import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#  自己下載 MNIST_data 資料集，  csdn  上下載很快
mnist_data_folder="/home/zhangjun/miniconda3/envs/tensorflow/MNIST_data"  
mnist=input_data.read_data_sets(mnist_data_folder,one_hot=True)

x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b

# Define loss and optimizer 

y_ = tf.placeholder(tf.float32, [None, 10]) 

# The raw formulation of cross-entropy, 

# # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)), 

# reduction_indices=[1])) # 
# can be numerically unstable. # 
# So here we use tf.nn.softmax_cross_entropy_with_logits on the raw 
# outputs of 'y', and then average across the batch.

cross_entropy = tf.reduce_mean( 
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
    
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) 
sess = tf.InteractiveSession()

tf.global_variables_initializer().run()

# Train 
for _ in range(1000): 
  batch_xs, batch_ys = mnist.train.next_batch(100) 
  sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) 
# Test trained model

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))