geht fast

1 files changed, 20 insertions, 35 deletions

diff --git a/boston.py b/boston.py
index ecf35c2..532b872 100644
--- a/boston.py
+++ b/boston.py
@@ -9,6 +9,8 @@ data, target = dataset.data, dataset.target
 
 data_scaler = preprocessing.MinMaxScaler()
 data = data_scaler.fit_transform(data)
+target_scaler = preprocessing.MinMaxScaler()
+target = target_scaler.fit_transform(target)
 
 # use sklearn to fit the data values between 0 and 1
 x_train, x_test, y_train, y_test = train_test_split(
@@ -17,44 +19,33 @@ x_train, x_test, y_train, y_test = train_test_split(
 
 
 with tf.device('/cpu:0'):
+    # TODO use name scopes
+
     sess = tf.InteractiveSession()
 
     vecInput = tf.placeholder(tf.float32, [13], name="input")
-    # 'unfitted' means not squeezed between 0 and 1, but real values
-    numUnfittedTarget = tf.placeholder(tf.float32, [1], name="target")
-
-    # numbers to scale the output between 0-1 and back to prices
-    numFactor = tf.placeholder(tf.float32, [1], name="factor")
-    numOffset = tf.placeholder(tf.float32, [1], name="offset")
+    numTarget = tf.placeholder(tf.float32, [1], name="target")
 
-    vecBias = tf.Variable(tf.random_uniform([13], minval=0, maxval=1), name="bias")
-    vecBias = tf.Print(vecBias, [vecBias], "vecbias")
+    vecBias = tf.Variable(tf.zeros([13]), name="bias")
     # vecWeights is not a true vector, it is turned by 90 degrees
-    vecWeights = tf.Variable(tf.random_uniform([1, 13], minval=0, maxval=1), name="weight")
-    vecWeights = tf.Print(vecWeights, [vecWeights], "vecweights")
+    vecWeights = tf.Variable(tf.zeros([1, 13]), name="weight")
 
     # sum((i + b) * w)
     matWeighted = tf.mul(tf.add(vecInput, vecBias), vecWeights)
-    matWeighted = tf.Print(matWeighted, [matWeighted], "matweighted: ")  # DEBUG
     vecNetinput = tf.reduce_sum(matWeighted, 1)
-    vecNetinput = tf.Print(vecNetinput, [vecNetinput], "netinput: ")  # DEBUG
     vecLayerOutput = tf.sigmoid(vecNetinput)
-    vecLayerOutput = tf.Print(vecLayerOutput, [vecLayerOutput], "vecLayerOutput: ")  # DEBUG
 
     # since this is the last layer, vecOutput is a numOutput
-    numOutput = tf.reduce_sum(vecLayerOutput)
-    # unscale the output
-    numUnfittedOutput = tf.add(tf.mul(numOutput, numFactor), numOffset)
-    numUnfittedOutput = tf.Print(numUnfittedOutput, [numUnfittedOutput], "unfitted output: ")  # DEBUG
+    numOutput = tf.reduce_sum(vecLayerOutput)  # TODO needed?
 
-    numUnfittedTarget = tf.Print(numUnfittedTarget, [numUnfittedTarget], "numUnfittedTarget: ")  # DEBUG
-    numDifference = tf.sub(numUnfittedOutput, numUnfittedTarget)
-    numDifference = tf.Print(numDifference, [numDifference], "numDifference: ")
-    numMSE = tf.reduce_sum(tf.square(numDifference))
-    numMSE = tf.Print(numMSE, [numMSE], "MSE: ")
+    numDifference = tf.sub(numOutput, numTarget)
+    numMSE = tf.square(numDifference)
 
-    summaryMSE = tf.scalar_summary("MSE", numMSE)
-    summaryDifference = tf.scalar_summary("Mean difference", numDifference)
+    summaryBias = tf.histogram_summary("bias", vecBias)
+    summaryWeights = tf.histogram_summary("weigths", vecWeights)
+    summaryDifference = tf.scalar_summary(["difference"], numDifference)
+    summaryMSE = tf.scalar_summary(["MSE"], numMSE)
+    summaryDifference = tf.scalar_summary(["Mean difference"], numDifference)
     summaries = tf.merge_all_summaries()
     writer = tf.train.SummaryWriter("/tmp/boston_logs", sess.graph_def)
 
@@ -63,30 +54,24 @@ with tf.device('/cpu:0'):
     train_step = tf.train.GradientDescentOptimizer(0.01).minimize(numMSE)
 
 
-factor = max(target) - min(target)
-offset = min(target)
-
 for count in range(0, len(x_train)):
     trainsteps = 100
     print("count " + str(count))
     for i in range(0, trainsteps):  # 100 epochs
         if i % 10 == 9:
+            # TODO mean over test data
             feed = {
                 vecInput: x_test[0],
-                numUnfittedTarget: [y_test[0]],
-                numFactor: [factor],
-                numOffset: [offset]
+                numTarget: [y_test[0]]
             }
             result = sess.run([summaries, numMSE], feed_dict=feed)
-            #print("test " + result[0])
-            #writer.add_summary(result[0], count * trainsteps + i)
+            writer.add_summary(result[0], count * trainsteps + i)
 
+        # TODO run a complete set
         sess.run(train_step,
                  feed_dict={
                      vecInput: x_train[count],
-                     numUnfittedTarget: [y_train[count]],
-                     numFactor: [factor],
-                     numOffset: [offset]
+                     numTarget: [y_train[count]]
                  })
 
 print("finished training")