TensorBoard基礎篇

TensorBoard簡介

TensorBoard是Tensorflow自帶的一個強大的可視化工具，也是一個web應用程序套件。在衆多機器學習庫中，Tensorflow是目前唯一自帶可視化工具的庫，這也是Tensorflow的一個優點。學會使用TensorBoard，將可以幫助我們構建複雜模型。

這裏需要理解“可視化”的意義。“可視化”也叫做數據可視化。是關於數據之視覺表現形式的研究。這種數據的視覺表現形式被定義爲一種以某種概要形式抽提出來的信息，包括相應信息單位的各種屬性和變量。例如我們需要可視化算法運行的錯誤率，那麼我們可以取算法每次訓練的錯誤率，繪製成折線圖或曲線圖，來表達訓練過程中錯誤率的變化。可視化的方法有很多種。但無論哪一種，均是對數據進行摘要(summary)與處理。

通常使用TensorBoard有三個步驟：

• 首先需要在需要可視化的相關部位添加可視化代碼，即創建摘要、添加摘要；
• 其次運行代碼，可以生成了一個或多個事件文件(event files)；
• 最後啓動TensorBoard的Web服務器。

完成以上三個步驟，就可以在瀏覽器中可視化結果，Web服務器將會分析這個事件文件中的內容，並在瀏覽器中將結果繪製出來。

如果我們已經擁有了一個事件文件，也可以直接利用TensorBoard查看這個事件文件中的摘要。
TensorBoard視圖如下所示：

Logistic迴歸的Tensorboard可視化

from __future__ import print_function

import tensorflow as tf

導入數據集

# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("./data/", one_hot=True)

---

Extracting ./data/train-images-idx3-ubyte.gz
Extracting ./data/train-labels-idx1-ubyte.gz
Extracting ./data/t10k-images-idx3-ubyte.gz
Extracting ./data/t10k-labels-idx1-ubyte.gz

參數設置

# Parameters
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_epoch = 1
logs_path = './log/example/' # log存放位置

# tf Graph Input
# mnist data image of shape 28*28=784
#（name=''將在Tensorboard中顯示）
x = tf.placeholder(tf.float32, [None, 784], name='InputData') #輸入數據（InputData）
# 0-9 digits recognition => 10 classes
y = tf.placeholder(tf.float32, [None, 10], name='LabelData') # 輸出標籤（LabelData）

# Set model weights
W = tf.Variable(tf.zeros([784, 10]), name='Weights') #權重（Weights）
b = tf.Variable(tf.zeros([10]), name='Bias') #偏置（Bias）

構建模型和操作（模型+損失函數+優化+準確率）

# Construct model and encapsulating all ops into scopes, making
# Tensorboard's Graph visualization more convenient
with tf.name_scope('Model'):
    # Model
    pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax
with tf.name_scope('Loss'):
    # Minimize error using cross entropy
    cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=1))
with tf.name_scope('SGD'):
    # Gradient Descent
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
with tf.name_scope('Accuracy'):
    # Accuracy
    acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    acc = tf.reduce_mean(tf.cast(acc, tf.float32))

# Initializing the variables
init = tf.global_variables_initializer()

# Create a summary to monitor cost tensor
tf.summary.scalar("loss", cost)
# Create a summary to monitor accuracy tensor
tf.summary.scalar("accuracy", acc)
# Merge all summaries into a single op
merged_summary_op = tf.summary.merge_all()

訓練並保存log

# Start Training
with tf.Session() as sess:
    sess.run(init)

    # op to write logs to Tensorboard
    summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())

    # Training cycle
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples / batch_size)
        # Loop over all batches
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # Run optimization op (backprop), cost op (to get loss value)
            # and summary nodes
            _, c, summary = sess.run([optimizer, cost, merged_summary_op],
                                     feed_dict={x: batch_xs, y: batch_ys})
            # Write logs at every iteration
            summary_writer.add_summary(summary, epoch * total_batch + i)
            # Compute average loss
            avg_cost += c / total_batch
        # Display logs per epoch step
        if (epoch+1) % display_epoch == 0:
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))

    print("Optimization Finished!")

    # Test model
    # Calculate accuracy
    print("Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels}))

    print("Run the command line:\n" \
          "--> tensorboard --logdir=./log" \
          "\nThen open http://0.0.0.0:6006/ into your web browser")

---

Epoch: 0001 cost= 1.183717763
Epoch: 0002 cost= 0.665147323
Epoch: 0003 cost= 0.552818966
Epoch: 0004 cost= 0.498699070
Epoch: 0005 cost= 0.465521080
Epoch: 0006 cost= 0.442596199
Epoch: 0007 cost= 0.425560050
Epoch: 0008 cost= 0.412205354
Epoch: 0009 cost= 0.401337254
Epoch: 0010 cost= 0.392412475
Epoch: 0011 cost= 0.384738669
Epoch: 0012 cost= 0.378180920
Epoch: 0013 cost= 0.372407395
Epoch: 0014 cost= 0.367316018
Epoch: 0015 cost= 0.362715464
Epoch: 0016 cost= 0.358595766
Epoch: 0017 cost= 0.354887394
Epoch: 0018 cost= 0.351458600
Epoch: 0019 cost= 0.348339875
Epoch: 0020 cost= 0.345448156
Epoch: 0021 cost= 0.342770365
Epoch: 0022 cost= 0.340232303
Epoch: 0023 cost= 0.337901928
Epoch: 0024 cost= 0.335753958
Epoch: 0025 cost= 0.333657109
Optimization Finished!
Accuracy: 0.9136
Run the command line:
--> tensorboard --logdir=./log
Then open http://0.0.0.0:6006/ into your web browser

損失和準確率的可視化

計算圖模型的基本單元

計算圖模型的可視化

參考

[TensorBoard: 圖表可視化]http://wiki.jikexueyuan.com/project/tensorflow-zh/how_tos/graph_viz.html

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