--- a
+++ b/scripts/plot_meanlosses.py
@@ -0,0 +1,49 @@
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import os
+import numpy as np
+import cPickle as pickle
+import time
+import sys
+
+filename = sys.argv[1]
+file = open(filename)
+
+last_chunk = -1
+training_errors = []
+validation_errors = []
+training_idcs = []
+validation_idcs=[]
+
+for line in file:
+	if 'Chunk' in line :
+		last_chunk = int(line.split()[1].split('/')[0])
+	if 'Sum of mean losses' in line:
+		validation_errors.append(float(line.split(':')[1].rsplit()[0]))
+		validation_idcs.append(last_chunk)
+	if 'Sum of train losses' in line:
+		training_errors.append(float(line.split(':')[1].rsplit()[0]))
+		training_idcs.append(last_chunk)
+
+
+print 'training errors'
+print training_errors
+print training_idcs
+print 'validation errors'
+print validation_errors
+print validation_idcs
+
+print 'min training error', np.amin(np.array(training_errors)), 'at', np.argmin(np.array(training_errors))
+print 'min validation error', np.amin(np.array(validation_errors)), 'at', np.argmin(np.array(validation_errors))
+
+plt.plot(training_errors, label='training errors')
+plt.plot(validation_errors, label='validation errors')
+plt.legend(loc="upper right")
+plt.title(sys.argv[1])
+plt.xlabel('Epoch')
+#plt.ylim(0, 0.7)
+plt.ylabel('Error')	
+plt.savefig(sys.argv[2])
+
+