我正在尝试Python和scikit-learn.我无法让MLPRegressor接近数据.哪里出错了？
from sklearn.neural_network import MLPRegressor
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0.0, 1, 0.01).reshape(-1, 1)
y = np.sin(2 * np.pi * x).ravel()
reg = MLPRegressor(hidden_layer_sizes=(10,), activation='relu', solver='adam', alpha=0.001,batch_size='auto',
learning_rate='constant', learning_rate_init=0.01, power_t=0.5, max_iter=1000, 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)
reg = reg.fit(x, y)
test_x = np.arange(0.0, 1, 0.05).reshape(-1, 1)
test_y = reg.predict(test_x)
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.scatter(x, y, s=10, c='b', marker="s", label='real')
ax1.scatter(test_x,test_y, s=10, c='r', marker="o", label='NN Prediction')
plt.show() …