通过多层感知机，直接对样本ASCII进行监督学习，隐藏层1层，节点数目50个，

功能分析：
准确率 92.8%，准确率还行，实际应用有问题，这里有坑的，黑白样本比例对准确率影响很大，当1:1的时候，几乎无法区分黑白测试样本，所以无特征MLP几乎是个笑话。同样，开始反思，之前的模型对新数据的预测能力不能仅仅看准确率。

性能分析：
1个样本耗时 0.000466秒，均摊 0.466ms，
10个样本耗时 0.000619秒，均摊 0.06ms，
100个样本耗时 0.000939秒，均摊 9.93us，
1000个样本耗时 0.003112秒，均摊 3.11us
完整代码：
import sys
import urllib
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical, pad_sequences
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
import time
NUM = 100
def elt(line):
x = []
for i, c in enumerate(line):
c = c.lower()
x.append(ord(c))
return x

def load_file(filename,label,ms=[],ns=[]):
with open(filename) as f:
for line in f:
line = line.strip('\n')
line = urllib.unquote(line)
if len(line)<= NUM:
m = elt(line)
if(label):
n = 1
else:
n = 0
ms.append(m)
ns.append(n)
print(len(ms))

def load_files(file1,file2):
xs = []
ys = []
load_file(file1,1,xs,ys)
load_file(file2,0,xs,ys)
return xs,ys
def train(x,y):
graph1 = tf.Graph()
with graph1.as_default():
x_train, x_test, y_train, y_test=train_test_split( x,y, test_size=0.4,random_state=0)
x_train = pad_sequences(x_train,maxlen=NUM,value=0.)
x_test = pad_sequences(x_test,maxlen=NUM,value=0.)
y_train = to_categorical(y_train, nb_classes=2)
y_test = to_categorical(y_test, nb_classes=2)
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)
n = mlp.score(x_test, y_test)
p1 = mlp.predict(x_test[1100:1101])

print("Training set score: %f"%n)
print(p1)

if __name__ == "__main__":
xs,ys = load_files(sys.argv[1],sys.argv[2])
train(xs,ys)