【Tensorboard使用】动态显示loss曲线

超、凢脫俗 2022-03-08 02:50 604阅读 0赞

# 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 #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BsU29uZ19DU0RO_size_16_color_FFFFFF_t_70][]

# accyracy #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BsU29uZ19DU0RO_size_16_color_FFFFFF_t_70 1][]

# loss #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BsU29uZ19DU0RO_size_16_color_FFFFFF_t_70 2][]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BsU29uZ19DU0RO_size_16_color_FFFFFF_t_70]: /images/20220308/2dd9e2f8cdf74e348b513a324e9cf172.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BsU29uZ19DU0RO_size_16_color_FFFFFF_t_70 1]: /images/20220308/64c890302ca14f7ba6b04ac969bd69be.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BsU29uZ19DU0RO_size_16_color_FFFFFF_t_70 2]: /images/20220308/3da21d4dba0245bdb3a0d472db442dc5.png