data scientists, engineers, and researchers understand improve performance of their models with visualization by using the DLProf Viewer in a web browser or by analyzing text reports. DL Prof is available data scientists, engineers, and researchers understand improve performance of their models with visualization by using the DLProf Viewer in a web browser or by analyzing text reports. DL Prof is available data scientists, engineers, and researchers understand improve performance of their models with visualization by using the DLProf Viewer in a web browser or by analyzing text reports. DL Prof is available

PyTorch • Tensors • Autograd • Modular structure • Models / Layers • Datasets • Dataloader • Visualization Tools like • TensorboardX (monitor training) • PyTorchViz (visualise computation graph) • Various compiler which mode to run on. Visualization • TensorboardX (visualise training) • PyTorchViz (visualise computation graph) https://github.com/lanpa/tensorboardX/ Visualization (continued) • PyTorchViz

and just training the prediction head, or training the entire model. Refer to figure 6-5 for a visualization of creating a fine-tuning a pre-trained model on a downstream task. Figure 6-5: Fine-tuning a parameters having smaller absolute values due to occam’s razor23. Refer to figure 6-14 for a visualization of steep and flat local minimas. The left hand side image shows a loss landscape that has a sharp neighborhood of the minima shows a gradual drop as compared to the left. Figure 6-14: Partial visualization of a loss landscape with a steep local minima (left) and a relatively flatter local minima (right)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 18 可视化 Visualization 234 19 Scikit-learn API 235 20 工具 236 20.1 CustomObjectScope [source] . . . . . . . . . . 有关如何使用此类预训练的模型进行特征提取或微调的详细示例，请参阅 此博客文章。 VGG16 模型也是以下几个 Keras 示例脚本的基础： • Style transfer • Feature visualization • Deep dream 3.3.18 如何在 Keras 中使用 HDF5 输入？ 你可以使用 keras.utils.io_utils 中的 HDF5Matrix 类。有关详细信息，请参阅 • min_max_norm(min_value=0.0, max_value=1.0, rate=1.0, axis=0): 最小/最大范数约束 可视化 VISUALIZATION 234 18 可视化 Visualization 模型可视化⁇ keras.utils.vis_utils 模块提供了一些绘制 Keras 模型的实用功能 (使用 graphviz)。 以下实例，将绘制一张模型图，并保存为文件：

alternative-to-batch-normalization-fb0699bffae7 Pipeline nn.BatchNorm2d Class variables Test Visualization Advantages ▪ Converge faster ▪ Better performance ▪ Robust ▪ stable ▪ larger learning rate

Gradient Vanishing: 1997 http://harinisuresh.com/2016/10/09/lstms/ RNN V.S. LSTM Gradient Visualization https://imgur.com/gallery/vaNahKE 下一课时 LSTM Thank You.

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needed ▪ Dimension reduction ▪ Preprocessing: Huge dimension, say 224x224, is hard to process ▪ Visualization: https://projector.tensorflow.org/ ▪ Taking advantages of unsupervised data ▪ Compression, denoising

values (an integration problem). How can we do this efficiently when there are many parame- ters. Visualization Understanding what’s happening is hard, 2D? 3D? All these challenges become greater when there

Campello R J G B, Moulavi D, Zimek A, et al. Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection[J]. Acm Transactions on Knowledge Discovery from Data, 2015. [11] 彭 涛