diff options
Diffstat (limited to 'boston.py')
| -rw-r--r-- | boston.py | 27 |
1 files changed, 14 insertions, 13 deletions
@@ -40,7 +40,9 @@ with tf.device('/cpu:0'): numDifference = tf.sub(numOutput, numTarget) numMSE = tf.square(numDifference) + train_step = tf.train.GradientDescentOptimizer(0.01).minimize(numMSE) + # summaries summaryBias = tf.histogram_summary("bias", vecBias) summaryWeights = tf.histogram_summary("weigths", vecWeights) summaryDifference = tf.scalar_summary(["difference"], numDifference) @@ -51,14 +53,13 @@ with tf.device('/cpu:0'): # all variables have to be specified here sess.run(tf.initialize_all_variables()) - train_step = tf.train.GradientDescentOptimizer(0.01).minimize(numMSE) 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: + if False: # if i % 10 == 9: # TODO mean over test data feed = { vecInput: x_test[0], @@ -68,17 +69,17 @@ for count in range(0, len(x_train)): writer.add_summary(result[0], count * trainsteps + i) # TODO run a complete set - sess.run(train_step, - feed_dict={ - vecInput: x_train[count], - numTarget: [y_train[count]] - }) + result = sess.run([summaries, train_step], + feed_dict={ + vecInput: x_train[count], + numTarget: [y_train[count]] + }) + writer.add_summary(result[0], count * trainsteps + i) # TODO this slows down print("finished training") -#yt = [y_test] -## debug -#print(" --------- ") -#print("mean difference to test data: ") -#print(sess.run(mse, feed_dict={input_matrix: x_test, real: yt, fact: factor, offs: offset})) -#print(sess.run(diff, feed_dict={input_matrix: x_test, real: yt, fact: factor, offs: offset})) +# yt = [y_test] +# # debug +# print(" --------- ") +# print("mean difference to test data: ") +# TODO |