Add requirements file
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.vscode/settings.json
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.vscode/settings.json
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{
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"python.pythonPath": "/usr/bin/python3.6"
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}
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SZI1/__pycache__/__init__.cpython-36.pyc
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SZI1/__pycache__/__init__.cpython-36.pyc
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SZI1/__pycache__/settings.cpython-36.pyc
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SZI1/__pycache__/settings.cpython-36.pyc
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SZI1/__pycache__/urls.cpython-36.pyc
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SZI1/__pycache__/urls.cpython-36.pyc
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SZI1/__pycache__/wsgi.cpython-36.pyc
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SZI1/__pycache__/wsgi.cpython-36.pyc
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iris_model.h5
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iris_model.h5
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magazine/__pycache__/__init__.cpython-36.pyc
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magazine/__pycache__/__init__.cpython-36.pyc
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magazine/__pycache__/admin.cpython-36.pyc
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magazine/__pycache__/admin.cpython-36.pyc
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magazine/__pycache__/urls.cpython-36.pyc
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magazine/__pycache__/urls.cpython-36.pyc
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magazine/__pycache__/views.cpython-36.pyc
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magazine/__pycache__/views.cpython-36.pyc
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magazine/migrations/__pycache__/__init__.cpython-36.pyc
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magazine/migrations/__pycache__/__init__.cpython-36.pyc
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requirements.txt
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requirements.txt
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tensorflow
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django
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django-cors-headers
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@ -17,12 +17,12 @@ train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.2)
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# Building the model
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model = tf.keras.Sequential()
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model.add(layers.Dense(8, activation='relu', input_dim=4))
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model.add(layers.Dense(20, activation='relu', input_dim=4))
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model.add(layers.Dense(3, activation='sigmoid'))
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model.compile(optimizer='adam', loss='categorical_crossentropy',
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metrics=['accuracy'])
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# Training the model
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model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=2000)
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model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=1000)
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model.save('iris_model.h5')
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