added lab-09-1

jennykang · jennykang · commit a75a82dfb609 · 2017-02-24T14:50:25.000+08:00
diff --git a/lab-07-2-learning_rate_and_evaluation.py b/lab-07-2-learning_rate_and_evaluation.py
@@ -13,9 +13,9 @@
 b = tf.Variable(tf.zeros([10]))
 
 # MNIST data image of shape 28 * 28 = 784
-X = tf.placeholder("float", [None, 784])
+X = tf.placeholder(tf.float32, [None, 784])
 # 0 - 9 digits recognition = 10 classes
-Y = tf.placeholder("float", [None, 10])
+Y = tf.placeholder(tf.float32, [None, 10])
 
 # Hypothesis (using softmax)
 hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)
@@ -55,8 +55,8 @@
     plt.show()
 
     # Test model
-    correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))
+    correct_prediction = tf.equal(tf.arg_max(hypothesis, 1), tf.arg_max(Y, 1))
     # Calculate accuracy
-    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
     print("Accuracy: ", accuracy.eval(session=sess, feed_dict={
           X: mnist.test.images, Y: mnist.test.labels}))
diff --git a/lab-09-1-xor.py b/lab-09-1-xor.py
@@ -0,0 +1,46 @@
+# Lab 9 XOR
+# This example does not work
+import tensorflow as tf
+import numpy as np
+
+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)
+Y = tf.placeholder(tf.float32)
+
+
+W = tf.Variable(tf.random_uniform(
+    shape=[2, 1], minval=-1.0, maxval=1.0, dtype=tf.float32))
+
+# Hypothesis
+h = tf.matmul(X, W)
+hypothesis = tf.div(1., 1. + tf.exp(-h))
+
+# Cost function
+cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y)
+                       * tf.log(1 - hypothesis))
+
+train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
+
+# Initialize variables
+init = tf.global_variables_initializer()
+
+# Launch graph
+with tf.Session() as sess:
+    sess.run(init)
+
+    for step in range(1001):
+        sess.run(train, feed_dict={X: x_data, Y: y_data})
+        if step % 200 == 0:
+            print(step, sess.run(cost, feed_dict={
+                  X: x_data, Y: y_data}), sess.run(W))
+
+    # Test model
+    correct_prediction = tf.equal(tf.floor(hypothesis + 0.5), Y)
+
+    # Accuracy
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, dtype=tf.float32))
+    print(sess.run([hypothesis, tf.floor(hypothesis + 0.5),
+                    correct_prediction, accuracy], feed_dict={X: x_data, Y: y_data}))
+    print("Accuracy: ", accuracy.eval({X: x_data, Y: y_data}))
