使用tensorflow进行mnist数字识别【模型训练+预测+模型保存+模型恢复】

 

import sys,os
sys.path.append(os.pardir)
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
from PIL import Image
import tensorflow as tf

def predict():
    meta_path = 'ckpt/mnist.ckpt.meta'
    model_path = 'ckpt/mnist.ckpt'
    sess = tf.InteractiveSession ()
    saver = tf.train.import_meta_graph (meta_path)
    saver.restore (sess, model_path)
    graph = tf.get_default_graph ()
    W = graph.get_tensor_by_name ("w:0")
    b = graph.get_tensor_by_name ("b:0")
    x = tf.placeholder (tf.float32, [None, 784])
    y = tf.nn.softmax (tf.matmul (x, W) + b)
    keep_prob = tf.placeholder (tf.float32)
    batch_xs, batch_ys=mnist.train.next_batch (100)
    one_img = batch_xs[0].reshape ((1, 784))
    one_num = batch_ys[0].reshape ((1, 10))
    temp = sess.run (y, feed_dict={x: one_img, keep_prob: 1.0})
    b = sess.run (tf.argmax (temp, 1))
    a = sess.run (tf.arg_max (one_num, 1))
    print(temp)
    print(one_num)
    if b == a:
        print ("success! the num is :", (b[0]))
        showImgTest(one_img)
    else:
        print ("mistakes predict.")

def trainNet():
    x = tf.placeholder (tf.float32, [None, 784])
    W = tf.Variable (tf.zeros ([784, 10]),name="w")
    b = tf.Variable (tf.zeros ([10]),name="b")
    y = tf.nn.softmax (tf.matmul (x, W) + b)
    y_ = tf.placeholder (tf.float32, [None, 10])
    keep_prob = tf.placeholder (tf.float32)
    # 定义测试的准确率
    correct_prediction = tf.equal (tf.argmax (y, 1), tf.argmax (y_, 1))
    accuracy = tf.reduce_mean (tf.cast (correct_prediction, tf.float32))
    #
    saver = tf.train.Saver (max_to_keep=1)
    max_acc = 0
    train_accuracy = 0
    #交叉熵
    cross_entropy = tf.reduce_mean (-tf.reduce_sum (y_ * tf.log (y)))
    # cross_error=cross_entropy_error_batch(y,y_)
    train_step = tf.train.GradientDescentOptimizer (0.01).minimize (cross_entropy)
    sess = tf.InteractiveSession ()
    tf.global_variables_initializer ().run ()
    for i in range (1000):
        batch_xs, batch_ys = mnist.train.next_batch (100)
        sess.run (train_step, feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 1.0})
        if i % 100 == 0:
            train_accuracy = accuracy.eval (feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 1.0})
            print ("step %d, training accuracy %g" % (i, train_accuracy))
        if train_accuracy > max_acc:
            max_acc = train_accuracy
            saver.save (sess, 'ckpt/mnist.ckpt')

if __name__ == '__main__':
    mnist = input_data.read_data_sets ("MNIST_data/", one_hot=True)
    choice=0
    while choice == 0:
        print ("------------------------tensorflow--------------------------")
        print ("\t\t\t1\ttrain model..")
        print("\t\t\t2\tpredict model")
        print("\t\t\t3\tshow the first image")
        print ("\t\t\t0\texit")
        choice = input ("please input your choice！")
        print(choice)
        if choice == "1":
            print("start train...")
            trainNet()
        if choice=="2":
            predict()
        if choice=="3":
            showImg()

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注：正在学习CNN，选项4还没有来的及做。后面补上