这里用到了MNIST数据集，这个数据集是入门级别的计算机视觉集合。
#-*- coding:utf-8 -*-
print(__doc__)
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_mldata
from sklearn.neural_network import MLPClassifier
mnist=fetch_mldata("MNIST original")
x,y = mnist.data / 255. , mnist.target
x_train , x_test = x[:60000],x[60000:]
y_train,y_test = y[:60000],y[60000:]
#以上都在做特征提取
mlp=MLPClassifier(hidden_layer_sizes=(50,),max_iter=10,alpha=1e-4,
solver='sgd',verbose=10,tol=1e-4,random_state=1,
learning_rate_init=.1)
mlp.fit(x_train,y_train)
print("Training set score: %f" % mlp.score(x_train, y_train))
print("Test set score: %f" % mlp.score(x_test, y_test))
fig,axes=plt.subplots(4,4)
vmin,vmax = mlp.coefs_[0].min(),mlp.coefs_[0].max()
for coef,ax in zip(mlp.coefs_[0].T,axes.ravel()):
ax.matshow(coef.reshape(28, 28), cmap=plt.cm.gray, vmin=.5 * vmin,
vmax=.5 * vmax)
ax.set_xticks(())
ax.set_yticks(())
plt.show()
输出：Iteration 1, loss = 0.32212731
Iteration 2, loss = 0.15738787
Iteration 3, loss = 0.11647274
Iteration 4, loss = 0.09631113
Iteration 5, loss = 0.08074513
Iteration 6, loss = 0.07163224
Iteration 7, loss = 0.06351392
Iteration 8, loss = 0.05694146
Iteration 9, loss = 0.05213487
Iteration 10, loss = 0.04708320
/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (10) reached and the optimization hasn't converged yet.
% self.max_iter, ConvergenceWarning)
Training set score: 0.985733
Test set score: 0.971000