【Tensorboard使用】动态显示loss曲线

程序

下面的例子是两层全连接网络，实现手写数字识别的案例。

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import os

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"  # 忽略tensorflow警告信息

mnist = input_data.read_data_sets('MNIST_data',one_hot=True)

batch_size = 100
n_batch = mnist.train.num_examples // batch_size

lr = 0.01

#初始化
#命名空间
with tf.name_scope("input"):
    x = tf.placeholder(tf.float32,[None,784],name='x')
    y_ = tf.placeholder(tf.float32,[None,10],name='y_')

#创建网络
with tf.name_scope('Layer1'):
    with tf.name_scope('weight1'):
        w1 = tf.Variable(tf.truncated_normal([784,500]),name='w1')
    with tf.name_scope("biases"):
        b1 = tf.Variable(tf.zeros([500]),name='b1')
    with tf.name_scope("y1"):
        y1 = tf.nn.tanh(tf.matmul(x,w1)+b1,name='y1')
with tf.name_scope("Layer2"):
    with tf.name_scope('weight2'):
        w2 = tf.Variable(tf.truncated_normal([500,10]),name='w2')
    with tf.name_scope("biases2"):
        b2 = tf.Variable(tf.zeros([10]),name='b2')
    with tf.name_scope("y"):
        y = tf.nn.softmax(tf.matmul(y1,w2)+b2,name='y') #输出信号总和

with tf.name_scope("loss"):
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y))
    tf.summary.scalar('loss',loss)

#训练
with tf.name_scope("train"):
    train_stap = tf.train.GradientDescentOptimizer(lr).minimize(loss)

#准确率
with tf.name_scope("accuracy"):
    correct_prediction = tf.equal(tf.argmax(y_,1),tf.argmax(y,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    tf.summary.scalar('accuracy',accuracy)

merged = tf.summary.merge_all()

with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    write = tf.summary.FileWriter('E://TensorBoard//test',sess.graph)

    for epoch in range(500):
        for batch in range(n_batch):
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            _,summery,loss_val=sess.run([train_stap,merged,loss],feed_dict={x:batch_xs, y_:batch_ys})

        write.add_summary(summery,epoch)
        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y_:mnist.test.labels})
        print("Atfer %d steps, Accuracy is %g, loss is %g" %(epoch,acc,loss_val))

 

网络结构

 

accyracy

 

loss