代码：

# -*- coding: utf-8 -*
"""
训练常用视觉基础网络，用于分类任务
需要将训练图片，类别文件 label_list.txt 放置在同一个文件夹下
程序会先读取 train.txt 文件获取类别数和图片数量
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import time
import math
import paddle
import paddle.fluid as fluid
import codecs
import logging

from paddle.fluid.initializer import MSRA
from paddle.fluid.initializer import Uniform
from paddle.fluid.param_attr import ParamAttr
from PIL import Image
from PIL import ImageEnhance

train_parameters = {
    "input_size": [3, 224, 224],
    "class_dim": -1,  # 分类数，会在初始化自定义 reader 的时候获得
    "image_count": -1,  # 训练图片数量，会在初始化自定义 reader 的时候获得
    "label_dict": {},
    "data_dir": "data/data12479/hackathon-blossom-flower-classification",  # 训练数据存储地址
    "train_file_list": "train.txt",
    "label_file": "label_list.txt",
    "save_freeze_dir": "./freeze-model",
    "save_persistable_dir": "./persistable-params",
    "continue_train": True,        # 是否接着上一次保存的参数接着训练，优先级高于预训练模型
    "pretrained": True,            # 是否使用预训练的模型
    "pretrained_dir": "data/data6487/ResNet50_pretrained", 
    "mode": "train",
    "num_epochs": 120,
    "train_batch_size": 20,
    "mean_rgb": [127.5, 127.5, 127.5],  # 常用图片的三通道均值，通常来说需要先对训练数据做统计，此处仅取中间值
    "use_gpu": True,
    "image_enhance_strategy": {  # 图像增强相关策略
        "need_distort": True,  # 是否启用图像颜色增强
        "need_rotate": True,   # 是否需要增加随机角度
        "need_crop": True,      # 是否要增加裁剪
        "need_flip": True,      # 是否要增加水平随机翻转
        "hue_prob": 0.5,
        "hue_delta": 18,
        "contrast_prob": 0.5,
        "contrast_delta": 0.5,
        "saturation_prob": 0.5,
        "saturation_delta": 0.5,
        "brightness_prob": 0.5,
        "brightness_delta": 0.125
    },
    "early_stop": {
        "sample_frequency": 50,
        "successive_limit": 3,
        "good_acc1": 0.92
    },
    "rsm_strategy": {
        "learning_rate": 0.002,
        "lr_epochs": [20, 40, 60, 80, 100],
        "lr_decay": [1, 0.5, 0.25, 0.1, 0.01, 0.002]
    },
    "momentum_strategy": {
        "learning_rate": 0.002,
        "lr_epochs": [20, 40, 60, 80, 100],
        "lr_decay": [1, 0.5, 0.25, 0.1, 0.01, 0.002]
    },
    "sgd_strategy": {
        "learning_rate": 0.002,
        "lr_epochs": [20, 40, 60, 80, 100],
        "lr_decay": [1, 0.5, 0.25, 0.1, 0.01, 0.002]
    },
    "adam_strategy": {
        "learning_rate": 0.002
    }
}


class ResNet(object):
    """
    resnet的网络结构类
    """

    def __init__(self, layers=50):
        """
        resnet的网络构造函数
        :param layers: 网络层数
        """
        self.layers = layers

    def name(self):
        """
        获取网络结构名字
        :return:
        """
        return 'resnet'

    def net(self, input, class_dim=1000):
        """
        构建网络结构
        :param input: 输入图片
        :param class_dim: 分类类别
        :return:
        """
        layers = self.layers
        supported_layers = [50, 101, 152]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(supported_layers, layers)

        if layers == 50:
            depth = [3, 4, 6, 3]
        elif layers == 101:
            depth = [3, 4, 23, 3]
        elif layers == 152:
            depth = [3, 8, 36, 3]
        num_filters = [64, 128, 256, 512]

        conv = self.conv_bn_layer(
            input=input,
            num_filters=64,
            filter_size=7,
            stride=2,
            act='relu',
            name="conv1")
        conv = fluid.layers.pool2d(
            input=conv,
            pool_size=3,
            pool_stride=2,
            pool_padding=1,
            pool_type='max')

        for block in range(len(depth)):
            for i in range(depth[block]):
                if layers in [101, 152] and block == 2:
                    if i == 0:
                        conv_name = "res" + str(block + 2) + "a"
                    else:
                        conv_name = "res" + str(block + 2) + "b" + str(i)
                else:
                    conv_name = "res" + str(block + 2) + chr(97 + i)
                conv = self.bottleneck_block(
                    input=conv,
                    num_filters=num_filters[block],
                    stride=2 if i == 0 and block != 0 else 1,
                    name=conv_name)

        pool = fluid.layers.pool2d(input=conv, pool_size=7, pool_type='avg', global_pooling=True)
        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
        out = fluid.layers.fc(input=pool,
                              size=class_dim,
                              act='softmax',
                              param_attr=fluid.param_attr.ParamAttr(initializer=Uniform(-stdv, stdv)))
        return out

    def conv_bn_layer(self,
                      input,
                      num_filters,
                      filter_size,
                      stride=1,
                      groups=1,
                      act=None,
                      name=None):
        """
        便捷型卷积结构，包含了batch_normal处理
        :param input: 输入图片
        :param num_filters: 卷积核个数
        :param filter_size: 卷积核大小
        :param stride: 平移
        :param groups: 分组
        :param act: 激活函数
        :param name: 卷积层名字
        :return:
        """
        conv = fluid.layers.conv2d(
            input=input,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            act=None,
            param_attr=ParamAttr(name=name + "_weights"),
            bias_attr=False,
            name=name + '.conv2d.output.1')
        if name == "conv1":
            bn_name = "bn_" + name
        else:
            bn_name = "bn" + name[3:]
        return fluid.layers.batch_norm(
            input=conv,
            act=act,
            name=bn_name + '.output.1',
            param_attr=ParamAttr(name=bn_name + '_scale'),
            bias_attr=ParamAttr(bn_name + '_offset'),
            moving_mean_name=bn_name + '_mean',
            moving_variance_name=bn_name + '_variance', )

    def shortcut(self, input, ch_out, stride, name):
        """
        转换结构，转换输入和输出一致，方便最后的短链接结构
        :param input:
        :param ch_out:
        :param stride:
        :param name:
        :return:
        """
        ch_in = input.shape[1]
        if ch_in != ch_out or stride != 1:
            return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
        else:
            return input

    def bottleneck_block(self, input, num_filters, stride, name):
        """
        resnet的短路链接结构中的一种，采用压缩方式先降维，卷积后再升维
        利用转换结构将输入变成瓶颈卷积一样的尺寸，最后将两者按照位相加，完成短路链接
        :param input:
        :param num_filters:
        :param stride:
        :param name:
        :return:
        """
        conv0 = self.conv_bn_layer(
            input=input,
            num_filters=num_filters,
            filter_size=1,
            act='relu',
            name=name + "_branch2a")
        conv1 = self.conv_bn_layer(
            input=conv0,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act='relu',
            name=name + "_branch2b")
        conv2 = self.conv_bn_layer(
            input=conv1,
            num_filters=num_filters * 4,
            filter_size=1,
            act=None,
            name=name + "_branch2c")

        short = self.shortcut(
            input, num_filters * 4, stride, name=name + "_branch1")

        return fluid.layers.elementwise_add(
            x=short, y=conv2, act='relu', name=name + ".add.output.5")


def init_log_config():
    """
    初始化日志相关配置
    :return:
    """
    global logger
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)
    log_path = os.path.join(os.getcwd(), 'logs')
    if not os.path.exists(log_path):
        os.makedirs(log_path)
    log_name = os.path.join(log_path, 'train.log')
    sh = logging.StreamHandler()
    fh = logging.FileHandler(log_name, mode='w')
    fh.setLevel(logging.DEBUG)
    formatter = logging.Formatter("%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s")
    fh.setFormatter(formatter)
    sh.setFormatter(formatter)
    logger.addHandler(sh)
    logger.addHandler(fh)


def init_train_parameters():
    """
    初始化训练参数，主要是初始化图片数量，类别数
    :return:
    """
    train_file_list = os.path.join(train_parameters['data_dir'], train_parameters['train_file_list'])
    label_list = os.path.join(train_parameters['data_dir'], train_parameters['label_file'])
    index = 0
    with codecs.open(label_list, encoding='utf-8') as flist:
        lines = [line.strip() for line in flist]
        for line in lines:
            parts = line.strip().split()
            train_parameters['label_dict'][parts[1]] = int(parts[0])
            index += 1
        train_parameters['class_dim'] = index
    with codecs.open(train_file_list, encoding='utf-8') as flist:
        lines = [line.strip() for line in flist]
        train_parameters['image_count'] = len(lines)


def resize_img(img, target_size):
    """
    强制缩放图片
    :param img:
    :param target_size:
    :return:
    """
    target_size = input_size
    img = img.resize((target_size[1], target_size[2]), Image.BILINEAR)
    return img


def random_crop(img, scale=[0.08, 1.0], ratio=[3. / 4., 4. / 3.]):
    aspect_ratio = math.sqrt(np.random.uniform(*ratio))
    w = 1. * aspect_ratio
    h = 1. / aspect_ratio

    bound = min((float(img.size[0]) / img.size[1]) / (w**2),
                (float(img.size[1]) / img.size[0]) / (h**2))
    scale_max = min(scale[1], bound)
    scale_min = min(scale[0], bound)

    target_area = img.size[0] * img.size[1] * np.random.uniform(scale_min,
                                                                scale_max)
    target_size = math.sqrt(target_area)
    w = int(target_size * w)
    h = int(target_size * h)

    i = np.random.randint(0, img.size[0] - w + 1)
    j = np.random.randint(0, img.size[1] - h + 1)

    img = img.crop((i, j, i + w, j + h))
    img = img.resize((train_parameters['input_size'][1], train_parameters['input_size'][2]), Image.BILINEAR)
    return img


def rotate_image(img):
    """
    图像增强，增加随机旋转角度
    """
    angle = np.random.randint(-14, 15)
    img = img.rotate(angle)
    return img


def random_brightness(img):
    """
    图像增强，亮度调整
    :param img:
    :return:
    """
    prob = np.random.uniform(0, 1)
    if prob < train_parameters['image_enhance_strategy']['brightness_prob']:
        brightness_delta = train_parameters['image_enhance_strategy']['brightness_delta']
        delta = np.random.uniform(-brightness_delta, brightness_delta) + 1
        img = ImageEnhance.Brightness(img).enhance(delta)
    return img


def random_contrast(img):
    """
    图像增强，对比度调整
    :param img:
    :return:
    """
    prob = np.random.uniform(0, 1)
    if prob < train_parameters['image_enhance_strategy']['contrast_prob']:
        contrast_delta = train_parameters['image_enhance_strategy']['contrast_delta']
        delta = np.random.uniform(-contrast_delta, contrast_delta) + 1
        img = ImageEnhance.Contrast(img).enhance(delta)
    return img


def random_saturation(img):
    """
    图像增强，饱和度调整
    :param img:
    :return:
    """
    prob = np.random.uniform(0, 1)
    if prob < train_parameters['image_enhance_strategy']['saturation_prob']:
        saturation_delta = train_parameters['image_enhance_strategy']['saturation_delta']
        delta = np.random.uniform(-saturation_delta, saturation_delta) + 1
        img = ImageEnhance.Color(img).enhance(delta)
    return img


def random_hue(img):
    """
    图像增强，色度调整
    :param img:
    :return:
    """
    prob = np.random.uniform(0, 1)
    if prob < train_parameters['image_enhance_strategy']['hue_prob']:
        hue_delta = train_parameters['image_enhance_strategy']['hue_delta']
        delta = np.random.uniform(-hue_delta, hue_delta)
        img_hsv = np.array(img.convert('HSV'))
        img_hsv[:, :, 0] = img_hsv[:, :, 0] + delta
        img = Image.fromarray(img_hsv, mode='HSV').convert('RGB')
    return img


def distort_color(img):
    """
    概率的图像增强
    :param img:
    :return:
    """
    prob = np.random.uniform(0, 1)
    # Apply different distort order
    if prob < 0.35:
        img = random_brightness(img)
        img = random_contrast(img)
        img = random_saturation(img)
        img = random_hue(img)
    elif prob < 0.7:
        img = random_brightness(img)
        img = random_saturation(img)
        img = random_hue(img)
        img = random_contrast(img)
    return img


def custom_image_reader(file_list, data_dir, mode):
    """
    自定义用户图片读取器，先初始化图片种类，数量
    :param file_list:
    :param data_dir:
    :param mode:
    :return:
    """
    with codecs.open(file_list) as flist:
        lines = [line.strip() for line in flist]

    def reader():
        np.random.shuffle(lines)
        for line in lines:
            if mode == 'train' or mode == 'val':
                img_path, label = line.split()
                img = Image.open(img_path)
                try:
                    if img.mode != 'RGB':
                        img = img.convert('RGB')
                    if train_parameters['image_enhance_strategy']['need_distort'] == True:
                        img = distort_color(img)
                    if train_parameters['image_enhance_strategy']['need_rotate'] == True:
                        img = rotate_image(img)
                    if train_parameters['image_enhance_strategy']['need_crop'] == True:
                        img = random_crop(img, train_parameters['input_size'])
                    if train_parameters['image_enhance_strategy']['need_flip'] == True:
                        mirror = int(np.random.uniform(0, 2))
                        if mirror == 1:
                            img = img.transpose(Image.FLIP_LEFT_RIGHT)
                    # HWC--->CHW && normalized
                    img = np.array(img).astype('float32')
                    img -= train_parameters['mean_rgb']
                    img = img.transpose((2, 0, 1))  # HWC to CHW
                    img *= 0.007843                 # 像素值归一化
                    yield img, int(label)
                except Exception as e:
                    pass                            # 以防某些图片读取处理出错，加异常处理
            elif mode == 'test':
                img_path = os.path.join(data_dir, line)
                img = Image.open(img_path)
                if img.mode != 'RGB':
                    img = img.convert('RGB')
                img = resize_img(img, train_parameters['input_size'])
                # HWC--->CHW && normalized
                img = np.array(img).astype('float32')
                img -= train_parameters['mean_rgb']
                img = img.transpose((2, 0, 1))  # HWC to CHW
                img *= 0.007843  # 像素值归一化
                yield img

    return reader


def optimizer_momentum_setting():
    """
    阶梯型的学习率适合比较大规模的训练数据
    """
    learning_strategy = train_parameters['momentum_strategy']
    batch_size = train_parameters["train_batch_size"]
    iters = train_parameters["image_count"] // batch_size
    lr = learning_strategy['learning_rate']

    boundaries = [i * iters for i in learning_strategy["lr_epochs"]]
    values = [i * lr for i in learning_strategy["lr_decay"]]
    learning_rate = fluid.layers.piecewise_decay(boundaries, values)
    optimizer = fluid.optimizer.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9)
    return optimizer


def optimizer_rms_setting():
    """
    阶梯型的学习率适合比较大规模的训练数据
    """
    batch_size = train_parameters["train_batch_size"]
    iters = train_parameters["image_count"] // batch_size
    learning_strategy = train_parameters['rsm_strategy']
    lr = learning_strategy['learning_rate']

    boundaries = [i * iters for i in learning_strategy["lr_epochs"]]
    values = [i * lr for i in learning_strategy["lr_decay"]]

    optimizer = fluid.optimizer.RMSProp(
        learning_rate=fluid.layers.piecewise_decay(boundaries, values))

    return optimizer


def optimizer_sgd_setting():
    """
    loss下降相对较慢，但是最终效果不错，阶梯型的学习率适合比较大规模的训练数据
    """
    learning_strategy = train_parameters['sgd_strategy']
    batch_size = train_parameters["train_batch_size"]
    iters = train_parameters["image_count"] // batch_size
    lr = learning_strategy['learning_rate']

    boundaries = [i * iters for i in learning_strategy["lr_epochs"]]
    values = [i * lr for i in learning_strategy["lr_decay"]]
    learning_rate = fluid.layers.piecewise_decay(boundaries, values)
    optimizer = fluid.optimizer.SGD(learning_rate=learning_rate)
    return optimizer


def optimizer_adam_setting():
    """
    能够比较快速的降低 loss，但是相对后期乏力
    """
    learning_strategy = train_parameters['adam_strategy']
    learning_rate = learning_strategy['learning_rate']
    optimizer = fluid.optimizer.Adam(learning_rate=learning_rate)
    return optimizer


def load_params(exe, program):
    if train_parameters['continue_train'] and os.path.exists(train_parameters['save_persistable_dir']):
        logger.info('load params from retrain model')
        fluid.io.load_persistables(executor=exe,
                                   dirname=train_parameters['save_persistable_dir'],
                                   main_program=program)
    elif train_parameters['pretrained'] and os.path.exists(train_parameters['pretrained_dir']):
        logger.info('load params from pretrained model')
        def if_exist(var):
            return os.path.exists(os.path.join(train_parameters['pretrained_dir'], var.name))

        fluid.io.load_vars(exe, train_parameters['pretrained_dir'], main_program=program,
                           predicate=if_exist)


def train():
    train_prog = fluid.Program()
    train_startup = fluid.Program()
    logger.info("create prog success")
    logger.info("train config: %s", str(train_parameters))
    logger.info("build input custom reader and data feeder")
    file_list = os.path.join(train_parameters['data_dir'], "train.txt")
    mode = train_parameters['mode']
    batch_reader = paddle.batch(custom_image_reader(file_list, train_parameters['data_dir'], mode),
                                batch_size=train_parameters['train_batch_size'],
                                drop_last=True)
                                
    place = fluid.CUDAPlace(0) if train_parameters['use_gpu'] else fluid.CPUPlace()
    # 定义输入数据的占位符
    img = fluid.layers.data(name='img', shape=train_parameters['input_size'], dtype='float32')
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
    feeder = fluid.DataFeeder(feed_list=[img, label], place=place)

    # 选取不同的网络
    logger.info("build newwork")
    model = ResNet()
    out = model.net(input=img, class_dim=train_parameters['class_dim'])
    cost = fluid.layers.cross_entropy(out, label)
    avg_cost = fluid.layers.mean(x=cost)
    acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
    # 选取不同的优化器
    optimizer = optimizer_rms_setting()
    # optimizer = optimizer_momentum_setting()
    # optimizer = optimizer_sgd_setting()
    # optimizer = optimizer_adam_setting()
    optimizer.minimize(avg_cost)
    exe = fluid.Executor(place)

    main_program = fluid.default_main_program()
    exe.run(fluid.default_startup_program())
    train_fetch_list = [avg_cost.name, acc_top1.name, out.name]
    
    load_params(exe, main_program)

    # 训练循环主体
    stop_strategy = train_parameters['early_stop']
    successive_limit = stop_strategy['successive_limit']
    sample_freq = stop_strategy['sample_frequency']
    good_acc1 = stop_strategy['good_acc1']
    successive_count = 0
    stop_train = False
    total_batch_count = 0
    for pass_id in range(train_parameters["num_epochs"]):
        logger.info("current pass: %d, start read image", pass_id)
        batch_id = 0
        for step_id, data in enumerate(batch_reader()):
            t1 = time.time()
            loss, acc1, pred_ot = exe.run(main_program,
                                          feed=feeder.feed(data),
                                          fetch_list=train_fetch_list)
            t2 = time.time()
            batch_id += 1
            total_batch_count += 1
            period = t2 - t1
            loss = np.mean(np.array(loss))
            acc1 = np.mean(np.array(acc1))
            if batch_id % 10 == 0:
                logger.info("Pass {0}, trainbatch {1}, loss {2}, acc1 {3}, time {4}".format(pass_id, batch_id, loss, acc1,
                                                                                            "%2.2f sec" % period))
            # 简单的提前停止策略，认为连续达到某个准确率就可以停止了
            if acc1 >= good_acc1:
                successive_count += 1
                logger.info("current acc1 {0} meets good {1}, successive count {2}".format(acc1, good_acc1, successive_count))
                fluid.io.save_inference_model(dirname=train_parameters['save_freeze_dir'],
                                              feeded_var_names=['img'],
                                              target_vars=[out],
                                              main_program=main_program,
                                              executor=exe)
                if successive_count >= successive_limit:
                    logger.info("end training")
                    stop_train = True
                    break
            else:
                successive_count = 0

            # 通用的保存策略，减小意外停止的损失
            if total_batch_count % sample_freq == 0:
                logger.info("temp save {0} batch train result, current acc1 {1}".format(total_batch_count, acc1))
                fluid.io.save_persistables(dirname=train_parameters['save_persistable_dir'],
                                           main_program=main_program,
                                           executor=exe)
        if stop_train:
            break
    logger.info("training till last epcho, end training")
    fluid.io.save_persistables(dirname=train_parameters['save_persistable_dir'],
                                           main_program=main_program,
                                           executor=exe)
    fluid.io.save_inference_model(dirname=train_parameters['save_freeze_dir'],
                                              feeded_var_names=['img'],
                                              target_vars=[out],
                                              main_program=main_program.clone(for_test=True),
                                              executor=exe)


if __name__ == '__main__':
    init_log_config()
    init_train_parameters()
    train()

日志：

ose': 74, u'buttercup': 48, u'anthurium': 80, u'hard-leaved': 2, u'geranium': 58, u'orange': 59, u'fritillary': 23, u'carnation': 31, u'blanket': 100, u'pincushion': 22, u'cape': 37, u'monkshood': 9, u'alpine': 35, u'columbine': 84, u'morning': 76, u'windflower': 69, u'lenten': 40, u'californian': 65, u'wallflower': 46, u'spring': 67, u'globe': 10, u'japanese': 62, u'hippeastrum': 91, u'barbeton': 41, u'hibiscus': 83, u'desert-rose': 85, u'cyclamen': 88, u'purple': 17, u'magnolia': 87, u'clematis': 82, u'moon': 7, u'passion': 77, u'marigold': 47, u'bird': 8, u'red': 24, u'common': 50, u'corn': 26, u'water': 73, u'pelargonium': 55, u'great': 38, u'oxeye': 49, u'frangipani': 81, u'cautleya': 61, u'canterbury': 3, u'love': 33, u'spear': 14, u'sweet': 4, u'gaura': 57, u'osteospermum': 66, u'poinsettia': 44, u'blackberry': 102, u'bougainvillea': 95, u'siam': 39, u'gazania': 71, u'silverbush': 64, u'fire': 21, u'tiger': 6, u'king': 13, u'daffodil': 42, u'tree': 86, u'bee': 92, u'black-eyed': 63, u'wild': 52, u'bolero': 45, u'ruby-lipped': 36, u'peruvian': 18, u"colt's": 12, u'mallow': 97, u'petunia': 51, u'trumpet': 101, u'bishop': 56, u'garden': 32, u'pink': 1, u'stemless': 28, u'balloon': 19, u'camellia': 96, u'snapdragon': 11, u'sword': 43, u'canna': 90, u'bearded': 68, u'ball': 93, u'toad': 79, u'pink-yellow': 60, u'watercress': 89, u'globe-flower': 16, u'thorn': 75, u'english': 5, u'bromelia': 99, u'primula': 53}, 'image_count': 6552, 'pretrained': True}
2019-09-15 22:07:27,243 - train.py[line:555] - INFO: build input custom reader and data feeder
2019-09-15 22:07:27,244 - train.py[line:569] - INFO: build newwork
W0915 22:07:28.376420 5309 device_context.cc:259] Please NOTE: device: 0, CUDA Capability: 61, Driver API Version: 9.0, Runtime API Version: 8.0
W0915 22:07:28.378059 5309 device_context.cc:267] device: 0, cuDNN Version: 7.1.
2019-09-15 22:07:28,402 - train.py[line:537] - INFO: load params from retrain model
2019-09-15 22:07:28,640 - train.py[line:598] - INFO: current pass: 0, start read image
2019-09-15 22:07:35,169 - train.py[line:613] - INFO: Pass 0, trainbatch 10, loss 4.66865539551, acc1 0.0, time 0.21 sec
2019-09-15 22:07:40,741 - train.py[line:613] - INFO: Pass 0, trainbatch 20, loss 4.40447187424, acc1 0.0, time 0.21 sec
Exception: /paddle/paddle/fluid/operators/cross_entropy_op.h:159 Assertion `label >= 0 && label < feature_size_` failed (The label is out of the range. 102).
F0915 22:07:43.791612 5309 device_context.cc:333] cudaStreamSynchronize unspecified launch failure errno:4
*** Check failure stack trace: ***
@ 0x7fa5c8e857dd google::LogMessage::Fail()
@ 0x7fa5c8e8928c google::LogMessage::SendToLog()
@ 0x7fa5c8e85303 google::LogMessage::Flush()
@ 0x7fa5c8e8a79e google::LogMessageFatal::~LogMessageFatal()
@ 0x7fa5cae80fcd _ZNSt17_Function_handlerIFvvEZNK6paddle8platform17CUDADeviceContext4WaitEvEUlvE_E9_M_invokeERKSt9_Any_data
@ 0x7fa5cae8ea54 paddle::platform::TemporaryAllocator::Release()
@ 0x7fa5cae83fa1 paddle::platform::CUDADeviceContext::Wait()
@ 0x7fa5caddaa24 paddle::framework::TransDataDevice()
@ 0x7fa5cadd9ade paddle::framework::TransformData()
@ 0x7fa5cadd223d paddle::framework::OperatorWithKernel::PrepareData()
@ 0x7fa5cadd336d paddle::framework::OperatorWithKernel::RunImpl()
@ 0x7fa5cadd3811 paddle::framework::OperatorWithKernel::RunImpl()
@ 0x7fa5cadce3a3 paddle::framework::OperatorBase::Run()
@ 0x7fa5c8ecabae paddle::framework::Executor::RunPreparedContext()
@ 0x7fa5c8ecdc4f paddle::framework::Executor::Run()
@ 0x7fa5c8d2e92d _ZZN8pybind1112cpp_function10initializeIZN6paddle6pybindL22pybind11_init_core_avxERNS_6moduleEEUlRNS2_9framework8ExecutorERKNS6_11ProgramDescEPNS6_5ScopeEibbRKSt6vectorISsSaISsEEE85_vIS8_SB_SD_ibbSI_EINS_4nameENS_9is_methodENS_7siblingEEEEvOT_PFT0_DpT1_EDpRKT2_ENUlRNS_6detail13function_callEE1_4_FUNES10_
@ 0x7fa5c8d70976 pybind11::cpp_function::dispatcher()
@ 0x5573b6 PyEval_EvalFrameEx
@ 0x559921 PyEval_EvalCodeEx
@ 0x551f0d PyEval_EvalFrameEx
@ 0x559921 PyEval_EvalCodeEx
@ 0x550cca PyEval_EvalFrameEx
@ 0x559921 PyEval_EvalCodeEx
@ 0x551f0d PyEval_EvalFrameEx
@ 0x559921 PyEval_EvalCodeEx
@ 0x5af5e2 PyEval_EvalCode
@ 0x57975b (unknown)
@ 0x41a130 PyRun_FileExFlags
@ 0x41ab77 PyRun_SimpleFileExFlags
@ 0x41c3f3 Py_Main
@ 0x7fa627434f45 __libc_start_main
@ 0x5785ee (unknown)
Aborted (core dumped)