前言
本文基于TensorFlow官网How-Tos的Visualizing Learning和Graph Visualization写成。
TensorBoard是TensorFlow自带的一个可视化工具。本文在学习笔记（4）的基础上修改少量代码，以探索TensorBoard的使用方法。
代码
# -*- coding=utf-8 -*-
# @author: 陈水平
# @date: 2017-02-09
# @description: implement a softmax regression model upon MNIST handwritten digits
# @ref: http://yann.lecun.com/exdb/mnist/import gzip
import struct
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn import preprocessing
from sklearn.metrics import accuracy_score
import tensorflow as tf# MNIST data is stored in binary format,
# and we transform them into numpy ndarray objects by the following two utility functions
def read_image(file_name):with gzip.open(file_name, 'rb') as f:buf = f.read()index = 0magic, 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 = 0magic, 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 / 255.0
test_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 first
eval_sklearn = False
if 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 filetest_y_predict = reg.predict(test_x_minmax)print "Accuracy of test set: %f" % accuracy_score(test_y_data, test_y_predict)eval_tensorflow = True
batch_gradient = Falsedef 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 stylelb = 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) + btf.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('log/train', sess.graph)test_writer = tf.summary.FileWriter('log/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" % stepprint "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 fileprint "Accuracy of test set: %f" % sess.run(accuracy, feed_dict={x: test_x_minmax, y_: test_y_data_trans})思考
主要修改点有：
-
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。
效果
Visualizing Learning
Scalar
Histogram
首先是Distribution，显示取值范围：
更细节的取值概率信息在Historgram里，如下：
Graph Visualization
双击train后，可查看下一层级的详细信息：
