Update lab-06-1-softmax_classifier.py (hunkim#232)

qoocrab · kkweon · commit 107d2eb66d6b · 2019-01-12T18:57:29.000-08:00
* Update lab-06-1-softmax_classifier.py

More simplified code and added missing output.

* Update lab-06-1-softmax_classifier.py
diff --git a/lab-06-1-softmax_classifier.py b/lab-06-1-softmax_classifier.py
@@ -40,41 +40,48 @@
     sess.run(tf.global_variables_initializer())
 
     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}))
+            _, cost_val = sess.run([optimizer, cost], feed_dict={X: x_data, Y: y_data})
 
-    print('--------------')
+            if step % 200 == 0:
+                print(step, cost_val)
 
+    print('--------------')
     # Testing & One-hot encoding
     a = sess.run(hypothesis, feed_dict={X: [[1, 11, 7, 9]]})
     print(a, sess.run(tf.argmax(a, 1)))
 
     print('--------------')
-
     b = sess.run(hypothesis, feed_dict={X: [[1, 3, 4, 3]]})
     print(b, sess.run(tf.argmax(b, 1)))
 
     print('--------------')
-
     c = sess.run(hypothesis, feed_dict={X: [[1, 1, 0, 1]]})
     print(c, sess.run(tf.argmax(c, 1)))
 
     print('--------------')
-
-    all = sess.run(hypothesis, feed_dict={
-                   X: [[1, 11, 7, 9], [1, 3, 4, 3], [1, 1, 0, 1]]})
+    all = sess.run(hypothesis, feed_dict={X: [[1, 11, 7, 9], [1, 3, 4, 3], [1, 1, 0, 1]]})
     print(all, sess.run(tf.argmax(all, 1)))
 
 '''
---------------
-[[  1.38904958e-03   9.98601854e-01   9.06129117e-06]] [1]
---------------
-[[ 0.93119204  0.06290206  0.0059059 ]] [0]
---------------
-[[  1.27327668e-08   3.34112905e-04   9.99665856e-01]] [2]
---------------
-[[  1.38904958e-03   9.98601854e-01   9.06129117e-06]
- [  9.31192040e-01   6.29020557e-02   5.90589503e-03]
- [  1.27327668e-08   3.34112905e-04   9.99665856e-01]] [1 0 2]
+0 6.926112
+200 0.6005015
+400 0.47295815
+600 0.37342924
+800 0.28018373
+1000 0.23280522
+1200 0.21065344
+1400 0.19229904
+1600 0.17682323
+1800 0.16359556
+2000 0.15216158
+-------------
+[[1.3890490e-03 9.9860185e-01 9.0613084e-06]] [1]
+-------------
+[[0.9311919  0.06290216 0.00590591]] [0]
+-------------
+[[1.2732815e-08 3.3411323e-04 9.9966586e-01]] [2]
+-------------
+[[1.3890490e-03 9.9860185e-01 9.0613084e-06]
+ [9.3119192e-01 6.2902197e-02 5.9059085e-03]
+ [1.2732815e-08 3.3411323e-04 9.9966586e-01]] [1 0 2]
 '''
