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BML 全功能AI开发平台

    PaddlePaddle 2.0.0rc

    Paddle

    此处提供基于Paddle框架的MNIST图像分类示例代码,数据集请点击这里下载。

    单机训练时(计算节点等于1),示例代码如下:

    import os
    import numpy
    import paddle # 导入paddle模块
    import paddle.fluid as fluid
    import gzip
    import struct
    work_path = os.getcwd()
    cluster_train_dir = "%s/train_data" % work_path
    def load_data(file_dir, is_train=True):
        """
        :param file_dir:
        :param is_train:
        :return:
        """
        if is_train:
            image_path = file_dir + '/train-images-idx3-ubyte.gz'
            label_path = file_dir + '/train-labels-idx1-ubyte.gz'
        else:
            image_path = file_dir + '/t10k-images-idx3-ubyte.gz'
            label_path = file_dir + '/t10k-labels-idx1-ubyte.gz'
        with open(image_path.replace('.gz', ''), 'wb') as out_f, gzip.GzipFile(image_path) as zip_f:
            out_f.write(zip_f.read())
            os.unlink(image_path)
        with open(label_path.replace('.gz', ''), 'wb') as out_f, gzip.GzipFile(label_path) as zip_f:
            out_f.write(zip_f.read())
            os.unlink(label_path)
        with open(label_path[:-3], 'rb') as lbpath:
            magic, n = struct.unpack('>II', lbpath.read(8))
            labels = numpy.fromfile(lbpath, dtype=numpy.uint8)
        with open(image_path[:-3], 'rb') as imgpath:
            magic, num, rows, cols = struct.unpack('>IIII', imgpath.read(16))
            images = numpy.fromfile(imgpath, dtype=numpy.uint8).reshape(len(labels), 784)
        return images, labels
    def reader_creator(file_dir, is_train=True, buffer_size=100):
        """
        :param file_dir:
        :param is_train:
        :param buffer_size:
        :return:
        """
        images, labels = load_data(file_dir, is_train)
        def reader():
            """
            :return:
            """
            for num in range(int(len(labels) / buffer_size)):
                for i in range(buffer_size):
                    yield images[num * buffer_size + i, :], int(labels[num * buffer_size + i])
        return reader
    def softmax_regression():
        """
        定义softmax分类器:
            一个以softmax为激活函数的全连接层
        Return:
            predict_image -- 分类的结果
        """
        # 输入的原始图像数据,大小为28*28*1
        img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
        # 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
        predict = fluid.layers.fc(
            input=img, size=10, act='softmax')
        return predict
    def multilayer_perceptron():
        """
        定义多层感知机分类器:
            含有两个隐藏层(全连接层)的多层感知器
            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
        Return:
            predict_image -- 分类的结果
        """
        # 输入的原始图像数据,大小为28*28*1
        img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
        # 第一个全连接层,激活函数为ReLU
        hidden = fluid.layers.fc(input=img, size=200, act='relu')
        # 第二个全连接层,激活函数为ReLU
        hidden = fluid.layers.fc(input=hidden, size=200, act='relu')
        # 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
        prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
        return prediction
    def convolutional_neural_network():
        """
        定义卷积神经网络分类器:
            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
        Return:
            predict -- 分类的结果
        """
        # 输入的原始图像数据,大小为28*28*1
        img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
        # 第一个卷积-池化层
        # 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
        conv_pool_1 = fluid.nets.simple_img_conv_pool(
            input=img,
            filter_size=5,
            num_filters=20,
            pool_size=2,
            pool_stride=2,
            act="relu")
        conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
        # 第二个卷积-池化层
        # 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
        conv_pool_2 = fluid.nets.simple_img_conv_pool(
            input=conv_pool_1,
            filter_size=5,
            num_filters=50,
            pool_size=2,
            pool_stride=2,
            act="relu")
        # 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
        prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
        return prediction
    def train_program():
        """
        配置train_program
        Return:
            predict -- 分类的结果
            avg_cost -- 平均损失
            acc -- 分类的准确率
        """
        paddle.enable_static()
        # 标签层,名称为label,对应输入图片的类别标签
        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
        # predict = softmax_regression() # 取消注释将使用 Softmax回归
        # predict = multilayer_perceptron() # 取消注释将使用 多层感知器
        predict = convolutional_neural_network() # 取消注释将使用 LeNet5卷积神经网络
        # 使用类交叉熵函数计算predict和label之间的损失函数
        cost = fluid.layers.cross_entropy(input=predict, label=label)
        # 计算平均损失
        avg_cost = fluid.layers.mean(cost)
        # 计算分类准确率
        acc = fluid.layers.accuracy(input=predict, label=label)
        return predict, [avg_cost, acc]
    def optimizer_program():
        """
        :return:
        """
        return fluid.optimizer.Adam(learning_rate=0.001)
    # 一个minibatch中有64个数据
    BATCH_SIZE = 64
    # 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
    train_reader = paddle.batch(
            paddle.reader.shuffle(
                reader_creator(cluster_train_dir, is_train=True, buffer_size=100), buf_size=500),
            batch_size=BATCH_SIZE)
    # 读取测试集的数据,每次 yield 64个数据
    test_reader = paddle.batch(
                reader_creator(cluster_train_dir, is_train=False, buffer_size=100), batch_size=BATCH_SIZE)
    def event_handler(pass_id, batch_id, cost):
        # 打印训练的中间结果,训练轮次,batch数,损失函数
        print("Pass %d, Batch %d, Cost %f" % (pass_id, batch_id, cost))
    # 该模型运行在单个CPU上
    place = fluid.CPUPlace()
    # 调用train_program 获取预测值,损失值,
    prediction, [avg_loss, acc] = train_program()
    # 输入的原始图像数据,大小为28*28*1
    img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
    # 标签层,名称为label,对应输入图片的类别标签
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
    # 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
    feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
    # 选择Adam优化器
    optimizer = fluid.optimizer.Adam(learning_rate=0.001)
    optimizer.minimize(avg_loss)
    PASS_NUM = 1 #训练1轮
    epochs = [epoch_id for epoch_id in range(PASS_NUM)]
    # 将模型参数存储在名为 save_dirname 的文件中
    save_dirname = "./output/"
    def train_test(train_test_program,
                       train_test_feed, train_test_reader):
        # 将分类准确率存储在acc_set中
        acc_set = []
        # 将平均损失存储在avg_loss_set中
        avg_loss_set = []
        # 将测试 reader yield 出的每一个数据传入网络中进行训练
        for test_data in train_test_reader():
            acc_np, avg_loss_np = exe.run(
                program=train_test_program,
                feed=train_test_feed.feed(test_data),
                fetch_list=[acc, avg_loss])
            acc_set.append(float(acc_np))
            avg_loss_set.append(float(avg_loss_np))
        # 获得测试数据上的准确率和损失值
        acc_val_mean = numpy.array(acc_set).mean()
        avg_loss_val_mean = numpy.array(avg_loss_set).mean()
        # 返回平均损失值,平均准确率
        return avg_loss_val_mean, acc_val_mean
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())
    main_program = fluid.default_main_program()
    test_program = fluid.default_main_program().clone(for_test=True)
    lists = []
    step = 0
    for epoch_id in epochs:
        for step_id, data in enumerate(train_reader()):
            metrics = exe.run(main_program,
                              feed=feeder.feed(data),
                              fetch_list=[avg_loss, acc])
            if step % 100 == 0: #每训练100次 更新一次图片
                event_handler(step, epoch_id, metrics[0])
            step += 1
        # 测试每个epoch的分类效果
        avg_loss_val, acc_val = train_test(train_test_program=test_program,
                                           train_test_reader=test_reader,
                                           train_test_feed=feeder)
        print("Test with Epoch %d, avg_cost: %s, acc: %s" % (epoch_id, avg_loss_val, acc_val))
        lists.append((epoch_id, avg_loss_val, acc_val))
        # 保存训练好的模型参数用于预测
        if save_dirname is not None:
            fluid.io.save_inference_model(save_dirname,
                                          ["img"], [prediction], exe,
                                          model_filename='model',
                                          params_filename='params')
    # 选择效果最好的pass
    best = sorted(lists, key=lambda list: float(list[1]))[0]
    print('Best pass is %s, testing Avgcost is %s' % (best[0], best[1]))
    print('The classification accuracy is %.2f%%' % (float(best[2]) * 100))
        

    分布式训练时(计算节点大于1),示例代码如下: 说明:demo分布式程序没有做数据的分片操作,仅供参考

    #!/usr/bin/env python
    # -*- coding: utf-8 -*-
    """
    """
    import os
    import gzip
    import struct
    import numpy as np
    from PIL import Image
    import time
    import paddle
    import paddle.distributed.fleet as fleet
    import paddle.static.nn as nn
    import paddle.fluid as fluid
    from paddle.io import Dataset
    TEST_IMAGE = 't10k-images-idx3-ubyte.gz'
    TEST_LABEL = 't10k-labels-idx1-ubyte.gz'
    TRAIN_IMAGE = 'train-images-idx3-ubyte.gz'
    TRAIN_LABEL = 'train-labels-idx1-ubyte.gz'
    class MNIST(Dataset):
        """
        MNIST
        """
        def __init__(self,
                     data_dir=None,
                     mode='train',
                     transform=None,
                     backend=None):
            assert mode.lower() in ['train', 'test'], \
                    "mode should be 'train' or 'test', but got {}".format(mode)
            if backend is None:
                backend = paddle.vision.get_image_backend()
            if backend not in ['pil', 'cv2']:
                raise ValueError(
                    "Expected backend are one of ['pil', 'cv2'], but got {}"
                    .format(backend))
            self.backend = backend
            self.mode = mode.lower()
            if self.mode == 'train':
                self.image_path = os.path.join(data_dir, TRAIN_IMAGE)
                self.label_path = os.path.join(data_dir, TRAIN_LABEL)
            else:
                self.image_path = os.path.join(data_dir, TEST_IMAGE)
                self.label_path = os.path.join(data_dir, TEST_LABEL)
            self.transform = transform
            # read dataset into memory
            self._parse_dataset()
            self.dtype = paddle.get_default_dtype()
        def _parse_dataset(self, buffer_size=100):
            self.images = []
            self.labels = []
            with gzip.GzipFile(self.image_path, 'rb') as image_file:
                img_buf = image_file.read()
                with gzip.GzipFile(self.label_path, 'rb') as label_file:
                    lab_buf = label_file.read()
                    step_label = 0
                    offset_img = 0
                    # read from Big-endian
                    # get file info from magic byte
                    # image file : 16B
                    magic_byte_img = '>IIII'
                    magic_img, image_num, rows, cols = struct.unpack_from(
                        magic_byte_img, img_buf, offset_img)
                    offset_img += struct.calcsize(magic_byte_img)
                    offset_lab = 0
                    # label file : 8B
                    magic_byte_lab = '>II'
                    magic_lab, label_num = struct.unpack_from(magic_byte_lab,
                                                              lab_buf, offset_lab)
                    offset_lab += struct.calcsize(magic_byte_lab)
                    while True:
                        if step_label >= label_num:
                            break
                        fmt_label = '>' + str(buffer_size) + 'B'
                        labels = struct.unpack_from(fmt_label, lab_buf, offset_lab)
                        offset_lab += struct.calcsize(fmt_label)
                        step_label += buffer_size
                        fmt_images = '>' + str(buffer_size * rows * cols) + 'B'
                        images_temp = struct.unpack_from(fmt_images, img_buf,
                                                         offset_img)
                        images = np.reshape(images_temp, (buffer_size, rows *
                                                          cols)).astype('float32')
                        offset_img += struct.calcsize(fmt_images)
                        for i in range(buffer_size):
                            self.images.append(images[i, :])
                            self.labels.append(
                                np.array([labels[i]]).astype('int64'))
        def __getitem__(self, idx):
            image, label = self.images[idx], self.labels[idx]
            image = np.reshape(image, [28, 28])
            if self.backend == 'pil':
                image = Image.fromarray(image.astype('uint8'), mode='L')
            if self.transform is not None:
                image = self.transform(image)
            if self.backend == 'pil':
                return image, label.astype('int64')
            return image.astype(self.dtype), label.astype('int64')
        def __len__(self):
            return len(self.labels)
    def mlp_model():
        """
        mlp_model
        """
        x = paddle.static.data(name="x", shape=[64, 28, 28], dtype='float32')
        y = paddle.static.data(name="y", shape=[64, 1], dtype='int64')
        x_flatten = paddle.reshape(x, [64, 784])
        fc_1 = nn.fc(x=x_flatten, size=128, activation='tanh')
        fc_2 = nn.fc(x=fc_1, size=128, activation='tanh')
        prediction = nn.fc(x=[fc_2], size=10, activation='softmax')
        cost = paddle.fluid.layers.cross_entropy(input=prediction, label=y)
        acc_top1 = paddle.metric.accuracy(input=prediction, label=y, k=1)
        avg_cost = paddle.mean(x=cost)
        res = [x, y, prediction, avg_cost, acc_top1]
        return res
    def train(epoch, exe, train_dataloader, cost, acc):
        """
        train
        """
        total_time = 0
        step = 0
        for data in train_dataloader():
            step += 1
            start_time = time.time()
            loss_val, acc_val = exe.run(
            paddle.static.default_main_program(),
            feed=data, fetch_list=[cost.name, acc.name])
            if step % 100 == 0:
                end_time = time.time()
                total_time += (end_time - start_time)
                print(
                        "epoch: %d, step:%d, train_loss: %f, train_acc: %f, total time cost = %f, speed: %f"
                    % (epoch, step, loss_val[0], acc_val[0], total_time,
                    1 / (end_time - start_time) ))
    def test(exe, test_dataloader, cost, acc):
        """
        test
        """
        total_time = 0
        step = 0
        for data in test_dataloader():
            step += 1
            start_time = time.time()
            loss_val, acc_val = exe.run(
            paddle.static.default_main_program(),
            feed=data, fetch_list=[cost.name, acc.name])
            if step % 100 == 0:
                end_time = time.time()
                total_time += (end_time - start_time)
                print(
                        "step:%d, test_loss: %f, test_acc: %f, total time cost = %f, speed: %f"
                    % (step, loss_val[0], acc_val[0], total_time,
                    1 / (end_time - start_time) ))
    def save(save_dir, feed_vars, fetch_vars, exe):
        """
        save
        """
        path_prefix = os.path.join(save_dir, 'model')
        if fleet.is_first_worker():
            paddle.static.save_inference_model(path_prefix, feed_vars, fetch_vars, exe)
    if __name__ == '__main__':
        # 设置训练集路径
        train_data = './train_data'
        # 设置验证集路径
        test_data = './test_data'
        # 设置输出路径
        save_dir = './output'
        # 设置迭代轮数
        epochs = 10
        # 设置验证间隔轮数
        test_interval = 2
        # 设置模型保存间隔轮数
        save_interval = 2
        paddle.enable_static()
        paddle.vision.set_image_backend('cv2')
        # 训练数据集 
        train_dataset = MNIST(data_dir=train_data, mode='train')
        # 验证数据集
        test_dataset = MNIST(data_dir=test_data, mode='test')
        # 设置模型
        [x, y, pred, cost, acc] = mlp_model()
        place = paddle.CUDAPlace(int(os.environ.get('FLAGS_selected_gpus', 0)))
        # 数据加载
        train_dataloader = paddle.io.DataLoader(
            train_dataset, feed_list=[x, y], drop_last=True,
            places=place, batch_size=64, shuffle=True, return_list=False)
        test_dataloader = paddle.io.DataLoader(
            test_dataset, feed_list=[x, y], drop_last=True,
            places=place, batch_size=64, return_list=False)
        # fleet初始化
        strategy = fleet.DistributedStrategy()
        fleet.init(is_collective=True, strategy=strategy)
        # 设置优化器
        optimizer = paddle.optimizer.Adam()
        optimizer = fleet.distributed_optimizer(optimizer)
        optimizer.minimize(cost)
        exe = paddle.static.Executor(place)
        exe.run(paddle.static.default_startup_program())
        prog = paddle.static.default_main_program()
        for epoch in range(epochs):
            train(epoch, exe, train_dataloader, cost, acc)
            if epoch % test_interval == 0:
                test(exe, test_dataloader, cost, acc)
            # save model
            if epoch % save_interval == 0:
                save(save_dir, [x], [pred], exe)
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