今天来写正经八百的训练函数文件，然后开始训练！
我的机器发出了异常的巨响，于是借用了一台散热器，现在写博客园只能开这一个页面，QQ都不能挂……
我头一次感觉到我的电脑在性能方面不行了?!
以下是代码：
import os import shutil import webnet import paddle as paddle import firstdo import paddle.fluid as fluid paddle.enable_static() crop_size = 224 resize_size = 250 # 定义输入层 image = fluid.layers.data(name='image', shape=[3, crop_size, crop_size], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') # 获取分类器 model = webnet.net(image, 61) # 获取损失函数和准确率函数 cost = fluid.layers.cross_entropy(input=model, label=label) avg_cost = fluid.layers.mean(cost) acc = fluid.layers.accuracy(input=model, label=label) # 获取训练和测试程序 test_program = fluid.default_main_program().clone(for_test=True) # 定义优化方法 optimizer = fluid.optimizer.AdamOptimizer(learning_rate=1e-3, regularization=fluid.regularizer.L2DecayRegularizer(1e-4)) opts = optimizer.minimize(avg_cost) database = 'C:/Users/14997/Desktop/database/' # 获取自定义数据 train_reader = paddle.batch(reader=firstdo.train_reader(database+'train.list', crop_size, resize_size), batch_size=32) test_reader = paddle.batch(reader=firstdo. test_reader(database+'test.list', crop_size), batch_size=32) # 定义一个使用GPU的执行器 # 这里可以用CPU，但奇慢无比，预计跑完全部的流程可能要200+小时 # 但使用GPU对内存要求太大了 place = fluid.CUDAPlace(0) # place = fluid.CPUPlace() exe = fluid.Executor(place) # 进行参数初始化 exe.run(fluid.default_startup_program()) # 定义输入数据维度 feeder = fluid.DataFeeder(place=place, feed_list=[image, label]) # 训练100次 for pass_id in range(50): # 进行训练 for batch_id, data in enumerate(train_reader()): train_cost, train_acc = exe.run(program=fluid.default_main_program(), feed=feeder.feed(data), fetch_list=[avg_cost, acc]) # 每100个batch打印一次信息 if batch_id % 100 == 0: print('Pass:%d, Batch:%d, Cost:%0.5f, Accuracy:%0.5f' % (pass_id, batch_id, train_cost[0], train_acc[0])) # 保存预测模型 save_path = 'infer_model/' # 删除旧的模型文件 shutil.rmtree(save_path, ignore_errors=True) # 创建保持模型文件目录 os.makedirs(save_path) # 保存预测模型 fluid.io.save_inference_model(save_path, feeded_var_names=[image.name], target_vars=[model], executor=exe) # 进行测试 test_accs = [] test_costs = [] for batch_id, data in enumerate(test_reader()): test_cost, test_acc = exe.run(program=test_program, feed=feeder.feed(data), fetch_list=[avg_cost, acc]) test_accs.append(test_acc[0]) test_costs.append(test_cost[0]) # 求测试结果的平均值 test_cost = (sum(test_costs) / len(test_costs)) test_acc = (sum(test_accs) / len(test_accs)) print('Test:%d, Cost:%0.5f, Accuracy:%0.5f' % (pass_id, test_cost, test_acc))