• windows下使用tensorboard
• tensorflow 官网上的例子程序都是针对Linux下的；文件路径需要更改
• tensorflow1.1和1.3的启动方式不一样 ：参考：

• Could you try using python -m tensorboard --logdir "${MODEL_DIR}" instead? I suspect that this will fix your issue.
• I should have written tensorboard.main instead of TensorBoard: python -m tensorboard.main --logdir "${MODEL_DIR}"

• 但是虽然启动了6006端口，但是加载文件失败，这个问题留到后面解决，继续跟进tensorflow学习

• 更新几个现有的demo

如路径为：E:\MyTensorBoard\logs, logs中又包含train和test。此时，TensorBoard通过读取事件文件来运行，通过在cmd 中键入命令：tensorboard --logdir=log文件路径。按照我们当前目录，若写成：tensorboard --logdir=E:\MyTensorBoard\logs显示结果是：No scalar、No image...，然而查了几遍代码也没有问题，事件文件也没有问题。解决方法：方法一：将cmd的默认路径cd到log文件的上一层，即cd /d E:\MyTensorBoard，之后等号后面直接键入log文件名即可，不需写全路径，即 tensorboard --logdir=logs。方法二：双斜杠，即tensorboard --logdir=E://MyTensorBoard//logs。最后根据得到的网址http://hostIP:6006，在chrome里打开，就可以可视化我们的图表了，幸福来的太突然

Tensor与Graph可视化

• Summary：所有需要在TensorBoard上展示的统计结果。
• tf.name_scope()：为Graph中的Tensor添加层级，TensorBoard会按照代码指定的层级进行展示，初始状态下只绘制最高层级的效果，点击后可展开层级看到下一层的细节。
• tf.summary.scalar()：添加标量统计结果。
• tf.summary.histogram()：添加任意shape的Tensor，统计这个Tensor的取值分布。
• tf.summary.merge_all()：添加一个操作，代表执行所有summary操作，这样可以避免人工执行每一个summary op。
• tf.summary.FileWrite：用于将Summary写入磁盘，需要制定存储路径logdir，如果传递了Graph对象，则在Graph Visualization会显示Tensor Shape Information。执行summary op后，将返回结果传递给add_summary()方法即可。

import gzipimport structimport numpy as npfrom sklearn.linear_model import LogisticRegressionfrom sklearn import preprocessingfrom sklearn.metrics import accuracy_scoreimport tensorflow as tf# MNIST data is stored in binary format,# and we transform them into numpy ndarray objects by the following two utility functionsdef read_image(file_name):    with gzip.open(file_name, 'rb') as f:        buf = f.read()        index = 0        magic, images, rows, columns = struct.unpack_from('>IIII', buf, index)        index += struct.calcsize('>IIII')        image_size = '>' + str(images * rows * columns) + 'B'        ims = struct.unpack_from(image_size, buf, index)        im_array = np.array(ims).reshape(images, rows, columns)        return im_arraydef read_label(file_name):    with gzip.open(file_name, 'rb') as f:        buf = f.read()        index = 0        magic, labels = struct.unpack_from('>II', buf, index)        index += struct.calcsize('>II')        label_size = '>' + str(labels) + 'B'        labels = struct.unpack_from(label_size, buf, index)        label_array = np.array(labels)        return label_arrayprint ("Start processing MNIST handwritten digits data...")train_x_data = read_image("MNIST_data/train-images-idx3-ubyte.gz")train_x_data = train_x_data.reshape(train_x_data.shape[0], -1).astype(np.float32)train_y_data = read_label("MNIST_data/train-labels-idx1-ubyte.gz")test_x_data = read_image("MNIST_data/t10k-images-idx3-ubyte.gz")test_x_data = test_x_data.reshape(test_x_data.shape[0], -1).astype(np.float32)test_y_data = read_label("MNIST_data/t10k-labels-idx1-ubyte.gz")train_x_minmax = train_x_data / 255.0test_x_minmax = test_x_data / 255.0# Of course you can also use the utility function to read in MNIST provided by tensorflow# from tensorflow.examples.tutorials.mnist import input_data# mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)# train_x_minmax = mnist.train.images# train_y_data = mnist.train.labels# test_x_minmax = mnist.test.images# test_y_data = mnist.test.labels# We evaluate the softmax regression model by sklearn firsteval_sklearn = Falseif eval_sklearn:    print ("Start evaluating softmax regression model by sklearn...")    reg = LogisticRegression(solver="lbfgs", multi_class="multinomial")    reg.fit(train_x_minmax, train_y_data)    np.savetxt('coef_softmax_sklearn.txt', reg.coef_, fmt='%.6f')  # Save coefficients to a text file    test_y_predict = reg.predict(test_x_minmax)    print ("Accuracy of test set: %f" % accuracy_score(test_y_data, test_y_predict))eval_tensorflow = Truebatch_gradient = False# Summary：所有需要在TensorBoard上展示的统计结果。def variable_summaries(var):    with tf.name_scope('summaries'):        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)if eval_tensorflow:    print ("Start evaluating softmax regression model by tensorflow...")    # reformat y into one-hot encoding style    lb = preprocessing.LabelBinarizer()    lb.fit(train_y_data)    train_y_data_trans = lb.transform(train_y_data)    test_y_data_trans = lb.transform(test_y_data)    x = tf.placeholder(tf.float32, [None, 784])    with tf.name_scope('weights'):        W = tf.Variable(tf.zeros([784, 10]))        variable_summaries(W)    with tf.name_scope('biases'):        b = tf.Variable(tf.zeros([10]))        variable_summaries(b)    with tf.name_scope('Wx_plus_b'):        V = tf.matmul(x, W) + b        tf.summary.histogram('pre_activations', V)    with tf.name_scope('softmax'):        y = tf.nn.softmax(V)        tf.summary.histogram('activations', y)    y_ = tf.placeholder(tf.float32, [None, 10])    with tf.name_scope('cross_entropy'):        loss = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))        tf.summary.scalar('cross_entropy', loss)    with tf.name_scope('train'):        optimizer = tf.train.GradientDescentOptimizer(0.5)        train = optimizer.minimize(loss)    with tf.name_scope('evaluate'):        with tf.name_scope('correct_prediction'):            correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))        with tf.name_scope('accuracy'):            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))            tf.summary.scalar('accuracy', accuracy)    init = tf.global_variables_initializer()    sess = tf.Session()    sess.run(init)    merged = tf.summary.merge_all()    train_writer = tf.summary.FileWriter('tmp/train', sess.graph)    test_writer = tf.summary.FileWriter('tmp/test')    if batch_gradient:        for step in range(300):            sess.run(train, feed_dict={x: train_x_minmax, y_: train_y_data_trans})            if step % 10 == 0:                print ("Batch Gradient Descent processing step %d" % step)        print ("Finally we got the estimated results, take such a long time...")    else:        for step in range(1000):            if step % 10 == 0:                summary, acc = sess.run([merged, accuracy], feed_dict={x: test_x_minmax, y_: test_y_data_trans})                test_writer.add_summary(summary, step)                print ("Stochastic Gradient Descent processing step %d accuracy=%.2f" % (step, acc))            else:                sample_index = np.random.choice(train_x_minmax.shape[0], 100)                batch_xs = train_x_minmax[sample_index, :]                batch_ys = train_y_data_trans[sample_index, :]                summary, _ = sess.run([merged, train], feed_dict={x: batch_xs, y_: batch_ys})                train_writer.add_summary(summary, step)    np.savetxt('coef_softmax_tf.txt', np.transpose(sess.run(W)), fmt='%.6f')  # Save coefficients to a text file    print ("Accuracy of test set: %f" % sess.run(accuracy, feed_dict={x: test_x_minmax, y_: test_y_data_trans}))

Embeddings

• TensorBoard是TensorFlow自带的一个可视化工具，Embeddings是其中的一个功能，用于在二维或三维空间对高维数据进行探索

# -*- coding: utf-8 -*-# @author: ranjiewen# @date: 2017-02-08# @description: hello world program to set up embedding projector in TensorBoard based on MNIST# @ref: http://yann.lecun.com/exdb/mnist/, https://www.tensorflow.org/images/mnist_10k_sprite.png#import numpy as npimport tensorflow as tffrom tensorflow.contrib.tensorboard.plugins import projectorfrom tensorflow.examples.tutorials.mnist import input_dataimport osPATH_TO_MNIST_DATA = "MNIST_data"LOG_DIR = "emd"IMAGE_NUM = 10000# Read in MNIST data by utility functions provided by TensorFlowmnist = input_data.read_data_sets(PATH_TO_MNIST_DATA, one_hot=False)# Extract target MNIST image dataplot_array = mnist.test.images[:IMAGE_NUM]  # shape: (n_observations, n_features)# Generate meta datanp.savetxt(os.path.join(LOG_DIR, 'metadata.tsv'), mnist.test.labels[:IMAGE_NUM], fmt='%d')# Download sprite image# https://www.tensorflow.org/images/mnist_10k_sprite.png, 100x100 thumbnailsPATH_TO_SPRITE_IMAGE = os.path.join(LOG_DIR, 'mnist_10k_sprite.png')# To visualise your embeddings, there are 3 things you need to do:# 1) Setup a 2D tensor variable(s) that holds your embedding(s)session = tf.InteractiveSession()embedding_var = tf.Variable(plot_array, name='embedding')tf.global_variables_initializer().run()# 2) Periodically save your embeddings in a LOG_DIR# Here we just save the Tensor once, so we set global_step to a fixed numbersaver = tf.train.Saver()saver.save(session, os.path.join(LOG_DIR, "model.ckpt"), global_step=0)# 3) Associate metadata and sprite image with your embedding# Use the same LOG_DIR where you stored your checkpoint.summary_writer = tf.summary.FileWriter(LOG_DIR)config = projector.ProjectorConfig()# You can add multiple embeddings. Here we add only one.embedding = config.embeddings.add()embedding.tensor_name = embedding_var.name# Link this tensor to its metadata file (e.g. labels).embedding.metadata_path = os.path.join(LOG_DIR, 'metadata.tsv')# Link this tensor to its sprite image.embedding.sprite.image_path = PATH_TO_SPRITE_IMAGEembedding.sprite.single_image_dim.extend([28, 28])# Saves a configuration file that TensorBoard will read during startup.projector.visualize_embeddings(summary_writer, config)

官网demo

• 注意更改文件路径

"""A simple MNIST classifier which displays summaries in TensorBoard.This is an unimpressive MNIST model, but it is a good example of usingtf.name_scope to make a graph legible in the TensorBoard graph explorer, and ofnaming summary tags so that they are grouped meaningfully in TensorBoard.It demonstrates the functionality of every TensorBoard dashboard."""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport argparseimport osimport sysimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataFLAGS = None #"F:\RANJIEWEN\Deep_learning\TensorFlow\log"def train():  # Import data  mnist = input_data.read_data_sets(FLAGS.data_dir,                                    one_hot=True,                                    fake_data=FLAGS.fake_data)  sess = tf.InteractiveSession()  # Create a multilayer model.  # Input placeholders  with tf.name_scope('input'):    x = tf.placeholder(tf.float32, [None, 784], name='x-input')    y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')  with tf.name_scope('input_reshape'):    image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])    tf.summary.image('input', image_shaped_input, 10)  # We can't initialize these variables to 0 - the network will get stuck.  def weight_variable(shape):    """Create a weight variable with appropriate initialization."""    initial = tf.truncated_normal(shape, stddev=0.1)    return tf.Variable(initial)  def bias_variable(shape):    """Create a bias variable with appropriate initialization."""    initial = tf.constant(0.1, shape=shape)    return tf.Variable(initial)  def variable_summaries(var):    """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""    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)  def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):    """Reusable code for making a simple neural net layer.    It does a matrix multiply, bias add, and then uses ReLU to nonlinearize.    It also sets up name scoping so that the resultant graph is easy to read,    and adds a number of summary ops.    """    # Adding a name scope ensures logical grouping of the layers in the graph.    with tf.name_scope(layer_name):      # This Variable will hold the state of the weights for the layer      with tf.name_scope('weights'):        weights = weight_variable([input_dim, output_dim])        variable_summaries(weights)      with tf.name_scope('biases'):        biases = bias_variable([output_dim])        variable_summaries(biases)      with tf.name_scope('Wx_plus_b'):        preactivate = tf.matmul(input_tensor, weights) + biases        tf.summary.histogram('pre_activations', preactivate)      activations = act(preactivate, name='activation')      tf.summary.histogram('activations', activations)      return activations  hidden1 = nn_layer(x, 784, 500, 'layer1')  with tf.name_scope('dropout'):    keep_prob = tf.placeholder(tf.float32)    tf.summary.scalar('dropout_keep_probability', keep_prob)    dropped = tf.nn.dropout(hidden1, keep_prob)  # Do not apply softmax activation yet, see below.  y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)  with tf.name_scope('cross_entropy'):    # The raw formulation of cross-entropy,    #    # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.softmax(y)),    #                               reduction_indices=[1]))    #    # can be numerically unstable.    #    # So here we use tf.nn.softmax_cross_entropy_with_logits on the    # raw outputs of the nn_layer above, and then average across    # the batch.    diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)    with tf.name_scope('total'):      cross_entropy = tf.reduce_mean(diff)  tf.summary.scalar('cross_entropy', cross_entropy)  with tf.name_scope('train'):    train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(        cross_entropy)  with tf.name_scope('accuracy'):    with tf.name_scope('correct_prediction'):      correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))    with tf.name_scope('accuracy'):      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))  tf.summary.scalar('accuracy', accuracy)  # Merge all the summaries and write them out to  # /tmp/tensorflow/mnist/logs/mnist_with_summaries (by default)# log/train or log/test  merged = tf.summary.merge_all()  train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)  test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test')  tf.global_variables_initializer().run()  # Train the model, and also write summaries.  # Every 10th step, measure test-set accuracy, and write test summaries  # All other steps, run train_step on training data, & add training summaries  def feed_dict(train):    """Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""    if train or FLAGS.fake_data:      xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)      k = FLAGS.dropout    else:      xs, ys = mnist.test.images, mnist.test.labels      k = 1.0    return {x: xs, y_: ys, keep_prob: k}  for i in range(FLAGS.max_steps):    if i % 10 == 0:  # Record summaries and test-set accuracy      summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))      test_writer.add_summary(summary, i)      print('Accuracy at step %s: %s' % (i, acc))    else:  # Record train set summaries, and train      if i % 100 == 99:  # Record execution stats        run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)        run_metadata = tf.RunMetadata()        summary, _ = sess.run([merged, train_step],                              feed_dict=feed_dict(True),                              options=run_options,                              run_metadata=run_metadata)        train_writer.add_run_metadata(run_metadata, 'step%03d' % i)        train_writer.add_summary(summary, i)        print('Adding run metadata for', i)      else:  # Record a summary        summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))        train_writer.add_summary(summary, i)  train_writer.close()  test_writer.close()def main(_):  if tf.gfile.Exists(FLAGS.log_dir):    tf.gfile.DeleteRecursively(FLAGS.log_dir)  tf.gfile.MakeDirs(FLAGS.log_dir)  train()if __name__ == '__main__':  parser = argparse.ArgumentParser()  parser.add_argument('--fake_data', nargs='?', const=True, type=bool,                      default=False,                      help='If true, uses fake data for unit testing.')  parser.add_argument('--max_steps', type=int, default=1000,                      help='Number of steps to run trainer.')  parser.add_argument('--learning_rate', type=float, default=0.001,                      help='Initial learning rate')  parser.add_argument('--dropout', type=float, default=0.9,                      help='Keep probability for training dropout.')  # parser.add_argument(  #     '--data_dir',  #     type=str,  #     default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),  #                          'tensorflow/mnist/input_data'),  #     help='Directory for storing input data')  # parser.add_argument(  #     '--log_dir',  #     type=str,  #     default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),  #                          'tensorflow/mnist/logs/mnist_with_summaries'),  #     help='Summaries log directory')  parser.add_argument(      '--data_dir',      type=str,      default=os.path.join(os.getenv('TEST_TMPDIR', 'F:\RANJIEWEN\Deep_learning\TensorFlow\MNIST_data')),      help='Directory for storing input data')  parser.add_argument(      '--log_dir',      type=str,      default=os.path.join(os.getenv('TEST_TMPDIR', 'F:\RANJIEWEN\Deep_learning\TensorFlow\log')),      help='Summaries log directory')  FLAGS, unparsed = parser.parse_known_args()  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)