Deep convolutional neural networks (CNNs) have achieved remarkable success in
various fields. However, training an excellent CNN is practically a
trial-and-error process that consumes a tremendous amount of time and computer
resources. To accelerate the training process and reduce the number of trials,
experts need to understand what has occurred in the training process and why
the resulting CNN behaves as such. However, current popular training platforms,
such as TensorFlow, only provide very little and general information, such as
training/validation errors, which is far from enough to serve this purpose. To
bridge this gap and help domain experts with their training tasks in a
practical environment, we propose a visual analytics system, DeepTracker, to
facilitate the exploration of the rich dynamics of CNN training processes and
to identify the unusual patterns that are hidden behind the huge amount of
training log. Specifically,we combine a hierarchical index mechanism and a set
of hierarchical small multiples to help experts explore the entire training log
from different levels of detail. We also introduce a novel cube-style
visualization to reveal the complex correlations among multiple types of
heterogeneous training data including neuron weights, validation images, and
training iterations. Three case studies are conducted to demonstrate how
DeepTracker provides its users with valuable knowledge in an industry-level CNN
training process, namely in our case, training ResNet-50 on the ImageNet
dataset. We show that our method can be easily applied to other
state-of-the-art "very deep" CNN models.

该研究提出了一种基于 DeepTracker 的可视化分析系统，结合分层索引机制、分层小满足和立体立式可视化等，帮助专家探索 CNN 训练过程中的丰富动态，更好地理解其过程并加速训练。通过三个案例研究，证明了该系统在训练 ResNet-50 模型上具有重要作用。