added lab-07-1

jennykang · jennykang · commit a3b32fd12e25 · 2017-02-23T13:47:48.000+08:00
diff --git a/lab-07-1-learning_rate_and_evaluation.py b/lab-07-1-learning_rate_and_evaluation.py
@@ -0,0 +1,37 @@
+# Lab 7 Learning rate and Evaluation
+import tensorflow as tf
+import numpy as np
+
+x_data = np.array([[1, 2, 1], [1, 3, 2], [1, 3, 4], [1, 5, 5],
+                   [1, 7, 5], [1, 2, 5], [1, 6, 6], [1, 7, 7]], dtype=np.float32)
+y_data = np.array([[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 1, 0],
+                   [0, 1, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0]], dtype=np.float32)
+
+X = tf.placeholder("float", [None, 3])
+Y = tf.placeholder("float", [None, 3])
+
+W = tf.Variable(tf.zeros([3, 3]))
+# Softmax
+hypothesis = tf.nn.softmax(tf.matmul(X, W))
+# tf.nn.softmax computes softmax activations
+# softmax = exp(logits) / reduce_sum(exp(logits), dim)
+
+# Cross entropy cost
+cost = tf.reduce_mean(-tf.reduce_sum(Y *
+                                     tf.log(hypothesis), reduction_indices=1))
+
+# Changed learning_rate to 10
+optimizer = tf.train.GradientDescentOptimizer(
+    learning_rate=10.).minimize(cost)
+
+init = tf.global_variables_initializer()
+
+# Launch graph
+with tf.Session() as sess:
+    sess.run(init)
+
+    for step in range(2001):
+        sess.run(optimizer, 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))
