#使用dropout解决overfitting（过拟合）问题
#如果有dropout,在feed_dict的参数中一定要加入dropout的值
import tensorflow as tf
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import LabelBinarizer

    #load datas 导入klearn中digits手写字体数据集
digits = load_digits()
X = digits.data         #加载从0-9的数字集
y = digits.target       #y为X所对应的标签
    #fit(y) 返回一个实例
    #fit_transform(y) 返回 和y一样的形状
y = LabelBinarizer().fit_transform(y)
    #train_test_split(train_data,train_target,test_size=0.4, random_state=0)
    # 是交叉验证中常用的函数，功能是从样本中随机的按比例选取train_data和test_data
    #参数解释：
    #train_data：所要划分的样本特征集
    #train_target：所要划分的样本结果
    #test_size：样本占比，如果是整数的话就是样本的数量
    #random_state：是随机数的种子。
    #随机数种子：其实就是该组随机数的编号，在需要重复试验的时候，保证得到一组一样的随机数。
    # 比如你每次都填1，其他参数一样的情况下你得到的随机数组是一样的。但填0或不填，每次都会不一样。
    #随机数的产生取决于种子，随机数和种子之间的关系遵从以下两个规则：
    #种子不同，产生不同的随机数；种子相同，即使实例不同也产生相同的随机数。
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)

'''
#fit_transform()、inverse_transform使用的例子
#程序
from sklearn import preprocessing
feature = [[0,1], [1,1], [0,0], [1,0]]
label= ['yes', 'no', 'yes', 'no']
lb = preprocessing.LabelBinarizer() #构建一个转换对象
Y = lb.fit_transform(label)
re_label = lb.inverse_transform(Y)#还原之前的label
print(Y)
print(re_label)
#结果
[[1]
 [0]
 [1]
 [0]]
['yes' 'no' 'yes' 'no']
'''

# 定义一个神经层
def add_layer(inputs, in_size, out_size,layer_name, activation_function=None):
    #add one more layer and return the output of the layer
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    Wx_plus_b = tf.nn.dropout(Wx_plus_b, keep_pro)#使用dropout机制，解决overfitting问题
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    tf.summary.histogram(layer_name+'/output',outputs)
    return outputs

#define placeholder for inputs to network
keep_pro = tf.placeholder(tf.float32)#dropout机制使用
xs = tf.placeholder(tf.float32, [None, 64])  # none表示无论给多少个例子都行，64=8*8
ys = tf.placeholder(tf.float32, [None, 10])  #表示10个需要识别的数字

#add output layer
l1 = add_layer(xs, 64, 50,'l1',activation_function=tf.nn.tanh)
prediction = add_layer(l1, 50, 10,'l2', activation_function=tf.nn.softmax)

#the error between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),reduction_indices=[1])) #loss function
tf.summary.scalar('loss',cross_entropy)
train_step = tf.train.GradientDescentOptimizer(0.6).minimize(cross_entropy)

sess = tf.Session()
merged = tf.summary.merge_all()
sess.run(tf.initialize_all_variables())#tf.initialize_all_variables()以被弃用
#sess.run(tf.global_variables_initializer())

#summary writer goes in here
train_writer = tf.summary.FileWriter("../../logs/train",sess.graph)
test_writer = tf.summary.FileWriter("../../logs/test",sess.graph)




for i in range(500):
    sess.run(train_step,feed_dict={xs: X_train, ys: y_train,keep_pro:0.6})#保持0.6的概率不被drop掉
    if i%50 == 0:
        # record loss
        train_result = sess.run(merged, feed_dict={xs: X_train, ys: y_train,keep_pro:1})
        test_result = sess.run(merged, feed_dict={xs: X_test, ys: y_test,keep_pro:1})
        train_writer.add_summary(train_result, i)
        test_writer.add_summary(test_result, i)