代码，逻辑回归(logistic_regression)实现mnist分类（TensorFlow实现）

#logistic_regression by ffzhang
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
os.environ["CUDA_VISIBLE_DEVICES"]='2'

import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time

mnist=input_data.read_data_sets('data/mnist',one_hot=True)

mnist.train.images.shape

mnist.train.labels.shape

batch_size=128
# X = tf.placeholder(tf.float32,[batch_siz,784],name='X_placeholder')
# Y = tf.placeholder(tf.int32, [batch_siz,10],name='Y_placehoder')

X = tf.placeholder(tf.float32,[None,784],name='X_placeholder')
Y = tf.placeholder(tf.int32, [None,10],name='Y_placehoder')

w = tf.Variable(tf.random_normal(shape=[784,10],stddev=0.01),name='weights')
b = tf.Variable(tf.zeros([1,10]),name='bias')

# W*x+b
logits=tf.matmul(X,w)+b

entropy=tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=Y,name='loss')
loss=tf.reduce_mean(entropy)

learning_rate=0.01
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)

n_epochs = 30
init=tf.global_variables_initializer()
with tf.Session() as sess:
writer=tf.summary.FileWriter('./graphs/logistic_reg',sess.graph)

start_time=time.time()
sess.run(init)
n_batches=int(mnist.train.num_examples/batch_size)

for i in range(n_epochs):

total_loss=0

for _ in range(n_batches):
X_batch, Y_batch =mnist.train.next_batch(batch_size)
_,loss_batch =sess.run([optimizer,loss],feed_dict={X:X_batch,Y:Y_batch})
total_loss +=loss_batch
print ('Average loss epoch {0}:{1}'.format(i,total_loss/n_batches))
print ('Total time: {0} seconds'.format(time.time()-start_time))

print ('optimizatin Finished')

preds = tf.nn.softmax(logits)
correct_preds=tf.equal(tf.argmax(preds,1),tf.argmax(Y,1))
accuracy=tf.reduce_sum(tf.cast(correct_preds,tf.float32))

n_batches = int(mnist.test.num_examples/batch_size)
total_correct_preds=0

for i in range(n_batches):
X_batch, Y_batch=mnist.test.next_batch(batch_size)
accuracy_batch =sess.run([accuracy],feed_dict={X:X_batch,Y:Y_batch})
total_correct_preds += accuracy_batch[0]

print ('Accuracy {0}'.format(total_correct_preds/mnist.test.num_examples))

writer.close()