Update lab-04-3-file_input_linear_regression.py

1. Add data output and train output example.
2. Change the data output format to make it easier to read.

Author: qoocrab

lab-04-3-file_input_linear_regression.py

@@ -8,8 +8,24 @@
 y_data = xy[:, [-1]]
 
 # Make sure the shape and data are OK
-print(x_data.shape, x_data, len(x_data))
-print(y_data.shape, y_data)
+print(x_data, "\nx_data shape:", x_data.shape)
+print(y_data, "\ny_data shape:", y_data.shape)
+
+# data output
+'''
+[[ 73.  80.  75.]
+ [ 93.  88.  93.]
+ ...
+ [ 76.  83.  71.]
+ [ 96.  93.  95.]] 
+x_data shape: (25, 3)
+[[152.]
+ [185.]
+ ...
+ [149.]
+ [192.]] 
+y_data shape: (25, 1)
+'''
 
 # placeholders for a tensor that will be always fed.
 X = tf.placeholder(tf.float32, shape=[None, 3])
@@ -34,14 +50,46 @@
 sess.run(tf.global_variables_initializer())
 
 for step in range(2001):
-    cost_val, hy_val, _ = sess.run(
-        [cost, hypothesis, train], feed_dict={X: x_data, Y: y_data})
+    cost_val, hy_val, _ = sess.run([cost, hypothesis, train], 
+                                   feed_dict={X: x_data, Y: y_data})
     if step % 10 == 0:
-        print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)
+        print(step, "Cost:", cost_val, "\nPrediction:\n", hy_val)
+
+# train output
+'''
+0 Cost: 21027.0 
+Prediction:
+ [[22.048063 ]
+ [21.619772 ]
+ ...
+ [31.36112  ]
+ [24.986364 ]]
+10 Cost: 95.976326 
+Prediction:
+ [[157.11063 ]
+ [183.99283 ]
+ ...
+ [167.48862 ]
+ [193.25117 ]]
+ 1990 Cost: 24.863274 
+Prediction:
+ [[154.4393  ]
+ [185.5584  ]
+ ...
+ [158.27443 ]
+ [192.79778 ]]
+2000 Cost: 24.722485 
+Prediction:
+ [[154.42894 ]
+ [185.5586  ]
+ ...
+ [158.24257 ]
+ [192.79166 ]]
+'''
 
 # Ask my score
-print("Your score will be ", sess.run(
-    hypothesis, feed_dict={X: [[100, 70, 101]]}))
+print("Your score will be ", sess.run(hypothesis, 
+                                      feed_dict={X: [[100, 70, 101]]}))
 
 print("Other scores will be ", sess.run(hypothesis,
                                         feed_dict={X: [[60, 70, 110], [90, 100, 80]]}))