#coding:utf-8
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import numpy as np
def ellipse(x):
y = np.sqrt(1 - x**2/4.0)
return y
data = np.linspace(-2, 2, 2000)
labels = ellipse(data)
X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.1)
X_train=X_train.reshape(-1,1)
X_test=X_test.reshape(-1,1)
mlp = MLPRegressor(hidden_layer_sizes=(5, 5, 5),
max_iter=1000,
batch_size=1,
activation='tanh')
mlp.fit(X_train, y_train)
y_pred = mlp.predict(X_test)
print('X_train.shape:', X_train.shape)
print('y_train.shape:', y_train.shape)
print('X_test.shape:', X_test.shape)
print('y_test.shape:', y_test.shape)
print('y_pred.shape:', y_pred.shape)
print("mlp train score:", mlp.score(X_train, y_train))
print("mlp test score:", mlp.score(X_test, y_test))
print("预测的损失值:", mean_squared_error(y_pred, y_test))
print('查看预测情况：')
for i in range(10):
print(y_pred[i], y_test[i])
通过几次实验发现
初始化的权重对结果影响很大， batch_size并不总是越大越好
