#!/usr/bin/env python2
-- coding: utf-8 --
"""
Created on Mon Jul 10 09:35:04 2017
@author: [email protected]，http://blog.csdn.net/myhaspl
"""
#邏輯或
import tensorflow as tf
batch_size=10
w1=tf.Variable(tf.random_normal([2,6],stddev=1,seed=1))
w2=tf.Variable(tf.random_normal([6,1],stddev=1,seed=1))
b=tf.Variable(tf.zeros([6]),tf.float32)
x=tf.placeholder(tf.float32,shape=(None,2),name="x")
y=tf.placeholder(tf.float32,shape=(None,1),name="y")
h=tf.matmul(x,w1)+b
yo=tf.matmul(h,w2)
#損失函數計算差異平均值
cross_entropy=tf.reduce_mean(tf.abs(y-yo))
#反向傳播
train_step=tf.train.AdamOptimizer(0.05).minimize(cross_entropy)
#生成樣本
x=[[0.,0.],[0.,1.],[1.,0.],[1.,1.]]
y=[[0.],[1.],[1.],[1.]]
b_=tf.zeros([6])
with tf.Session() as sess:
#初始化變量
init_op=tf.global_variables_initializer()
sess.run(init_op)
print sess.run(w1)
print sess.run(w2)
#設定訓練輪數
TRAINCOUNT=500
for i in range(TRAINCOUNT):
#開始訓練
sess.run(train_step,feed_dict={x:x_,y:y_})
if i%10==0:
total_cross_entropy=sess.run(cross_entropy,feed_dict={x:x_,y:y_})
print("%d 次訓練之後，損失:%g"%(i+1,total_cross_entropy))
print(sess.run(w1))
print(sess.run(w2))
#生成測試樣本，僅進行前向傳播驗證：
testyo=sess.run(yo,feed_dict={x:[[0.,1.],[1.,1.]]})
myout=[int(testout>0.5) for testout in testyo]
print myout兩個概率分佈p,q，其中p爲真實分佈，q爲非真實分佈
class tf.train.GradientDescentOptimizer
See the guide: Training > Optimizers
Optimizer that implements the gradient descent algorithm.
Methods
init(learning_rate, use_locking=False, name='GradientDescent')
Construct a new gradient descent optimizer.
Args:
learning_rate: A Tensor or a floating point value. The learning rate to use.
use_locking: If True use locks for update operations.
name: Optional name prefix for the operations created when applying gradients. Defaults to "GradientDescent".
Adam
Adam(Adaptive Moment Estimation)本質上是帶有動量項的RMSprop，它利用梯度的一階矩估計和二階矩估計動態調整每個參數的學習率。Adam的優點主要在於經過偏置校正後，每一次迭代學習率都有個確定範圍，使得參數比較平穩。
class tf.train.AdamOptimizer
Defined in tensorflow/python/training/adam.py.
See the guide: Training > Optimizers
Optimizer that implements the Adam algorithm.
See Kingma et. al., 2014 (pdf).
Methods
init
init(
learning_rate=0.001,
beta1=0.9,
beta2=0.999,
epsilon=1e-08,
use_locking=False,
name='Adam'
)
Construct a new Adam optimizer.
Initialization:
m_0 <- 0 (Initialize initial 1st moment vector)
v_0 <- 0 (Initialize initial 2nd moment vector)
t <- 0 (Initialize timestep)
The update rule for variable with gradient g uses an optimization described at the end of section2 of the paper:
t <- t + 1
lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t)
mt <- beta1 * m{t-1} + (1 - beta1) g
v_t <- beta2 v_{t-1} + (1 - beta2) g g
variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)
The default value of 1e-8 for epsilon might not be a good default in general. For example, when training an Inception network on ImageNet a current good choice is 1.0 or 0.1. Note that since AdamOptimizer uses the formulation just before Section 2.1 of the Kingma and Ba paper rather than the formulation in Algorithm 1, the "epsilon" referred to here is "epsilon hat" in the paper.
The sparse implementation of this algorithm (used when the gradient is an IndexedSlices object, typically because of tf.gather or an embedding lookup in the forward pass) does apply momentum to variable slices even if they were not used in the forward pass (meaning they have a gradient equal to zero). Momentum decay (beta1) is also applied to the entire momentum accumulator. This means that the sparse behavior is equivalent to the dense behavior (in contrast to some momentum implementations which ignore momentum unless a variable slice was actually used).