Deep Convolutional Neural Networks are opaque machine learning solutions. Opaque in the sense that the model state itself is neither self evident nor human interpretable. This has lead to a primarily trial and error driven approach for development, that relies on the comparison of abstract, model agnostic performance metrics like accuracy as a measure for classification performance, the number of parameters as a measure of capacity and the FLOPs per forward pass as a metric for computational efficiency Tan et al. (2018); Tan and Le (2019); He et al. (2015). To move towards a more efficient, less trial and error based design process, a deeper understanding of the model's state is required. This understanding does not have to be necessarily complete with regards to fully understanding the relation of the input and output of the model. The comparative analysis methods based on SVCCA Raghu et al. (2017) are good examples of such a non-holistic approaches. The information extracted from the model by using SVCCA is highly aggregated but allows for useful insights into the converged model. Logistic Regression Probes Alain and Bengio (2018) and saturation Richter et al. (2020) aggregate a single layer to a number, which allows for easy and intuitive analysis, similar to measuring with a thermometer. While logistic regression probes measure the intermediate solution quality very directly by training logistic regressions on the output of a layer, saturation is more task agnostic.