深度学习之路三 训练椭圆函数

Posted 2023-03-26 Please Call me 小强

#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