├── .gitignore
├── RECOGNITION_CATS_and_DOGS_1.ipynb
├── archive1
    ├── main
    └── main.zip
└── ensembledmodel.py


/.gitignore:
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  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | 
  9 | # Distribution / packaging
 10 | .Python
 11 | build/
 12 | develop-eggs/
 13 | dist/
 14 | downloads/
 15 | eggs/
 16 | .eggs/
 17 | lib/
 18 | lib64/
 19 | parts/
 20 | sdist/
 21 | var/
 22 | wheels/
 23 | pip-wheel-metadata/
 24 | share/python-wheels/
 25 | *.egg-info/
 26 | .installed.cfg
 27 | *.egg
 28 | MANIFEST
 29 | 
 30 | # PyInstaller
 31 | #  Usually these files are written by a python script from a template
 32 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 33 | *.manifest
 34 | *.spec
 35 | 
 36 | # Installer logs
 37 | pip-log.txt
 38 | pip-delete-this-directory.txt
 39 | 
 40 | # Unit test / coverage reports
 41 | htmlcov/
 42 | .tox/
 43 | .nox/
 44 | .coverage
 45 | .coverage.*
 46 | .cache
 47 | nosetests.xml
 48 | coverage.xml
 49 | *.cover
 50 | *.py,cover
 51 | .hypothesis/
 52 | .pytest_cache/
 53 | 
 54 | # Translations
 55 | *.mo
 56 | *.pot
 57 | 
 58 | # Django stuff:
 59 | *.log
 60 | local_settings.py
 61 | db.sqlite3
 62 | db.sqlite3-journal
 63 | 
 64 | # Flask stuff:
 65 | instance/
 66 | .webassets-cache
 67 | 
 68 | # Scrapy stuff:
 69 | .scrapy
 70 | 
 71 | # Sphinx documentation
 72 | docs/_build/
 73 | 
 74 | # PyBuilder
 75 | target/
 76 | 
 77 | # Jupyter Notebook
 78 | .ipynb_checkpoints
 79 | 
 80 | # IPython
 81 | profile_default/
 82 | ipython_config.py
 83 | 
 84 | # pyenv
 85 | .python-version
 86 | 
 87 | # pipenv
 88 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 89 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 90 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 91 | #   install all needed dependencies.
 92 | #Pipfile.lock
 93 | 
 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
 95 | __pypackages__/
 96 | 
 97 | # Celery stuff
 98 | celerybeat-schedule
 99 | celerybeat.pid
100 | 
101 | # SageMath parsed files
102 | *.sage.py
103 | 
104 | # Environments
105 | .env
106 | .venv
107 | env/
108 | venv/
109 | ENV/
110 | env.bak/
111 | venv.bak/
112 | 
113 | # Spyder project settings
114 | .spyderproject
115 | .spyproject
116 | 
117 | # Rope project settings
118 | .ropeproject
119 | 
120 | # mkdocs documentation
121 | /site
122 | 
123 | # mypy
124 | .mypy_cache/
125 | .dmypy.json
126 | dmypy.json
127 | 
128 | # Pyre type checker
129 | .pyre/
130 | 


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/archive1/main:
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1 | 
2 | 


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/archive1/main.zip:
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https://raw.githubusercontent.com/diglukhikh/neural-network-for-classifier/405c48b37c9a0cd28f14a7943a98db1403bab40f/archive1/main.zip


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/ensembledmodel.py:
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 1 | #скачать обучающий и валидационный файлы по ссылкам
 2 | #https://drive.google.com/file/d/10_ScZ-zbKTTDlOVSNCVKembLZX-CKnDp/view?usp=sharing
 3 | #https://drive.google.com/file/d/1F4lqUJ5UKQKMJhUbx0ICKwrEljjt975_/view?usp=sharing
 4 | 
 5 | from keras.models import Sequential,  Model
 6 | from keras.layers import Dense, Dropout, Input, concatenate
 7 | 
 8 | import numpy
 9 | 
10 | 
11 | train = numpy.loadtxt("newtrain.csv", delimiter=",")
12 | s = train.shape[1] - 1
13 | print(s)
14 | cl = int(numpy.amax(train[:, s]) + 1)
15 | print(f'Количество классов: {cl}')
16 | 
17 | X1 = train[:, 0:s]
18 | 
19 | Y = train[:, s]
20 | print(Y)
21 | 
22 | model_in1 = Input(shape=(s,))
23 | model_out11 = Dense(s*30, input_dim=s, activation="relu", name="layer1")(model_in1)
24 | model_out111 = Dense(s*30, activation="swish", name="layer111")(model_out11)
25 | model_out1 = Dense(s*15, activation="relu", name="layer11")(model_out111)
26 | model1 = Model(model_in1,model_out1)
27 | 
28 | model_in2 = Input(shape=(s,))
29 | model_out22 = Dense(s*15, input_dim=s, activation="relu", name="layer2")(model_in2)
30 | model_out2 = Dense(s*8, activation="swish", name="layer22")(model_out22)
31 | model2 = Model(model_in2,model_out2)
32 | 
33 | model_in3 = Input(shape=(s,))
34 | model_out3 = Dense(s*8, input_dim=s, activation="swish", name="layer3")(model_in3)
35 | model3 = Model(model_in3,model_out3)
36 | 
37 | model_in4 = Input(shape=(s,))
38 | model_out4 = Dense(s*4, input_dim=s, activation="swish", name="layer4")(model_in4)
39 | model4 = Model(model_in4,model_out4)
40 | 
41 | concatenated = concatenate([model_out1,model_out2,model_out3,model_out4])
42 | out = Dense(cl, activation="softmax", name="outputlayer")(concatenated)
43 | 
44 | merged_model = Model([model_in1,model_in2, model_in3,model_in4], out)
45 | merged_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=["accuracy"])
46 | merged_model.fit([X1,X1,X1,X1], Y, batch_size=5, epochs=10, verbose=1, validation_split=0.15)
47 | merged_model.save(f"adress")
48 | 


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