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# Lab 13 Tensorboard

import tensorflow as tf

import random

from tensorflow.examples.tutorials.mnist import input_data

tf.set_random_seed(777) # reproducibility

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# Check out https://www.tensorflow.org/get_started/mnist/beginners for

# more information about the mnist dataset

# parameters

learning_rate = 0.001

training_epochs = 15

batch_size = 100

TB_SUMMARY_DIR = './tb/mnist'

# input place holders

X = tf.placeholder(tf.float32, [None, 784])

Y = tf.placeholder(tf.float32, [None, 10])

# Image input

x_image = tf.reshape(X, [-1, 28, 28, 1])

tf.summary.image('input', x_image, 3)

# dropout (keep_prob) rate 0.7~0.5 on training, but should be 1 for testing

keep_prob = tf.placeholder(tf.float32)

# weights & bias for nn layers

# http://stackoverflow.com/questions/33640581/how-to-do-xavier-initialization-on-tensorflow

with tf.variable_scope('layer1') as scope:

W1 = tf.get_variable("W", shape=[784, 512],

initializer=tf.contrib.layers.xavier_initializer())

b1 = tf.Variable(tf.random_normal([512]))

L1 = tf.nn.relu(tf.matmul(X, W1) + b1)

L1 = tf.nn.dropout(L1, keep_prob=keep_prob)

tf.summary.histogram("X", X)

tf.summary.histogram("weights", W1)

tf.summary.histogram("bias", b1)

tf.summary.histogram("layer", L1)

with tf.variable_scope('layer2') as scope:

W2 = tf.get_variable("W", shape=[512, 512],

initializer=tf.contrib.layers.xavier_initializer())

b2 = tf.Variable(tf.random_normal([512]))

L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)

L2 = tf.nn.dropout(L2, keep_prob=keep_prob)

tf.summary.histogram("weights", W2)

tf.summary.histogram("bias", b2)

tf.summary.histogram("layer", L2)

with tf.variable_scope('layer3') as scope:

W3 = tf.get_variable("W", shape=[512, 512],

initializer=tf.contrib.layers.xavier_initializer())

b3 = tf.Variable(tf.random_normal([512]))

L3 = tf.nn.relu(tf.matmul(L2, W3) + b3)

L3 = tf.nn.dropout(L3, keep_prob=keep_prob)

tf.summary.histogram("weights", W3)

tf.summary.histogram("bias", b3)

tf.summary.histogram("layer", L3)

with tf.variable_scope('layer4') as scope:

W4 = tf.get_variable("W", shape=[512, 512],

initializer=tf.contrib.layers.xavier_initializer())

b4 = tf.Variable(tf.random_normal([512]))

L4 = tf.nn.relu(tf.matmul(L3, W4) + b4)

L4 = tf.nn.dropout(L4, keep_prob=keep_prob)

tf.summary.histogram("weights", W4)

tf.summary.histogram("bias", b4)

tf.summary.histogram("layer", L4)

with tf.variable_scope('layer5') as scope:

W5 = tf.get_variable("W", shape=[512, 10],

initializer=tf.contrib.layers.xavier_initializer())

b5 = tf.Variable(tf.random_normal([10]))

hypothesis = tf.matmul(L4, W5) + b5

tf.summary.histogram("weights", W5)

tf.summary.histogram("bias", b5)

tf.summary.histogram("hypothesis", hypothesis)

# define cost/loss & optimizer

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(

logits=hypothesis, labels=Y))

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

tf.summary.scalar("loss", cost)

# Summary

summary = tf.summary.merge_all()

# initialize

sess = tf.Session()

sess.run(tf.global_variables_initializer())

# Create summary writer

writer = tf.summary.FileWriter(TB_SUMMARY_DIR)

writer.add_graph(sess.graph)

global_step = 0

print('Start learning!')

# train my model

for epoch in range(training_epochs):

avg_cost = 0

total_batch = int(mnist.train.num_examples / batch_size)

for i in range(total_batch):

batch_xs, batch_ys = mnist.train.next_batch(batch_size)

feed_dict = {X: batch_xs, Y: batch_ys, keep_prob: 0.7}

s, _ = sess.run([summary, optimizer], feed_dict=feed_dict)

writer.add_summary(s, global_step=global_step)

global_step += 1

avg_cost += sess.run(cost, feed_dict=feed_dict) / total_batch

print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))

print('Learning Finished!')

# Test model and check accuracy

correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

print('Accuracy:', sess.run(accuracy, feed_dict={

X: mnist.test.images, Y: mnist.test.labels, keep_prob: 1}))

# Get one and predict

r = random.randint(0, mnist.test.num_examples - 1)

print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))

print("Prediction: ", sess.run(

tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1], keep_prob: 1}))

# plt.imshow(mnist.test.images[r:r + 1].

# reshape(28, 28), cmap='Greys', interpolation='nearest')

# plt.show()

'''

tensorboard --logdir tb/

Starting TensorBoard b'41' on port 6006

(You can navigate to http://10.0.1.4:6006)

'''

'''

Epoch: 0001 cost = 0.447322626

Epoch: 0002 cost = 0.157285590

Epoch: 0003 cost = 0.121884535

Epoch: 0004 cost = 0.098128681

Epoch: 0005 cost = 0.082901778

Epoch: 0006 cost = 0.075337573

Epoch: 0007 cost = 0.069752543

Epoch: 0008 cost = 0.060884363

Epoch: 0009 cost = 0.055276413

Epoch: 0010 cost = 0.054631256

Epoch: 0011 cost = 0.049675195

Epoch: 0012 cost = 0.049125314

Epoch: 0013 cost = 0.047231930

Epoch: 0014 cost = 0.041290121

Epoch: 0015 cost = 0.043621063

Learning Finished!

Accuracy: 0.9804

'''