tensorflow（迭代完成回归逻辑实例）

今天学习了利用梯度下降法处理数据与mnist数据集中的准确度的实例：

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
print("packs loaded")
trainimg = mnist.train.images
trainlabel = mnist.train.labels
testimg = mnist.train.images
testlabel = mnist.train.labels
print("MNIST loaded")
tf.compat.v1.disable_eager_execution()
x=tf.compat.v1.placeholder("float",[None,784])
y=tf.compat.v1.placeholder("float",[None,10])
W=tf.Variable(tf.zeros([784,10]))
b=tf.Variable(tf.zeros([10]))
actv = tf.nn.softmax(tf.matmul(x,W)+b)
cost=tf.compat.v1.reduce_mean(-tf.compat.v1.reduce_sum(y*tf.compat.v1.log(actv),reduction_indices=1))
learning_rate=0.01
optm = tf.compat.v1.train.GradientDescentOptimizer(learning_rate).minimize(cost)
pred = tf.equal(tf.argmax(actv,1),tf.argmax(y,1))
accr = tf.reduce_mean(tf.cast(pred,"float"))
init = tf.compat.v1.global_variables_initializer()
sess=tf.compat.v1.InteractiveSession()
arr=np.array([[31,23,4,24,27,34],
              [18,3,25,0,6,35],
              [28,14,33,22,20,8],
              [13,30,21,19,7,9],
              [16,1,26,32,2,29],
              [17,12,5,11,10,15]])
#tf.rank(arr).eval()
training_epochs = 50
batch_size = 100
display_step = 5
sess = tf.compat.v1.Session()
sess.run(init)
for epoch in range(training_epochs):
    avg_cost=0.
    num_batch =int(mnist.train.num_examples/batch_size)
    for i in range(num_batch):
        batch_xs,batch_ys = mnist.train.next_batch(batch_size)
        sess.run(optm,feed_dict={x:batch_xs,y:batch_ys})
        feeds ={x:batch_xs,y:batch_ys}
        avg_cost+=sess.run(cost,feed_dict=feeds)/num_batch
        if epoch%display_step==0:
            feeds_train = {x:batch_xs,y:batch_ys}
            feeds_test ={x:mnist.test.images,y:mnist.test.labels}
            train_acc =sess.run(accr,feed_dict=feeds_train)
            test_acc=sess.run(accr,feed_dict=feeds_test)
            print("Epoch: %03d/%03d cost: %.9f train_acc: %.3f test_acc: %.3f"
                 %(epoch,training_epochs,avg_cost,train_acc,test_acc))
print("DONE")