数字识别¶

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

    """
    # 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 fluid.optimizer.Adam(learning_rate=0.001)

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))

from paddle.utils.plot import Ploter

train_prompt = "Train cost"
test_prompt = "Test cost"
cost_ploter = Ploter(train_prompt, test_prompt)

# 将训练过程绘图表示
def event_handler_plot(ploter_title, step, cost):
    cost_ploter.append(ploter_title, step, cost)
    cost_ploter.plot()

# 该模型运行在单个CPU上
use_cuda = False # 如想使用GPU，请设置为 True
place = fluid.CUDAPlace(0) if use_cuda else 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，名称为img，大小为28*28*1
# 标签层，名称为label,对应输入图片的类别标签
# 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 = optimizer_program()
optimizer.minimize(avg_loss)

PASS_NUM = 5 #训练5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 = "recognize_digits.inference.model"

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 打印一次log
            print("Pass %d, Batch %d, Cost %f" % (step, epoch_id, metrics[0]))
            event_handler_plot(train_prompt, step, 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))
    event_handler_plot(test_prompt, step, metrics[0])

    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=None,
                                      params_filename=None)

# 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))

Pass 0, Batch 0, Cost 0.125650
Pass 100, Batch 0, Cost 0.161387
Pass 200, Batch 0, Cost 0.040036
Pass 300, Batch 0, Cost 0.023391
Pass 400, Batch 0, Cost 0.005856
Pass 500, Batch 0, Cost 0.003315
Pass 600, Batch 0, Cost 0.009977
Pass 700, Batch 0, Cost 0.020959
Pass 800, Batch 0, Cost 0.105560
Pass 900, Batch 0, Cost 0.239809
Test with Epoch 0, avg_cost: 0.053097883707459624, acc: 0.9822850318471338