XOR Tensorboard (hunkim#77)

* Added XOR Tensorboard
Updated .gitignore file to ignore logs directory

* Renamed lab-09-7 to lab-09-3

.gitignore

@@ -9,3 +9,4 @@ tb
 Untitled*.ipynb
 .ropeproject
 xlab*
+logs

lab-09-3-xor_tensorboard.py

@@ -0,0 +1,77 @@
+# Lab 9 XOR
+# This example does not work
+import tensorflow as tf
+import numpy as np
+tf.set_random_seed(777)  # for reproducibility
+
+x_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
+y_data = np.array([[0], [1], [1], [0]], dtype=np.float32)
+
+X = tf.placeholder(tf.float32, [None, 2], name='x-input')
+Y = tf.placeholder(tf.float32, [None, 1], name='y-input')
+
+W1 = tf.Variable(tf.random_normal([2, 2]), name='weight1')
+b1 = tf.Variable(tf.random_normal([2]), name='bias1')
+
+with tf.name_scope("layer2") as scope:
+    layer1 = tf.sigmoid(tf.matmul(X, W1) + b1)
+
+W2 = tf.Variable(tf.random_normal([2, 1]), name='weight2')
+b2 = tf.Variable(tf.random_normal([1]), name='bias2')
+
+with tf.name_scope("layer3") as scope:
+    hypothesis = tf.sigmoid(tf.matmul(layer1, W2) + b2)
+
+w1_hist = tf.summary.histogram("weights1", W1)
+w2_hist = tf.summary.histogram("weights2", W2)
+b1_hist = tf.summary.histogram("biases1", b1)
+b2_hist = tf.summary.histogram("biases2", b2)
+y_hist = tf.summary.histogram("y", Y)
+
+# cost/loss function
+with tf.name_scope("cost") as scope:
+    cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *
+                           tf.log(1 - hypothesis))
+    cost_summ = tf.summary.scalar("cost", cost)
+
+with tf.name_scope("train") as scope:
+    train = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)
+
+# Accuracy computation
+# True if hypothesis>0.5 else False
+predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
+accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))
+
+# Launch graph
+with tf.Session() as sess:
+    #tensorboard --logdir=./logs/xor_logs
+    merged = tf.summary.merge_all()
+    writer = tf.summary.FileWriter("./logs/xor_logs", sess.graph)
+
+    # Initialize TensorFlow variables
+    sess.run(tf.global_variables_initializer())
+
+    for step in range(10001):
+        summary, _ = sess.run([merged, train], feed_dict={X: x_data, Y: y_data})
+        if step % 100 == 0:
+            print(step, sess.run(cost, feed_dict={
+                  X: x_data, Y: y_data}), sess.run([W1, W2]))
+            writer.add_summary(summary, step)
+
+    # Accuracy report
+    h, c, a = sess.run([hypothesis, predicted, accuracy],
+                       feed_dict={X: x_data, Y: y_data})
+    print("\nHypothesis: ", h, "\nCorrect: ", c, "\nAccuracy: ", a)
+
+
+'''
+Hypothesis:  [[ 0.01338218]
+ [ 0.98166394]
+ [ 0.98809403]
+ [ 0.01135799]]
+Correct:  [[ 0.]
+ [ 1.]
+ [ 1.]
+ [ 0.]]
+Accuracy:  1.0
+'''