61 lines
2.2 KiB
Python
61 lines
2.2 KiB
Python
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import numpy as np
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from flask import Flask, request, render_template
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from sklearn.datasets import make_circles
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from sklearn.neural_network import MLPClassifier
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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import matplotlib.pyplot as plt
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from mlxtend.plotting import plot_decision_regions
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app = Flask(__name__)
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@app.route('/')
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def home():
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return render_template('index.html')
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@app.route('/compare_models', methods=['POST'])
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def compare_models():
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# Генерация данных
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rs = 0
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X, y = make_circles(noise=0.2, factor=0.5, random_state=rs)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=rs)
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# Персептрон
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perceptron = MLPClassifier(hidden_layer_sizes=(1,), max_iter=1000, alpha=0.01)
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perceptron.fit(X_train, y_train)
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perceptron_score = accuracy_score(y_test, perceptron.predict(X_test))
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# Многослойный персептрон с 10-ю нейронами в скрытом слое
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mlp_10 = MLPClassifier(hidden_layer_sizes=(10,), max_iter=1000, alpha=0.01)
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mlp_10.fit(X_train, y_train)
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mlp_10_score = accuracy_score(y_test, mlp_10.predict(X_test))
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# Многослойный персептрон со 100-ю нейронами в скрытом слое
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mlp_100 = MLPClassifier(hidden_layer_sizes=(100,), max_iter=1000, alpha=0.01)
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mlp_100.fit(X_train, y_train)
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mlp_100_score = accuracy_score(y_test, mlp_100.predict(X_test))
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# Создание графиков
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plt.figure(figsize=(12, 4))
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plt.subplot(131)
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plot_decision_regions(X_test, y_test, clf=perceptron, legend=2)
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plt.title('Персептрон\n(Accuracy: {:.2f})'.format(perceptron_score))
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plt.subplot(132)
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plot_decision_regions(X_test, y_test, clf=mlp_10, legend=2)
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plt.title('MLP (10 нейронов)\n(Accuracy: {:.2f})'.format(mlp_10_score))
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plt.subplot(133)
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plot_decision_regions(X_test, y_test, clf=mlp_100, legend=2)
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plt.title('MLP (100 нейронов)\n(Accuracy: {:.2f})'.format(mlp_100_score))
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plt.tight_layout()
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plt.savefig('static/models_comparison.png')
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return render_template('index.html', result=True)
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if __name__ == '__main__':
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app.run(debug=True)
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