final results ready for publishing

davidsteinar · Oct 7, 2018 · 8cb29a7 · 8cb29a7
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diff --git a/choose_threshold.ipynb b/choose_threshold.ipynb
diff --git a/data/concrete.txt b/data/concrete.txt
diff --git a/data/loaco2.csv b/data/loaco2.csv
diff --git a/find_damage_rugl.ipynb b/find_damage_rugl.ipynb
diff --git a/notebooks/testset_error_distribution.ipynb b/notebooks/testset_error_distribution.ipynb
@@ -119,11 +119,15 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/dsteinar/miniconda3/lib/python3.6/site-packages/torch/serialization.py:333: UserWarning: Couldn't retrieve source code for container of type Model. It won't be checked for correctness upon loading.\n",
-      "  \"type \" + container_type.__name__ + \". It won't be checked \"\n"
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '../results/trained_autoencoder_correct.pt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-4-49b5fb759409>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'../results/trained_autoencoder_correct.pt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'cpu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mloss_criterion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMSELoss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module)\u001b[0m\n\u001b[1;32m    299\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mversion_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpathlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0mnew_fd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 301\u001b[0;31m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    302\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../results/trained_autoencoder_correct.pt'"
      ]
     }
    ],

diff --git a/notebooks/z24-data-explore.ipynb b/notebooks/z24-data-explore.ipynb
@@ -857,7 +857,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.7.0"
   }
  },
  "nbformat": 4,

diff --git a/results/autoencoder_full_orthogonal.log b/results/autoencoder_full_orthogonal.log
@@ -0,0 +1,78 @@
+###Starting script###
+torch version: 0.4.0
+Number CUDA Devices: 1
+batch size: 1024
+epochs: 50
+Model(
+  (h1): Linear(in_features=753, out_features=512, bias=True)
+  (h2): Linear(in_features=512, out_features=256, bias=True)
+  (z): Linear(in_features=256, out_features=128, bias=True)
+  (h4): Linear(in_features=128, out_features=256, bias=True)
+  (h5): Linear(in_features=256, out_features=512, bias=True)
+  (h6): Linear(in_features=512, out_features=700, bias=True)
+)
+1073980
+{'dropout_p': 0.1, 'learning_rate': 0.0001, 'weight_init': <function orthogonal_ at 0x7f333012ad08>, 'hidden_size1': 512, 'hidden_size2': 256, 'z_size': 128}
+Epoch: 0
+train loss: 1.3198069856885006
+valiation loss: 0.558456106178211
+Epoch: 1
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+valiation loss: 0.5091420509518451
+Epoch: 2
+train loss: 0.8863865082711992
+valiation loss: 0.4833347361597708
+Epoch: 3
+train loss: 0.8200794545329572
+valiation loss: 0.4704232626449207
+Epoch: 4
+train loss: 0.7788513942858797
+valiation loss: 0.4596094799970713
+Epoch: 5
+train loss: 0.749287265726529
+valiation loss: 0.4501727740575053
+###Starting script###
+torch version: 0.4.0
+Number CUDA Devices: 1
+batch size: 1024
+epochs: 10
+Model(
+  (h1): Linear(in_features=1453, out_features=512, bias=True)
+  (h2): Linear(in_features=512, out_features=256, bias=True)
+  (z): Linear(in_features=256, out_features=128, bias=True)
+  (h4): Linear(in_features=128, out_features=256, bias=True)
+  (h5): Linear(in_features=256, out_features=512, bias=True)
+  (h6): Linear(in_features=512, out_features=1400, bias=True)
+)
+1791480
+{'dropout_p': 0.1, 'learning_rate': 0.0001, 'weight_init': <function orthogonal_ at 0x7fdbd646ed08>, 'hidden_size1': 512, 'hidden_size2': 256, 'z_size': 128}
+Epoch: 0
+train loss: 1.4884252085255971
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+Epoch: 1
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+Epoch: 3
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+Epoch: 4
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+Epoch: 5
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+valiation loss: 0.5057342650942536
+Epoch: 6
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+valiation loss: 0.4985074367971486
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+valiation loss: 0.48701937147710894
diff --git a/results/autoencoder_orthogonal100.log b/results/autoencoder_orthogonal100.log
@@ -0,0 +1,45 @@
+###Starting script###
+torch version: 0.4.0
+Number CUDA Devices: 1
+batch size: 1024
+epochs: 10
+Model(
+  (h1): Linear(in_features=753, out_features=512, bias=True)
+  (h2): Linear(in_features=512, out_features=256, bias=True)
+  (z): Linear(in_features=256, out_features=128, bias=True)
+  (h4): Linear(in_features=128, out_features=256, bias=True)
+  (h5): Linear(in_features=256, out_features=512, bias=True)
+  (h6): Linear(in_features=512, out_features=700, bias=True)
+)
+1073980
+{'dropout_p': 0.1, 'learning_rate': 0.0001, 'weight_init': <function orthogonal_ at 0x7fe37f67fd08>, 'hidden_size1': 512, 'hidden_size2': 256, 'z_size': 128}
+Epoch: 0
+train loss: 1.3358571395675611
+valiation loss: 0.5798759653236921
+Epoch: 1
+train loss: 1.0068298294729154
+valiation loss: 0.5185036406526146
+Epoch: 2
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+valiation loss: 0.4944814006102425
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+valiation loss: 0.4559700830981041
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+Epoch: 7
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+valiation loss: 0.43812326487067316
+Epoch: 8
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+valiation loss: 0.4353056059156069
+Epoch: 9
+train loss: 0.6999109750188136
+valiation loss: 0.435301588744098
diff --git a/results/autoencoder_orthogonal500_lr001.log b/results/autoencoder_orthogonal500_lr001.log
@@ -0,0 +1,45 @@
+###Starting script###
+torch version: 0.4.0
+Number CUDA Devices: 1
+batch size: 1024
+epochs: 10
+Model(
+  (h1): Linear(in_features=3553, out_features=512, bias=True)
+  (h2): Linear(in_features=512, out_features=256, bias=True)
+  (z): Linear(in_features=256, out_features=128, bias=True)
+  (h4): Linear(in_features=128, out_features=256, bias=True)
+  (h5): Linear(in_features=256, out_features=512, bias=True)
+  (h6): Linear(in_features=512, out_features=3500, bias=True)
+)
+3943980
+{'dropout_p': 0.1, 'learning_rate': 0.001, 'weight_init': <function orthogonal_ at 0x7f483339bd08>, 'hidden_size1': 512, 'hidden_size2': 256, 'z_size': 128}
+Epoch: 0
+train loss: 1.2955295446898545
+valiation loss: 0.5972153081705696
+Epoch: 1
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+valiation loss: 0.5656329549456898
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+valiation loss: 0.5478053587831949
+Epoch: 3
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+Epoch: 4
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+Epoch: 5
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+valiation loss: 0.5332107117301539
+Epoch: 6
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+Epoch: 7
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+Epoch: 8
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+valiation loss: 0.5212440446019173
+Epoch: 9
+train loss: 0.9678559675308167
+valiation loss: 0.5215851792379429