摘要：是自帶的一個可視化工具。本文在學(xué)習(xí)筆記的基礎(chǔ)上修改少量代碼，以探索的使用方法。添加標(biāo)量統(tǒng)計結(jié)果。執(zhí)行后，將返回結(jié)果傳遞給方法即可。效果首先是，顯示取值范圍更細(xì)節(jié)的取值概率信息在里，如下雙擊后，可查看下一層級的詳細(xì)信息

本文基于TensorFlow官網(wǎng)How-Tos的Visualizing Learning和Graph Visualization寫成。

TensorBoard是TensorFlow自帶的一個可視化工具。本文在學(xué)習(xí)筆記（4）的基礎(chǔ)上修改少量代碼，以探索TensorBoard的使用方法。

# -*- coding=utf-8 -*-
# @author: 陳水平
# @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 = 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_array

def 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_array

print "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 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 = True
batch_gradient = False

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("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" % 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})

主要修改點有：

Summary：所有需要在TensorBoard上展示的統(tǒng)計結(jié)果。

tf.name_scope()：為Graph中的Tensor添加層級，TensorBoard會按照代碼指定的層級進(jìn)行展示，初始狀態(tài)下只繪制最高層級的效果，點擊后可展開層級看到下一層的細(xì)節(jié)。

tf.summary.scalar()：添加標(biāo)量統(tǒng)計結(jié)果。

tf.summary.histogram()：添加任意shape的Tensor，統(tǒng)計這個Tensor的取值分布。

tf.summary.merge_all()：添加一個操作，代表執(zhí)行所有summary操作，這樣可以避免人工執(zhí)行每一個summary op。

tf.summary.FileWrite：用于將Summary寫入磁盤，需要制定存儲路徑logdir，如果傳遞了Graph對象，則在Graph Visualization會顯示Tensor Shape Information。執(zhí)行summary op后，將返回結(jié)果傳遞給add_summary()方法即可。

首先是Distribution，顯示取值范圍：

更細(xì)節(jié)的取值概率信息在Historgram里，如下：

雙擊train后，可查看下一層級的詳細(xì)信息：