46 lines
2.1 KiB
Python
46 lines
2.1 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.datasets import make_moons
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import Perceptron
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from sklearn.neural_network import MLPClassifier
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from sklearn.metrics import accuracy_score
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# Генерируем данные
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rs = 42
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X, y = make_moons(n_samples=1000, noise=0.3, random_state=rs)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=rs)
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def train_and_evaluate_model(model, X_train, y_train, X_test, y_test):
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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accuracy = accuracy_score(y_test, y_pred)
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return accuracy
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# Построение моделей
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perceptron_model = Perceptron(random_state=rs)
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perceptron_accuracy = train_and_evaluate_model(perceptron_model, X_train, y_train, X_test, y_test)
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mlp_model_10_neurons = MLPClassifier(hidden_layer_sizes=(10,), alpha=0.01, random_state=rs)
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mlp_10_neurons_accuracy = train_and_evaluate_model(mlp_model_10_neurons, X_train, y_train, X_test, y_test)
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mlp_model_100_neurons = MLPClassifier(hidden_layer_sizes=(100,), alpha=0.01, random_state=rs)
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mlp_100_neurons_accuracy = train_and_evaluate_model(mlp_model_100_neurons, X_train, y_train, X_test, y_test)
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# Построение графиков
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plt.figure(figsize=(12, 4))
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plt.subplot(131)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap='viridis', marker='.')
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plt.title("Персептрон\nТочность: {:.2f}%".format(perceptron_accuracy * 100))
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plt.subplot(132)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap='viridis', marker='.')
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plt.title("Многослойный персептрон\nс 10-ю нейронами в скрытом слое\nТочность: {:.2f}%".format(mlp_10_neurons_accuracy * 100))
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plt.subplot(133)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap='viridis', marker='.')
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plt.title("Многослойный персептрон\nс 100-а нейронами в скрытом слое\nТочность: {:.2f}%".format(mlp_100_neurons_accuracy * 100))
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plt.tight_layout()
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plt.savefig('models.png')
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plt.show()
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