diff --git a/cnnBase.ipynb b/cnnBase.ipynb
index 010e27c8dfc4c4157e8ec3ca812bc8e3da129522..d46ccde1e2381736dc934292c623447f2fad8c17 100644
--- a/cnnBase.ipynb
+++ b/cnnBase.ipynb
@@ -456,392 +456,92 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 35,
"metadata": {},
"outputs": [
- {
- "data": {
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- "model_id": "336f68875de74650a9ac2363ce42d612",
- "version_major": 2,
- "version_minor": 0
- },
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- " 0%| | 0/110 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Validation loss decreased (inf --> 2.069703). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.069702927102434, Validation Acc: 0.29, Learning Rate: 0.001\n",
- "Finished Training\n"
- ]
- },
- {
- "data": {
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- "model_id": "1e5e707110aa44be809d24f1c312f2c6",
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- "version_minor": 0
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- "text/plain": [
- " 0%| | 0/110 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Validation loss decreased (inf --> 2.077011). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.0770112732623485, Validation Acc: 0.32, Learning Rate: 0.001\n",
- "Finished Training\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "170f9b4346b04fd2a079af5f05a7681b",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/110 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Validation loss decreased (inf --> 2.064547). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.0645472039567663, Validation Acc: 0.316, Learning Rate: 0.001\n",
- "Finished Training\n"
- ]
- },
- {
- "data": {
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- "version_major": 2,
- "version_minor": 0
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- "text/plain": [
- " 0%| | 0/110 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Validation loss decreased (inf --> 2.070623). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.070623255790548, Validation Acc: 0.32066666666666666, Learning Rate: 0.001\n",
- "Finished Training\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "1a40d7da439341eda67d25cc6cbe88c6",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/110 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Validation loss decreased (inf --> 2.063928). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.063927566751521, Validation Acc: 0.336, Learning Rate: 0.001\n",
- "Finished Training\n"
- ]
- },
- {
- "data": {
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- "model_id": "c2b54646b199482a85586726e1c2ee83",
- "version_major": 2,
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- "text/plain": [
- " 0%| | 0/110 [00:00<?, ?it/s]"
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- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Validation loss decreased (inf --> 2.063790). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.063790004304115, Validation Acc: 0.33, Learning Rate: 0.001\n",
- "Finished Training\n"
- ]
- },
- {
- "data": {
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- " 0%| | 0/110 [00:00<?, ?it/s]"
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- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Validation loss decreased (inf --> 2.061159). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.0611586824376533, Validation Acc: 0.30266666666666664, Learning Rate: 0.001\n",
- "Finished Training\n"
- ]
- },
- {
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- "text/plain": [
- " 0%| | 0/110 [00:00<?, ?it/s]"
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- "metadata": {},
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- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Validation loss decreased (inf --> 2.066780). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.06678029324146, Validation Acc: 0.2906666666666667, Learning Rate: 0.001\n",
- "Finished Training\n"
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- },
- {
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- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Validation loss decreased (inf --> 2.075776). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.0757758414491696, Validation Acc: 0.288, Learning Rate: 0.001\n",
- "Finished Training\n"
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- },
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"name": "stdout",
"output_type": "stream",
"text": [
- "Validation loss decreased (inf --> 2.076347). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.0763469275007855, Validation Acc: 0.29533333333333334, Learning Rate: 0.001\n",
+ "Validation loss decreased (inf --> 2.069341). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.069341246117937, Validation Acc: 0.326, Learning Rate: 0.001\n",
"Finished Training\n",
- "Generation 1/50\n"
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- "text": [
- "Validation loss decreased (inf --> 2.062321). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.0623205291463975, Validation Acc: 0.326, Learning Rate: 0.001\n",
- "Finished Training\n"
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- "text": [
- "Validation loss decreased (inf --> 2.110578). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.1105781742867005, Validation Acc: 0.2986666666666667, Learning Rate: 0.001\n",
- "Finished Training\n"
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- "output_type": "stream",
- "text": [
- "Validation loss decreased (inf --> 2.072264). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.072263854615232, Validation Acc: 0.29533333333333334, Learning Rate: 0.001\n",
- "Finished Training\n"
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- " 0%| | 0/110 [00:00<?, ?it/s]"
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- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Validation loss decreased (inf --> 2.083575). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.083575045808833, Validation Acc: 0.288, Learning Rate: 0.001\n",
- "Finished Training\n"
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- " 0%| | 0/110 [00:00<?, ?it/s]"
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- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Validation loss decreased (inf --> 2.093737). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.093737287724272, Validation Acc: 0.31, Learning Rate: 0.001\n",
- "Finished Training\n"
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- " 0%| | 0/110 [00:00<?, ?it/s]"
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- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Validation loss decreased (inf --> 2.081190). Saving model ...\n",
- "Epoch 1/ 1, Validation Loss: 2.081189992580008, Validation Acc: 0.32866666666666666, Learning Rate: 0.001\n",
+ "Validation loss decreased (inf --> 2.081061). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.081061193283568, Validation Acc: 0.3, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.077981). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.077980510731961, Validation Acc: 0.30933333333333335, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.057297). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.057297257666892, Validation Acc: 0.29133333333333333, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.058545). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.05854465352728, Validation Acc: 0.30266666666666664, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.058182). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.05818188444097, Validation Acc: 0.2866666666666667, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.077766). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.07776573110134, Validation Acc: 0.298, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.057639). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.0576388835906982, Validation Acc: 0.29533333333333334, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.081729). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.0817288033505705, Validation Acc: 0.30666666666666664, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.092310). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.0923096575635545, Validation Acc: 0.31733333333333336, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Generation 1/50\n",
+ "Validation loss decreased (inf --> 2.078669). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.0786693324433996, Validation Acc: 0.332, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.064316). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.064316229617342, Validation Acc: 0.2926666666666667, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.081282). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.081282280861063, Validation Acc: 0.312, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.080946). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.0809461157372655, Validation Acc: 0.30466666666666664, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.072785). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.072785425693431, Validation Acc: 0.29733333333333334, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.085408). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.085408312209109, Validation Acc: 0.2813333333333333, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.073224). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.0732235249052655, Validation Acc: 0.288, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.054482). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.054482099857736, Validation Acc: 0.32066666666666666, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.090219). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.0902189295342626, Validation Acc: 0.29533333333333334, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.097155). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.0971552590106395, Validation Acc: 0.312, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Generation 2/50\n",
+ "Validation loss decreased (inf --> 2.092001). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.0920009359400322, Validation Acc: 0.2926666666666667, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.073072). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.073072228025883, Validation Acc: 0.28933333333333333, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.114132). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.114131823499152, Validation Acc: 0.264, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.084767). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.0847667886855756, Validation Acc: 0.33666666666666667, Learning Rate: 0.001\n",
+ "Finished Training\n",
+ "Validation loss decreased (inf --> 2.077225). Saving model ...\n",
+ "Epoch 1/ 1, Validation Loss: 2.077225426410107, Validation Acc: 0.30533333333333335, Learning Rate: 0.001\n",
"Finished Training\n"
]
},
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- "version_major": 2,
- "version_minor": 0
- },
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{
"ename": "KeyboardInterrupt",
"evalue": "",
@@ -852,9 +552,7 @@
"\u001b[1;32mc:\\Users\\Main\\Documents\\com3013-comp-intelligence\\cnnBase.ipynb Cell 11\u001b[0m line \u001b[0;36m1\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=135'>136</a>\u001b[0m \u001b[39m# Print the best individual (weights)\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=136'>137</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mBest Individual (Weights):\u001b[39m\u001b[39m\"\u001b[39m, best_ind)\n\u001b[1;32m--> <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=138'>139</a>\u001b[0m DEAPStuff()\n",
"\u001b[1;32mc:\\Users\\Main\\Documents\\com3013-comp-intelligence\\cnnBase.ipynb Cell 11\u001b[0m line \u001b[0;36m1\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=120'>121</a>\u001b[0m \u001b[39mdel\u001b[39;00m mutant\u001b[39m.\u001b[39mfitness\u001b[39m.\u001b[39mvalues\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=122'>123</a>\u001b[0m \u001b[39m# Evaluate the offspring\u001b[39;00m\n\u001b[1;32m--> <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=123'>124</a>\u001b[0m fitnesses \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39;49m(\u001b[39mmap\u001b[39;49m(toolbox\u001b[39m.\u001b[39;49mevaluate, offspring))\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=124'>125</a>\u001b[0m \u001b[39mfor\u001b[39;00m ind, fit \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(offspring, fitnesses):\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=125'>126</a>\u001b[0m ind\u001b[39m.\u001b[39mfitness\u001b[39m.\u001b[39mvalues \u001b[39m=\u001b[39m fit\n",
"\u001b[1;32mc:\\Users\\Main\\Documents\\com3013-comp-intelligence\\cnnBase.ipynb Cell 11\u001b[0m line \u001b[0;36m7\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=66'>67</a>\u001b[0m scheduler \u001b[39m=\u001b[39m optim\u001b[39m.\u001b[39mlr_scheduler\u001b[39m.\u001b[39mReduceLROnPlateau(optimizer, mode\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mmin\u001b[39m\u001b[39m'\u001b[39m, patience\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m, factor\u001b[39m=\u001b[39m\u001b[39m0.1\u001b[39m, verbose\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=68'>69</a>\u001b[0m \u001b[39m# Train the model and return the validation loss\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=69'>70</a>\u001b[0m fitness \u001b[39m=\u001b[39m train_model_for_fitness(eval_model, train_loader, val_loader, criterion, optimizer, scheduler, num_epochs\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=71'>72</a>\u001b[0m \u001b[39mreturn\u001b[39;00m (fitness,)\n",
- "\u001b[1;32mc:\\Users\\Main\\Documents\\com3013-comp-intelligence\\cnnBase.ipynb Cell 11\u001b[0m line \u001b[0;36m1\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m model\u001b[39m.\u001b[39mtrain()\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m running_loss \u001b[39m=\u001b[39m \u001b[39m0.0\u001b[39m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m \u001b[39mfor\u001b[39;00m inputs, labels \u001b[39min\u001b[39;00m tqdm(train_loader):\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m inputs, labels \u001b[39m=\u001b[39m inputs\u001b[39m.\u001b[39mto(device), labels\u001b[39m.\u001b[39mto(device)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n",
- "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\tqdm\\notebook.py:249\u001b[0m, in \u001b[0;36mtqdm_notebook.__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 247\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 248\u001b[0m it \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39m(tqdm_notebook, \u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__iter__\u001b[39m()\n\u001b[1;32m--> 249\u001b[0m \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m it:\n\u001b[0;32m 250\u001b[0m \u001b[39m# return super(tqdm...) will not catch exception\u001b[39;00m\n\u001b[0;32m 251\u001b[0m \u001b[39myield\u001b[39;00m obj\n\u001b[0;32m 252\u001b[0m \u001b[39m# NB: except ... [ as ...] breaks IPython async KeyboardInterrupt\u001b[39;00m\n",
- "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\tqdm\\std.py:1182\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1179\u001b[0m time \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_time\n\u001b[0;32m 1181\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 1182\u001b[0m \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m iterable:\n\u001b[0;32m 1183\u001b[0m \u001b[39myield\u001b[39;00m obj\n\u001b[0;32m 1184\u001b[0m \u001b[39m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[0;32m 1185\u001b[0m \u001b[39m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
+ "\u001b[1;32mc:\\Users\\Main\\Documents\\com3013-comp-intelligence\\cnnBase.ipynb Cell 11\u001b[0m line \u001b[0;36m1\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m model\u001b[39m.\u001b[39mtrain()\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m running_loss \u001b[39m=\u001b[39m \u001b[39m0.0\u001b[39m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m \u001b[39mfor\u001b[39;00m inputs, labels \u001b[39min\u001b[39;00m (train_loader):\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m inputs, labels \u001b[39m=\u001b[39m inputs\u001b[39m.\u001b[39mto(device), labels\u001b[39m.\u001b[39mto(device)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X13sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n",
"File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:630\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 627\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sampler_iter \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 628\u001b[0m \u001b[39m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 629\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reset() \u001b[39m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 630\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_next_data()\n\u001b[0;32m 631\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m 632\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataset_kind \u001b[39m==\u001b[39m _DatasetKind\u001b[39m.\u001b[39mIterable \u001b[39mand\u001b[39;00m \\\n\u001b[0;32m 633\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \\\n\u001b[0;32m 634\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called:\n",
"File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:674\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 672\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_next_data\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m 673\u001b[0m index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_next_index() \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 674\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dataset_fetcher\u001b[39m.\u001b[39;49mfetch(index) \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m 675\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory:\n\u001b[0;32m 676\u001b[0m data \u001b[39m=\u001b[39m _utils\u001b[39m.\u001b[39mpin_memory\u001b[39m.\u001b[39mpin_memory(data, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory_device)\n",
"File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:49\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mauto_collation:\n\u001b[0;32m 48\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset, \u001b[39m\"\u001b[39m\u001b[39m__getitems__\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__:\n\u001b[1;32m---> 49\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset\u001b[39m.\u001b[39;49m__getitems__(possibly_batched_index)\n\u001b[0;32m 50\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 51\u001b[0m data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n",
@@ -867,10 +565,12 @@
"File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torchvision\\transforms\\v2\\_container.py:53\u001b[0m, in \u001b[0;36mCompose.forward\u001b[1;34m(self, *inputs)\u001b[0m\n\u001b[0;32m 51\u001b[0m needs_unpacking \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(inputs) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m 52\u001b[0m \u001b[39mfor\u001b[39;00m transform \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransforms:\n\u001b[1;32m---> 53\u001b[0m outputs \u001b[39m=\u001b[39m transform(\u001b[39m*\u001b[39;49minputs)\n\u001b[0;32m 54\u001b[0m inputs \u001b[39m=\u001b[39m outputs \u001b[39mif\u001b[39;00m needs_unpacking \u001b[39melse\u001b[39;00m (outputs,)\n\u001b[0;32m 55\u001b[0m \u001b[39mreturn\u001b[39;00m outputs\n",
"File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
"File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
- "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torchvision\\transforms\\v2\\_transform.py:50\u001b[0m, in \u001b[0;36mTransform.forward\u001b[1;34m(self, *inputs)\u001b[0m\n\u001b[0;32m 45\u001b[0m needs_transform_list \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_needs_transform_list(flat_inputs)\n\u001b[0;32m 46\u001b[0m params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_params(\n\u001b[0;32m 47\u001b[0m [inpt \u001b[39mfor\u001b[39;00m (inpt, needs_transform) \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(flat_inputs, needs_transform_list) \u001b[39mif\u001b[39;00m needs_transform]\n\u001b[0;32m 48\u001b[0m )\n\u001b[1;32m---> 50\u001b[0m flat_outputs \u001b[39m=\u001b[39m [\n\u001b[0;32m 51\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_transform(inpt, params) \u001b[39mif\u001b[39;49;00m needs_transform \u001b[39melse\u001b[39;49;00m inpt\n\u001b[0;32m 52\u001b[0m \u001b[39mfor\u001b[39;49;00m (inpt, needs_transform) \u001b[39min\u001b[39;49;00m \u001b[39mzip\u001b[39;49m(flat_inputs, needs_transform_list)\n\u001b[0;32m 53\u001b[0m ]\n\u001b[0;32m 55\u001b[0m \u001b[39mreturn\u001b[39;00m tree_unflatten(flat_outputs, spec)\n",
- "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torchvision\\transforms\\v2\\_transform.py:51\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 45\u001b[0m needs_transform_list \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_needs_transform_list(flat_inputs)\n\u001b[0;32m 46\u001b[0m params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_params(\n\u001b[0;32m 47\u001b[0m [inpt \u001b[39mfor\u001b[39;00m (inpt, needs_transform) \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(flat_inputs, needs_transform_list) \u001b[39mif\u001b[39;00m needs_transform]\n\u001b[0;32m 48\u001b[0m )\n\u001b[0;32m 50\u001b[0m flat_outputs \u001b[39m=\u001b[39m [\n\u001b[1;32m---> 51\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_transform(inpt, params) \u001b[39mif\u001b[39;00m needs_transform \u001b[39melse\u001b[39;00m inpt\n\u001b[0;32m 52\u001b[0m \u001b[39mfor\u001b[39;00m (inpt, needs_transform) \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(flat_inputs, needs_transform_list)\n\u001b[0;32m 53\u001b[0m ]\n\u001b[0;32m 55\u001b[0m \u001b[39mreturn\u001b[39;00m tree_unflatten(flat_outputs, spec)\n",
- "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torchvision\\transforms\\v2\\_deprecated.py:50\u001b[0m, in \u001b[0;36mToTensor._transform\u001b[1;34m(self, inpt, params)\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_transform\u001b[39m(\u001b[39mself\u001b[39m, inpt: Union[PIL\u001b[39m.\u001b[39mImage\u001b[39m.\u001b[39mImage, np\u001b[39m.\u001b[39mndarray], params: Dict[\u001b[39mstr\u001b[39m, Any]) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m torch\u001b[39m.\u001b[39mTensor:\n\u001b[1;32m---> 50\u001b[0m \u001b[39mreturn\u001b[39;00m _F\u001b[39m.\u001b[39;49mto_tensor(inpt)\n",
- "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torchvision\\transforms\\functional.py:170\u001b[0m, in \u001b[0;36mto_tensor\u001b[1;34m(pic)\u001b[0m\n\u001b[0;32m 168\u001b[0m \u001b[39mif\u001b[39;00m pic\u001b[39m.\u001b[39mmode \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m1\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m 169\u001b[0m img \u001b[39m=\u001b[39m \u001b[39m255\u001b[39m \u001b[39m*\u001b[39m img\n\u001b[1;32m--> 170\u001b[0m img \u001b[39m=\u001b[39m img\u001b[39m.\u001b[39;49mview(pic\u001b[39m.\u001b[39;49msize[\u001b[39m1\u001b[39;49m], pic\u001b[39m.\u001b[39;49msize[\u001b[39m0\u001b[39;49m], F_pil\u001b[39m.\u001b[39;49mget_image_num_channels(pic))\n\u001b[0;32m 171\u001b[0m \u001b[39m# put it from HWC to CHW format\u001b[39;00m\n\u001b[0;32m 172\u001b[0m img \u001b[39m=\u001b[39m img\u001b[39m.\u001b[39mpermute((\u001b[39m2\u001b[39m, \u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m))\u001b[39m.\u001b[39mcontiguous()\n",
+ "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:676\u001b[0m, in \u001b[0;36mRandomCrop.forward\u001b[1;34m(self, img)\u001b[0m\n\u001b[0;32m 668\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 669\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[0;32m 670\u001b[0m \u001b[39m img (PIL Image or Tensor): Image to be cropped.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 673\u001b[0m \u001b[39m PIL Image or Tensor: Cropped image.\u001b[39;00m\n\u001b[0;32m 674\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 675\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 676\u001b[0m img \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39;49mpad(img, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpadding, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfill, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpadding_mode)\n\u001b[0;32m 678\u001b[0m _, height, width \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mget_dimensions(img)\n\u001b[0;32m 679\u001b[0m \u001b[39m# pad the width if needed\u001b[39;00m\n",
+ "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torchvision\\transforms\\functional.py:539\u001b[0m, in \u001b[0;36mpad\u001b[1;34m(img, padding, fill, padding_mode)\u001b[0m\n\u001b[0;32m 537\u001b[0m _log_api_usage_once(pad)\n\u001b[0;32m 538\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(img, torch\u001b[39m.\u001b[39mTensor):\n\u001b[1;32m--> 539\u001b[0m \u001b[39mreturn\u001b[39;00m F_pil\u001b[39m.\u001b[39;49mpad(img, padding\u001b[39m=\u001b[39;49mpadding, fill\u001b[39m=\u001b[39;49mfill, padding_mode\u001b[39m=\u001b[39;49mpadding_mode)\n\u001b[0;32m 541\u001b[0m \u001b[39mreturn\u001b[39;00m F_t\u001b[39m.\u001b[39mpad(img, padding\u001b[39m=\u001b[39mpadding, fill\u001b[39m=\u001b[39mfill, padding_mode\u001b[39m=\u001b[39mpadding_mode)\n",
+ "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torchvision\\transforms\\_functional_pil.py:215\u001b[0m, in \u001b[0;36mpad\u001b[1;34m(img, padding, fill, padding_mode)\u001b[0m\n\u001b[0;32m 213\u001b[0m \u001b[39m# RGB image\u001b[39;00m\n\u001b[0;32m 214\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(img\u001b[39m.\u001b[39mshape) \u001b[39m==\u001b[39m \u001b[39m3\u001b[39m:\n\u001b[1;32m--> 215\u001b[0m img \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mpad(img, ((pad_top, pad_bottom), (pad_left, pad_right), (\u001b[39m0\u001b[39;49m, \u001b[39m0\u001b[39;49m)), padding_mode)\n\u001b[0;32m 216\u001b[0m \u001b[39m# Grayscale image\u001b[39;00m\n\u001b[0;32m 217\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(img\u001b[39m.\u001b[39mshape) \u001b[39m==\u001b[39m \u001b[39m2\u001b[39m:\n",
+ "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mpad\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
+ "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\numpy\\lib\\arraypad.py:862\u001b[0m, in \u001b[0;36mpad\u001b[1;34m(array, pad_width, mode, **kwargs)\u001b[0m\n\u001b[0;32m 857\u001b[0m roi \u001b[39m=\u001b[39m _view_roi(padded, original_area_slice, axis)\n\u001b[0;32m 858\u001b[0m \u001b[39mwhile\u001b[39;00m left_index \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m \u001b[39mor\u001b[39;00m right_index \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m 859\u001b[0m \u001b[39m# Iteratively pad until dimension is filled with reflected\u001b[39;00m\n\u001b[0;32m 860\u001b[0m \u001b[39m# values. This is necessary if the pad area is larger than\u001b[39;00m\n\u001b[0;32m 861\u001b[0m \u001b[39m# the length of the original values in the current dimension.\u001b[39;00m\n\u001b[1;32m--> 862\u001b[0m left_index, right_index \u001b[39m=\u001b[39m _set_reflect_both(\n\u001b[0;32m 863\u001b[0m roi, axis, (left_index, right_index),\n\u001b[0;32m 864\u001b[0m method, include_edge\n\u001b[0;32m 865\u001b[0m )\n\u001b[0;32m 867\u001b[0m \u001b[39melif\u001b[39;00m mode \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mwrap\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m 868\u001b[0m \u001b[39mfor\u001b[39;00m axis, (left_index, right_index) \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(axes, pad_width):\n",
+ "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\numpy\\lib\\arraypad.py:357\u001b[0m, in \u001b[0;36m_set_reflect_both\u001b[1;34m(padded, axis, width_pair, method, include_edge)\u001b[0m\n\u001b[0;32m 352\u001b[0m left_pad \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m chunk_length\n\u001b[0;32m 354\u001b[0m \u001b[39mif\u001b[39;00m right_pad \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m 355\u001b[0m \u001b[39m# Pad with reflected values on right side:\u001b[39;00m\n\u001b[0;32m 356\u001b[0m \u001b[39m# First limit chunk size which can't be larger than pad area\u001b[39;00m\n\u001b[1;32m--> 357\u001b[0m chunk_length \u001b[39m=\u001b[39m \u001b[39mmin\u001b[39;49m(old_length, right_pad)\n\u001b[0;32m 358\u001b[0m \u001b[39m# Slice right to left, start on or next to edge, stop relative to start\u001b[39;00m\n\u001b[0;32m 359\u001b[0m start \u001b[39m=\u001b[39m \u001b[39m-\u001b[39mright_pad \u001b[39m+\u001b[39m edge_offset \u001b[39m-\u001b[39m \u001b[39m2\u001b[39m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
@@ -910,7 +610,7 @@
" print(\"Early stopping\")\n",
" break\n",
"\n",
- " print(f'Epoch {epoch + 1}/ {num_epochs}, Validation Loss: {val_loss}, Validation Acc: {val_acc}, Learning Rate: {optimizer.param_groups[0][\"lr\"]}')\n",
+ " #print(f'Epoch {epoch + 1}/ {num_epochs}, Validation Loss: {val_loss}, Validation Acc: {val_acc}, Learning Rate: {optimizer.param_groups[0][\"lr\"]}')\n",
"\n",
" print('Finished Training')\n",
"\n",
@@ -1005,7 +705,7 @@
"\n",
" # Replace the old population by the offspring\n",
" population[:] = offspring\n",
- "\n",
+ " print(\"best: {best}\".format(best=tools.selBest(population, 1)[0]))\n",
" # Get the best individual\n",
" best_ind = tools.selBest(population, 1)[0]\n",
"\n",