speichere logs in CSV

2 files changed, 15 insertions, 1 deletions

diff --git a/inspect b/inspect
new file mode 120000
index 0000000..f11e19a
--- /dev/null
+++ b/inspect
@@ -0,0 +1 @@
+../viz/data
\ No newline at end of file
diff --git a/neuralnet.py b/neuralnet.py
index 079e587..8eb31cb 100644
--- a/neuralnet.py
+++ b/neuralnet.py
@@ -5,6 +5,7 @@ from sklearn.cross_validation import train_test_split
 import tensorflow as tf
 import numpy.random
 import numpy as np
+import os
 
 # Siehe Kommentar in dem Programm der linearen Regression
 SEED = 42
@@ -101,13 +102,25 @@ with tf.device('/cpu:0'):
     for count in range(0, int(len(x_train) / BATCH)):
         print("batch " + str(count))
         for i in range(0, STEPS):
-            result = sess.run([summaries, train_step],
+            result = sess.run([summaries,
+                              matWeights, matWeightsHidden, vecBias,
+                              train_step],
                               feed_dict={
                                 matInput: x_train[count*BATCH:(count+1)*BATCH],
                                 vecTarget: y_train[count*BATCH:(count+1)*BATCH]
                               })
             writer.add_summary(result[0], count * STEPS + i)
 
+            logdir = "inspect/" + str(count) + "/" + str(i) + "/"
+            if not os.path.exists(logdir):
+                os.makedirs(logdir)
+            numpy.savetxt(logdir + "/weights.csv",
+                          result[1], delimiter=",")
+            numpy.savetxt(logdir + "/weights-hidden.csv",
+                          result[2], delimiter=",")
+            numpy.savetxt(logdir + "/bias.csv",
+                          result[1], delimiter=",")
+
     print("Training beendet")
 
     print("Testergebnisse - Differenz zu echten Daten:")