These models are trained on specific datasets and are proven in terms of accuracy and processing speed. Developers need to evaluate ML models and ensure they meet specific expected thresholds and capabilities before deployment. There are many experiments to improve model performance, and visualizing the differences becomes critical when designing and training models. TensorBoard helps visualize the model, making analysis less complicated because debugging becomes easier when one can see where the problem is.

General practice for training ML models

A common practice is to use a pretrained model and perform transfer learning to retrain the model on a similar dataset. During transfer learning, a neural network model is first trained on a problem similar to the problem being solved. One or more layers in the trained model are then used for a new model trained on the problem of interest.

Most of the time, pretrained models are in binary format, which makes it difficult to get internal information and start processing right away. From an organization’s business perspective, it makes sense to have tools to gain insight into models to reduce project delivery time.

There are several options available to get model information such as the number of layers and related parameters. Model summaries and model diagrams are the basic options. These options are very simple considering a few lines of implementation and provide very basic details like number of layers, type of layers and input/output of each layer.

However, model summaries and model graphs are not that effective for understanding every detail of any large complex model in the form of protocol buffers. In such a scenario, it makes more sense to use the visualization tool TensorBoard provided by TensorFlow. Considering the variety of visualization options it offers, such as models, scalars and metrics (training and validation data), images (from datasets), hyperparameter tuning, and more, it’s very powerful.

Model diagrams to visualize custom models

This option is especially useful when custom models are received as protocol buffers and need to be understood before any modification or training of them is made. An overview of the sequential CNN is visualized on the board as shown in the figure below. Each block represents a separate layer, and selecting one will open a window in the upper right corner with input and output information.

If you need further information about what’s in each block, you can simply double-click on the block, which will expand the block and provide more details. Note that a block can contain one or more blocks that can be extended layer by layer. It will also provide more information about the relevant processing parameters after any specific action is selected.

Scalars and metrics for analytical model training and validation

The second important aspect of machine learning is analyzing the training and validation of a given model. From an accuracy and speed standpoint, performance is important to make it suitable for real-life practical applications. In the figure below, it can be seen that the accuracy of the model increases with the number of epochs/iterations. If the train and test validations are not up to par, something is not right. This could be a case of underfitting or overfitting, which can be corrected by modifying the layers/parameters or improving the dataset or both.

Image data to visualize the images in the dataset

As the name suggests, it helps to visualize images. It is not limited to visualizing the images in the dataset, it also displays the confusion matrix in the form of an image. This matrix represents the accuracy of detecting objects of each class. As you can see in the photo below, the model confused the coat with the jumper. To overcome this situation, it is proposed to improve class-specific datasets to provide distinguishable features to the model for better learning and improved accuracy.

Hyperparameter tuning to achieve desired model accuracy

The accuracy of the model depends on the input dataset, number of layers and related parameters. In most cases, during the initial training, the accuracy will never reach the expected accuracy, besides the dataset, the number of layers, layer types, related parameters need to be considered. This process is called hyperparameter tuning.

During this process, a series of hyperparameters are provided for the model to choose from, and the model is run in combination with these parameters. The accuracy of each combination is recorded on the board and visualized. It corrects for the effort and time spent manually training the model for every possible combination of hyperparameters.

Analysis tools for analyzing model processing speed

In addition to accuracy, processing speed is an equally important aspect for any model. It is necessary to analyze the processing time consumed by the individual blocks and whether it can be reduced with some modifications. Profiling Tool provides a graphical representation of the time consumption of each operation over different periods. With this visualization, one can easily pinpoint operations that require more time. Some known overheads might be resizing the input, translating the model code from Python, or running the code in the CPU instead of the GPU. Taking care of these things will help achieve optimal performance.

Overall, TensorBoard is a great tool to help with the development and training process. Scalar and Metrics, Image Data, and Hyperparameter-tuned data help improve accuracy, while profiling tools help increase processing speed. TensorBoard also helps reduce the debugging time involved, which would otherwise be a huge time frame for sure.


Reviewing Editor: Guo Ting


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