  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.
