add visualizations results

README.md

@@ -31,7 +31,7 @@ The MNIST database of handwritten digits, available from this [link](http://yann
 
 
 ## Results on MNIST:
-### Loss/ACC:
+### Loss/ACC: with 10 epochs.
 |               Models: 2 conv layers (29k parameters)              	|      Loss      	|  Accuracy (%)  	|
 |:-----------------------------------------------------------------:	|:--------------:	|:--------------:	|
 | No binary models                                                  	|     **0.0341**     	|      **98.79**     	|
@@ -73,20 +73,25 @@ The Omniglot data set is designed for developing more human-like learning algori
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Sd1zvImf4UzTiix7mnI6Hzk7vkeKNSLB#scrollTo=XVgZBwOCIdl0)
 
-## Results on MNIST:
-### ACC:
+## Results on Omniglot:
+### ACC: with this repository with 10 epochs.
 
-|               Models: matching Network (MN) [4]              	|     Accuracy (%)   	|    Accuracy (%)   	|
-|:-----------------------------------------------------------------:	|:--------------:	|:--------------:	|
-| k-way                                                  	|     5     	|      **     	|
-| n-shot                                                  	|    1    	|      **     	|
-|:-----------------------------------------------------------------:	|:--------------:	|:--------------:	|
-| No binary MN                                                  	|    1    	|      **     	|
-|:-----------------------------------------------------------------:	|:--------------:	|:--------------:	|
-| binary MN: first conv           	|     ?     	|     ?     	|
-| binary MN: second conv           	|     ?     	|     ?     	|
-| binary MN: third conv           	|     ?     	|     ?     	|
-| binary MN: fourth conv           	|     ?     	|     ?     	|
+|               Models: matching Network (MN) [4]              	|     Accuracy (%)   	|
+|:-----------------------------------------------------------------:	|:--------------:	|
+| k-way                                                  	|     5     	|
+| n-shot                                                  	|    1    	|
+|:-----------------------------------------------------------------:	|:--------------:	|
+| No binary MN                                                  	|    **84.4**    	|
+|:-----------------------------------------------------------------:	|:--------------:	|
+| binary MN: first conv           	|     **79.6**     	|
+| binary MN: second conv           	|     **79.6**     	|
+| binary MN: third conv           	|     64.8     	|
+| binary MN: fourth conv           	|     53.6     	|
+
+
+### Heatmap:
+heatmap No binary network, conv layer1:
+![heatmap binary network conv1|150x150](results/Omniglot_results/heatmapbinary_MN_first_conv_conv1.png)
 
 
 # References: 

config.py

@@ -4,10 +4,10 @@ import os
 PATH = os.path.dirname(os.path.realpath(__file__))
 
 # local DATA_PATH
-DATA_PATH = '/home/julien/PycharmProjects/thesis/work/Pytorch/MNIST_Binary_V2/data/'
+# DATA_PATH = '/home/julien/PycharmProjects/thesis/work/Pytorch/MNIST_Binary_V2/data/'
 
 # colab DATA_PATH
-# DATA_PATH = 'data/'
+DATA_PATH = 'data/'
 
 
 EPSILON = 1e-8

results/Omniglot_results/logs/matching_nets/NoBinary/_n=1_k=5_q=15_nv=1_kv=5_qv=1_dist=l2.csv

 epoch,categorical_accuracy,loss,lr,val_1-shot_5-way_acc,val_loss
-1,0.778,0.9533519837545685,0.001,0.8,0.616737681295656
-2,0.8622666666666668,0.4550229909799085,0.001,0.87,0.43833875964832636
-3,0.9007999999999998,0.320360649811288,0.001,0.896,0.3457465486303751
-4,0.9274666666666661,0.25274506511145584,0.001,0.894,0.3231195524051118
-5,0.9230666666666666,0.24372688643086973,0.001,0.91,0.33259988474469887
-6,0.9389333333333331,0.19657207039475327,0.001,0.89,0.2970636524840366
-7,0.9374666666666661,0.18138220079964518,0.001,0.898,0.3222561237411739
-8,0.9471999999999996,0.1596292485957262,0.001,0.926,0.2700234619845729
+1,0.7026666666666667,2.104287913513441,0.001,0.44,1.5924072702723575
+2,0.7920000000000001,1.1609025717093833,0.001,0.62,1.202949864179473
+3,0.7560000000000001,1.0870123655266073,0.001,0.74,0.769391296713978
+4,0.752,0.8491624999140818,0.001,0.82,0.5381571792938985
+5,0.768,0.7014528157320702,0.001,0.76,0.8621403412271347
+6,0.8013333333333333,0.5696974590719571,0.001,0.84,1.0207692265302541
+7,0.8133333333333332,0.5612485399407076,0.001,0.86,0.4157242334214697
+8,0.7973333333333333,0.5550898496917309,0.001,0.76,0.7614003585715885
+9,0.8213333333333332,0.451805199095923,0.001,0.8,0.6756177308612625
+10,0.8573333333333333,0.4525841653977821,0.001,0.84,0.43197904722653646