Tensorflow暑期实践——基于多隐层神经网络的手写数字识别(全部代码+tensorboard可视化)

 

 

import tensorflow as tf 
import numpy as np
import os 
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
print(tf.__version__)
print(tf.test.is_gpu_available())


from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# 定义占位符 
tf.reset_default_graph() #清除default graph和不断增加的节点

# 输入层
x = tf.placeholder(tf.float32, [None, 784], name="X")
# 输出层
y = tf.placeholder(tf.float32, [None, 10], name="Y")

image_shaped_input = tf.reshape(x,[-1,28,28,1])

H1_NN = 512
H2_NN = 256

regularizer = 0.0001
def get_weight(shape, regularizer):
    w = tf.Variable(tf.truncated_normal(shape, stddev=0.1))
    if regularizer != None:
        tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w))
    return w

w1 = get_weight([784, H1_NN], regularizer)
b1 = tf.Variable(tf.zeros(H1_NN))
y1 = tf.nn.relu(tf.matmul(x, w1) + b1)

w2 = get_weight([H1_NN, H2_NN], regularizer)
b2 = tf.Variable(tf.zeros(H2_NN))
y2 = tf.nn.relu(tf.matmul(y1, w2) + b2)

w3 = get_weight([H2_NN, 10], regularizer)
b3 = tf.Variable(tf.zeros(10))
pred = tf.matmul(y2,w3) + b3


BATCH_SIZE = 250
LEARNING_RATE_BASE = 0.1
LEARNING_RATE_DECAY = 0.99
REGULARIZER = 0.0001
STEPS = 10000
MOVING_AVERAGE_DECAY = 0.99

global_step = tf.Variable(0, trainable=False)

# 含正则化的loss
ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=tf.argmax(y,1))
cem = tf.reduce_mean(ce)
loss = cem + tf.add_n(tf.get_collection('losses'))

learning_rate = tf.train.exponential_decay(
    LEARNING_RATE_BASE,
    global_step,
    mnist.train.num_examples / BATCH_SIZE,
    LEARNING_RATE_DECAY,
    staircase=True)

train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)

# 滑动平均值
ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
ema_op = ema.apply(tf.trainable_variables())
with tf.control_dependencies([train_step, ema_op]):
    train_op = tf.no_op(name="train")
    
# 定义准确率
correct_prediction = tf.equal(tf.argmax(y, 1),tf.argmax(pred,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


image_shaped_input=tf.reshape(x,[-1,28,28,1])
tf.summary.image('input', image_shaped_input,10)
tf.summary.histogram('forward',pred)
tf.summary.scalar('loss',loss)
tf.summary.scalar('accuracy',accuracy)
merged_summary_op = tf.summary.merge_all()


from time import time
startTime = time()
MODEL_SAVE_PATH="./model2/"

saver = tf.train.Saver()
with tf.Session() as sess:
    
    writer = tf.summary.FileWriter('log/',sess.graph)

    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    
    '''有效防止宿舍断电，参数白跑的情况'''
    ckpt=tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
    if ckpt and ckpt.model_checkpoint_path:
        #把ckpt恢复到当前会话
        saver.restore(sess,ckpt.model_checkpoint_path)
        print("Restore model from"+ckpt.model_checkpoint_path)

    for i in range(STEPS):
        xs, ys = mnist.train.next_batch(BATCH_SIZE)
        _, loss_value, step,acc = sess.run([train_op, loss, global_step,accuracy], feed_dict={x: xs, y:ys})
        
        summary_str = sess.run(merged_summary_op,feed_dict={x:xs,y:ys})
        writer.add_summary(summary_str, i)
        
        if i % 500 == 0:
            print("After %d training step(s) .loss on training batch is %g." % (step, loss_value),
                 "Accuracy=","{:.4f}".format(acc))
            saver.save(sess, os.path.join(MODEL_SAVE_PATH, "mnist_model"),global_step=global_step)
            
duration = time() - startTime



correct_prediction = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.Session() as sess:
    ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
    if ckpt and ckpt.model_checkpoint_path:
        saver.restore(sess, ckpt.model_checkpoint_path)
        global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
        accuracy_score = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
        print("After %s training step(s) , test accuracy = %g." % (global_step, accuracy_score))

会在当前运行的文件所在的文件夹下生成一个log文件夹

 

 在log下打开cmd输入tensorboard --logdir=C:\Users\28746\Desktop\SummerProject\Day4_多层神经网络_手写识别\log

 

 在网页中打开localhost:6006就可以看到具体运行日志

Acc和Loss：

 

输入的图片：

 

 

神经网络搭建的结构图：