converted the code to use numpy random number generator.

caglar · caglar · commit 29b167a328a8 · 2015-05-11T11:06:01.000-04:00
diff --git a/code/lstm.py b/code/lstm.py
@@ -3,7 +3,6 @@
 '''
 from collections import OrderedDict
 import cPickle as pkl
-import random
 import sys
 import time
 
@@ -19,7 +18,6 @@
 
 # Set the random number generators' seeds for consistency
 SEED = 123
-random.seed(SEED)
 numpy.random.seed(SEED)
 
 def numpy_floatX(data):
@@ -34,7 +32,7 @@ def get_minibatches_idx(n, minibatch_size, shuffle=False):
     idx_list = numpy.arange(n, dtype="int32")
 
     if shuffle:
-        random.shuffle(idx_list)
+        numpy.random.shuffle(idx_list)
 
     minibatches = []
     minibatch_start = 0
@@ -423,7 +421,7 @@ def train_lstm(
         # size example.  So we must select a random selection of the
         # examples.
         idx = numpy.arange(len(test[0]))
-        random.shuffle(idx)
+        numpy.random.shuffle(idx)
         idx = idx[:test_size]
         test = ([test[0][n] for n in idx], [test[1][n] for n in idx])
 
@@ -472,6 +470,7 @@ def train_lstm(
     print "%d train examples" % len(train[0])
     print "%d valid examples" % len(valid[0])
     print "%d test examples" % len(test[0])
+
     history_errs = []
     best_p = None
     bad_count = 0
@@ -589,7 +588,6 @@ def train_lstm(
 if __name__ == '__main__':
     # See function train for all possible parameter and there definition.
     train_lstm(
-        #reload_model="lstm_model.npz",
         max_epochs=100,
         test_size=500,
     )
