In this example, we used the MNIST dataset (handwritten digits) to train a convolutional neural network (CNN) for image classification, and reached a validation accuracy of ~0.98 on our trained model.

Below is a population exploration plot. It represents a samples’ similarity map based on the model’s latent space, built using the extracted features of the trained model.

The samples shown below were taken from the training set and are colored based on their ground truth class. The dot’s size represents the model’s error loss for that specific sample. We can see that there is a clear separation of the samples based on their classes.

The true class label for this sample is 2, but the model’s prediction assigns it to 1. Furthermore, the visualization shows that this sample appears to be more closely related to samples from class 1 (indicated by the light blue color) rather than samples from class 2 (indicated by the turquoise color).

The Tensorleap platform provides a way to further explore the model’s response to specific data samples. For example, from the results below, we can see that the model’s prediction was 9 while the ground truth is 8.

In the image below we can see a sample with true class label of 7, but the model’s prediction assigns it to 4. When examining the loss analysis feature, we can identify specific regions within the image that contribute to the higher loss, thereby causing the model’s prediction to be inaccurate.

As explained, Tensorleap tracks how each learned feature responds to each sample. This allows finding samples that the model considers similar, as they activate similar learned features. This functionality also allows to automatically find candidates for labeling, detect ambiguities in the data and mislabeled samples. In the example below we ran a Fetch Similars operation to find similar samples to the sample . The result is a cluster of similar samples:

The Tensorleap Dashboard allows users to visualize the training progress and performance metrics of their models. Additionally, users can explore correlations and trends between various metadata to gain insights into their data. The Dashboard allows us to visualize and investigate each metadata interactively. The visualization below shows the average error loss per class separate into two groups-even and odd.

The visualization below displays a histogram of the average loss vs the Euclidean distance. It reveals a strong correlation between distance and loss – samples with high distance values tend to have higher losses.

The Tensorleap platform provides powerful tools for analyzing and understanding deep learning models. In this example, we presented only a few examples of the types of insights that can be gained using the platform.

This quick start guide will walk you through the steps to get started with this example repository project.

Before you begin, ensure that you have the following prerequisites installed:

• Python (version 3.7 or higher).
• Poetry.
• Tensorleap platform access. To request a free trial click here.
• Tensorleap CLI.

with curl:

curl -s https://raw.githubusercontent.com/tensorleap/leap-cli/master/install.sh | bash

To login to Tensorleap:

tensorleap auth login [api key] [api url].

• API Key is your Tensorleap token (see how to generate a CLI token in the section below).
• API URL is your Tensorleap environment URL: https://api.CLIENT_NAME.tensorleap.ai/api/v2

How To Generate CLI Token from the UI

1. Login to the platform in ‘CLIENT_NAME.tensorleap.ai’
2. Scroll down to the bottom of the Resources Management page, then click GENERATE CLI TOKEN in the bottom-left corner.
3. Once a CLI token is generated, just copy the whole text and paste it into your shell.

To deploy your local changes:

leap project push

Tensorleap files in the repository include leap_binder.py and leap.yaml. The files consist of the required configurations to make the code integrate with the Tensorleap engine:

leap.yaml

leap.yaml file is configured to a dataset in your Tensorleap environment and is synced to the dataset saved in the environment.

For any additional file being used, we add its path under include parameter:

include:
    - leap_binder.py
    - mnist/configs.py
    - [...]

leap_binder.py file

leap_binder.py configures all binding functions used to bind to Tensorleap engine. These are the functions used to evaluate and train the model, visualize the variables, and enrich the analysis with external metadata variables

To test the system we can run leap_test.py file using poetry:

poetry run test

This file will execute several tests on leap_binder.py script to assert that the implemented binding functions: preprocess, encoders, metadata, etc., run smoothly.

For further explanation please refer to the docs