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

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

#導入Minst數據集
import input_data
mnist = input_data.read_data_sets("data",one_hot=True)
 
#導入tensorflow庫
import tensorflow as tf
 
#輸入變量，把28*28的圖片變成一維數組（丟失結構信息）
x = tf.placeholder("float",[None,784])
 
#權重矩陣，把28*28=784的一維輸入，變成0-9這10個數字的輸出
w = tf.Variable(tf.zeros([784,10]))
#偏置
b = tf.Variable(tf.zeros([10]))
 
#核心運算，其實就是softmax（x*w+b）
y = tf.nn.softmax(tf.matmul(x,w) + b)
 
#這個是訓練集的正確結果
y_ = tf.placeholder("float",[None,10])
 
#交叉熵，作為損失函數
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之前必須進行的
init = tf.initialize_all_variables()
#創建session以便運算
sess = tf.Session()
sess.run(init)
 
#迭代1000次
for i in range(1000):
 #獲取訓練數據集的圖片輸入和正確表示數字
 batch_xs, batch_ys = mnist.train.next_batch(100)
 #運行剛才建立的梯度下降算法，x賦值為圖片輸入，y_賦值為正確的表示數字
 sess.run(train_step,feed_dict = {x:batch_xs, y_: batch_ys})
 
#tf.argmax獲取最大值的索引。比較運算后的結果和本身結果是否相同。
#這步的結果應該是[1,1,1,1,1,1,1,1,0,1...........1,1,0,1]這種形式。
#1代表正確，0代表錯誤
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
 
#tf.cast先將數據轉換成float，防止求平均不準確。
#tf.reduce_mean由于只有一個參數，就是上面那個數組的平均值。
accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
#輸出
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

#導入Minst數據集
import input_data
mnist = input_data.read_data_sets("data",one_hot=True)
 
#導入tensorflow庫
import tensorflow as tf
 
#輸入變量，把28*28的圖片變成一維數組（丟失結構信息）
x = tf.placeholder("float",[None,784])
 
#權重矩陣，把28*28=784的一維輸入，變成0-9這10個數字的輸出
w = tf.Variable(tf.zeros([784,10]))
#偏置
b = tf.Variable(tf.zeros([10]))
 
#核心運算，其實就是softmax（x*w+b）
y = tf.nn.softmax(tf.matmul(x,w) + b)
 
#這個是訓練集的正確結果
y_ = tf.placeholder("float",[None,10])
 
#交叉熵，作為損失函數
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之前必須進行的
init = tf.initialize_all_variables()
#創建session以便運算
sess = tf.Session()
sess.run(init)
 
#迭代1000次
for i in range(1000):
 #獲取訓練數據集的圖片輸入和正確表示數字
 batch_xs, batch_ys = mnist.train.next_batch(100)
 #運行剛才建立的梯度下降算法，x賦值為圖片輸入，y_賦值為正確的表示數字
 sess.run(train_step,feed_dict = {x:batch_xs, y_: batch_ys})
 
#tf.argmax獲取最大值的索引。比較運算后的結果和本身結果是否相同。
#這步的結果應該是[1,1,1,1,1,1,1,1,0,1...........1,1,0,1]這種形式。
#1代表正確，0代表錯誤
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
 
#tf.cast先將數據轉換成float，防止求平均不準確。
#tf.reduce_mean由于只有一個參數，就是上面那個數組的平均值。
accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
#輸出
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