mnist_into_tensorboard.py

# encoding: utf-8

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# MNIST 손글씨 데이터 읽어들이기
mnist = input_data.read_data_sets("mnist/", one_hot=True)

pixels = 28 * 28
nums = 10

# placeholder 정의
x = tf.placeholder(tf.float32, shape=(None, pixels), name="x")  # image data
y_ = tf.placeholder(tf.float32, shape=(None, nums), name="y_")  # answer label


# initiate function for weight and bias
def weight_variable(name, shape):
  W_init = tf.truncated_normal(shape, stddev=0.1)
  W = tf.Variable(W_init, name="W_" + name)
  return W


def bias_variable(name, size):
  b_init = tf.constant(0.1, shape=[size])
  b = tf.Variable(b_init, name="b_" + name)
  return b

# function to create convolution layer
def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

# function to create maximum pooling layer
def max_pool(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')

# conv layer 1
with tf.name_scope('conv1') as scope:
  W_conv1 = weight_variable('conv1', [5, 5, 1, 32])
  b_conv1 = bias_variable('conv1', 32)
  x_image = tf.reshape(x, [-1, 28, 28, 1])
  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

# pooling layer 1
with tf.name_scope('pool1') as scope:
  h_pool1 = max_pool(h_conv1)

# conv layer 2
with tf.name_scope('conv2') as scope:
  W_conv2 = weight_variable('conv2', [5, 5, 32, 64])
  b_conv2 = bias_variable('conv2', 64)
  h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

# pooling layer 2
with tf.name_scope('pool2') as scope:
  h_pool2 = max_pool(h_conv2)

# fully connected layer
with tf.name_scope('fully_connected') as scope:
  n = 7 * 7 * 64
  W_fc = weight_variable('fc', [n, 1024])
  b_fc = bias_variable('fc', 1024)
  h_pool2_flat = tf.reshape(h_pool2, [-1, n])
  h_fc = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc) + b_fc)

# keep from dropout
with tf.name_scope('dropout') as scope:
  keep_prob = tf.placeholder(tf.float32)
  h_fc_drop = tf.nn.dropout(h_fc, keep_prob)

# output layer
with tf.name_scope('readout') as scope:
  W_fc2 = weight_variable('fc2', [1024, 10])
  b_fc2 = bias_variable('fc2', 10)
  y_conv = tf.nn.softmax(tf.matmul(h_fc_drop, W_fc2) + b_fc2)

# training model
with tf.name_scope('loss') as scope:
  cross_entoropy = -tf.reduce_sum(y_ * tf.log(y_conv))
with tf.name_scope('training') as scope:
  optimizer = tf.train.AdamOptimizer(1e-4)
  train_step = optimizer.minimize(cross_entoropy)

# testing model
with tf.name_scope('predict') as scope:
  predict_step = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
  accuracy_step = tf.reduce_mean(tf.cast(predict_step, tf.float32))


# set feed_dict
def set_feed(images, labels, prob):
  return {x: images, y_: labels, keep_prob: prob}


# start session
with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())

  # prepare TensorBoard
  tw = tf.summary.FileWriter('log_dir', graph=sess.graph)

  # create feed for test
  test_fd = set_feed(mnist.test.images, mnist.test.labels, 1)

  # start training
  for step in range(10000):
    batch = mnist.train.next_batch(50)
    fd = set_feed(batch[0], batch[1], 0.5)
    _, loss = sess.run([train_step, cross_entoropy], feed_dict=fd)
    if step % 100 == 0:
      acc = sess.run(accuracy_step, feed_dict=test_fd)
      print('step=', step, 'loss=', loss, 'acc=', acc)

  # print final result
  acc = sess.run(accuracy_step, feed_dict=test_fd)
  print('accuracy=', acc)