dataset for project presentation (#35) from wozek_works_in_a_loop into master

Reviewed-on: #35

.gitignore

@@ -0,0 +1,160 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.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
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# 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/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# 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

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

DecisionTree/200permutations.txt

@@ -1,200 +0,0 @@
-10010010
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-12100101

DecisionTree/Source.gv

@@ -0,0 +1,197 @@
+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 ;
+3 [label="priorytet <= 0.5\nentropy = 0.887\nsamples = 23\nvalue = [7, 16]\nclass = 1", fillcolor="#90c8f0"] ;
+2 -> 3 ;
+4 [label="kruchosc <= 0.5\nentropy = 0.439\nsamples = 11\nvalue = [1, 10]\nclass = 1", fillcolor="#4da7e8"] ;
+3 -> 4 ;
+5 [label="entropy = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = 1", fillcolor="#399de5"] ;
+4 -> 5 ;
+6 [label="wielkosc <= 0.5\nentropy = 0.811\nsamples = 4\nvalue = [1, 3]\nclass = 1", fillcolor="#7bbeee"] ;
+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 ;
+10 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+9 -> 10 ;
+11 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+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 ;
+20 [label="ksztalt <= 0.5\nentropy = 0.684\nsamples = 11\nvalue = [9, 2]\nclass = 0", fillcolor="#eb9d65"] ;
+2 -> 20 ;
+21 [label="dolna <= 0.5\nentropy = 1.0\nsamples = 4\nvalue = [2, 2]\nclass = 0", fillcolor="#ffffff"] ;
+20 -> 21 ;
+22 [label="kruchosc <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
+21 -> 22 ;
+23 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+22 -> 23 ;
+24 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
+22 -> 24 ;
+25 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+21 -> 25 ;
+26 [label="entropy = 0.0\nsamples = 7\nvalue = [7, 0]\nclass = 0", fillcolor="#e58139"] ;
+20 -> 26 ;
+27 [label="gorna <= 0.5\nentropy = 0.449\nsamples = 64\nvalue = [58, 6]\nclass = 0", fillcolor="#e88e4d"] ;
+1 -> 27 ;
+28 [label="entropy = 0.0\nsamples = 33\nvalue = [33, 0]\nclass = 0", fillcolor="#e58139"] ;
+27 -> 28 ;
+29 [label="wielkosc <= 1.5\nentropy = 0.709\nsamples = 31\nvalue = [25, 6]\nclass = 0", fillcolor="#eb9f69"] ;
+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"] ;
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+35 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+33 -> 35 ;
+36 [label="entropy = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = 0", fillcolor="#e58139"] ;
+32 -> 36 ;
+37 [label="dolna <= 0.5\nentropy = 0.722\nsamples = 5\nvalue = [1, 4]\nclass = 1", fillcolor="#6ab6ec"] ;
+31 -> 37 ;
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+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 ;
+41 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+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 ;
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+44 -> 45 ;
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+44 -> 46 ;
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+46 -> 47 ;
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+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"] ;
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+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 ;
+64 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+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 ;
+71 [label="gorna <= 0.5\nentropy = 1.0\nsamples = 4\nvalue = [2, 2]\nclass = 0", fillcolor="#ffffff"] ;
+69 -> 71 ;
+72 [label="entropy = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = 0", fillcolor="#e58139"] ;
+71 -> 72 ;
+73 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
+71 -> 73 ;
+74 [label="entropy = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = 0", fillcolor="#e58139"] ;
+68 -> 74 ;
+75 [label="dolna <= 0.5\nentropy = 0.391\nsamples = 13\nvalue = [1, 12]\nclass = 1", fillcolor="#49a5e7"] ;
+67 -> 75 ;
+76 [label="entropy = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = 1", fillcolor="#399de5"] ;
+75 -> 76 ;
+77 [label="gorna <= 0.5\nentropy = 0.65\nsamples = 6\nvalue = [1, 5]\nclass = 1", fillcolor="#61b1ea"] ;
+75 -> 77 ;
+78 [label="priorytet <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
+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

@@ -0,0 +1,201 @@
+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
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NeuralNetwork/learning_results.txt

@@ -0,0 +1,31 @@
+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

@@ -0,0 +1,60 @@
+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

@@ -0,0 +1,147 @@
+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

@@ -0,0 +1,61 @@
+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

@@ -0,0 +1,31 @@
+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)