5.4 tensorflow学习与应用——Tensorboard可视化

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.tensorboard.plugins import projector
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
#运行次数
max_steps = 1001
#图片数量
image_num = 3000
#文件路径
DIR = "E:/correct_dove/MLSFromSegV2/2/MLSFromSegV2/"
#定义会话
sess = tf.Session()
#载入图片
embedding = tf.Variable(tf.stack(mnist.test.images[:image_num]), trainable=False, name='embedding')
#参数概要
def variable_summaries(var):
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean', mean)
        with tf.name_scope('stddev'):
            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)
#命名空间
with tf.name_scope("input"):
    #定义输入输出
    x = tf.placeholder(tf.float32, [None, 784], name='x-input')
    y = tf.placeholder(tf.float32, [None, 10], name='y-input')
#显示图片
with tf.name_scope('input_reshape'):
    image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
    tf.summary.image('input', image_shaped_input, 10)
with tf.name_scope("layer"):
    #创建一个简单的神经网络
    with tf.name_scope('weights'):
        W = tf.Variable(tf.zeros([784, 10]))
        variable_summaries(W)
    with tf.name_scope('bias'):
        b = tf.Variable(tf.zeros([1, 10]))
        variable_summaries(b)
    with tf.name_scope('predictions'):
        predictions_1 = tf.matmul(x, W) + b
#定义二次代价函数
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=predictions_1))
    tf.summary.scalar('loss', loss)
#使用梯度下降
with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
#初始化变量
sess.run(tf.global_variables_initializer())

#结果存放在一个bool型列表中
with tf.name_scope('acc'):
    with tf.name_scope('correction'):
        correction = tf.equal(tf.argmax(y, 1), tf.argmax(predictions_1, 1))#argmax返回一维张量中最大的值所在的位置
    #求准确率
    with tf.name_scope('accuracy'):
        accuracy = tf.reduce_mean(tf.cast(correction, tf.float32))
        tf.summary.scalar('accuracy', accuracy)

#产生metadata文件
if tf.gfile.Exists(DIR + 'projector/projector/metadata.tsv'):
    tf.gfile.DeleteRecursively(DIR + 'projector/projector/metadata.tsv')
with open(DIR + 'projector/projector/metadata.tsv', 'w') as f:
    labels = sess.run(tf.argmax(mnist.test.labels[:], 1))
    for i in range(image_num):
        f.write(str(labels[i]) + '\n')

#合并所有的summary
merged = tf.summary.merge_all()

projector_writer = tf.summary.FileWriter(DIR + 'projector/projector', sess.graph)
saver = tf.train.Saver() #存储训练好的模型
config = projector.ProjectorConfig()
embed = config.embeddings.add()
embed.tensor_name = embedding.name
embed.metadata_path = DIR + 'projector/projector/metadata.tsv'
embed.sprite.image_path = DIR + 'projector/data/mnist_10k_sprite.png'
embed.sprite.single_image_dim.extend([28, 28])
projector.visualize_embeddings(projector_writer, config)

for i in range(max_steps):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    run_options = tf.RunOptions(trace_level = tf.RunOptions.FULL_TRACE)
    run_metadata = tf.RunMetadata()
    summary, _ = sess.run([merged, train_step], feed_dict={x: batch_xs, y: batch_ys},
                          options=run_options, run_metadata=run_metadata)
    projector_writer.add_run_metadata(run_metadata, ' step%03d' % i)
    projector_writer.add_summary(summary, i)

    if i % 100 == 0:
        acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y:mnist.test.labels})
        print("Iter " + str(i) + ". Testing Accuracy = " + str(acc))
saver.save(sess, DIR + 'projector/projector/model.ckpt', global_step=max_steps)
projector_writer.close()
sess.close()