tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy,var_list=[w,b])

#导入Minst数据集
import input_data
mnist = input_data.read_data_sets("data",one_hot=True)
 
#导入tensorflow库
import tensorflow as tf
 
#输入变量，把28*28的图片变成一维数组（丢失结构信息）
x = tf.placeholder("float",[None,784])
 
#权重矩阵，把28*28=784的一维输入，变成0-9这10个数字的输出
w = tf.Variable(tf.zeros([784,10]))
#偏置
b = tf.Variable(tf.zeros([10]))
 
#核心运算，其实就是softmax（x*w+b）
y = tf.nn.softmax(tf.matmul(x,w) + b)
 
#这个是训练集的正确结果
y_ = tf.placeholder("float",[None,10])
 
#交叉熵，作为损失函数
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
 
#梯度下降算法，最小化交叉熵
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
 
#初始化，在run之前必须进行的
init = tf.initialize_all_variables()
#创建session以便运算
sess = tf.Session()
sess.run(init)
 
#迭代1000次
for i in range(1000):
 #获取训练数据集的图片输入和正确表示数字
 batch_xs, batch_ys = mnist.train.next_batch(100)
 #运行刚才建立的梯度下降算法，x赋值为图片输入，y_赋值为正确的表示数字
 sess.run(train_step,feed_dict = {x:batch_xs, y_: batch_ys})
 
#tf.argmax获取最大值的索引。比较运算后的结果和本身结果是否相同。
#这步的结果应该是[1,1,1,1,1,1,1,1,0,1...........1,1,0,1]这种形式。
#1代表正确，0代表错误
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
 
#tf.cast先将数据转换成float，防止求平均不准确。
#tf.reduce_mean由于只有一个参数，就是上面那个数组的平均值。
accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
#输出
print(sess.run(accuracy,feed_dict={x:mnist.test.images,y_: mnist.test.labels}))

"C:\Program Files\Anaconda3\python.exe" D:/pycharmprogram/tensorflow_learn/softmax_learn/softmax_learn.py
Extracting data\train-images-idx3-ubyte.gz
Extracting data\train-labels-idx1-ubyte.gz
Extracting data\t10k-images-idx3-ubyte.gz
Extracting data\t10k-labels-idx1-ubyte.gz
WARNING:tensorflow:From C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\util\tf_should_use.py:175: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
2018-05-14 15:49:45.866600: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-05-14 15:49:45.866600: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
0.9163
 
Process finished with exit code 0

"C:\Program Files\Anaconda3\python.exe" D:/pycharmprogram/tensorflow_learn/softmax_learn/softmax_learn.py
Extracting data\train-images-idx3-ubyte.gz
Extracting data\train-labels-idx1-ubyte.gz
Extracting data\t10k-images-idx3-ubyte.gz
Extracting data\t10k-labels-idx1-ubyte.gz
WARNING:tensorflow:From C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\util\tf_should_use.py:175: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
2018-05-14 15:51:08.543600: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-05-14 15:51:08.544600: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
0.9187
 
Process finished with exit code 0

#导入Minst数据集
import input_data
mnist = input_data.read_data_sets("data",one_hot=True)
 
#导入tensorflow库
import tensorflow as tf
 
#输入变量，把28*28的图片变成一维数组（丢失结构信息）
x = tf.placeholder("float",[None,784])
 
#权重矩阵，把28*28=784的一维输入，变成0-9这10个数字的输出
w = tf.Variable(tf.zeros([784,10]))
#偏置
b = tf.Variable(tf.zeros([10]))
 
#核心运算，其实就是softmax（x*w+b）
y = tf.nn.softmax(tf.matmul(x,w) + b)
 
#这个是训练集的正确结果
y_ = tf.placeholder("float",[None,10])
 
#交叉熵，作为损失函数
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
 
#梯度下降算法，最小化交叉熵
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy,var_list=[b])
 
#初始化，在run之前必须进行的
init = tf.initialize_all_variables()
#创建session以便运算
sess = tf.Session()
sess.run(init)
 
#迭代1000次
for i in range(1000):
 #获取训练数据集的图片输入和正确表示数字
 batch_xs, batch_ys = mnist.train.next_batch(100)
 #运行刚才建立的梯度下降算法，x赋值为图片输入，y_赋值为正确的表示数字
 sess.run(train_step,feed_dict = {x:batch_xs, y_: batch_ys})
 
#tf.argmax获取最大值的索引。比较运算后的结果和本身结果是否相同。
#这步的结果应该是[1,1,1,1,1,1,1,1,0,1...........1,1,0,1]这种形式。
#1代表正确，0代表错误
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
 
#tf.cast先将数据转换成float，防止求平均不准确。
#tf.reduce_mean由于只有一个参数，就是上面那个数组的平均值。
accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
#输出
print(sess.run(accuracy,feed_dict={x:mnist.test.images,y_: mnist.test.labels}))

"C:\Program Files\Anaconda3\python.exe" D:/pycharmprogram/tensorflow_learn/softmax_learn/softmax_learn.py
Extracting data\train-images-idx3-ubyte.gz
Extracting data\train-labels-idx1-ubyte.gz
Extracting data\t10k-images-idx3-ubyte.gz
Extracting data\t10k-labels-idx1-ubyte.gz
WARNING:tensorflow:From C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\util\tf_should_use.py:175: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
2018-05-14 15:52:04.483600: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-05-14 15:52:04.483600: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
0.1135
 
Process finished with exit code 0