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lab-13-2-mnist_tensorboard.py
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lab-13-2-mnist_tensorboard.py
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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
'''