.gitignore

@@ -1,160 +0,0 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
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-
-# Distribution / packaging
-.Python
-build/
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-dist/
-downloads/
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-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
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-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
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-
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-.cache
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-*.py,cover
-.hypothesis/
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-cover/
-
-# Translations
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-*.pot
-
-# Django stuff:
-*.log
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-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
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-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
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-.spyproject
-
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-
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-/site
-
-# mypy
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-
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-
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-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/

.vscode/settings.json

@@ -1,5 +0,0 @@
-{
-    "python.analysis.extraPaths": [
-        "./DecisionTree"
-    ]
-}

DecisionTree/200permutations.txt

@@ -0,0 +1,200 @@
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DecisionTree/Source.gv

@@ -1,197 +0,0 @@
-digraph Tree {
-node [shape=box, style="filled, rounded", color="black", fontname="helvetica"] ;
-edge [fontname="helvetica"] ;
-0 [label="g > d <= 0.5\nentropy = 0.997\nsamples = 200\nvalue = [94, 106]\nclass = 1", fillcolor="#e9f4fc"] ;
-1 [label="waga, <= 0.5\nentropy = 0.803\nsamples = 98\nvalue = [74, 24]\nclass = 0", fillcolor="#edaa79"] ;
-0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
-2 [label="wielkosc <= 1.5\nentropy = 0.998\nsamples = 34\nvalue = [16, 18]\nclass = 1", fillcolor="#e9f4fc"] ;
-1 -> 2 ;
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-2 -> 3 ;
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-3 -> 4 ;
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-4 -> 6 ;
-7 [label="ksztalt <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
-6 -> 7 ;
-8 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
-7 -> 8 ;
-9 [label="gorna <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
-7 -> 9 ;
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-9 -> 10 ;
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-9 -> 11 ;
-12 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
-6 -> 12 ;
-13 [label="kruchosc <= 0.5\nentropy = 1.0\nsamples = 12\nvalue = [6, 6]\nclass = 0", fillcolor="#ffffff"] ;
-3 -> 13 ;
-14 [label="entropy = 0.0\nsamples = 5\nvalue = [5, 0]\nclass = 0", fillcolor="#e58139"] ;
-13 -> 14 ;
-15 [label="ksztalt <= 0.5\nentropy = 0.592\nsamples = 7\nvalue = [1, 6]\nclass = 1", fillcolor="#5aade9"] ;
-13 -> 15 ;
-16 [label="entropy = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = 1", fillcolor="#399de5"] ;
-15 -> 16 ;
-17 [label="gorna <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
-15 -> 17 ;
-18 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
-17 -> 18 ;
-19 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
-17 -> 19 ;
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-20 -> 21 ;
-22 [label="kruchosc <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
-21 -> 22 ;
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-20 -> 26 ;
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-1 -> 27 ;
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-27 -> 28 ;
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-27 -> 29 ;
-30 [label="ksztalt <= 0.5\nentropy = 0.918\nsamples = 18\nvalue = [12, 6]\nclass = 0", fillcolor="#f2c09c"] ;
-29 -> 30 ;
-31 [label="kruchosc <= 0.5\nentropy = 1.0\nsamples = 10\nvalue = [5, 5]\nclass = 0", fillcolor="#ffffff"] ;
-30 -> 31 ;
-32 [label="dolna <= 0.5\nentropy = 0.722\nsamples = 5\nvalue = [4, 1]\nclass = 0", fillcolor="#eca06a"] ;
-31 -> 32 ;
-33 [label="priorytet <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
-32 -> 33 ;
-34 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
-33 -> 34 ;
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-37 [label="dolna <= 0.5\nentropy = 0.722\nsamples = 5\nvalue = [1, 4]\nclass = 1", fillcolor="#6ab6ec"] ;
-31 -> 37 ;
-38 [label="entropy = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = 1", fillcolor="#399de5"] ;
-37 -> 38 ;
-39 [label="waga, <= 1.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
-37 -> 39 ;
-40 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
-39 -> 40 ;
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-39 -> 41 ;
-42 [label="waga, <= 1.5\nentropy = 0.544\nsamples = 8\nvalue = [7, 1]\nclass = 0", fillcolor="#e99355"] ;
-30 -> 42 ;
-43 [label="entropy = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = 0", fillcolor="#e58139"] ;
-42 -> 43 ;
-44 [label="wielkosc <= 0.5\nentropy = 0.811\nsamples = 4\nvalue = [3, 1]\nclass = 0", fillcolor="#eeab7b"] ;
-42 -> 44 ;
-45 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
-44 -> 45 ;
-46 [label="kruchosc <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [2, 1]\nclass = 0", fillcolor="#f2c09c"] ;
-44 -> 46 ;
-47 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
-46 -> 47 ;
-48 [label="priorytet <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
-46 -> 48 ;
-49 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
-48 -> 49 ;
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-48 -> 50 ;
-51 [label="entropy = 0.0\nsamples = 13\nvalue = [13, 0]\nclass = 0", fillcolor="#e58139"] ;
-29 -> 51 ;
-52 [label="wielkosc <= 1.5\nentropy = 0.714\nsamples = 102\nvalue = [20, 82]\nclass = 1", fillcolor="#69b5eb"] ;
-0 -> 52 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
-53 [label="waga, <= 0.5\nentropy = 0.469\nsamples = 70\nvalue = [7, 63]\nclass = 1", fillcolor="#4fa8e8"] ;
-52 -> 53 ;
-54 [label="entropy = 0.0\nsamples = 21\nvalue = [0, 21]\nclass = 1", fillcolor="#399de5"] ;
-53 -> 54 ;
-55 [label="ksztalt <= 0.5\nentropy = 0.592\nsamples = 49\nvalue = [7, 42]\nclass = 1", fillcolor="#5aade9"] ;
-53 -> 55 ;
-56 [label="wielkosc <= 0.5\nentropy = 0.25\nsamples = 24\nvalue = [1, 23]\nclass = 1", fillcolor="#42a1e6"] ;
-55 -> 56 ;
-57 [label="entropy = 0.0\nsamples = 15\nvalue = [0, 15]\nclass = 1", fillcolor="#399de5"] ;
-56 -> 57 ;
-58 [label="kruchosc <= 0.5\nentropy = 0.503\nsamples = 9\nvalue = [1, 8]\nclass = 1", fillcolor="#52a9e8"] ;
-56 -> 58 ;
-59 [label="dolna <= 0.5\nentropy = 0.722\nsamples = 5\nvalue = [1, 4]\nclass = 1", fillcolor="#6ab6ec"] ;
-58 -> 59 ;
-60 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
-59 -> 60 ;
-61 [label="gorna <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
-59 -> 61 ;
-62 [label="priorytet <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
-61 -> 62 ;
-63 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
-62 -> 63 ;
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-62 -> 64 ;
-65 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
-61 -> 65 ;
-66 [label="entropy = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = 1", fillcolor="#399de5"] ;
-58 -> 66 ;
-67 [label="kruchosc <= 0.5\nentropy = 0.795\nsamples = 25\nvalue = [6, 19]\nclass = 1", fillcolor="#78bced"] ;
-55 -> 67 ;
-68 [label="priorytet <= 0.5\nentropy = 0.98\nsamples = 12\nvalue = [5, 7]\nclass = 1", fillcolor="#c6e3f8"] ;
-67 -> 68 ;
-69 [label="dolna <= 0.5\nentropy = 0.764\nsamples = 9\nvalue = [2, 7]\nclass = 1", fillcolor="#72b9ec"] ;
-68 -> 69 ;
-70 [label="entropy = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = 1", fillcolor="#399de5"] ;
-69 -> 70 ;
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-71 -> 72 ;
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-71 -> 73 ;
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-68 -> 74 ;
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-75 -> 76 ;
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-75 -> 77 ;
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-77 -> 78 ;
-79 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
-78 -> 79 ;
-80 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
-78 -> 80 ;
-81 [label="entropy = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = 1", fillcolor="#399de5"] ;
-77 -> 81 ;
-82 [label="gorna <= 0.5\nentropy = 0.974\nsamples = 32\nvalue = [13, 19]\nclass = 1", fillcolor="#c0e0f7"] ;
-52 -> 82 ;
-83 [label="kruchosc <= 0.5\nentropy = 0.65\nsamples = 12\nvalue = [10, 2]\nclass = 0", fillcolor="#ea9a61"] ;
-82 -> 83 ;
-84 [label="entropy = 0.0\nsamples = 7\nvalue = [7, 0]\nclass = 0", fillcolor="#e58139"] ;
-83 -> 84 ;
-85 [label="waga, <= 1.5\nentropy = 0.971\nsamples = 5\nvalue = [3, 2]\nclass = 0", fillcolor="#f6d5bd"] ;
-83 -> 85 ;
-86 [label="priorytet <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
-85 -> 86 ;
-87 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
-86 -> 87 ;
-88 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
-86 -> 88 ;
-89 [label="entropy = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = 0", fillcolor="#e58139"] ;
-85 -> 89 ;
-90 [label="dolna <= 0.5\nentropy = 0.61\nsamples = 20\nvalue = [3, 17]\nclass = 1", fillcolor="#5caeea"] ;
-82 -> 90 ;
-91 [label="entropy = 0.0\nsamples = 11\nvalue = [0, 11]\nclass = 1", fillcolor="#399de5"] ;
-90 -> 91 ;
-92 [label="kruchosc <= 0.5\nentropy = 0.918\nsamples = 9\nvalue = [3, 6]\nclass = 1", fillcolor="#9ccef2"] ;
-90 -> 92 ;
-93 [label="waga, <= 0.5\nentropy = 0.811\nsamples = 4\nvalue = [3, 1]\nclass = 0", fillcolor="#eeab7b"] ;
-92 -> 93 ;
-94 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
-93 -> 94 ;
-95 [label="entropy = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = 0", fillcolor="#e58139"] ;
-93 -> 95 ;
-96 [label="entropy = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = 1", fillcolor="#399de5"] ;
-92 -> 96 ;
-}

DecisionTree/dane.csv

@@ -1,201 +0,0 @@
-wielkosc,"waga,",priorytet,ksztalt,kruchosc,dolna,gorna,g > d,polka
-1,0,0,1,0,0,1,0,1
-0,0,1,0,1,1,0,1,1
-2,0,1,1,0,0,0,1,0
-2,2,1,0,1,1,1,0,0
-1,0,0,1,0,0,0,1,1
-2,1,0,0,1,1,0,0,0
-1,0,0,0,1,0,0,1,1
-1,1,0,1,0,0,0,1,1
-0,0,1,0,1,1,1,0,1
-0,2,0,0,0,1,1,0,0
-0,0,1,0,0,1,0,1,1
-0,0,0,0,0,1,1,0,1
-0,2,1,0,1,1,0,0,0
-2,0,0,0,1,0,0,0,1
-2,1,0,1,0,1,1,1,0
-0,1,1,0,1,1,1,0,0
-0,2,0,1,1,1,0,1,1
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DecisionTree/training_data.txt

@@ -1,200 +0,0 @@
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NeuralNetwork/learning_results.txt

@@ -1,31 +0,0 @@
-Epoch:  1 Train Loss: 65 Train Accuracy: 0.5754245754245755
-Epoch:  2 Train Loss: 25 Train Accuracy: 0.7457542457542458
-Epoch:  3 Train Loss: 8 Train Accuracy: 0.8431568431568431
-Epoch:  4 Train Loss: 2 Train Accuracy: 0.9010989010989011
-Epoch:  5 Train Loss: 1 Train Accuracy: 0.9335664335664335
-Epoch:  6 Train Loss: 0 Train Accuracy: 0.9545454545454546
-Epoch:  7 Train Loss: 0 Train Accuracy: 0.972027972027972
-Epoch:  8 Train Loss: 0 Train Accuracy: 0.9820179820179821
-Epoch:  9 Train Loss: 0 Train Accuracy: 0.994005994005994
-Epoch:  10 Train Loss: 0 Train Accuracy: 0.9945054945054945
-
-Epoch:  1 Train Loss: 42 Train Accuracy: 0.6428571428571429
-Epoch:  2 Train Loss: 11 Train Accuracy: 0.8306693306693307
-Epoch:  3 Train Loss: 3 Train Accuracy: 0.8921078921078921
-Epoch:  4 Train Loss: 2 Train Accuracy: 0.8891108891108891
-Epoch:  5 Train Loss: 1 Train Accuracy: 0.9335664335664335
-Epoch:  6 Train Loss: 0 Train Accuracy: 0.952047952047952
-Epoch:  7 Train Loss: 0 Train Accuracy: 0.9545454545454546
-Epoch:  8 Train Loss: 0 Train Accuracy: 0.9655344655344655
-Epoch:  9 Train Loss: 0 Train Accuracy: 0.9815184815184815
-Epoch:  10 Train Loss: 0 Train Accuracy: 0.9805194805194806
-Epoch:  11 Train Loss: 0 Train Accuracy: 0.9855144855144855
-Epoch:  12 Train Loss: 0 Train Accuracy: 0.989010989010989
-Epoch:  13 Train Loss: 0 Train Accuracy: 0.9925074925074925
-Epoch:  14 Train Loss: 0 Train Accuracy: 0.9915084915084915
-Epoch:  15 Train Loss: 0 Train Accuracy: 0.9885114885114885
-Epoch:  16 Train Loss: 0 Train Accuracy: 0.994005994005994
-Epoch:  17 Train Loss: 0 Train Accuracy: 0.997002997002997
-Epoch:  18 Train Loss: 0 Train Accuracy: 0.9965034965034965
-Epoch:  19 Train Loss: 0 Train Accuracy: 0.999000999000999
-Epoch:  20 Train Loss: 0 Train Accuracy: 1.0

NeuralNetwork/neural_network_learning.py

@@ -1,60 +0,0 @@
-import glob
-from src.torchvision_resize_dataset import combined_dataset, images_path, classes
-import src.data_model
-from torch.optim import Adam
-import torch 
-import torch.nn as nn 
-from torch.utils.data import DataLoader 
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-train_loader = DataLoader(
-    combined_dataset,   #dataset of images
-    batch_size=256,     # accuracy
-    shuffle=True    # rand order
-)
-
-model = src.data_model.DataModel(num_objects=2).to(device)
-
-#optimizer 
-optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)
-#loss function
-criterion = nn.CrossEntropyLoss()
-
-num_epochs = 20
-# train_size = len(glob.glob(images_path+'*.jpg'))
-train_size = 2002
-
-go_to_accuracy = 0.0
-for epoch in range(num_epochs):
-    #training on dataset
-    model.train()
-    train_accuracy = 0.0
-    train_loss = 0.0
-    for i, (images, labels) in enumerate(train_loader):
-        if torch.cuda.is_available():
-            images = torch.Variable(images.cuda())
-            labels = torch.Variable(labels.cuda())
-        # clearing the optimizer gradients
-        optimizer.zero_grad()
-
-        outputs = model(images) # predoction
-        loss = criterion(outputs, labels)   #loss calculation
-        loss.backward()
-        optimizer.step()
-
-        train_loss += loss.cpu().data*images.size(0)
-        _, prediction = torch.max(outputs.data, 1)
-
-        train_accuracy += int(torch.sum(prediction == labels.data))
-
-    train_accuracy = train_accuracy/train_size
-    train_loss = train_loss/train_size
-
-    model.eval()
-
-    print('Epoch:  '+ str(epoch+1) +' Train Loss: '+ str(int(train_loss)) +' Train Accuracy: '+ str(train_accuracy))
-
-    if train_accuracy > go_to_accuracy:
-        go_to_accuracy= train_accuracy
-        torch.save(model.state_dict(), "best_model.pth") 

NeuralNetwork/prediction.py

@@ -1,147 +0,0 @@
-import torch
-import torch.nn as nn
-from torchvision.transforms import transforms
-import numpy as np
-from torch.autograd import Variable
-from torchvision.models import squeezenet1_1
-import torch.functional as F
-from io import open
-import os
-from PIL import Image
-import pathlib
-import glob
-from tkinter import Tk, Label
-from PIL import Image, ImageTk
-
-absolute_path = os.path.abspath('NeuralNetwork/src/train_images')
-train_path = absolute_path
-absolute_path = os.path.abspath('Images/Items_test')
-pred_path = absolute_path
-
-root=pathlib.Path(train_path)
-classes=sorted([j.name.split('/')[-1] for j in root.iterdir()])
-
-
-class DataModel(nn.Module):
-    def __init__(self, num_classes):
-        super(DataModel, self).__init__()
-        #input (batch=256, nr of channels rgb=3 , size=244x244)
-
-        # convolution
-        self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
-        #shape (256, 12, 224x224)
-
-        # batch normalization
-        self.bn1 = nn.BatchNorm2d(num_features=12)
-        #shape (256, 12, 224x224)
-        self.reul1 = nn.ReLU()
-
-        self.pool=nn.MaxPool2d(kernel_size=2, stride=2)
-        # reduce image size by factor 2
-        # pooling window moves by 2 pixels at a time instead of 1
-        # shape (256, 12, 112x112)
-
-
-
-        self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1)
-        self.bn2 = nn.BatchNorm2d(num_features=24)
-        self.reul2 = nn.ReLU()
-        # shape (256, 24, 112x112)
-
-        self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=1)
-        #shape (256, 48, 112x112)
-        self.bn3 = nn.BatchNorm2d(num_features=48)
-        #shape (256, 48, 112x112)
-        self.reul3 = nn.ReLU()
-
-        # connected layer
-        self.fc = nn.Linear(in_features=48*112*112, out_features=num_classes)
-
-    def forward(self, input):
-            output = self.conv1(input)
-            output = self.bn1(output)
-            output = self.reul1(output)
-
-            output = self.pool(output)
-            output = self.conv2(output)
-            output  = self.bn2(output)
-            output = self.reul2(output)
-            
-            output = self.conv3(output)
-            output = self.bn3(output)
-            output = self.reul3(output)
-
-            # output shape matrix (256, 48, 112x112)
-            #print(output.shape)
-            #print(self.fc.weight.shape)
-
-            output = output.view(-1, 48*112*112)
-            output = self.fc(output)
-            
-            return output
-
-script_dir = os.path.dirname(os.path.abspath(__file__))
-file_path = os.path.join(script_dir, 'best_model.pth')
-checkpoint=torch.load(file_path)
-model = DataModel(num_classes=2)
-model.load_state_dict(checkpoint)
-model.eval()
-
-transformer = transforms.Compose([
-    transforms.Resize((224, 224)),  # Resize images to (224, 224)
-    transforms.ToTensor(),  # Convert images to tensors, 0-255 to 0-1
-    # transforms.RandomHorizontalFlip(),  # 0.5 chance to flip the image
-    transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5]) 
-])
-
-def prediction(img_path,transformer):
-    
-    image=Image.open(img_path)
-    
-    image_tensor=transformer(image).float()
-    
-    image_tensor=image_tensor.unsqueeze_(0)
-    
-    if torch.cuda.is_available():
-        image_tensor.cuda()
-        
-    input=Variable(image_tensor)
-    
-    output=model(input)
-    
-    index=output.data.numpy().argmax()
-    
-    pred=classes[index]
-    
-    return pred
-
-def prediction_keys():
-
-    #funkcja zwracajaca sciezki do kazdego pliku w folderze w postaci listy
-
-    images_path=glob.glob(pred_path+'/*.jpg')
-
-    pred_list=[]
-
-    for i in images_path:
-        pred_list.append(i)
-
-    return pred_list
-
-def predict_one(path):
-
-    #wyswietlanie obrazka po kazdym podniesieniu itemu
-    root = Tk()
-    root.title("Okno z obrazkiem")
-
-    image = Image.open(path)
-    photo = ImageTk.PhotoImage(image)
-    label = Label(root, image=photo)
-    label.pack()
-
-    root.mainloop()
-
-    #uruchamia sie funkcja spr czy obrazek to paczka czy list
-    pred_print = prediction(path,transformer)
-    print('Zdjecie jest: '+pred_print)
-    return pred_print

NeuralNetwork/src/data_model.py

@@ -1,61 +0,0 @@
-import torch.nn as nn 
-import torch
-
-
-class DataModel(nn.Module):
-    def __init__(self, num_objects):
-        super(DataModel, self).__init__()
-        #input (batch=256, nr of channels rgb=3 , size=244x244)
-
-        # convolution
-        self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
-        #shape (256, 12, 224x224)
-
-        # batch normalization
-        self.bn1 = nn.BatchNorm2d(num_features=12)
-        #shape (256, 12, 224x224)
-        self.reul1 = nn.ReLU()
-
-        self.pool=nn.MaxPool2d(kernel_size=2, stride=2)
-        # reduce image size by factor 2
-        # pooling window moves by 2 pixels at a time instead of 1
-        # shape (256, 12, 112x112)
-
-
-
-        self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1)
-        self.bn2 = nn.BatchNorm2d(num_features=24)
-        self.reul2 = nn.ReLU()
-        # shape (256, 24, 112x112)
-
-        self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=1)
-        #shape (256, 48, 112x112)
-        self.bn3 = nn.BatchNorm2d(num_features=48)
-        #shape (256, 48, 112x112)
-        self.reul3 = nn.ReLU()
-
-        # connected layer
-        self.fc = nn.Linear(in_features=48*112*112, out_features=num_objects)
-
-    def forward(self, input):
-            output = self.conv1(input)
-            output = self.bn1(output)
-            output = self.reul1(output)
-
-            output = self.pool(output)
-            output = self.conv2(output)
-            output  = self.bn2(output)
-            output = self.reul2(output)
-            
-            output = self.conv3(output)
-            output = self.bn3(output)
-            output = self.reul3(output)
-
-            # output shape matrix (256, 48, 112x112)
-            #print(output.shape)
-            #print(self.fc.weight.shape)
-
-            output = output.view(-1, 48*112*112)
-            output = self.fc(output)
-            
-            return output

NeuralNetwork/src/torchvision_resize_dataset.py

@@ -1,31 +0,0 @@
-import glob
-import pathlib
-import torchvision.transforms as transforms
-from torchvision.datasets import ImageFolder
-from torch.utils.data import ConcatDataset
-
-# images have to be the same size for the algorithm to work
-transform = transforms.Compose([
-    transforms.Resize((224, 224)),  # Resize images to (224, 224)
-    transforms.ToTensor(),  # Convert images to tensors, 0-255 to 0-1
-    # transforms.RandomHorizontalFlip(),  # 0.5 chance to flip the image
-    transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5]) 
-])
-
-letters_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images/letters'
-package_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images/package'
-images_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images'
-
-# # Load images from folders
-# letter_folder = ImageFolder(letters_path, transform=transform)
-# package_folder = ImageFolder(package_path, transform=transform)
-
-# Combine the both datasets into a single dataset
-#combined_dataset = ConcatDataset([letter_folder, package_folder])
-combined_dataset = ImageFolder(images_path, transform=transform)
-
-#image classes
-path=pathlib.Path(images_path)
-classes = sorted([i.name.split("/")[-1] for i in path.iterdir()])
-
-# print(classes)