80
81

Delete article

Deleted articles cannot be recovered.

Draft of this article would be also deleted.

Are you sure you want to delete this article?

More than 5 years have passed since last update.

[TF]Tensorboardを使って学習結果をVisualizationしてみた

Last updated at Posted at 2016-01-06

Tensorboardを使って学習結果をVisualizationする方法を調べてみた。
Tensorboardに出力する方法はここを参考にした。

簡単な例

いきなり複雑なやつだと大変なので、簡単な例を作って試した。
テストデータは下記の図のように、xは5次元、yを3次元とし、xをランダムで生成したあとに、あらかじめ指定しておいたWをかけてbを足してyを生成した。xが5次元、yが3次元なので、Wのサイズは5x3になる。
実行したコードは下記の通り、これはTensorflowのホームページのコードをテストデータに合わせてちょっとだけ変更した。

sample08.png

コードのポイントは下記の通り。

  1. writer = tf.train.SummaryWriter("/tmp/tensorflow_log", sess.graph_def)のようにしてSummaryWriterのobjectを作る。そのときにlogを出力するdirectoryを指定する。
  2. merged = tf.merge_all_summaries()でsummary dataを生成するoprationをまとめる。operationとはscalar_summaryとかhistogram_summaryのこと。
  3. operationをコードに埋め込む。w_hist = tf.histogram_summary("weights", W)こんな感じ。
  4. 何回かに1回(コードでは10回に1回)result = sess.run([merged, loss])のようにmergedを実行する。lossの方は、標準出力に出力するためなのでTensorboardに出したいだけであれば実行しなくてもいいと思う。
  5. sess.runの戻り値をwriteに渡す。コードでは、mergedとlossをsess.runに渡しているのでresultがlistで戻ってくる。mergedの方なのでresult[0]をwriter.add_summary(summary_str, step)こんな感じで渡す。
    6.学習を実行して終わったら、tensorboard --logdir=/tmp/tensorflow_log を実行する。
    7.その状態でchromeを起動し、http://localhost:6006 にアクセスするとTensorBoardが表示され、logの出力がうまく行っているとGRAPHが表示される。

以下にTensorboardの結果と実行したコードを示す。

GRAPH

00_graph.png

EVENTS

範囲を選択_003.png

HISTOGRAMS

範囲を選択_004.png

Code

python
import tensorflow as tf
import numpy as np

WW = np.array([[0.1, 0.6, -0.9], 
               [0.2, 0.5, -0.8], 
               [0.3, 0.4, -0.7],
               [0.4, 0.3, -0.6],
               [0.5, 0.2, -0.5]]).astype(np.float32)
bb = np.array([0.3, 0.4, 0.5]).astype(np.float32)
x_data = np.random.rand(100,5).astype(np.float32)
y_data = np.dot(x_data, WW) + bb

with tf.Session() as sess:

    W = tf.Variable(tf.random_uniform([5,3], -1.0, 1.0))
    # The zeros set to zero with all elements.
    b = tf.Variable(tf.zeros([3]))
    #y = W * x_data + b
    y = tf.matmul(x_data, W) + b
    
    # Add summary ops to collect data
    w_hist = tf.histogram_summary("weights", W)
    b_hist = tf.histogram_summary("biases", b)
    y_hist = tf.histogram_summary("y", y)
    
    # Minimize the mean squared errors.
    loss = tf.reduce_mean(tf.square(y - y_data))
    # Outputs a Summary protocol buffer with scalar values
    loss_summary = tf.scalar_summary("loss", loss)
    
    # Gradient descent algorithm
    optimizer = tf.train.GradientDescentOptimizer(0.5)
    train = optimizer.minimize(loss)
    
    # Before starting, initialize the variables.  We will 'run' this first.
    init = tf.initialize_all_variables()
    
    # Creates a SummaryWriter
    # Merges all summaries collected in the default graph
    merged = tf.merge_all_summaries()
    writer = tf.train.SummaryWriter("/tmp/tensorflow_log", sess.graph_def)
    sess.run(init)
    
    # Fit the line
    for step in xrange(501):
        if step % 10 == 0:
            result = sess.run([merged, loss])
            summary_str = result[0]
            acc = result[1]
            writer.add_summary(summary_str, step)
            print"step = %s acc = %s W = %s b = %s" % (step, acc, sess.run(W), sess.run(b))
        else:
            sess.run(train)

もうちょっと複雑な例

histogramをまとめるときは、tf.histogram_summary("xxx/weights", w), tf.histogram_summary("xxx/biases", b)のようにまとめたいhistogramのxxxを同じにする。
以下にTensorboardの結果と実行したコードを示す。

GRAPH

20160107_mnist_graph.png

EVENTS

20160107_mnist_accuracy.png

HISTOGRAMS

20160107_mnist_histogram.png

Code

python
import input_data
import tensorflow as tf

print 'load MNIST dataset'
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape = shape)
    return tf.Variable(initial)
    
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

def _write_histogram_summary(parent, infos):
    for i in infos:
        tf.histogram_summary("%s/%s" % (parent, i[0]), i[1])
    
with tf.Session() as sess:
    x = tf.placeholder("float", [None, 784])
    y_ = tf.placeholder("float", [None, 10])
    
    # 1x28x28 -> 32x28x28 -> 32x14x14
    x_image = tf.reshape(x, [-1,28,28,1])    
    with tf.variable_scope('conv1') as scope:
        W_conv1 = weight_variable([5, 5, 1, 32])
        b_conv1 = bias_variable([32])
        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1, name=scope.name)
        
        _write_histogram_summary('conv1', [['weights', W_conv1],['biases', b_conv1], ['activations', h_conv1]])
        
    h_pool1 = max_pool_2x2(h_conv1)  
    
    # 32x14x14 -> 64x7x7
    with tf.variable_scope('conv1') as scope:
        W_conv2 = weight_variable([5, 5, 32, 64])
        b_conv2 = bias_variable([64])
        h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2, name=scope.name)
        
        _write_histogram_summary('conv2', [['weights', W_conv2],['biases', b_conv2], ['activations', h_conv2]])
        
    h_pool2 = max_pool_2x2(h_conv2)    
    
    # 64x7x7 -> 1024
    with tf.variable_scope('full1') as scope:
        W_fc1 = weight_variable([7 * 7 * 64, 1024])
        b_fc1 = bias_variable([1024])
        h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1, name=scope.name)    

        _write_histogram_summary('full1', [['weights', W_fc1],['biases', b_fc1], ['activations', h_fc1]])
            
    # dropout
    keep_prob = tf.placeholder("float")
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
    
    # Readout
    with tf.variable_scope('full2') as scope:
        W_fc2 = weight_variable([1024, 10])
        b_fc2 = bias_variable([10])
    
        y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2, name=scope.name)  
    
        _write_histogram_summary('full2', [['weights', W_fc2],['biases', b_fc2]])
    
    tf.histogram_summary("y", y_conv)
    
    cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
    
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    
    
    tf.scalar_summary("cross_entropy", cross_entropy)
    tf.scalar_summary("accuracy", accuracy)
    
    # Before starting, initialize the variables.  We will 'run' this first.
    init = tf.initialize_all_variables()
    
    # Creates a SummaryWriter
    # Merges all summaries collected in the default graph
    merged = tf.merge_all_summaries()
    writer = tf.train.SummaryWriter("/tmp/tensorflow_log_mnist", sess.graph_def)
    
    sess.run(init)
    
    # training
    N = len(mnist.train.images)
    N_test = len(mnist.test.images)
    n_epoch = 20000
    batchsize = 50
    
    for i in range(n_epoch):
        batch = mnist.train.next_batch(batchsize)
        if i%100 == 0:
            summary_str, loss_value = sess.run([merged, accuracy], feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
            writer.add_summary(summary_str, i)
            print "step %d %.2f" % (i, loss_value)
        sess.run(train_step, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})    
        
    tacc = 0
    tbatchsize = 1000
    for i in range(0,N_test,tbatchsize):
        acc = sess.run(accuracy, feed_dict={
        x: mnist.test.images[i:i+tbatchsize], y_: mnist.test.labels[i:i+tbatchsize], keep_prob: 1.0})
        tacc += acc * tbatchsize
        
        print "test step %d acc = %.2f" % (i//tbatchsize, acc)
    tacc /= N_test
    print "test accuracy %.2f" % tacc       
80
81
0

Register as a new user and use Qiita more conveniently

  1. You get articles that match your needs
  2. You can efficiently read back useful information
  3. You can use dark theme
What you can do with signing up
80
81

Delete article

Deleted articles cannot be recovered.

Draft of this article would be also deleted.

Are you sure you want to delete this article?