Create 1_1_2d_kymatio_output_try1.txt

spectral/experiment_1/1_1_2d_kymatio_output_try1.txt

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+> Functional, time to write the decomposition analysis software.
+
+Raw scattering output: torch.Size([2, 3, 81, 8, 8])
+Reshaped: torch.Size([2, 243, 8, 8])
+K=243, spatial=8x8, flat=15552
+
+============================================================
+Scattering Feature Test — kymatio Official Recipe
+  K=243, spatial=8x8, flat=15552
+  Norm: ImageNet, BN on scattering, SGD lr=0.1
+  BATCH=128, EPOCHS=90
+  Device: cuda
+============================================================
+
+  [linear] params=156,016
+    E  1: train=47.0% val=55.2% loss=2.0217 (5s) *
+    E  2: train=52.6% val=59.0% loss=1.7041 (5s) *
+    E  3: train=55.3% val=62.0% loss=1.4345 (5s) *
+    E  4: train=56.8% val=64.4% loss=1.2670 (5s) *
+    E  5: train=58.3% val=64.0% loss=1.2185 (5s)
+    E  7: train=59.4% val=65.5% loss=1.2014 (5s) *
+    E  9: train=59.8% val=65.8% loss=1.1841 (5s) *
+    E 10: train=59.4% val=63.5% loss=1.1872 (5s)
+    E 15: train=59.6% val=64.5% loss=1.1769 (5s)
+    E 20: train=60.3% val=63.0% loss=1.1713 (5s)
+    E 21: train=63.8% val=67.8% loss=1.0662 (5s) *
+    E 22: train=64.8% val=68.1% loss=1.0445 (5s) *
+    E 24: train=64.8% val=68.5% loss=1.0406 (5s) *
+    E 25: train=64.9% val=67.7% loss=1.0371 (5s)
+    E 28: train=65.2% val=68.6% loss=1.0308 (5s) *
+    E 29: train=65.1% val=69.1% loss=1.0287 (5s) *
+    E 30: train=65.1% val=69.0% loss=1.0264 (5s)
+    E 32: train=65.5% val=69.2% loss=1.0219 (5s) *
+    E 33: train=65.3% val=69.6% loss=1.0200 (5s) *
+    E 35: train=65.4% val=68.1% loss=1.0184 (5s)
+    E 40: train=65.7% val=68.1% loss=1.0130 (5s)
+    E 41: train=66.9% val=69.7% loss=0.9838 (5s) *
+    E 42: train=67.0% val=69.9% loss=0.9802 (5s) *
+    E 43: train=67.1% val=70.0% loss=0.9806 (5s) *
+    E 44: train=67.2% val=70.0% loss=0.9786 (5s) *
+    E 45: train=67.0% val=70.1% loss=0.9766 (5s) *
+    E 46: train=67.4% val=70.2% loss=0.9728 (5s) *
+    E 47: train=67.3% val=70.3% loss=0.9766 (5s) *
+    E 50: train=67.3% val=70.0% loss=0.9730 (5s)
+    E 53: train=67.5% val=70.3% loss=0.9712 (5s) *
+    E 55: train=67.2% val=70.3% loss=0.9722 (5s) *
+    E 60: train=67.4% val=70.2% loss=0.9685 (5s)
+    E 64: train=67.9% val=70.4% loss=0.9588 (5s) *
+    E 65: train=68.1% val=70.3% loss=0.9568 (5s)
+    E 66: train=67.8% val=70.6% loss=0.9601 (5s) *
+    E 70: train=67.6% val=70.3% loss=0.9610 (5s)
+    E 75: train=67.7% val=70.4% loss=0.9619 (5s)
+    E 80: train=67.9% val=70.5% loss=0.9591 (5s)
+    E 85: train=67.8% val=70.5% loss=0.9584 (5s)
+    E 86: train=67.9% val=70.6% loss=0.9586 (5s) *
+    E 90: train=67.8% val=70.4% loss=0.9578 (5s)
+  [linear] BEST: 70.6%
+
+  [mlp] params=16,986,608
+    E  1: train=51.5% val=62.8% loss=1.3598 (5s) *
+    E  2: train=61.3% val=67.3% loss=1.0950 (5s) *
+    E  3: train=64.6% val=68.7% loss=1.0030 (5s) *
+    E  4: train=66.8% val=70.8% loss=0.9440 (5s) *
+    E  5: train=68.7% val=72.1% loss=0.8955 (5s) *
+    E  6: train=69.4% val=72.6% loss=0.8667 (5s) *
+    E  7: train=70.7% val=73.5% loss=0.8366 (5s) *
+    E  8: train=71.6% val=73.6% loss=0.8131 (5s) *
+    E  9: train=72.2% val=74.3% loss=0.7949 (5s) *
+    E 10: train=72.8% val=74.4% loss=0.7803 (5s) *
+    E 11: train=73.3% val=74.9% loss=0.7631 (5s) *
+    E 12: train=73.9% val=75.7% loss=0.7453 (5s) *
+    E 15: train=74.7% val=75.1% loss=0.7250 (5s)
+---------------------------------------------------------------------------
+KeyboardInterrupt                         Traceback (most recent call last)
+/tmp/ipykernel_66190/3946613479.py in <cell line: 0>()
+    168             imgs, tgts = imgs.to(DEVICE), tgts.to(DEVICE)
+    169             with torch.no_grad():
+--> 170                 feats = get_scat(imgs)
+    171             logits = model(feats)
+    172             loss = F.cross_entropy(logits, tgts)
+
+6 frames
+/usr/local/lib/python3.12/dist-packages/kymatio/scattering2d/backend/torch_backend.py in rfft(cls, x)
+    136         cls.real_check(x)
+    137 
+--> 138         x_r = torch.zeros((x.shape[:-1] + (2,)), dtype=x.dtype, layout=x.layout, device=x.device)
+    139         x_r[..., 0] = x[..., 0]
+    140 
+
+KeyboardInterrupt: