Fixed bug in redshifting while training.

daniel-muthukrishna · Jul 22, 2018 · c325b2a · c325b2a
1 parent d273d75
commit c325b2a
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Showing 2 changed files with 3 additions and 2 deletions.
diff --git a/dash/create_and_save_all_data_files.py b/dash/create_and_save_all_data_files.py
@@ -42,6 +42,7 @@
         f.write("Redshift: Zero\n")
         f.write("Redshift Range: {} to {}\n".format(minZ, maxZ))
         f.write("Num of Redshifts: {}\n".format(numOfRedshifts))
+        f.write("Redshift During Training: {}\n".format(redshiftDuringTraining))
         f.write("Fraction of Training Set Used: {}\n".format(trainFraction))
         f.write("Training Amount: 50 x {}\n".format(numTrainBatches))
         f.write("Changed wavelength range to 3500 to 10000A\n")

diff --git a/dash/deep_learning_multilayer.py b/dash/deep_learning_multilayer.py
@@ -101,8 +101,8 @@ def train_model(dataDirName, overwrite=False, numTrainBatches=500000, minZ=0, ma
             # Redshift arrays
             if redshifting is True:
                 redshifts = np.random.uniform(low=minZ, high=maxZ, size=len(batch_xs))
-                for i, z in enumerate(redshifts):
-                    batch_xs[i] = redshift_binned_spectrum(batch_xs[i], z, nIndexes, dwlog, w0, w1, nw, outerVal=0.5)
+                for j, z in enumerate(redshifts):
+                    batch_xs[j] = redshift_binned_spectrum(batch_xs[j], z, nIndexes, dwlog, w0, w1, nw, outerVal=0.5)
 
             train_step.run(feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5})
             if i % 100 == 0: