[ML] TensorBoard 사용하기

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TensorBoard 사용하기

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TensorBoard의 장점

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1. Visualize your TF graph

2. Plot quantitative metrics

3. Show additional data

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5 steps of using TensorBoard

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TensorBoard 사용법

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1. 위의 step에 따라 코딩을 한 후 실행

2. 터미널에 들어가 아래 빨간 네모 칸과 같이 입력

3. 입력 후 밑에 나오는 URL(ex : http://127.0.0.1:6006, tensorboard의 기본 포트 번호는 6006이다.)을 브라우저에 입력하면 tensorflow 확인 가능

이전에 짠 XOR 코드를 tensorboard를 사용해서 실행시켜보자.

소스 코드

import tensorflow as tf
import numpy as np
 
tf.set_random_seed(777)  # for reproducibility
learning_rate = 0.01
 
x_data = [[0, 0],
          [0, 1],
          [1, 0],
          [1, 1]]
y_data = [[0],
          [1],
          [1],
          [0]]
x_data = np.array(x_data, dtype=np.float32)
y_data = np.array(y_data, dtype=np.float32)
 
X = tf.placeholder(tf.float32, [None, 2], name='x-input')
Y = tf.placeholder(tf.float32, [None, 1], name='y-input')
 
with tf.name_scope("layer1"):
    W1 = tf.Variable(tf.random_normal([2, 2]), name='weight1')
    b1 = tf.Variable(tf.random_normal([2]), name='bias1')
    layer1 = tf.sigmoid(tf.matmul(X, W1) + b1)
 
    w1_hist = tf.summary.histogram("weights1", W1)
    b1_hist = tf.summary.histogram("biases1", b1)
    layer1_hist = tf.summary.histogram("layer1", layer1)
 
 
with tf.name_scope("layer2"):
    W2 = tf.Variable(tf.random_normal([2, 1]), name='weight2')
    b2 = tf.Variable(tf.random_normal([1]), name='bias2')
    hypothesis = tf.sigmoid(tf.matmul(layer1, W2) + b2)
 
    w2_hist = tf.summary.histogram("weights2", W2)
    b2_hist = tf.summary.histogram("biases2", b2)
    hypothesis_hist = tf.summary.histogram("hypothesis", hypothesis)
 
# cost/loss function
with tf.name_scope("cost"):
    cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *
                           tf.log(1 - hypothesis))
    cost_summ = tf.summary.scalar("cost", cost)
 
with tf.name_scope("train"):
    train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
 
# Accuracy computation
# True if hypothesis>0.5 else False
predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))
accuracy_summ = tf.summary.scalar("accuracy", accuracy)
 
# Launch graph
with tf.Session() as sess:
    # tensorboard --logdir=./logs/xor_logs
    merged_summary = tf.summary.merge_all()
    writer = tf.summary.FileWriter("./logs/xor_logs_r0_01")
    writer.add_graph(sess.graph)  # Show the graph
 
    # Initialize TensorFlow variables
    sess.run(tf.global_variables_initializer())
 
    for step in range(10001):
        summary, _ = sess.run([merged_summary, train], feed_dict={X: x_data, Y: y_data})
        writer.add_summary(summary, global_step=step)
 
        if step % 100 == 0:
            print(step, sess.run(cost, feed_dict={
                  X: x_data, Y: y_data}), sess.run([W1, W2]))
 
    # Accuracy report
    h, c, a = sess.run([hypothesis, predicted, accuracy],
                       feed_dict={X: x_data, Y: y_data})
    print("\nHypothesis: ", h, "\nCorrect: ", c, "\nAccuracy: ", a)

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실행 결과

TensorBoard 실행 결과