Use numpy.inf instead of float('inf').

Idem for nan.

Author: lamblin

code/DBN.py

@@ -324,7 +324,7 @@ def test_DBN( finetune_lr = 0.1, pretraining_epochs = 100, \
 
 
     best_params          = None
-    best_validation_loss = float('inf')
+    best_validation_loss = numpy.inf
     test_score           = 0.
     start_time = time.clock()
 

code/SdA.py

@@ -376,7 +376,7 @@ def test_SdA( finetune_lr = 0.1, pretraining_epochs = 15, \
 
 
     best_params          = None
-    best_validation_loss = float('inf')
+    best_validation_loss = numpy.inf
     test_score           = 0.
     start_time = time.clock()
 

code/convolutional_mlp.py

@@ -226,7 +226,7 @@ def evaluate_lenet5(learning_rate=0.1, n_epochs=200, dataset='../data/mnist.pkl.
                                   # check every epoch 
 
     best_params          = None
-    best_validation_loss = float('inf')
+    best_validation_loss = numpy.inf
     best_iter            = 0
     test_score           = 0.
     start_time = time.clock()

code/logistic_cg.py

@@ -268,7 +268,7 @@ def train_fn_grad(theta_value):
         return grad/n_train_batches
 
 
-    validation_scores = [float('inf'), 0]
+    validation_scores = [numpy.inf, 0]
 
     # creates the validation function
     def callback(theta_value):

code/logistic_sgd.py

@@ -300,7 +300,7 @@ def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000, dataset='../data/m
                                   # check every epoch 
 
     best_params          = None
-    best_validation_loss = float('inf')
+    best_validation_loss = numpy.inf
     test_score           = 0.
     start_time = time.clock()
 

code/mlp.py

@@ -284,7 +284,7 @@ def test_mlp( learning_rate=0.01, L1_reg = 0.00, L2_reg = 0.0001, n_epochs=1000,
 
 
     best_params          = None
-    best_validation_loss = float('inf')
+    best_validation_loss = numpy.inf
     best_iter            = 0
     test_score           = 0.
     start_time = time.clock()

code/test.py

@@ -59,15 +59,15 @@ def speed():
 
     def time_test(m,l,idx,f,**kwargs):
         if not to_exec[idx]:
-            l[idx]=float('nan')
+            l[idx]=numpy.nan
             return
         print algo[idx]
         ts=m.call_time
         try:
             f(**kwargs)
         except Exception, e:
             print >> sys.stderr, 'test', algo[idx], 'FAILED', e
-            l[idx]=float('nan')
+            l[idx]=numpy.nan
             return
         te=m.call_time
         l[idx]=te-ts

doc/gettingstarted.txt

@@ -569,7 +569,7 @@ of a strategy based on a geometrically increasing amount of patience.
                                   # check every epoch 
 
     best_params          = None
-    best_validation_loss = float('inf')
+    best_validation_loss = numpy.inf
     test_score           = 0.
     start_time = time.clock()