2.tensorflow——Softmax回归

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data

#download data
mnist=input_data.read_data_sets('data/',one_hot=True)
trainimg=mnist.train.images
trainlabel=mnist.train.labels
testimg=mnist.test.images

print("downloading...")
print("type:%s" % (type(mnist)))
print("tain data size:%d" % (mnist.train.num_examples))
print("test data size:%d" % (mnist.test.num_examples))
print("tarin lable's shape: %s" % (trainlabel.shape,))

#show example
# nsample = 5
# randidx=np.random.randint(trainimg.shape[0],size=nsample)
# for i in randidx:
#     cur_img=np.reshape(trainimg[i,:],(28,28))
#     cur_label=np.argmax(trainlabel[i,:])
#     plt.matshow(cur_img)
#     print(""+str(i)+"th training data,"+"which label is:"+str(cur_label))
#     plt.show()

#batch
batch_size=100
batch_xs,batch_ys=mnist.train.next_batch(batch_size)#x-data,y-label

####start train
#1.set up
numClasses=10
inputSize=784#28*28
trainningIterations=50000#total steps
batchSize=64#

#2.model #64:x(1*784)*w(784*10)+b1(10)=y(1*10)
X=tf.placeholder(tf.float32,shape=[None,inputSize])
y=tf.placeholder(tf.float32,shape=[None,numClasses])

#2.1 initial
W1 = tf.Variable(tf.zeros([784,10]))
B1 = tf.Variable(tf.zeros([10]))

#2.2 model set
y_pred=tf.nn.softmax(tf.matmul(X,W1)+B1)#10*1
loss=tf.reduce_mean(tf.square(y-y_pred))
cross_entropy=-tf.reduce_sum(y*tf.log(y_pred))
opt=tf.train.GradientDescentOptimizer(learning_rate=0.05).minimize(cross_entropy)
correct_prediction=tf.equal(tf.argmax(y_pred,1),tf.argmax(y,1))#
accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))#bool 2 float

#2.3 run train
sess=tf.Session()
init=tf.global_variables_initializer()
sess.run(init)
for i in range(trainningIterations):
    batch=mnist.train.next_batch(batch_size)
    batchInput=batch[0]
    batchLabels=batch[1]
    sess.run(opt,feed_dict={X:batchInput,y:batchLabels})
    if i%1000 == 0:
         train_accuracy=sess.run(accuracy,feed_dict={X:batchInput,y:batchLabels})
         print("step %d, tarinning accuracy %g" % (i,train_accuracy))

#2.4 run test to accuracy
batch=mnist.test.next_batch(batch_size)
testAccuracy=sess.run(accuracy,feed_dict={X:batch[0],y:batch[1]})
print("test accuracy %g" % (testAccuracy))

 理论参考：

http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html