diff --git a/main/experiments/graph_drawing/november_2018/classif_end_to_end_with_augment/classif_end_to_end.ipynb b/main/experiments/graph_drawing/november_2018/classif_end_to_end_with_augment/classif_end_to_end.ipynb
index 2b729e5c419996fd1a23e1ebbd17e3bea8c75b7e..93eb6f7cdc7e6ef60705804f73cae74601b78f3a 100644
--- a/main/experiments/graph_drawing/november_2018/classif_end_to_end_with_augment/classif_end_to_end.ipynb
+++ b/main/experiments/graph_drawing/november_2018/classif_end_to_end_with_augment/classif_end_to_end.ipynb
@@ -2,9 +2,17 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2018-11-21 16:41:23,509 [24156] DEBUG matplotlib.backends: backend module://ipykernel.pylab.backend_inline version unknown\n"
+ ]
+ }
+ ],
"source": [
"import pandas as pd\n",
"import matplotlib\n",
@@ -46,7 +54,7 @@
"\n",
" return df\n",
"\n",
- "DIRNAME_BIG = \"/home/luc/Resultats/Deepstrom/october_2018/classif_end_to_end\"\n",
+ "DIRNAME_BIG = \"/home/luc/Resultats/Deepstrom/november_2018/end_to_end_with_augment\"\n",
"FILENAME_BIG = \"gathered_results.csv\"\n",
"df = build_df()"
]
@@ -99,267 +107,226 @@
" <th>training_time</th>\n",
" <th>val_acc</th>\n",
" <th>val_eval_time</th>\n",
+ " <th>file_timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>53</th>\n",
+ " <th>5</th>\n",
+ " <td>None</td>\n",
" <td>None</td>\n",
- " <td>1024</td>\n",
" <td>mnist</td>\n",
" <td>None</td>\n",
- " <td>dense</td>\n",
- " <td>0.9908</td>\n",
- " <td>0.046835</td>\n",
- " <td>692.606522</td>\n",
- " <td>0.9908</td>\n",
- " <td>0.093102</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
+ " <td>deepfriedconvnet</td>\n",
" <td>None</td>\n",
- " <td>128</td>\n",
- " <td>mnist</td>\n",
" <td>None</td>\n",
- " <td>dense</td>\n",
- " <td>0.9879</td>\n",
- " <td>0.059532</td>\n",
- " <td>647.076592</td>\n",
- " <td>0.9888</td>\n",
- " <td>0.070084</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>45</th>\n",
" <td>None</td>\n",
- " <td>64</td>\n",
- " <td>mnist</td>\n",
" <td>None</td>\n",
- " <td>dense</td>\n",
- " <td>0.9874</td>\n",
- " <td>0.058991</td>\n",
- " <td>643.130677</td>\n",
- " <td>0.9887</td>\n",
- " <td>0.068697</td>\n",
+ " <td>None</td>\n",
+ " <td>1542128021</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>68</th>\n",
- " <td>256</td>\n",
+ " <th>6</th>\n",
+ " <td>None</td>\n",
" <td>None</td>\n",
" <td>mnist</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.9852</td>\n",
- " <td>0.052776</td>\n",
- " <td>1258.943433</td>\n",
- " <td>0.9846</td>\n",
- " <td>0.103054</td>\n",
+ " <td>None</td>\n",
+ " <td>deepfriedconvnet</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>1542128036</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>18</th>\n",
- " <td>128</td>\n",
+ " <th>28</th>\n",
+ " <td>None</td>\n",
" <td>None</td>\n",
" <td>mnist</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.9845</td>\n",
- " <td>0.064160</td>\n",
- " <td>1008.505888</td>\n",
- " <td>0.9848</td>\n",
- " <td>0.079071</td>\n",
+ " <td>None</td>\n",
+ " <td>deepfriedconvnet</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>1542128029</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>59</th>\n",
+ " <th>39</th>\n",
" <td>None</td>\n",
- " <td>16</td>\n",
+ " <td>1024</td>\n",
" <td>mnist</td>\n",
" <td>None</td>\n",
" <td>dense</td>\n",
- " <td>0.9838</td>\n",
- " <td>0.063164</td>\n",
- " <td>637.988250</td>\n",
- " <td>0.9849</td>\n",
- " <td>0.078794</td>\n",
+ " <td>0.9822000086307525</td>\n",
+ " <td>0.0282437801361084</td>\n",
+ " <td>11942.309963703156</td>\n",
+ " <td>0.9835000038146973</td>\n",
+ " <td>0.05691170692443848</td>\n",
+ " <td>1542093243</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>41</th>\n",
- " <td>64</td>\n",
+ " <th>19</th>\n",
" <td>None</td>\n",
+ " <td>128</td>\n",
" <td>mnist</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.9816</td>\n",
- " <td>0.044161</td>\n",
- " <td>864.483099</td>\n",
- " <td>0.9813</td>\n",
- " <td>0.093081</td>\n",
+ " <td>None</td>\n",
+ " <td>dense</td>\n",
+ " <td>0.9712999999523163</td>\n",
+ " <td>0.04774975776672363</td>\n",
+ " <td>18801.122860193253</td>\n",
+ " <td>0.9713999986648559</td>\n",
+ " <td>0.09031915664672852</td>\n",
+ " <td>1542093242</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>63</th>\n",
- " <td>512</td>\n",
+ " <th>38</th>\n",
" <td>None</td>\n",
+ " <td>64</td>\n",
" <td>mnist</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.9813</td>\n",
- " <td>0.064762</td>\n",
- " <td>1815.580290</td>\n",
- " <td>0.9813</td>\n",
- " <td>0.115415</td>\n",
+ " <td>None</td>\n",
+ " <td>dense</td>\n",
+ " <td>0.9674999952316284</td>\n",
+ " <td>0.05219531059265137</td>\n",
+ " <td>18916.09988093376</td>\n",
+ " <td>0.9656000018119812</td>\n",
+ " <td>0.10185027122497559</td>\n",
+ " <td>1542093242</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>13</th>\n",
- " <td>16</td>\n",
+ " <th>11</th>\n",
" <td>None</td>\n",
+ " <td>16</td>\n",
" <td>mnist</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.9813</td>\n",
- " <td>0.043917</td>\n",
- " <td>773.847790</td>\n",
- " <td>0.9783</td>\n",
- " <td>0.092328</td>\n",
+ " <td>None</td>\n",
+ " <td>dense</td>\n",
+ " <td>0.9584999918937683</td>\n",
+ " <td>0.07531499862670898</td>\n",
+ " <td>18834.147827863693</td>\n",
+ " <td>0.9583999991416932</td>\n",
+ " <td>0.07740616798400879</td>\n",
+ " <td>1542093237</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>0</th>\n",
- " <td>128</td>\n",
+ " <th>32</th>\n",
+ " <td>64</td>\n",
" <td>None</td>\n",
" <td>mnist</td>\n",
" <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.9813</td>\n",
- " <td>0.186224</td>\n",
- " <td>1401.125618</td>\n",
- " <td>0.9782</td>\n",
- " <td>0.202431</td>\n",
+ " <td>0.9003000020980835</td>\n",
+ " <td>0.10981917381286621</td>\n",
+ " <td>10661.762295007706</td>\n",
+ " <td>0.8931000113487244</td>\n",
+ " <td>0.13260126113891602</td>\n",
+ " <td>1542138607</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>24</th>\n",
- " <td>64</td>\n",
+ " <th>26</th>\n",
+ " <td>128</td>\n",
" <td>None</td>\n",
" <td>mnist</td>\n",
" <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.9795</td>\n",
- " <td>0.121712</td>\n",
- " <td>1100.418470</td>\n",
- " <td>0.9778</td>\n",
- " <td>0.136375</td>\n",
+ " <td>0.8997999966144562</td>\n",
+ " <td>0.10766291618347168</td>\n",
+ " <td>12511.990970373154</td>\n",
+ " <td>0.8817000031471253</td>\n",
+ " <td>0.11288857460021973</td>\n",
+ " <td>1542142166</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>39</th>\n",
+ " <th>37</th>\n",
" <td>256</td>\n",
" <td>None</td>\n",
" <td>mnist</td>\n",
" <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.9792</td>\n",
- " <td>0.300938</td>\n",
- " <td>1987.493944</td>\n",
- " <td>0.9801</td>\n",
- " <td>0.326709</td>\n",
+ " <td>0.8875000059604645</td>\n",
+ " <td>0.1719503402709961</td>\n",
+ " <td>24715.69637322426</td>\n",
+ " <td>0.8808999955654144</td>\n",
+ " <td>0.2258143424987793</td>\n",
+ " <td>1542143626</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>26</th>\n",
+ " <th>12</th>\n",
" <td>16</td>\n",
" <td>None</td>\n",
" <td>mnist</td>\n",
" <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.9789</td>\n",
- " <td>0.058784</td>\n",
- " <td>871.508166</td>\n",
- " <td>0.9780</td>\n",
- " <td>0.109170</td>\n",
+ " <td>0.8655000030994415</td>\n",
+ " <td>0.060543060302734375</td>\n",
+ " <td>10251.9246032238</td>\n",
+ " <td>0.8617999970912933</td>\n",
+ " <td>0.1010580062866211</td>\n",
+ " <td>1542138349</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>14</th>\n",
+ " <th>31</th>\n",
" <td>512</td>\n",
" <td>None</td>\n",
" <td>mnist</td>\n",
" <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.9758</td>\n",
- " <td>0.564625</td>\n",
- " <td>3119.552520</td>\n",
- " <td>0.9751</td>\n",
- " <td>0.616923</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>35</th>\n",
- " <td>8</td>\n",
- " <td>None</td>\n",
- " <td>mnist</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.9729</td>\n",
- " <td>0.059439</td>\n",
- " <td>727.739111</td>\n",
- " <td>0.9707</td>\n",
- " <td>0.070918</td>\n",
+ " <td>0.8584999978542328</td>\n",
+ " <td>0.3059966564178467</td>\n",
+ " <td>27894.391397476196</td>\n",
+ " <td>0.857099997997284</td>\n",
+ " <td>0.30718469619750977</td>\n",
+ " <td>1542143762</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>37</th>\n",
+ " <th>29</th>\n",
" <td>8</td>\n",
" <td>None</td>\n",
" <td>mnist</td>\n",
" <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.9718</td>\n",
- " <td>0.066200</td>\n",
- " <td>804.819344</td>\n",
- " <td>0.9663</td>\n",
- " <td>0.079810</td>\n",
+ " <td>0.8299999952316284</td>\n",
+ " <td>0.054628849029541016</td>\n",
+ " <td>10385.974974393845</td>\n",
+ " <td>0.8270999968051911</td>\n",
+ " <td>0.09607958793640137</td>\n",
+ " <td>1542128211</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>58</th>\n",
- " <td>4</td>\n",
- " <td>None</td>\n",
- " <td>mnist</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.9498</td>\n",
- " <td>0.059206</td>\n",
- " <td>728.185049</td>\n",
- " <td>0.9453</td>\n",
- " <td>0.072418</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>70</th>\n",
+ " <th>9</th>\n",
" <td>4</td>\n",
" <td>None</td>\n",
" <td>mnist</td>\n",
" <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.9456</td>\n",
- " <td>0.047744</td>\n",
- " <td>750.139674</td>\n",
- " <td>0.9452</td>\n",
- " <td>0.091661</td>\n",
+ " <td>0.7988000035285949</td>\n",
+ " <td>0.05095171928405762</td>\n",
+ " <td>10202.119398117065</td>\n",
+ " <td>0.8037999987602233</td>\n",
+ " <td>0.09100127220153809</td>\n",
+ " <td>1542128137</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " --nys-size --out-dim dataset kernel network test_acc test_eval_time training_time val_acc val_eval_time\n",
- "53 None 1024 mnist None dense 0.9908 0.046835 692.606522 0.9908 0.093102\n",
- "25 None 128 mnist None dense 0.9879 0.059532 647.076592 0.9888 0.070084\n",
- "45 None 64 mnist None dense 0.9874 0.058991 643.130677 0.9887 0.068697\n",
- "68 256 None mnist linear deepstrom 0.9852 0.052776 1258.943433 0.9846 0.103054\n",
- "18 128 None mnist linear deepstrom 0.9845 0.064160 1008.505888 0.9848 0.079071\n",
- "59 None 16 mnist None dense 0.9838 0.063164 637.988250 0.9849 0.078794\n",
- "41 64 None mnist linear deepstrom 0.9816 0.044161 864.483099 0.9813 0.093081\n",
- "63 512 None mnist linear deepstrom 0.9813 0.064762 1815.580290 0.9813 0.115415\n",
- "13 16 None mnist linear deepstrom 0.9813 0.043917 773.847790 0.9783 0.092328\n",
- "0 128 None mnist chi2_cpd deepstrom 0.9813 0.186224 1401.125618 0.9782 0.202431\n",
- "24 64 None mnist chi2_cpd deepstrom 0.9795 0.121712 1100.418470 0.9778 0.136375\n",
- "39 256 None mnist chi2_cpd deepstrom 0.9792 0.300938 1987.493944 0.9801 0.326709\n",
- "26 16 None mnist chi2_cpd deepstrom 0.9789 0.058784 871.508166 0.9780 0.109170\n",
- "14 512 None mnist chi2_cpd deepstrom 0.9758 0.564625 3119.552520 0.9751 0.616923\n",
- "35 8 None mnist linear deepstrom 0.9729 0.059439 727.739111 0.9707 0.070918\n",
- "37 8 None mnist chi2_cpd deepstrom 0.9718 0.066200 804.819344 0.9663 0.079810\n",
- "58 4 None mnist linear deepstrom 0.9498 0.059206 728.185049 0.9453 0.072418\n",
- "70 4 None mnist chi2_cpd deepstrom 0.9456 0.047744 750.139674 0.9452 0.091661"
+ " --nys-size --out-dim dataset kernel network test_acc test_eval_time training_time val_acc val_eval_time file_timestamp\n",
+ "5 None None mnist None deepfriedconvnet None None None None None 1542128021\n",
+ "6 None None mnist None deepfriedconvnet None None None None None 1542128036\n",
+ "28 None None mnist None deepfriedconvnet None None None None None 1542128029\n",
+ "39 None 1024 mnist None dense 0.9822000086307525 0.0282437801361084 11942.309963703156 0.9835000038146973 0.05691170692443848 1542093243\n",
+ "19 None 128 mnist None dense 0.9712999999523163 0.04774975776672363 18801.122860193253 0.9713999986648559 0.09031915664672852 1542093242\n",
+ "38 None 64 mnist None dense 0.9674999952316284 0.05219531059265137 18916.09988093376 0.9656000018119812 0.10185027122497559 1542093242\n",
+ "11 None 16 mnist None dense 0.9584999918937683 0.07531499862670898 18834.147827863693 0.9583999991416932 0.07740616798400879 1542093237\n",
+ "32 64 None mnist chi2_cpd deepstrom 0.9003000020980835 0.10981917381286621 10661.762295007706 0.8931000113487244 0.13260126113891602 1542138607\n",
+ "26 128 None mnist chi2_cpd deepstrom 0.8997999966144562 0.10766291618347168 12511.990970373154 0.8817000031471253 0.11288857460021973 1542142166\n",
+ "37 256 None mnist chi2_cpd deepstrom 0.8875000059604645 0.1719503402709961 24715.69637322426 0.8808999955654144 0.2258143424987793 1542143626\n",
+ "12 16 None mnist chi2_cpd deepstrom 0.8655000030994415 0.060543060302734375 10251.9246032238 0.8617999970912933 0.1010580062866211 1542138349\n",
+ "31 512 None mnist chi2_cpd deepstrom 0.8584999978542328 0.3059966564178467 27894.391397476196 0.857099997997284 0.30718469619750977 1542143762\n",
+ "29 8 None mnist chi2_cpd deepstrom 0.8299999952316284 0.054628849029541016 10385.974974393845 0.8270999968051911 0.09607958793640137 1542128211\n",
+ "9 4 None mnist chi2_cpd deepstrom 0.7988000035285949 0.05095171928405762 10202.119398117065 0.8037999987602233 0.09100127220153809 1542128137"
]
},
"execution_count": 4,
@@ -407,267 +374,226 @@
" <th>training_time</th>\n",
" <th>val_acc</th>\n",
" <th>val_eval_time</th>\n",
+ " <th>file_timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>38</th>\n",
+ " <th>4</th>\n",
+ " <td>None</td>\n",
" <td>None</td>\n",
- " <td>64</td>\n",
" <td>cifar10</td>\n",
" <td>None</td>\n",
- " <td>dense</td>\n",
- " <td>0.7776</td>\n",
- " <td>3.851561</td>\n",
- " <td>14389.172500</td>\n",
- " <td>0.7790</td>\n",
- " <td>6.267717</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>55</th>\n",
+ " <td>deepfriedconvnet</td>\n",
" <td>None</td>\n",
- " <td>128</td>\n",
- " <td>cifar10</td>\n",
" <td>None</td>\n",
- " <td>dense</td>\n",
- " <td>0.7656</td>\n",
- " <td>3.849130</td>\n",
- " <td>14380.838030</td>\n",
- " <td>0.7683</td>\n",
- " <td>6.270864</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>52</th>\n",
" <td>None</td>\n",
- " <td>1024</td>\n",
- " <td>cifar10</td>\n",
" <td>None</td>\n",
- " <td>dense</td>\n",
- " <td>0.7643</td>\n",
- " <td>3.862283</td>\n",
- " <td>14470.796952</td>\n",
- " <td>0.7606</td>\n",
- " <td>6.280409</td>\n",
+ " <td>None</td>\n",
+ " <td>1542128044</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>7</th>\n",
+ " <th>20</th>\n",
+ " <td>None</td>\n",
" <td>None</td>\n",
- " <td>16</td>\n",
" <td>cifar10</td>\n",
" <td>None</td>\n",
- " <td>dense</td>\n",
- " <td>0.7588</td>\n",
- " <td>3.575382</td>\n",
- " <td>14157.014869</td>\n",
- " <td>0.7657</td>\n",
- " <td>5.747157</td>\n",
+ " <td>deepfriedconvnet</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>1542128054</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>51</th>\n",
- " <td>512</td>\n",
+ " <th>24</th>\n",
+ " <td>None</td>\n",
" <td>None</td>\n",
" <td>cifar10</td>\n",
- " <td>chi2_cpd</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.7479</td>\n",
- " <td>6.846244</td>\n",
- " <td>87033.216096</td>\n",
- " <td>0.7405</td>\n",
- " <td>9.329525</td>\n",
+ " <td>None</td>\n",
+ " <td>deepfriedconvnet</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>1542128066</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>22</th>\n",
- " <td>4</td>\n",
+ " <th>34</th>\n",
+ " <td>16</td>\n",
" <td>None</td>\n",
" <td>cifar10</td>\n",
" <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.7433</td>\n",
- " <td>4.010236</td>\n",
- " <td>19910.613283</td>\n",
- " <td>0.7361</td>\n",
- " <td>6.501417</td>\n",
+ " <td>0.8763999938964844</td>\n",
+ " <td>1.166285514831543</td>\n",
+ " <td>36617.78374505043</td>\n",
+ " <td>0.8763000011444092</td>\n",
+ " <td>1.849858283996582</td>\n",
+ " <td>1542146397</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>43</th>\n",
- " <td>256</td>\n",
+ " <th>3</th>\n",
" <td>None</td>\n",
+ " <td>16</td>\n",
" <td>cifar10</td>\n",
- " <td>chi2_cpd</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.7405</td>\n",
- " <td>5.432825</td>\n",
- " <td>53063.696264</td>\n",
- " <td>0.7363</td>\n",
- " <td>7.898510</td>\n",
+ " <td>None</td>\n",
+ " <td>dense</td>\n",
+ " <td>0.8750999987125396</td>\n",
+ " <td>3.556856632232666</td>\n",
+ " <td>51417.4389526844</td>\n",
+ " <td>0.8725000023841858</td>\n",
+ " <td>5.722635269165039</td>\n",
+ " <td>1542093242</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>36</th>\n",
+ " <th>17</th>\n",
" <td>128</td>\n",
" <td>None</td>\n",
" <td>cifar10</td>\n",
" <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.7379</td>\n",
- " <td>4.370653</td>\n",
- " <td>34882.018647</td>\n",
- " <td>0.7358</td>\n",
- " <td>6.602529</td>\n",
+ " <td>0.8719999969005585</td>\n",
+ " <td>4.408368825912476</td>\n",
+ " <td>55802.59868788719</td>\n",
+ " <td>0.8729000091552734</td>\n",
+ " <td>6.515403985977173</td>\n",
+ " <td>1542149279</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>2</th>\n",
- " <td>16</td>\n",
+ " <th>14</th>\n",
" <td>None</td>\n",
+ " <td>64</td>\n",
" <td>cifar10</td>\n",
- " <td>chi2_cpd</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.7306</td>\n",
- " <td>4.136106</td>\n",
- " <td>22086.981499</td>\n",
- " <td>0.7336</td>\n",
- " <td>6.612154</td>\n",
+ " <td>None</td>\n",
+ " <td>dense</td>\n",
+ " <td>0.8715999960899353</td>\n",
+ " <td>3.90252685546875</td>\n",
+ " <td>51397.38422727585</td>\n",
+ " <td>0.8755999982357026</td>\n",
+ " <td>6.284409046173096</td>\n",
+ " <td>1542093242</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>20</th>\n",
- " <td>64</td>\n",
+ " <th>18</th>\n",
" <td>None</td>\n",
+ " <td>1024</td>\n",
" <td>cifar10</td>\n",
- " <td>chi2_cpd</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.7257</td>\n",
- " <td>3.999178</td>\n",
- " <td>27069.076405</td>\n",
- " <td>0.7262</td>\n",
- " <td>6.233111</td>\n",
+ " <td>None</td>\n",
+ " <td>dense</td>\n",
+ " <td>0.8707000076770782</td>\n",
+ " <td>3.899188756942749</td>\n",
+ " <td>34750.86156606674</td>\n",
+ " <td>0.8687999963760376</td>\n",
+ " <td>6.288317680358887</td>\n",
+ " <td>1542093242</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>46</th>\n",
+ " <th>10</th>\n",
" <td>8</td>\n",
" <td>None</td>\n",
" <td>cifar10</td>\n",
" <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.7246</td>\n",
- " <td>4.051840</td>\n",
- " <td>21110.480839</td>\n",
- " <td>0.7287</td>\n",
- " <td>6.506875</td>\n",
+ " <td>0.8703999996185303</td>\n",
+ " <td>3.6414413452148438</td>\n",
+ " <td>57386.47595191002</td>\n",
+ " <td>0.8709000051021576</td>\n",
+ " <td>5.832661390304565</td>\n",
+ " <td>1542144697</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>31</th>\n",
- " <td>4</td>\n",
+ " <th>40</th>\n",
" <td>None</td>\n",
+ " <td>128</td>\n",
" <td>cifar10</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.1000</td>\n",
- " <td>4.011486</td>\n",
- " <td>19580.249264</td>\n",
- " <td>0.0947</td>\n",
- " <td>6.453061</td>\n",
+ " <td>None</td>\n",
+ " <td>dense</td>\n",
+ " <td>0.8699000000953674</td>\n",
+ " <td>3.515281915664673</td>\n",
+ " <td>34945.89453077316</td>\n",
+ " <td>0.8782000064849853</td>\n",
+ " <td>5.652684450149536</td>\n",
+ " <td>1542093242</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>28</th>\n",
- " <td>8</td>\n",
+ " <th>8</th>\n",
+ " <td>4</td>\n",
" <td>None</td>\n",
" <td>cifar10</td>\n",
- " <td>linear</td>\n",
+ " <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.1000</td>\n",
- " <td>3.999425</td>\n",
- " <td>20588.263982</td>\n",
- " <td>0.0947</td>\n",
- " <td>6.449730</td>\n",
+ " <td>0.8697000086307526</td>\n",
+ " <td>3.9117791652679443</td>\n",
+ " <td>56443.23889231682</td>\n",
+ " <td>0.8759000062942505</td>\n",
+ " <td>6.313701391220093</td>\n",
+ " <td>1542144666</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>47</th>\n",
+ " <th>36</th>\n",
" <td>512</td>\n",
" <td>None</td>\n",
" <td>cifar10</td>\n",
- " <td>linear</td>\n",
+ " <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.1000</td>\n",
- " <td>5.874875</td>\n",
- " <td>82356.760107</td>\n",
- " <td>0.0947</td>\n",
- " <td>8.360661</td>\n",
+ " <td>0.8689000010490417</td>\n",
+ " <td>2.193476676940918</td>\n",
+ " <td>85243.51559782028</td>\n",
+ " <td>0.8681999981403351</td>\n",
+ " <td>2.8480000495910645</td>\n",
+ " <td>1542156968</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>23</th>\n",
+ " <th>30</th>\n",
" <td>256</td>\n",
" <td>None</td>\n",
" <td>cifar10</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.1000</td>\n",
- " <td>4.876681</td>\n",
- " <td>50572.784957</td>\n",
- " <td>0.0947</td>\n",
- " <td>7.366312</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>128</td>\n",
- " <td>None</td>\n",
- " <td>cifar10</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.1000</td>\n",
- " <td>4.089114</td>\n",
- " <td>34239.797556</td>\n",
- " <td>0.0947</td>\n",
- " <td>6.307771</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>66</th>\n",
- " <td>16</td>\n",
- " <td>None</td>\n",
- " <td>cifar10</td>\n",
- " <td>linear</td>\n",
+ " <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.1000</td>\n",
- " <td>3.705690</td>\n",
- " <td>21834.584799</td>\n",
- " <td>0.0947</td>\n",
- " <td>5.931691</td>\n",
+ " <td>0.8680999994277954</td>\n",
+ " <td>1.638434648513794</td>\n",
+ " <td>44774.475116968155</td>\n",
+ " <td>0.8678999960422515</td>\n",
+ " <td>2.325268268585205</td>\n",
+ " <td>1542154705</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>67</th>\n",
+ " <th>27</th>\n",
" <td>64</td>\n",
" <td>None</td>\n",
" <td>cifar10</td>\n",
- " <td>linear</td>\n",
+ " <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.1000</td>\n",
- " <td>3.857690</td>\n",
- " <td>26705.858757</td>\n",
- " <td>0.0947</td>\n",
- " <td>6.085833</td>\n",
+ " <td>0.8679000020027161</td>\n",
+ " <td>4.357625722885132</td>\n",
+ " <td>47625.56103491783</td>\n",
+ " <td>0.8693000018596649</td>\n",
+ " <td>6.8435378074646</td>\n",
+ " <td>1542148610</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " --nys-size --out-dim dataset kernel network test_acc test_eval_time training_time val_acc val_eval_time\n",
- "38 None 64 cifar10 None dense 0.7776 3.851561 14389.172500 0.7790 6.267717\n",
- "55 None 128 cifar10 None dense 0.7656 3.849130 14380.838030 0.7683 6.270864\n",
- "52 None 1024 cifar10 None dense 0.7643 3.862283 14470.796952 0.7606 6.280409\n",
- "7 None 16 cifar10 None dense 0.7588 3.575382 14157.014869 0.7657 5.747157\n",
- "51 512 None cifar10 chi2_cpd deepstrom 0.7479 6.846244 87033.216096 0.7405 9.329525\n",
- "22 4 None cifar10 chi2_cpd deepstrom 0.7433 4.010236 19910.613283 0.7361 6.501417\n",
- "43 256 None cifar10 chi2_cpd deepstrom 0.7405 5.432825 53063.696264 0.7363 7.898510\n",
- "36 128 None cifar10 chi2_cpd deepstrom 0.7379 4.370653 34882.018647 0.7358 6.602529\n",
- "2 16 None cifar10 chi2_cpd deepstrom 0.7306 4.136106 22086.981499 0.7336 6.612154\n",
- "20 64 None cifar10 chi2_cpd deepstrom 0.7257 3.999178 27069.076405 0.7262 6.233111\n",
- "46 8 None cifar10 chi2_cpd deepstrom 0.7246 4.051840 21110.480839 0.7287 6.506875\n",
- "31 4 None cifar10 linear deepstrom 0.1000 4.011486 19580.249264 0.0947 6.453061\n",
- "28 8 None cifar10 linear deepstrom 0.1000 3.999425 20588.263982 0.0947 6.449730\n",
- "47 512 None cifar10 linear deepstrom 0.1000 5.874875 82356.760107 0.0947 8.360661\n",
- "23 256 None cifar10 linear deepstrom 0.1000 4.876681 50572.784957 0.0947 7.366312\n",
- "11 128 None cifar10 linear deepstrom 0.1000 4.089114 34239.797556 0.0947 6.307771\n",
- "66 16 None cifar10 linear deepstrom 0.1000 3.705690 21834.584799 0.0947 5.931691\n",
- "67 64 None cifar10 linear deepstrom 0.1000 3.857690 26705.858757 0.0947 6.085833"
+ " --nys-size --out-dim dataset kernel network test_acc test_eval_time training_time val_acc val_eval_time file_timestamp\n",
+ "4 None None cifar10 None deepfriedconvnet None None None None None 1542128044\n",
+ "20 None None cifar10 None deepfriedconvnet None None None None None 1542128054\n",
+ "24 None None cifar10 None deepfriedconvnet None None None None None 1542128066\n",
+ "34 16 None cifar10 chi2_cpd deepstrom 0.8763999938964844 1.166285514831543 36617.78374505043 0.8763000011444092 1.849858283996582 1542146397\n",
+ "3 None 16 cifar10 None dense 0.8750999987125396 3.556856632232666 51417.4389526844 0.8725000023841858 5.722635269165039 1542093242\n",
+ "17 128 None cifar10 chi2_cpd deepstrom 0.8719999969005585 4.408368825912476 55802.59868788719 0.8729000091552734 6.515403985977173 1542149279\n",
+ "14 None 64 cifar10 None dense 0.8715999960899353 3.90252685546875 51397.38422727585 0.8755999982357026 6.284409046173096 1542093242\n",
+ "18 None 1024 cifar10 None dense 0.8707000076770782 3.899188756942749 34750.86156606674 0.8687999963760376 6.288317680358887 1542093242\n",
+ "10 8 None cifar10 chi2_cpd deepstrom 0.8703999996185303 3.6414413452148438 57386.47595191002 0.8709000051021576 5.832661390304565 1542144697\n",
+ "40 None 128 cifar10 None dense 0.8699000000953674 3.515281915664673 34945.89453077316 0.8782000064849853 5.652684450149536 1542093242\n",
+ "8 4 None cifar10 chi2_cpd deepstrom 0.8697000086307526 3.9117791652679443 56443.23889231682 0.8759000062942505 6.313701391220093 1542144666\n",
+ "36 512 None cifar10 chi2_cpd deepstrom 0.8689000010490417 2.193476676940918 85243.51559782028 0.8681999981403351 2.8480000495910645 1542156968\n",
+ "30 256 None cifar10 chi2_cpd deepstrom 0.8680999994277954 1.638434648513794 44774.475116968155 0.8678999960422515 2.325268268585205 1542154705\n",
+ "27 64 None cifar10 chi2_cpd deepstrom 0.8679000020027161 4.357625722885132 47625.56103491783 0.8693000018596649 6.8435378074646 1542148610"
]
},
"execution_count": 5,
@@ -715,564 +641,229 @@
" <th>training_time</th>\n",
" <th>val_acc</th>\n",
" <th>val_eval_time</th>\n",
+ " <th>file_timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>50</th>\n",
+ " <th>2</th>\n",
" <td>None</td>\n",
- " <td>1024</td>\n",
- " <td>cifar100</td>\n",
" <td>None</td>\n",
- " <td>dense</td>\n",
- " <td>0.3479</td>\n",
- " <td>3.547975</td>\n",
- " <td>14130.525702</td>\n",
- " <td>0.3364</td>\n",
- " <td>5.697969</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
+ " <td>svhn</td>\n",
" <td>None</td>\n",
- " <td>128</td>\n",
- " <td>cifar100</td>\n",
+ " <td>deepfriedconvnet</td>\n",
" <td>None</td>\n",
- " <td>dense</td>\n",
- " <td>0.3388</td>\n",
- " <td>3.524847</td>\n",
- " <td>14074.119773</td>\n",
- " <td>0.3329</td>\n",
- " <td>5.684675</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>64</td>\n",
" <td>None</td>\n",
- " <td>cifar100</td>\n",
- " <td>chi2_cpd</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.2585</td>\n",
- " <td>4.339325</td>\n",
- " <td>27720.096254</td>\n",
- " <td>0.2508</td>\n",
- " <td>6.800864</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>57</th>\n",
- " <td>4</td>\n",
" <td>None</td>\n",
- " <td>cifar100</td>\n",
- " <td>chi2_cpd</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.2043</td>\n",
- " <td>3.971535</td>\n",
- " <td>19768.391855</td>\n",
- " <td>0.1966</td>\n",
- " <td>6.418263</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>256</td>\n",
" <td>None</td>\n",
- " <td>cifar100</td>\n",
- " <td>chi2_cpd</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.1991</td>\n",
- " <td>5.475031</td>\n",
- " <td>53419.870877</td>\n",
- " <td>0.1887</td>\n",
- " <td>7.927623</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>128</td>\n",
" <td>None</td>\n",
- " <td>cifar100</td>\n",
- " <td>chi2_cpd</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.1983</td>\n",
- " <td>4.717442</td>\n",
- " <td>36788.966420</td>\n",
- " <td>0.1876</td>\n",
- " <td>7.193454</td>\n",
+ " <td>1542128113</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>40</th>\n",
- " <td>16</td>\n",
+ " <th>22</th>\n",
" <td>None</td>\n",
- " <td>cifar100</td>\n",
- " <td>chi2_cpd</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.1952</td>\n",
- " <td>3.694020</td>\n",
- " <td>21831.538531</td>\n",
- " <td>0.1890</td>\n",
- " <td>5.934045</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>62</th>\n",
- " <td>8</td>\n",
" <td>None</td>\n",
- " <td>cifar100</td>\n",
- " <td>chi2_cpd</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.1623</td>\n",
- " <td>3.718879</td>\n",
- " <td>20875.334670</td>\n",
- " <td>0.1606</td>\n",
- " <td>5.946196</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>64</th>\n",
- " <td>512</td>\n",
+ " <td>svhn</td>\n",
" <td>None</td>\n",
- " <td>cifar100</td>\n",
- " <td>chi2_cpd</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.1587</td>\n",
- " <td>6.489950</td>\n",
- " <td>81977.486589</td>\n",
- " <td>0.1563</td>\n",
- " <td>8.722982</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>61</th>\n",
+ " <td>deepfriedconvnet</td>\n",
" <td>None</td>\n",
- " <td>64</td>\n",
- " <td>cifar100</td>\n",
" <td>None</td>\n",
- " <td>dense</td>\n",
- " <td>0.0100</td>\n",
- " <td>3.809767</td>\n",
- " <td>14162.506632</td>\n",
- " <td>0.0083</td>\n",
- " <td>6.197990</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>54</th>\n",
- " <td>256</td>\n",
" <td>None</td>\n",
- " <td>cifar100</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.0100</td>\n",
- " <td>4.521444</td>\n",
- " <td>48345.980519</td>\n",
- " <td>0.0083</td>\n",
- " <td>6.749054</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>8</td>\n",
" <td>None</td>\n",
- " <td>cifar100</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.0100</td>\n",
- " <td>3.634902</td>\n",
- " <td>20741.875956</td>\n",
- " <td>0.0083</td>\n",
- " <td>5.885382</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49</th>\n",
- " <td>16</td>\n",
" <td>None</td>\n",
- " <td>cifar100</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.0100</td>\n",
- " <td>3.655856</td>\n",
- " <td>21732.213368</td>\n",
- " <td>0.0083</td>\n",
- " <td>5.886336</td>\n",
+ " <td>1542128100</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>33</th>\n",
- " <td>64</td>\n",
+ " <th>23</th>\n",
" <td>None</td>\n",
- " <td>cifar100</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.0100</td>\n",
- " <td>4.235058</td>\n",
- " <td>26857.990116</td>\n",
- " <td>0.0083</td>\n",
- " <td>6.687950</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
" <td>None</td>\n",
- " <td>16</td>\n",
- " <td>cifar100</td>\n",
+ " <td>svhn</td>\n",
" <td>None</td>\n",
- " <td>dense</td>\n",
- " <td>0.0100</td>\n",
- " <td>3.837857</td>\n",
- " <td>14150.901807</td>\n",
- " <td>0.0083</td>\n",
- " <td>6.251673</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>512</td>\n",
+ " <td>deepfriedconvnet</td>\n",
" <td>None</td>\n",
- " <td>cifar100</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.0100</td>\n",
- " <td>5.467176</td>\n",
- " <td>78125.019147</td>\n",
- " <td>0.0083</td>\n",
- " <td>7.815155</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>128</td>\n",
" <td>None</td>\n",
- " <td>cifar100</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.0100</td>\n",
- " <td>4.414209</td>\n",
- " <td>35154.659761</td>\n",
- " <td>0.0083</td>\n",
- " <td>6.863228</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>69</th>\n",
- " <td>4</td>\n",
" <td>None</td>\n",
- " <td>cifar100</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.0100</td>\n",
- " <td>3.613290</td>\n",
- " <td>19602.906152</td>\n",
- " <td>0.0083</td>\n",
- " <td>5.825223</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " --nys-size --out-dim dataset kernel network test_acc test_eval_time training_time val_acc val_eval_time\n",
- "50 None 1024 cifar100 None dense 0.3479 3.547975 14130.525702 0.3364 5.697969\n",
- "16 None 128 cifar100 None dense 0.3388 3.524847 14074.119773 0.3329 5.684675\n",
- "3 64 None cifar100 chi2_cpd deepstrom 0.2585 4.339325 27720.096254 0.2508 6.800864\n",
- "57 4 None cifar100 chi2_cpd deepstrom 0.2043 3.971535 19768.391855 0.1966 6.418263\n",
- "29 256 None cifar100 chi2_cpd deepstrom 0.1991 5.475031 53419.870877 0.1887 7.927623\n",
- "27 128 None cifar100 chi2_cpd deepstrom 0.1983 4.717442 36788.966420 0.1876 7.193454\n",
- "40 16 None cifar100 chi2_cpd deepstrom 0.1952 3.694020 21831.538531 0.1890 5.934045\n",
- "62 8 None cifar100 chi2_cpd deepstrom 0.1623 3.718879 20875.334670 0.1606 5.946196\n",
- "64 512 None cifar100 chi2_cpd deepstrom 0.1587 6.489950 81977.486589 0.1563 8.722982\n",
- "61 None 64 cifar100 None dense 0.0100 3.809767 14162.506632 0.0083 6.197990\n",
- "54 256 None cifar100 linear deepstrom 0.0100 4.521444 48345.980519 0.0083 6.749054\n",
- "1 8 None cifar100 linear deepstrom 0.0100 3.634902 20741.875956 0.0083 5.885382\n",
- "49 16 None cifar100 linear deepstrom 0.0100 3.655856 21732.213368 0.0083 5.886336\n",
- "33 64 None cifar100 linear deepstrom 0.0100 4.235058 26857.990116 0.0083 6.687950\n",
- "21 None 16 cifar100 None dense 0.0100 3.837857 14150.901807 0.0083 6.251673\n",
- "10 512 None cifar100 linear deepstrom 0.0100 5.467176 78125.019147 0.0083 7.815155\n",
- "9 128 None cifar100 linear deepstrom 0.0100 4.414209 35154.659761 0.0083 6.863228\n",
- "69 4 None cifar100 linear deepstrom 0.0100 3.613290 19602.906152 0.0083 5.825223"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "get_sorted_acc_for_dataset(df, \"cifar100\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
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- " }\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>--nys-size</th>\n",
- " <th>--out-dim</th>\n",
- " <th>dataset</th>\n",
- " <th>kernel</th>\n",
- " <th>network</th>\n",
- " <th>test_acc</th>\n",
- " <th>test_eval_time</th>\n",
- " <th>training_time</th>\n",
- " <th>val_acc</th>\n",
- " <th>val_eval_time</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>8</th>\n",
" <td>None</td>\n",
- " <td>128</td>\n",
- " <td>svhn</td>\n",
" <td>None</td>\n",
- " <td>dense</td>\n",
- " <td>0.941009</td>\n",
- " <td>9.443329</td>\n",
- " <td>22538.016228</td>\n",
- " <td>0.9339</td>\n",
- " <td>5.747079</td>\n",
+ " <td>1542128076</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>32</th>\n",
+ " <th>33</th>\n",
+ " <td>8</td>\n",
" <td>None</td>\n",
- " <td>16</td>\n",
" <td>svhn</td>\n",
- " <td>None</td>\n",
- " <td>dense</td>\n",
- " <td>0.940815</td>\n",
- " <td>9.270262</td>\n",
- " <td>22534.473243</td>\n",
- " <td>0.9361</td>\n",
- " <td>5.683755</td>\n",
+ " <td>chi2_cpd</td>\n",
+ " <td>deepstrom</td>\n",
+ " <td>0.950157399530764</td>\n",
+ " <td>3.156935930252075</td>\n",
+ " <td>157795.88800406456</td>\n",
+ " <td>0.9431999981403351</td>\n",
+ " <td>1.888800859451294</td>\n",
+ " <td>1542171673</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>6</th>\n",
+ " <th>1</th>\n",
" <td>None</td>\n",
- " <td>64</td>\n",
+ " <td>128</td>\n",
" <td>svhn</td>\n",
" <td>None</td>\n",
" <td>dense</td>\n",
- " <td>0.940787</td>\n",
- " <td>9.433461</td>\n",
- " <td>22535.162152</td>\n",
- " <td>0.9350</td>\n",
- " <td>5.748816</td>\n",
+ " <td>0.9500648246871101</td>\n",
+ " <td>9.564645528793335</td>\n",
+ " <td>118978.64107084274</td>\n",
+ " <td>0.9454999923706054</td>\n",
+ " <td>5.882847309112549</td>\n",
+ " <td>1542112081</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>44</th>\n",
+ " <th>21</th>\n",
" <td>256</td>\n",
" <td>None</td>\n",
" <td>svhn</td>\n",
" <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.937454</td>\n",
- " <td>14.281184</td>\n",
- " <td>83866.884516</td>\n",
- " <td>0.9326</td>\n",
- " <td>7.820895</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>30</th>\n",
- " <td>512</td>\n",
- " <td>None</td>\n",
- " <td>svhn</td>\n",
- " <td>chi2_cpd</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.937407</td>\n",
- " <td>18.118679</td>\n",
- " <td>137458.785191</td>\n",
- " <td>0.9312</td>\n",
- " <td>9.242190</td>\n",
+ " <td>0.9472685147214819</td>\n",
+ " <td>14.435875177383423</td>\n",
+ " <td>129790.18364858627</td>\n",
+ " <td>0.940499997138977</td>\n",
+ " <td>7.935451030731201</td>\n",
+ " <td>1542196274</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>56</th>\n",
+ " <th>7</th>\n",
" <td>None</td>\n",
" <td>1024</td>\n",
" <td>svhn</td>\n",
" <td>None</td>\n",
" <td>dense</td>\n",
- " <td>0.937000</td>\n",
- " <td>10.360103</td>\n",
- " <td>23155.441453</td>\n",
- " <td>0.9342</td>\n",
- " <td>6.369419</td>\n",
+ " <td>0.9470370301493892</td>\n",
+ " <td>10.098342180252075</td>\n",
+ " <td>119093.35198688507</td>\n",
+ " <td>0.9433000087738037</td>\n",
+ " <td>6.1705663204193115</td>\n",
+ " <td>1542112171</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>5</th>\n",
- " <td>512</td>\n",
+ " <th>41</th>\n",
+ " <td>64</td>\n",
" <td>None</td>\n",
" <td>svhn</td>\n",
- " <td>linear</td>\n",
+ " <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.936324</td>\n",
- " <td>15.367678</td>\n",
- " <td>133449.591165</td>\n",
- " <td>0.9303</td>\n",
- " <td>8.193455</td>\n",
+ " <td>0.9463981500378361</td>\n",
+ " <td>3.4341957569122314</td>\n",
+ " <td>78279.07663154602</td>\n",
+ " <td>0.9418999910354614</td>\n",
+ " <td>1.9714641571044922</td>\n",
+ " <td>1542177149</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>65</th>\n",
+ " <th>15</th>\n",
" <td>128</td>\n",
" <td>None</td>\n",
" <td>svhn</td>\n",
" <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.935120</td>\n",
- " <td>12.290664</td>\n",
- " <td>57845.390112</td>\n",
- " <td>0.9309</td>\n",
- " <td>7.054313</td>\n",
+ " <td>0.9461481416666949</td>\n",
+ " <td>3.6263587474823</td>\n",
+ " <td>83843.22896647453</td>\n",
+ " <td>0.9402000069618225</td>\n",
+ " <td>2.0779218673706055</td>\n",
+ " <td>1542183046</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>17</th>\n",
- " <td>64</td>\n",
+ " <th>0</th>\n",
" <td>None</td>\n",
+ " <td>64</td>\n",
" <td>svhn</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.933824</td>\n",
- " <td>11.180088</td>\n",
- " <td>43996.453977</td>\n",
- " <td>0.9315</td>\n",
- " <td>6.699816</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>8</td>\n",
" <td>None</td>\n",
- " <td>svhn</td>\n",
- " <td>chi2_cpd</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.932556</td>\n",
- " <td>9.698029</td>\n",
- " <td>33257.878631</td>\n",
- " <td>0.9280</td>\n",
- " <td>5.919757</td>\n",
+ " <td>dense</td>\n",
+ " <td>0.9458981443334509</td>\n",
+ " <td>10.34985876083374</td>\n",
+ " <td>114540.45786070824</td>\n",
+ " <td>0.9479000031948089</td>\n",
+ " <td>6.307359933853149</td>\n",
+ " <td>1542112063</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>15</th>\n",
- " <td>64</td>\n",
+ " <th>25</th>\n",
" <td>None</td>\n",
+ " <td>16</td>\n",
" <td>svhn</td>\n",
- " <td>chi2_cpd</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.931815</td>\n",
- " <td>10.448107</td>\n",
- " <td>42970.862907</td>\n",
- " <td>0.9307</td>\n",
- " <td>6.198442</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>48</th>\n",
- " <td>128</td>\n",
" <td>None</td>\n",
- " <td>svhn</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.931787</td>\n",
- " <td>11.711458</td>\n",
- " <td>56833.877055</td>\n",
- " <td>0.9291</td>\n",
- " <td>6.837168</td>\n",
+ " <td>dense</td>\n",
+ " <td>0.9455648152916519</td>\n",
+ " <td>2.8233609199523926</td>\n",
+ " <td>71913.0057694912</td>\n",
+ " <td>0.943999993801117</td>\n",
+ " <td>1.6415505409240723</td>\n",
+ " <td>1542105209</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>34</th>\n",
+ " <th>13</th>\n",
" <td>4</td>\n",
" <td>None</td>\n",
" <td>svhn</td>\n",
" <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.931528</td>\n",
- " <td>9.722512</td>\n",
- " <td>31789.523167</td>\n",
- " <td>0.9292</td>\n",
- " <td>5.931942</td>\n",
+ " <td>0.9449722214981362</td>\n",
+ " <td>3.056546449661255</td>\n",
+ " <td>157415.13459396362</td>\n",
+ " <td>0.941399997472763</td>\n",
+ " <td>1.819403886795044</td>\n",
+ " <td>1542168358</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>4</th>\n",
+ " <th>16</th>\n",
" <td>16</td>\n",
" <td>None</td>\n",
" <td>svhn</td>\n",
" <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.929741</td>\n",
- " <td>9.828513</td>\n",
- " <td>34855.966583</td>\n",
- " <td>0.9305</td>\n",
- " <td>5.963734</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>42</th>\n",
- " <td>16</td>\n",
- " <td>None</td>\n",
- " <td>svhn</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.196694</td>\n",
- " <td>9.705651</td>\n",
- " <td>34650.985853</td>\n",
- " <td>0.1881</td>\n",
- " <td>5.946775</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>8</td>\n",
- " <td>None</td>\n",
- " <td>svhn</td>\n",
- " <td>linear</td>\n",
- " <td>deepstrom</td>\n",
- " <td>0.196694</td>\n",
- " <td>9.592059</td>\n",
- " <td>33089.771927</td>\n",
- " <td>0.1881</td>\n",
- " <td>5.880033</td>\n",
+ " <td>0.9448888897895813</td>\n",
+ " <td>3.1488840579986572</td>\n",
+ " <td>161223.36014437675</td>\n",
+ " <td>0.9398000001907348</td>\n",
+ " <td>1.856621503829956</td>\n",
+ " <td>1542173177</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>60</th>\n",
- " <td>4</td>\n",
+ " <th>35</th>\n",
+ " <td>512</td>\n",
" <td>None</td>\n",
" <td>svhn</td>\n",
- " <td>linear</td>\n",
+ " <td>chi2_cpd</td>\n",
" <td>deepstrom</td>\n",
- " <td>0.196694</td>\n",
- " <td>9.709503</td>\n",
- " <td>31571.430093</td>\n",
- " <td>0.1881</td>\n",
- " <td>5.931994</td>\n",
+ " <td>0.9432222269199513</td>\n",
+ " <td>16.69476819038391</td>\n",
+ " <td>219266.66595840454</td>\n",
+ " <td>0.9396999895572662</td>\n",
+ " <td>8.49440312385559</td>\n",
+ " <td>1542196669</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " --nys-size --out-dim dataset kernel network test_acc test_eval_time training_time val_acc val_eval_time\n",
- "8 None 128 svhn None dense 0.941009 9.443329 22538.016228 0.9339 5.747079\n",
- "32 None 16 svhn None dense 0.940815 9.270262 22534.473243 0.9361 5.683755\n",
- "6 None 64 svhn None dense 0.940787 9.433461 22535.162152 0.9350 5.748816\n",
- "44 256 None svhn chi2_cpd deepstrom 0.937454 14.281184 83866.884516 0.9326 7.820895\n",
- "30 512 None svhn chi2_cpd deepstrom 0.937407 18.118679 137458.785191 0.9312 9.242190\n",
- "56 None 1024 svhn None dense 0.937000 10.360103 23155.441453 0.9342 6.369419\n",
- "5 512 None svhn linear deepstrom 0.936324 15.367678 133449.591165 0.9303 8.193455\n",
- "65 128 None svhn chi2_cpd deepstrom 0.935120 12.290664 57845.390112 0.9309 7.054313\n",
- "17 64 None svhn linear deepstrom 0.933824 11.180088 43996.453977 0.9315 6.699816\n",
- "19 8 None svhn chi2_cpd deepstrom 0.932556 9.698029 33257.878631 0.9280 5.919757\n",
- "15 64 None svhn chi2_cpd deepstrom 0.931815 10.448107 42970.862907 0.9307 6.198442\n",
- "48 128 None svhn linear deepstrom 0.931787 11.711458 56833.877055 0.9291 6.837168\n",
- "34 4 None svhn chi2_cpd deepstrom 0.931528 9.722512 31789.523167 0.9292 5.931942\n",
- "4 16 None svhn chi2_cpd deepstrom 0.929741 9.828513 34855.966583 0.9305 5.963734\n",
- "42 16 None svhn linear deepstrom 0.196694 9.705651 34650.985853 0.1881 5.946775\n",
- "12 8 None svhn linear deepstrom 0.196694 9.592059 33089.771927 0.1881 5.880033\n",
- "60 4 None svhn linear deepstrom 0.196694 9.709503 31571.430093 0.1881 5.931994"
+ " --nys-size --out-dim dataset kernel network test_acc test_eval_time training_time val_acc val_eval_time file_timestamp\n",
+ "2 None None svhn None deepfriedconvnet None None None None None 1542128113\n",
+ "22 None None svhn None deepfriedconvnet None None None None None 1542128100\n",
+ "23 None None svhn None deepfriedconvnet None None None None None 1542128076\n",
+ "33 8 None svhn chi2_cpd deepstrom 0.950157399530764 3.156935930252075 157795.88800406456 0.9431999981403351 1.888800859451294 1542171673\n",
+ "1 None 128 svhn None dense 0.9500648246871101 9.564645528793335 118978.64107084274 0.9454999923706054 5.882847309112549 1542112081\n",
+ "21 256 None svhn chi2_cpd deepstrom 0.9472685147214819 14.435875177383423 129790.18364858627 0.940499997138977 7.935451030731201 1542196274\n",
+ "7 None 1024 svhn None dense 0.9470370301493892 10.098342180252075 119093.35198688507 0.9433000087738037 6.1705663204193115 1542112171\n",
+ "41 64 None svhn chi2_cpd deepstrom 0.9463981500378361 3.4341957569122314 78279.07663154602 0.9418999910354614 1.9714641571044922 1542177149\n",
+ "15 128 None svhn chi2_cpd deepstrom 0.9461481416666949 3.6263587474823 83843.22896647453 0.9402000069618225 2.0779218673706055 1542183046\n",
+ "0 None 64 svhn None dense 0.9458981443334509 10.34985876083374 114540.45786070824 0.9479000031948089 6.307359933853149 1542112063\n",
+ "25 None 16 svhn None dense 0.9455648152916519 2.8233609199523926 71913.0057694912 0.943999993801117 1.6415505409240723 1542105209\n",
+ "13 4 None svhn chi2_cpd deepstrom 0.9449722214981362 3.056546449661255 157415.13459396362 0.941399997472763 1.819403886795044 1542168358\n",
+ "16 16 None svhn chi2_cpd deepstrom 0.9448888897895813 3.1488840579986572 161223.36014437675 0.9398000001907348 1.856621503829956 1542173177\n",
+ "35 512 None svhn chi2_cpd deepstrom 0.9432222269199513 16.69476819038391 219266.66595840454 0.9396999895572662 8.49440312385559 1542196669"
]
},
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -1283,17 +874,17 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "2018-11-07 09:46:39,143 [16826] DEBUG root: Nystrom possible sizes are: {'4', '512', '8', '256', '128', '64', '16'}\n",
- "2018-11-07 09:46:39,145 [16826] DEBUG root: Kernel functions are: {'linear', 'chi2_cpd'}\n",
- "2018-11-07 09:46:39,146 [16826] DEBUG root: Compared network types are: {'deepstrom', 'dense'}\n",
- "2018-11-07 09:46:39,147 [16826] DEBUG root: Tested representation dimension are: {'1024', '128', '64', '16'}\n"
+ "2018-11-21 16:41:23,643 [24156] DEBUG root: Nystrom possible sizes are: {'128', '512', '8', '256', '64', '4', '16'}\n",
+ "2018-11-21 16:41:23,644 [24156] DEBUG root: Kernel functions are: {'chi2_cpd'}\n",
+ "2018-11-21 16:41:23,646 [24156] DEBUG root: Compared network types are: {'deepstrom', 'dense', 'deepfriedconvnet'}\n",
+ "2018-11-21 16:41:23,647 [24156] DEBUG root: Tested representation dimension are: {'128', '64', '1024', '16'}\n"
]
}
],
@@ -1315,7 +906,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -1339,7 +930,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -1365,7 +956,7 @@
" f.subplots_adjust(bottom=0.3)\n",
"\n",
" out_name = \"end_to_end_{}\".format(DATANAME)\n",
- "\n",
+ " return\n",
" base_out_dir = os.path.abspath(__file__.split(\".\")[0])\n",
" base_out_dir_path = pathlib.Path(base_out_dir) / \"images\"\n",
" base_out_dir_path.mkdir(parents=True, exist_ok=True)\n",
@@ -1376,24 +967,67 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
- "ename": "NameError",
- "evalue": "name '__file__' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-47-01e3edb84776>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0msorted_idx_dense\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp_param_dense\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp_param_dense\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msorted_idx_dense\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracies_dense\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msorted_idx_dense\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"o\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"Dense\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mpost_processing_figures\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubsample_sizes_kernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m<ipython-input-46-99b79cbcf53c>\u001b[0m in \u001b[0;36mpost_processing_figures\u001b[0;34m(f, ax, nbparamdeepstrom, subsample_sizes)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mout_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"end_to_end_{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDATANAME\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mbase_out_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__file__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\".\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 25\u001b[0m \u001b[0mbase_out_dir_path\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[0mbase_out_dir\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m\"images\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0mbase_out_dir_path\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparents\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mNameError\u001b[0m: name '__file__' is not defined"
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2018-11-21 16:41:23,741 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0 to DejaVu Sans ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,766 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,772 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=STIXGeneral:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXGeneral ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,778 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=STIXGeneral:style=italic:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXGeneral ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,779 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=STIXGeneral:style=normal:variant=normal:weight=bold:stretch=normal:size=10.0 to STIXGeneral ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf') with score of 0.000000\n",
+ "2018-11-21 16:41:23,785 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=STIXNonUnicode:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXNonUnicode ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,790 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=STIXNonUnicode:style=italic:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXNonUnicode ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,792 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=STIXNonUnicode:style=normal:variant=normal:weight=bold:stretch=normal:size=10.0 to STIXNonUnicode ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf') with score of 0.000000\n",
+ "2018-11-21 16:41:23,798 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=STIXSizeOneSym:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXSizeOneSym ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,803 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=STIXSizeTwoSym:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXSizeTwoSym ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,808 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=STIXSizeThreeSym:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXSizeThreeSym ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,813 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=STIXSizeFourSym:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXSizeFourSym ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,821 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=STIXSizeFiveSym:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXSizeFiveSym ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,830 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=cmsy10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmsy10 ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,837 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=cmr10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmr10 ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/cmr10.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,846 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=cmtt10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmtt10 ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,853 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=cmmi10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmmi10 ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,862 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=cmb10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmb10 ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/cmb10.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,868 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=cmss10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmss10 ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,876 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=cmex10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmex10 ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,882 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=DejaVu Sans:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,888 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=DejaVu Sans:style=italic:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf') with score of 0.150000\n",
+ "2018-11-21 16:41:23,890 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=DejaVu Sans:style=normal:variant=normal:weight=bold:stretch=normal:size=10.0 to DejaVu Sans ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf') with score of 0.000000\n",
+ "2018-11-21 16:41:23,897 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=DejaVu Sans Mono:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans Mono ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf') with score of 0.050000\n",
+ "2018-11-21 16:41:23,902 [24156] DEBUG matplotlib.font_manager: findfont: Matching :family=DejaVu Sans Display:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans Display ('/home/luc/anaconda3/envs/ml/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf') with score of 0.050000\n"
]
},
{
"data": {
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\n",
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x432 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x432 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
@@ -1417,7 +1051,9 @@
" np_param = (np.square(subsample_sizes_kernel) + # m x m\n",
" subsample_sizes_kernel * nb_classes_dataset) # m x c\n",
" sorted_idx = np.argsort(np_param.values)\n",
- " ax.plot(np_param.values[sorted_idx], accuracies_kernel.values[sorted_idx], marker=\"x\", label=f\"Deepstrom {k_name}\")\n",
+ " xx = np_param.values[sorted_idx]\n",
+ " yy = accuracies_kernel.values[sorted_idx].astype(float)\n",
+ " ax.plot(xx, yy, marker=\"x\", label=f\"Deepstrom {k_name}\")\n",
" \n",
" df_dense = df_data[df_data[\"network\"] == \"dense\"]\n",
" accuracies_dense = df_dense[\"test_acc\"]\n",
@@ -1425,7 +1061,9 @@
" np_param_dense = (nb_feature_conv * out_dim_dense + # d x D\n",
" out_dim_dense * nb_classes_dataset) # D x c\n",
" sorted_idx_dense = np.argsort(np_param_dense.values)\n",
- " ax.plot(np_param_dense.values[sorted_idx_dense], accuracies_dense.values[sorted_idx_dense], marker=\"o\", label=f\"Dense\")\n",
+ " xx = np_param_dense.values[sorted_idx_dense]\n",
+ " yy = accuracies_dense.values[sorted_idx_dense].astype(float)\n",
+ " ax.plot(xx, yy, marker=\"o\", label=f\"Dense\")\n",
" post_processing_figures(f, ax, np_param, subsample_sizes_kernel)"
]
},
diff --git a/main/experiments/graph_drawing/november_2018/classif_end_to_end_with_augment/classif_end_to_end.py b/main/experiments/graph_drawing/november_2018/classif_end_to_end_with_augment/classif_end_to_end.py
index e326ac6a99cf3ccbaaf7df04386af23aa8d13823..5472e78799336117f505651703d8f296c5d12784 100644
--- a/main/experiments/graph_drawing/november_2018/classif_end_to_end_with_augment/classif_end_to_end.py
+++ b/main/experiments/graph_drawing/november_2018/classif_end_to_end_with_augment/classif_end_to_end.py
@@ -1,7 +1,7 @@
# coding: utf-8
-# In[38]:
+# In[1]:
import pandas as pd
@@ -41,7 +41,7 @@ def build_df():
return df
-DIRNAME_BIG = "/home/luc/Resultats/Deepstrom/october_2018/classif_end_to_end"
+DIRNAME_BIG = "/home/luc/Resultats/Deepstrom/november_2018/end_to_end_with_augment"
FILENAME_BIG = "gathered_results.csv"
df = build_df()
@@ -70,16 +70,10 @@ get_sorted_acc_for_dataset(df, "cifar10")
# In[6]:
-get_sorted_acc_for_dataset(df, "cifar100")
-
-
-# In[7]:
-
-
get_sorted_acc_for_dataset(df, "svhn")
-# In[10]:
+# In[7]:
method_names = set(df["network"].values)
@@ -97,7 +91,7 @@ logger.debug("Compared network types are: {}".format(method_names))
logger.debug("Tested representation dimension are: {}".format(repr_dim))
-# In[43]:
+# In[8]:
nb_classes_datasets = {
@@ -117,12 +111,8 @@ nb_feature_convs = {
min_acc = 0
max_acc = 1
-deepstrom_markers = {
- "linear": "x",
- "chi2_cpd": "o"
-}
-# In[46]:
+# In[9]:
def post_processing_figures(f, ax, nbparamdeepstrom, subsample_sizes):
@@ -147,7 +137,6 @@ def post_processing_figures(f, ax, nbparamdeepstrom, subsample_sizes):
f.subplots_adjust(bottom=0.3)
out_name = "end_to_end_{}".format(DATANAME)
-
base_out_dir = os.path.abspath(__file__.split(".")[0])
base_out_dir_path = pathlib.Path(base_out_dir) / "images"
base_out_dir_path.mkdir(parents=True, exist_ok=True)
@@ -156,7 +145,7 @@ def post_processing_figures(f, ax, nbparamdeepstrom, subsample_sizes):
f.savefig(out_path)
-# In[47]:
+# In[10]:
for DATANAME in datasets:
@@ -171,8 +160,9 @@ for DATANAME in datasets:
np_param = (np.square(subsample_sizes_kernel) + # m x m
subsample_sizes_kernel * nb_classes_dataset) # m x c
sorted_idx = np.argsort(np_param.values)
- ax.plot(np_param.values[sorted_idx], accuracies_kernel.values[sorted_idx], color="g", marker=deepstrom_markers[k_name], label=f"Deepstrom {k_name}")
-
+ xx = np_param.values[sorted_idx]
+ yy = accuracies_kernel.values[sorted_idx].astype(float)
+ ax.plot(xx, yy, marker="x", label=f"Deepstrom {k_name}")
df_dense = df_data[df_data["network"] == "dense"]
accuracies_dense = df_dense["test_acc"]
@@ -180,6 +170,8 @@ for DATANAME in datasets:
np_param_dense = (nb_feature_conv * out_dim_dense + # d x D
out_dim_dense * nb_classes_dataset) # D x c
sorted_idx_dense = np.argsort(np_param_dense.values)
- ax.plot(np_param_dense.values[sorted_idx_dense], accuracies_dense.values[sorted_idx_dense], color="r", marker="s", label=f"Dense")
+ xx = np_param_dense.values[sorted_idx_dense]
+ yy = accuracies_dense.values[sorted_idx_dense].astype(float)
+ ax.plot(xx, yy, marker="o", label=f"Dense")
post_processing_figures(f, ax, np_param, subsample_sizes_kernel)