TensorFlow学习笔记--logistics回归

import tensorflow as tf

#Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("F:\workspace\example\MNIST",one_hot='True')

#parameters
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1

#tf Graph input
x = tf.placeholder(tf.float32, [None,784])
y = tf.placeholder(tf.float32, [None,10])

#Set model weights
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

#Construct model
y_pre = tf.nn.softmax(tf.matmul(x, W) + b)

#Minimize error use cross entropy
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(y_pre), reduction_indices=1))

#Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

#initialize the variables
init = tf.global_variables_initializer()

#launch the graph
with tf.Session() as sess:
   sess.run(init)
   for epoch in range(training_epochs):
      avg_cost = 0.
      total_batch = int(mnist.train.num_examples/batch_size)
      #loop over all batchs
      for i in range(total_batch):
         batch_xs, batch_ys = mnist.train.next_batch(batch_size)
         #Fit training using batch data
         _, c = sess.run([optimizer, cost], feed_dict={x:batch_xs, y:batch_ys})
         avg_cost += c/total_batch
      #Display logs per epoch step
      if (epoch+1) % display_step == 0:
         print("Epoch:%04d cost=%.9f" % ((epoch+1), avg_cost))	  	  
   print("Optimization Finished!")
   correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(y, 1))   
   #Calculate accuracy for 3000 examples
   accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
   print("Accuracy:",accuracy.eval({x:mnist.test.images[:3000],y:mnist.test.labels[:3000]}))