implemented data loading and normalization

TicTacToe_AI/Net/pytorch_ai.py

@@ -0,0 +1,42 @@
+import random
+import torch
+from tqdm import tqdm
+
+
+def to_set(raw_list):
+    out_set = []
+    for line in tqdm(raw_list):
+        line = line.replace('\n', '')
+        raw_board, raw_label = line.split('|')[0], line.split('|')[1]
+
+        # convert string label to tensor
+        label = torch.zeros([1, 9])
+        if not (int(raw_label) is -1):
+            label[0][int(raw_label)] = 1
+
+        # convert board to tensor
+        raw_board = raw_board.split(',')
+        board = torch.zeros([1, 9])
+        for n, block in enumerate(raw_board):
+            if int(block) is -1:
+                board[0][n] = 0
+            elif int(block) is 0:
+                board[0][n] = 0.5
+            elif int(block) is 1:
+                board[0][n] = 1
+
+        out_set.append((board, label))
+
+    return out_set
+
+
+with open('boards.bds', 'r') as infile:
+    print('Loading file...')
+    alllines = infile.readlines()
+    random.shuffle(alllines)
+
+    print('Generating testset...')
+    testset = to_set(alllines[0:50000])
+
+    print('Generating trainset...')
+    trainset = to_set(alllines[50001:])

doc/Grundlagen_des_maschinellen_lernens.log

@@ -1,4 +1,4 @@
-This is pdfTeX, Version 3.14159265-2.6-1.40.18 (TeX Live 2017/Debian) (preloaded format=pdflatex 2019.9.28)  17 NOV 2019 18:39
+This is pdfTeX, Version 3.14159265-2.6-1.40.18 (TeX Live 2017/Debian) (preloaded format=pdflatex 2019.9.28)  7 DEC 2019 21:41
 entering extended mode
  restricted \write18 enabled.
  %&-line parsing enabled.
@@ -1418,7 +1418,7 @@ xmf/fonts/type1/public/cm-super/sfrm0600.pfb></usr/share/texmf/fonts/type1/publ
 ic/cm-super/sfrm0700.pfb></usr/share/texmf/fonts/type1/public/cm-super/sfrm0800
 .pfb></usr/share/texmf/fonts/type1/public/cm-super/sfrm1000.pfb></usr/share/tex
 mf/fonts/type1/public/cm-super/sfsl1000.pfb>
-Output written on Grundlagen_des_maschinellen_lernens.pdf (8 pages, 445894 byte
+Output written on Grundlagen_des_maschinellen_lernens.pdf (8 pages, 445892 byte
 s).
 PDF statistics:
  276 PDF objects out of 1000 (max. 8388607)

mnist_classifier.py

@@ -17,6 +17,7 @@ test = datasets.MNIST('./datasets', train=False, download=True,
 trainset = torch.utils.data.DataLoader(train, batch_size=10, shuffle=True)
 testset = torch.utils.data.DataLoader(test, batch_size=10, shuffle=False)
 
+
 class Net(nn.Module):
     def __init__(self):
         super().__init__()