}-val_accuracy-{val_accuracy:.4f}.h5' checkpoints = ModelCheckpoint(tmpl, save_best_only=True, monitor="val_accuracy") history = model.fit( batch_tds, validation_data=batch_vds, epochs=epochs, c cb_checkpoint = ModelCheckpoint( str(CHKPT_TMPL), monitor="val_accuracy", save_best_only=True, save_weights_only=True ) cb_earlystopping = EarlyStopping( monitor='val_accuracy', patience= 15, restore_best_weights=True able to keep track of and save the best checkpoint, and the categorical accuracy as the metric to monitor. The callback will save the checkpoint with the maximum accuracy during evaluation. The load_best_checkpoint

你可以使用 EarlyStopping 回调函数： from keras.callbacks import EarlyStopping early_stopping = EarlyStopping(monitor='val_loss', patience=2) model.fit(x, y, validation_split=0.2, callbacks=[early_stopping]) 更多信息请查看 模型。History 对象会被模型的 fit 方法返回。 11.1.6 ModelCheckpoint [source] keras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', period=1) 在每个训练期之后保存模型。 weights.{epoch:02d}-{val_loss:.2f}.hdf5，那么模型被保存 的的文件名就会有训练轮数和验证损失。 参数 • filepath: 字符串，保存模型的路径。 • monitor: 被监测的数据。 • verbose: 详细信息模式，0 或者 1 。 • save_best_only: 如果 save_best_only=True，被监测数据的最佳模型就不会被覆盖。

callbacks = [EarlyStopping(monitor = 'val_loss', patience = 20)]) Here, we have used callback function, EarlyStopping. The purpose of this callback is to monitor the loss value during each epoch

Gradient Descent Early Stopping ▪ Regularization How-To ▪ Validation set to select parameters ▪ Monitor validation performance ▪ Stop at the highest val perf. Dropout ▪ Learning less to learn better

bicycle, boat, bus, car, motorbike, train • bottle, chair, dining table, potted plant, sofa, tv/monitor • VOC 2007：9,963张标注过的图片， 由train/val/test三部分组成， 共标注出 24,640个物体。 • VOC 2012：trainval/test包含08-11年的所有对应图片。

Modular structure • Models / Layers • Datasets • Dataloader • Visualization Tools like • TensorboardX (monitor training) • PyTorchViz (visualise computation graph) • Various other functions • loss (MSE,CE etc

越来越多的数据量，图像从百万到千万，数据从图像到视频 • 越来越复杂的网络结构，从Resnet，ResNeXt，DenseNet，DPN，SENET SACC2017 加快训练速度 - RapidFlow Mysql App 1 master agent agent agent App 2 PS Job 1 App 2 App 3 SSH Job 1 Train Job 1 Val Job

bostondynamics.com/ ④ 图片来自 https://venturebeat.com/2019/01/08/baidu-announces-apollo-3-5-and-apollo-enterprise-says-it-has- over-130-partners/ 预览版202112 1.5 深度学习框架 13 是一个基于 Python 语言、定位底层运算的计算库，Theano

'diningtable', 'dog', 'horse', 'motorbike', 'person', 'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor'] 通过上面定义的两个常量，我们可以方便地查找标签中每个像素的类索引。我们定义了voc_colormap2label函 数 来 构 建 从 上 述RGB颜 色 值 到 类 别 索 引 的 映