Analysis of CNN evaluation results

Xuei: <p>Hello everyone, I’m a beginner in the field of machine learning, and I’m starting to learn CNN. I have some evaluation results using batch sizes 16, 32, 64, and optimizers Adam, RMSProp, and SGD. I conducted training for 100 epochs with early stop patience set to 2. I don’t understand how to analyze these evaluation results, is there anyone who can help me?</p>
<p><div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/5/9/59b5420701befb3435f7faebf1c1290935d78a72.jpeg" data-download-href="https://discuss.pytorch.org/uploads/default/59b5420701befb3435f7faebf1c1290935d78a72" title="batch 16"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/5/9/59b5420701befb3435f7faebf1c1290935d78a72_2_690x388.jpeg" alt="batch 16" data-base62-sha1="cNAPXVGEQhA9mMBfsUxEQBRDlYu" width="690" height="388" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/5/9/59b5420701befb3435f7faebf1c1290935d78a72_2_690x388.jpeg, https://discuss.pytorch.org/uploads/default/optimized/3X/5/9/59b5420701befb3435f7faebf1c1290935d78a72_2_1035x582.jpeg 1.5x, https://discuss.pytorch.org/uploads/default/optimized/3X/5/9/59b5420701befb3435f7faebf1c1290935d78a72_2_1380x776.jpeg 2x" data-dominant-color="FCFCFC"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">batch 16</span><span class="informations">1920×1080 133 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div><br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/1/5/15ca1a119262d9937b099754814358fbef87e225.png" data-download-href="https://discuss.pytorch.org/uploads/default/15ca1a119262d9937b099754814358fbef87e225" title="batch 64"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/1/5/15ca1a119262d9937b099754814358fbef87e225_2_690x388.png" alt="batch 64" data-base62-sha1="36L1JQbELg5Xm8xKTvTiDc684E5" width="690" height="388" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/1/5/15ca1a119262d9937b099754814358fbef87e225_2_690x388.png, https://discuss.pytorch.org/uploads/default/optimized/3X/1/5/15ca1a119262d9937b099754814358fbef87e225_2_1035x582.png 1.5x, https://discuss.pytorch.org/uploads/default/optimized/3X/1/5/15ca1a119262d9937b099754814358fbef87e225_2_1380x776.png 2x" data-dominant-color="FCFCFC"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">batch 64</span><span class="informations">1920×1080 189 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div><br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/c/c/ccbf266ea7a3086cc851033562c613e206f8055d.jpeg" data-download-href="https://discuss.pytorch.org/uploads/default/ccbf266ea7a3086cc851033562c613e206f8055d" title="batch 32"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/c/c/ccbf266ea7a3086cc851033562c613e206f8055d_2_690x388.jpeg" alt="batch 32" data-base62-sha1="tdgWm4URvPtrla8jGkIdS99sZwp" width="690" height="388" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/c/c/ccbf266ea7a3086cc851033562c613e206f8055d_2_690x388.jpeg, https://discuss.pytorch.org/uploads/default/optimized/3X/c/c/ccbf266ea7a3086cc851033562c613e206f8055d_2_1035x582.jpeg 1.5x, https://discuss.pytorch.org/uploads/default/optimized/3X/c/c/ccbf266ea7a3086cc851033562c613e206f8055d_2_1380x776.jpeg 2x" data-dominant-color="FCFCFC"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">batch 32</span><span class="informations">1920×1080 133 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>