speichere in JSON

1 files changed, 14 insertions, 13 deletions

diff --git a/neuralnet.py b/neuralnet.py
index 8eb31cb..6434e8a 100644
--- a/neuralnet.py
+++ b/neuralnet.py
@@ -6,6 +6,7 @@ import tensorflow as tf
 import numpy.random
 import numpy as np
 import os
+import json
 
 # Siehe Kommentar in dem Programm der linearen Regression
 SEED = 42
@@ -98,28 +99,28 @@ with tf.device('/cpu:0'):
     # Alle Tensorflow-Variablen müssen bis hier spezifiziert sein!
     sess.run(tf.initialize_all_variables())
 
+    jsonLog = dict()
     # Training
     for count in range(0, int(len(x_train) / BATCH)):
+        jsonLog[count] = dict()
         print("batch " + str(count))
         for i in range(0, STEPS):
             result = sess.run([summaries,
-                              matWeights, matWeightsHidden, vecBias,
-                              train_step],
+                               vecBias, matWeights, matWeightsHidden,
+                               train_step],
                               feed_dict={
-                                matInput: x_train[count*BATCH:(count+1)*BATCH],
-                                vecTarget: y_train[count*BATCH:(count+1)*BATCH]
+                                  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=",")
+            if not os.path.exists("inspect"):
+                os.makedirs("inspect")
+            jsonLog[count][i] =\
+                [[result[1].tolist()]] + [a.tolist() for a in result[2:4]]
+    f = open("inspect/log.json", "w+")
+    json.dump(jsonLog, f)
+    f.close()
 
     print("Training beendet")