geht so schon fast

2 files changed, 8 insertions, 12 deletions

diff --git a/boston.py b/boston.py
index 0ad4fa4..3ae8785 100644
--- a/boston.py
+++ b/boston.py
@@ -6,6 +6,8 @@ import tensorflow as tf
 import numpy.random
 import numpy as np
 
+# TODO: vorzeigbar machen, aufräumen
+
 SEED = 42
 numpy.random.seed(SEED)
 tf.set_random_seed(SEED)
@@ -70,12 +72,7 @@ with tf.device('/cpu:0'):
 
     with tf.name_scope('cost'):
         vecDifference = tf.sub(vecTarget, vecLayeroutHidden, name='diff')
-        vecLogdifference = tf.sub(tf.log(tf.add(vecTarget, 1)),
-                                  tf.log(tf.add(vecLayeroutHidden, 1)))
-        scalarLogdiffsum = tf.reduce_sum(tf.abs(vecLogdifference))
-        scalarDiffsum = tf.reduce_sum(tf.abs(vecDifference))
-        scalarMSE = tf.reduce_sum(tf.square(vecDifference))  # ! do not unscale
-        scalarError = scalarDiffsum
+        scalarError = tf.reduce_sum(tf.abs(vecDifference), name='error')
         summaryError = tf.scalar_summary('error', scalarError)
 
     with tf.name_scope('train'):
@@ -106,8 +103,7 @@ with tf.device('/cpu:0'):
     print("finished training")
 
     print("Test results, difference to real data:")
-    result = sess.run([vecDifference, vecLayeroutHidden,
-                       scalarLogdiffsum, scalarMSE],
+    result = sess.run([vecLayeroutHidden],
                       feed_dict={
                           matInput: x_test,
                           vecTarget: y_test
@@ -118,17 +114,15 @@ with tf.device('/cpu:0'):
     print(target_scaler.inverse_transform(y_test))
     print("--------------------------------")
     print("predicted")
-    print(target_scaler.inverse_transform(result[1]))
+    print(target_scaler.inverse_transform(result[0]))
     print("--------------------------------")
     print("")
     print("difference")
     print("--------------------------------")
     diff = target_scaler.inverse_transform(
-            result[1]) - target_scaler.inverse_transform(y_test)
+            result[0]) - target_scaler.inverse_transform(y_test)
     print(diff)
     print("--------------------------------")
     print("")
-    print("total difference")
-    print(str(result[2]))
     print("MSE")
     print(np.mean(diff ** 2))
diff --git a/linear.py b/linear.py
index f6d1568..bec85db 100644
--- a/linear.py
+++ b/linear.py
@@ -4,6 +4,8 @@ from sklearn.linear_model import LinearRegression
 from sklearn.cross_validation import train_test_split
 import numpy as np
 
+# TODO schön machen und verweisen
+
 SEED = 42
 np.random.seed(SEED)