跟着Leo机器学习实战：sklearn之Neural network models

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1.17. Neural network models

sklearn框架

函数导图

1.17.1. Multi-layer Perceptron

1.17.2. Classification

from sklearn.neural_network import MLPClassifier
X = [[0., 0.], [1., 1.]]
y = [0, 1]
clf = MLPClassifier(solver='lbfgs', alpha=1e-5,
                    hidden_layer_sizes=(5, 2), random_state=1)
clf.fit(X, y)

clf.predict([[2., 2.], [-1., -2.]])

[coef.shape for coef in clf.coefs_]

clf.predict_proba([[2., 2.], [1., 2.]])

1.17.3. Regression

https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor

sklearn.neural_network.MLPRegressor(hidden_layer_sizes=(100, ), activation='relu', solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000)