tensorboard可视化

https://www.cnblogs.com/felixwang2/p/9184404.html 

 根据上面的这个博客学习的。

中间遇到过问题，就是运行时报错：

Failed precondition: could not dlopen DSO: cupti64_90.dll; dlerror: cupti64_90.dll not found

 好在后来找到办法：

 

将cupti64_90.dll从目录

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\extras\CUPTI\libx64

复制到设置了环境变量的这个目录下：

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin

 

#*- coding:utf-8 -*
# https://www.cnblogs.com/felixwang2/p/9184404.html
# TensorFlow(八)：tensorboard可视化

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.tensorboard.plugins import projector

# 载入数据集
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  # 最多10000，因为测试集为10000
# 文件路径
DIR = "F:/document/PyCharm/temp/"

# 定义会话
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

# 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)  # 平均值
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar('stddev', stddev)  # 标准差
        tf.summary.scalar('max', tf.reduce_max(var))  # 最大值
        tf.summary.scalar('min', tf.reduce_min(var))  # 最小值
        tf.summary.histogram('histogram', var)  # 直方图


# 命名空间
with tf.name_scope('input'):
    # 这里的none表示第一个维度可以是任意的长度
    x = tf.placeholder(tf.float32, [None, 784], name='x-input')
    # 正确的标签
    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])  # -1表示不确定的值
    tf.summary.image('input', image_shaped_input, 10)  # 一共放10张图片

with tf.name_scope('layer'):
    # 创建一个简单神经网络
    with tf.name_scope('weights'):
        W = tf.Variable(tf.zeros([784, 10]), name='W')
        variable_summaries(W)
    with tf.name_scope('biases'):
        b = tf.Variable(tf.zeros([10]), name='b')
        variable_summaries(b)
    with tf.name_scope('wx_plus_b'):
        wx_plus_b = tf.matmul(x, W) + b
    with tf.name_scope('softmax'):
        prediction = tf.nn.softmax(wx_plus_b)

with tf.name_scope('loss'):
    # 交叉熵代价函数
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=prediction))
    tf.summary.scalar('loss', loss)
with tf.name_scope('train'):
    # 使用梯度下降法
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

# 初始化变量
sess.run(tf.global_variables_initializer())

with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
        # 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
    with tf.name_scope('accuracy'):
        # 求准确率
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))  # 把correct_prediction变为float32类型
        tf.summary.scalar('accuracy', accuracy)

# 产生metadata文件
if tf.gfile.Exists(DIR + 'projector/projector/metadata.tsv'):  # 检测是否已存在
    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()  # 用来保存网络模型
config = projector.ProjectorConfig()  # 定义了配置文件
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)  # 可视化的一个工具

for i in range(max_steps):
    # 每个批次100个样本
    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)

    # 每训练100次打印准确率
    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))

# 训练完保存模型
saver.save(sess, DIR + 'projector/projector/a_model.ckpt', global_step=max_steps)
projector_writer.close()
sess.close()