一、准备神经网络程序

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # 载入数据集 mnist = input_data.read_data_sets("MNIST_data", one_hot=True) # 每个批次的大小 batch_size = 100 # 计算一共有多少批次 n_batch = mnist.train.num_examples // batch_size # 定义两个placeholder x = tf.placeholder(tf.float32, [None, 784]) y = tf.placeholder(tf.float32, [None, 10]) keep_prob = tf.placeholder(tf.float32) # 创建一个简单的神经网络 W_1 = tf.Variable(tf.truncated_normal([784, 2000], stddev=0.1)) b_1 = tf.Variable(tf.zeros([2000]) + 0.1) L_1 = tf.nn.relu(tf.matmul(x, W_1) + b_1) L1_drop=tf.nn.dropout(L_1, keep_prob) W2=tf.Variable(tf.truncated_normal([2000,1000],stddev=0.1)) b2=tf.Variable(tf.zeros([1000]) + 0.1) L2=tf.nn.tanh(tf.matmul(L1_drop, W2)+b2) L2_drop=tf.nn.dropout(L2, keep_prob) W_3 = tf.Variable(tf.truncated_normal([1000,10],stddev=0.1)) b_3 = tf.Variable(tf.zeros([10]) + 0.1) prediction = tf.nn.softmax(tf.matmul(L2_drop,W_3) + b_3) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)) train_step = tf.train.MomentumOptimizer(0.2,0.9).minimize(loss) # 初始化变量 init = tf.global_variables_initializer() # 结果存放在一个布尔型列表中 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) # argmax返回一维张量中最大的值所在的位置 # 求准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.333) with tf.Session() as sess: sess.run(init) for epoch in range(50): for batch in range(n_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys, keep_prob:0.5}) test_acc=sess.run(accuracy,feed_dict={x:mnist.test.images, y:mnist.test.labels,keep_prob:0.1}) train_acc=sess.run(accuracy,feed_dict={x:mnist.train.images, y:mnist.train.labels,keep_prob:1.0}) print("Iter"+str(epoch)+",Testing Accuracy "+str(test_acc)+"Training Accuracy "+str(train_acc))

二、添加命名空间

          添加类似 with tf.name_scope("input")的代码，定义命名空间。如下，添加了4行with

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # 载入数据集 mnist = input_data.read_data_sets("MNIST_data", one_hot=True) # 每个批次的大小 batch_size = 100 # 计算一共有多少批次 n_batch = mnist.train.num_examples // batch_size # 定义两个placeholder with tf.name_scope("input"): x = tf.placeholder(tf.float32, [None, 784]) y = tf.placeholder(tf.float32, [None, 10]) keep_prob = tf.placeholder(tf.float32) # 创建一个简单的神经网络 with tf.name_scope("layer1"): W_1 = tf.Variable(tf.truncated_normal([784, 2000], stddev=0.1)) b_1 = tf.Variable(tf.zeros([2000]) + 0.1) L_1 = tf.nn.relu(tf.matmul(x, W_1) + b_1) L1_drop=tf.nn.dropout(L_1, keep_prob) with tf.name_scope("layer2"): W2=tf.Variable(tf.truncated_normal([2000,1000],stddev=0.1)) b2=tf.Variable(tf.zeros([1000]) + 0.1) L2=tf.nn.tanh(tf.matmul(L1_drop, W2)+b2) L2_drop=tf.nn.dropout(L2, keep_prob) with tf.name_scope("output"): W_3 = tf.Variable(tf.truncated_normal([1000,10],stddev=0.1)) b_3 = tf.Variable(tf.zeros([10]) + 0.1) prediction = tf.nn.softmax(tf.matmul(L2_drop,W_3) + b_3) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)) train_step = tf.train.MomentumOptimizer(0.2,0.9).minimize(loss) # 初始化变量 init = tf.global_variables_initializer() # 结果存放在一个布尔型列表中 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) # argmax返回一维张量中最大的值所在的位置 # 求准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.333) with tf.Session() as sess: sess.run(init) for epoch in range(50): for batch in range(n_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys, keep_prob:0.5}) test_acc=sess.run(accuracy,feed_dict={x:mnist.test.images, y:mnist.test.labels,keep_prob:0.1}) train_acc=sess.run(accuracy,feed_dict={x:mnist.train.images, y:mnist.train.labels,keep_prob:1.0}) print("Iter"+str(epoch)+",Testing Accuracy "+str(test_acc)+"Training Accuracy "+str(train_acc))

 三、添加summary

     添加各种summary

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data def varibale_summary(var): with tf.name_scope("summary"): mean = tf.reduce_mean(var) tf.summary.scalar("mean", mean) stddev = tf.sqrt(tf.reduce_mean(tf.square(var-mean))) tf.summary.scalar("stddev", stddev) tf.summary.scalar("max", tf.reduce_max(var)) tf.summary.scalar("min", tf.reduce_min(var)) tf.summary.histogram("histogram", var) # 载入数据集 mnist = input_data.read_data_sets("MNIST_data", one_hot=True) # 每个批次的大小 batch_size = 100 # 计算一共有多少批次 n_batch = mnist.train.num_examples // batch_size # 定义两个placeholder with tf.name_scope("input"): x = tf.placeholder(tf.float32, [None, 784]) y = tf.placeholder(tf.float32, [None, 10]) keep_prob = tf.placeholder(tf.float32) # 创建一个简单的神经网络 with tf.name_scope("layer1"): W_1 = tf.Variable(tf.truncated_normal([784, 2000], stddev=0.1)) b_1 = tf.Variable(tf.zeros([2000]) + 0.1) L_1 = tf.nn.relu(tf.matmul(x, W_1) + b_1) L1_drop=tf.nn.dropout(L_1, keep_prob) varibale_summary(W_1) varibale_summary(b_1) with tf.name_scope("layer2"): W2=tf.Variable(tf.truncated_normal([2000,1000],stddev=0.1)) b2=tf.Variable(tf.zeros([1000]) + 0.1) L2=tf.nn.tanh(tf.matmul(L1_drop, W2)+b2) L2_drop=tf.nn.dropout(L2, keep_prob) varibale_summary(W2) varibale_summary(b2) with tf.name_scope("output"): W_3 = tf.Variable(tf.truncated_normal([1000,10],stddev=0.1)) b_3 = tf.Variable(tf.zeros([10]) + 0.1) varibale_summary(W_3) varibale_summary(b_3) prediction = tf.nn.softmax(tf.matmul(L2_drop,W_3) + b_3) with tf.name_scope("loss"): loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)) tf.summary.scalar("loss", loss) with tf.name_scope("train"): train_step = tf.train.MomentumOptimizer(0.2,0.9).minimize(loss) # 初始化变量 init = tf.global_variables_initializer() with tf.name_scope("accurary"): # 结果存放在一个布尔型列表中 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) # argmax返回一维张量中最大的值所在的位置 # 求准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) tf.summary.scalar("accurary", accuracy) merged = tf.summary.merge_all() with tf.Session() as sess: sess.run(init) writer = tf.summary.FileWriter("logs/", sess.graph) for epoch in range(5): for batch in range(n_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) _summary, _ = sess.run([merged, train_step], feed_dict={x: batch_xs, y: batch_ys, keep_prob:0.5}) writer.add_summary(_summary, epoch) test_acc=sess.run(accuracy,feed_dict={x:mnist.test.images, y:mnist.test.labels,keep_prob:0.1}) train_acc=sess.run(accuracy,feed_dict={x:mnist.train.images, y:mnist.train.labels,keep_prob:1.0}) print("Iter"+str(epoch)+",Testing Accuracy "+str(test_acc)+"Training Accuracy "+str(train_acc))

四、CMD终端启动 

tensorboard --logdir=F:\code\MINST_test\logs

输入命令行以后，控制台会输出网址，浏览器打开即可。