2，激活函数为Relu
101    conv_pool_2 = fluid.nets.simple_img_conv_pool(
102        input=conv_pool_1,
103        filter_size=5,
104        num_filters=50,
105        pool_size=2,
106        pool_stride=2,
107        act="relu")
108    # 以softmax为激活函数的全连接输出层，输出层的大小必须为数字的个数10
109    prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
110    return prediction
111def train_program():
112    """
113    配置train_program
114    Return:
115        predict -- 分类的结果
116        avg_cost -- 平均损失
117        acc -- 分类的准确率
118    """
119    paddle.enable_static()
120    # 标签层，名称为label,对应输入图片的类别标签
121    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
122    # predict = softmax_regression() # 取消注释将使用 Softmax回归
123    # predict = multilayer_perceptron() # 取消注释将使用 多层感知器
124    predict = convolutional_neural_network() # 取消注释将使用 LeNet5卷积神经网络
125    # 使用类交叉熵函数计算predict和label之间的损失函数
126    cost = fluid.layers.cross_entropy(input=predict, label=label)
127    # 计算平均损失
128    avg_cost = fluid.layers.mean(cost)
129    # 计算分类准确率
130    acc = fluid.layers.accuracy(input=predict, label=label)
131    return predict, [avg_cost, acc]
132def optimizer_program():
133    """
134    :return:
135    """
136    return fluid.optimizer.Adam(learning_rate=0.001)
137# 一个minibatch中有64个数据
138BATCH_SIZE = 64
139# 每次读取训练集中的500个数据并随机打乱，传入batched reader中，batched reader 每次 yield 64个数据
140train_reader = paddle.batch(
141        paddle.reader.shuffle(
142            reader_creator(cluster_train_dir, is_train=True, buffer_size=100), buf_size=500),
143        batch_size=BATCH_SIZE)
144# 读取测试集的数据，每次 yield 64个数据
145test_reader = paddle.batch(
146            reader_creator(cluster_train_dir, is_train=False, buffer_size=100), batch_size=BATCH_SIZE)
147def event_handler(pass_id, batch_id, cost):
148    # 打印训练的中间结果，训练轮次，batch数，损失函数
149    print("Pass %d, Batch %d, Cost %f" % (pass_id, batch_id, cost))
150# 该模型运行在单个CPU上
151place = fluid.CPUPlace()
152# 调用train_program 获取预测值，损失值，
153prediction, [avg_loss, acc] = train_program()
154# 输入的原始图像数据，大小为28*28*1
155img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
156# 标签层，名称为label,对应输入图片的类别标签
157label = fluid.layers.data(name='label', shape=[1], dtype='int64')
158# 告知网络传入的数据分为两部分，第一部分是img值，第二部分是label值
159feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
160# 选择Adam优化器
161optimizer = fluid.optimizer.Adam(learning_rate=0.001)
162optimizer.minimize(avg_loss)
163PASS_NUM = 1 #训练1轮
164epochs = [epoch_id for epoch_id in range(PASS_NUM)]
165# 将模型参数存储在名为 save_dirname 的文件中
166save_dirname = "./output/"
167def train_test(train_test_program,
168                   train_test_feed, train_test_reader):
169    # 将分类准确率存储在acc_set中
170    acc_set = []
171    # 将平均损失存储在avg_loss_set中
172    avg_loss_set = []
173    # 将测试 reader yield 出的每一个数据传入网络中进行训练
174    for test_data in train_test_reader():
175        acc_np, avg_loss_np = exe.run(
176            program=train_test_program,
177            feed=train_test_feed.feed(test_data),
178            fetch_list=[acc, avg_loss])
179        acc_set.append(float(acc_np))
180        avg_loss_set.append(float(avg_loss_np))
181    # 获得测试数据上的准确率和损失值
182    acc_val_mean = numpy.array(acc_set).mean()
183    avg_loss_val_mean = numpy.array(avg_loss_set).mean()
184    # 返回平均损失值，平均准确率
185    return avg_loss_val_mean, acc_val_mean
186exe = fluid.Executor(place)
187exe.run(fluid.default_startup_program())
188main_program = fluid.default_main_program()
189test_program = fluid.default_main_program().clone(for_test=True)
190lists = []
191step = 0
192for epoch_id in epochs:
193    for step_id, data in enumerate(train_reader()):
194        metrics = exe.run(main_program,
195                          feed=feeder.feed(data),
196                          fetch_list=[avg_loss, acc])
197        if step % 100 == 0: #每训练100次 更新一次图片
198            event_handler(step, epoch_id, metrics[0])
199        step += 1
200    # 测试每个epoch的分类效果
201    avg_loss_val, acc_val = train_test(train_test_program=test_program,
202                                       train_test_reader=test_reader,
203                                       train_test_feed=feeder)
204    print("Test with Epoch %d, avg_cost: %s, acc: %s" % (epoch_id, avg_loss_val, acc_val))
205    lists.append((epoch_id, avg_loss_val, acc_val))
206    # 保存训练好的模型参数用于预测
207    if save_dirname is not None:
208        fluid.io.save_inference_model(save_dirname,
209                                      ["img"], [prediction], exe,
210                                      model_filename='model',
211                                      params_filename='params')
212# 选择效果最好的pass
213best = sorted(lists, key=lambda list: float(list[1]))[0]
214print('Best pass is %s, testing Avgcost is %s' % (best[0], best[1]))
215print('The classification accuracy is %.2f%%' % (float(best[2]) * 100))
216    

1#!/usr/bin/env python
2# -*- coding: utf-8 -*-
3"""
4"""
5import os
6import gzip
7import struct
8import numpy as np
9from PIL import Image
10import time
11import paddle
12import paddle.distributed.fleet as fleet
13import paddle.static.nn as nn
14import paddle.fluid as fluid
15from paddle.io import Dataset
16TEST_IMAGE = 't10k-images-idx3-ubyte.gz'
17TEST_LABEL = 't10k-labels-idx1-ubyte.gz'
18TRAIN_IMAGE = 'train-images-idx3-ubyte.gz'
19TRAIN_LABEL = 'train-labels-idx1-ubyte.gz'
20class MNIST(Dataset):
21    """
22    MNIST
23    """
24    def __init__(self,
25                 data_dir=None,
26                 mode='train',
27                 transform=None,
28                 backend=None):
29        assert mode.lower() in ['train', 'test'], \
30                "mode should be 'train' or 'test', but got {}".format(mode)
31        if backend is None:
32            backend = paddle.vision.get_image_backend()
33        if backend not in ['pil', 'cv2']:
34            raise ValueError(
35                "Expected backend are one of ['pil', 'cv2'], but got {}"
36                .format(backend))
37        self.backend = backend
38        self.mode = mode.lower()
39        if self.mode == 'train':
40            self.image_path = os.path.join(data_dir, TRAIN_IMAGE)
41            self.label_path = os.path.join(data_dir, TRAIN_LABEL)
42        else:
43            self.image_path = os.path.join(data_dir, TEST_IMAGE)
44            self.label_path = os.path.join(data_dir, TEST_LABEL)
45        self.transform = transform
46        # read dataset into memory
47        self._parse_dataset()
48        self.dtype = paddle.get_default_dtype()
49    def _parse_dataset(self, buffer_size=100):
50        self.images = []
51        self.labels = []
52        with gzip.GzipFile(self.image_path, 'rb') as image_file:
53            img_buf = image_file.read()
54            with gzip.GzipFile(self.label_path, 'rb') as label_file:
55                lab_buf = label_file.read()
56                step_label = 0
57                offset_img = 0
58                # read from Big-endian
59                # get file info from magic byte
60                # image file : 16B
61                magic_byte_img = '>IIII'
62                magic_img, image_num, rows, cols = struct.unpack_from(
63                    magic_byte_img, img_buf, offset_img)
64                offset_img += struct.calcsize(magic_byte_img)
65                offset_lab = 0
66                # label file : 8B
67                magic_byte_lab = '>II'
68                magic_lab, label_num = struct.unpack_from(magic_byte_lab,
69                                                          lab_buf, offset_lab)
70                offset_lab += struct.calcsize(magic_byte_lab)
71                while True:
72                    if step_label >= label_num:
73                        break
74                    fmt_label = '>' + str(buffer_size) + 'B'
75                    labels = struct.unpack_from(fmt_label, lab_buf, offset_lab)
76                    offset_lab += struct.calcsize(fmt_label)
77                    step_label += buffer_size
78                    fmt_images = '>' + str(buffer_size * rows * cols) + 'B'
79                    images_temp = struct.unpack_from(fmt_images, img_buf,
80                                                     offset_img)
81                    images = np.reshape(images_temp, (buffer_size, rows *
82                                                      cols)).astype('float32')
83                    offset_img += struct.calcsize(fmt_images)
84                    for i in range(buffer_size):
85                        self.images.append(images[i, :])
86                        self.labels.append(
87                            np.array([labels[i]]).astype('int64'))
88    def __getitem__(self, idx):
89        image, label = self.images[idx], self.labels[idx]
90        image = np.reshape(image, [28, 28])
91        if self.backend == 'pil':
92            image = Image.fromarray(image.astype('uint8'), mode='L')
93        if self.transform is not None:
94            image = self.transform(image)
95        if self.backend == 'pil':
96            return image, label.astype('int64')
97        return image.astype(self.dtype), label.astype('int64')
98    def __len__(self):
99        return len(self.labels)
100def mlp_model():
101    """
102    mlp_model
103    """
104    x = paddle.static.data(name="x", shape=[64, 28, 28], dtype='float32')
105    y = paddle.static.data(name="y", shape=[64, 1], dtype='int64')
106    x_flatten = paddle.reshape(x, [64, 784])
107    fc_1 = nn.fc(x=x_flatten, size=128, activation='tanh')
108    fc_2 = nn.fc(x=fc_1, size=128, activation='tanh')
109    prediction = nn.fc(x=[fc_2], size=10, activation='softmax')
110    cost = paddle.fluid.layers.cross_entropy(input=prediction, label=y)
111    acc_top1 = paddle.metric.accuracy(input=prediction, label=y, k=1)
112    avg_cost = paddle.mean(x=cost)
113    res = [x, y, prediction, avg_cost, acc_top1]
114    return res
115def train(epoch, exe, train_dataloader, cost, acc):
116    """
117    train
118    """
119    total_time = 0
120    step = 0
121    for data in train_dataloader():
122        step += 1
123        start_time = time.time()
124        loss_val, acc_val = exe.run(
125        paddle.static.default_main_program(),
126        feed=data, fetch_list=[cost.name, acc.name])
127        if step % 100 == 0:
128            end_time = time.time()
129            total_time += (end_time - start_time)
130            print(
131                    "epoch: %d, step:%d, train_loss: %f, train_acc: %f, total time cost = %f, speed: %f"
132                % (epoch, step, loss_val[0], acc_val[0], total_time,
133                1 / (end_time - start_time) ))
134def test(exe, test_dataloader, cost, acc):
135    """
136    test
137    """
138    total_time = 0
139    step = 0
140    for data in test_dataloader():
141        step += 1
142        start_time = time.time()
143        loss_val, acc_val = exe.run(
144        paddle.static.default_main_program(),
145        feed=data, fetch_list=[cost.name, acc.name])
146        if step % 100 == 0:
147            end_time = time.time()
148            total_time += (end_time - start_time)
149            print(
150                    "step:%d, test_loss: %f, test_acc: %f, total time cost = %f, speed: %f"
151                % (step, loss_val[0], acc_val[0], total_time,
152                1 / (end_time - start_time) ))
153def save(save_dir, feed_vars, fetch_vars, exe):
154    """
155    save
156    """
157    path_prefix = os.path.join(save_dir, 'model')
158    if fleet.is_first_worker():
159        paddle.static.save_inference_model(path_prefix, feed_vars, fetch_vars, exe)
160if __name__ == '__main__':
161    # 设置训练集路径
162    train_data = './train_data'
163    # 设置验证集路径
164    test_data = './test_data'
165    # 设置输出路径
166    save_dir = './output'
167    # 设置迭代轮数
168    epochs = 10
169    # 设置验证间隔轮数
170    test_interval = 2
171    # 设置模型保存间隔轮数
172    save_interval = 2
173    paddle.enable_static()
174    paddle.vision.set_image_backend('cv2')
175    # 训练数据集 
176    train_dataset = MNIST(data_dir=train_data, mode='train')
177    # 验证数据集
178    test_dataset = MNIST(data_dir=test_data, mode='test')
179    # 设置模型
180    [x, y, pred, cost, acc] = mlp_model()
181    place = paddle.CUDAPlace(int(os.environ.get('FLAGS_selected_gpus', 0)))
182    # 数据加载
183    train_dataloader = paddle.io.DataLoader(
184        train_dataset, feed_list=[x, y], drop_last=True,
185        places=place, batch_size=64, shuffle=True, return_list=False)
186    test_dataloader = paddle.io.DataLoader(
187        test_dataset, feed_list=[x, y], drop_last=True,
188        places=place, batch_size=64, return_list=False)
189    # fleet初始化
190    strategy = fleet.DistributedStrategy()
191    fleet.init(is_collective=True, strategy=strategy)
192    # 设置优化器
193    optimizer = paddle.optimizer.Adam()
194    optimizer = fleet.distributed_optimizer(optimizer)
195    optimizer.minimize(cost)
196    exe = paddle.static.Executor(place)
197    exe.run(paddle.static.default_startup_program())
198    prog = paddle.static.default_main_program()
199    for epoch in range(epochs):
200        train(epoch, exe, train_dataloader, cost, acc)
201        if epoch % test_interval == 0:
202            test(exe, test_dataloader, cost, acc)
203        # save model
204        if epoch % save_interval == 0:
205            save(save_dir, [x], [pred], exe)