tensorflow之tensorboard

tensorflow之tensorboard

前言

tensorboard是TensorFlow中自帶的一個數據可視化工具，在安裝TensorFlow的同時，系統會自動安裝。

在不同的TensorFlow版本中，記錄訓練過程所需要的API也不一樣，1.3主要需要summary，而之前的版本，則直接封裝在了tf下面

在使用tensorboard時，必須使用name_scope來創建一個域，然後在每個域內定義變量的名稱。tf.summary中提供了一系列函數，用來幫忙統計。

• histogram用來繪製直方圖。
• scale用來統計標量

---

除此之外，還需要用到

• tf.summary.merge_all用來一次性生成所有摘要
• tf.summary.Filewriter用來生成一個寫入的文件夾（訓練結果和測試結果可以放在不同的文件夾中）
• add_summary講新生成的summer寫入記錄器

代碼

# -*- coding: utf-8 -*-
"""
Created on Wed Oct 25 11:41:50 2017

@author: Sky_Gao
"""


import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
max_steps = 100
learning_rate = 0.001
dropout = 0.9
data_dir = 'MNIST_data/'
log_dir = 'mnist_with_summaries/'
# 定義一個存儲的目錄

mnist = input_data.read_data_sets(data_dir, one_hot=True)
sess = tf.InteractiveSession()

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


def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


with tf.name_scope('input'):
    x = tf.placeholder(tf.float32, [None, 784], name='x-input')
    y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')
# 定義輸入域，並在其中利用placeholder實現佔位
#
with tf.name_scope('input_reshape'):
    image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
    tf.summary.image('input', image_shaped_input, 10)


def Variable_summaries(var):
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean', mean)
        with tf.name_scope('stddev'):
            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)


def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
    with tf.name_scope(layer_name):
        with tf.name_scope('weights'):
            weights = weight_variable([input_dim, output_dim])
            Variable_summaries(weights)
        with tf.name_scope('biases'):
            biases = bias_variable([output_dim])
            Variable_summaries(biases)

        with tf.name_scope('Wx_plus_b'):
            preactivate = tf.matmul(input_tensor, weights) + biases
            tf.summary.histogram('preactivate', preactivate)

        activations = act(preactivate, name='activations')
        tf.summary.histogram('activations', activations)

    return activations


hidden1 = nn_layer(x, 784, 500, 'layer1')

with tf.name_scope('dropout'):
    keep_prob = tf.placeholder(dtype=tf.float32)
    tf.summary.scalar('dropout_keep_probability', keep_prob)
    dropped = tf.nn.dropout(hidden1, keep_prob)

y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)

with tf.name_scope('cross_entropy'):
    diff = tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_)
    with tf.name_scope('total'):
        cross_entropy = tf.reduce_mean(diff)

tf.summary.scalar('cross_entropy', cross_entropy)

with tf.name_scope('train'):
    train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)

with tf.name_scope('accuracy'):
    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)

#merge = tf.summary.merge_all()

with tf.Session() as sess:


merged = tf.summary.merge_all()
#定義合併變量操作，一次性生成所有摘要數據
#    sess.run(merged)
train_writer = tf.summary.FileWriter(log_dir + '/train', sess.graph)
test_writer = tf.summary.FileWriter(log_dir + '/test', sess.graph)
tf.global_variables_initializer().run()

def feed_dict(train):
    if train:
        xs, ys = mnist.train.next_batch(100)
        k = dropout
    else:
        xs, ys = mnist.test.images, mnist.test.labels
        k = 1.0

    return {x: xs, y_:ys, keep_prob:k}

saver = tf.train.Saver()

for i in range(max_steps):
    if i % 10 == 0:
        summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
        test_writer.add_summary(summary, i)
        print('accuracy at step %s : %s' % (i, acc))
    else:
        if i % 100 == 99:
            run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
            run_metadata = tf.RunMetadata()
            summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True),
                                options=run_options, run_metadata=run_metadata)
            train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
            train_writer.add_summary(summary, i)
            saver.save(sess, log_dir+"/model.ckpt", i)
        else:
            summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
            train_writer.add_summary(summary, i)

train_writer.close()
test_writer.close()
#sess.close()