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")
