{"version":"1.0","type":"rich","title":"shape issue","description":"This visualization compares the tensor shapes of batches across training epochs for two car models, Impreza and JaguarXF, in an automotive cybersecurity dataset. The displayed output shows TensorFlow dataset shapes ((128, 11), (128, 5)) and a sequence of batch shapes like (128, 11), (136, 11), (140, 11) that vary as training progresses, revealing that the final batches have non-uniform sizes. This highlights the \"shape issue\": the data pipeline produces batches of inconsistent lengths despite the model expecting fixed (128, 11) inputs, which can cause errors or require dynamic batching. The example illustrates a common challenge in preparing variable-length time-series data for deep learning models.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-e80001d933f8e6aebcd546fd6c0985e2?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}