Remove .value in DBN

Author: lamblin

code/DBN.py

@@ -144,7 +144,7 @@ def pretraining_functions(self, train_set_x, batch_size,k):
         learning_rate    = T.scalar('lr')    # learning rate to use
 
         # number of batches
-        n_batches = train_set_x.value.shape[0] / batch_size
+        n_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
         # begining of a batch, given `index`
         batch_begin = index * batch_size
         # ending of a batch given `index`
@@ -190,8 +190,8 @@ def build_finetune_functions(self, datasets, batch_size, learning_rate):
         (test_set_x , test_set_y ) = datasets[2]
 
         # compute number of minibatches for training, validation and testing
-        n_valid_batches = valid_set_x.value.shape[0] / batch_size
-        n_test_batches  = test_set_x.value.shape[0]  / batch_size
+        n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size
+        n_test_batches  = test_set_x.get_value(borrow=True).shape[0]  / batch_size
 
         index   = T.lscalar('index')    # index to a [mini]batch 
 
@@ -263,7 +263,7 @@ def test_DBN( finetune_lr = 0.1, pretraining_epochs = 100, \
     test_set_x , test_set_y  = datasets[2]
 
     # compute number of minibatches for training, validation and testing
-    n_train_batches = train_set_x.value.shape[0] / batch_size
+    n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
 
     # numpy random generator
     numpy_rng = numpy.random.RandomState(123)

doc/DBN.txt

@@ -294,7 +294,7 @@ Theano variable to it that has a default value.
         learning_rate    = T.scalar('lr')    # learning rate to use
 
         # number of batches
-        n_batches = train_set_x.value.shape[0] / batch_size
+        n_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
         # begining of a batch, given `index`
         batch_begin = index * batch_size
         # ending of a batch given `index`
@@ -358,8 +358,8 @@ and a ``test_model`` function).
         (test_set_x , test_set_y ) = datasets[2]
 
         # compute number of minibatches for training, validation and testing
-        n_valid_batches = valid_set_x.value.shape[0] / batch_size
-        n_test_batches  = test_set_x.value.shape[0]  / batch_size
+        n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size
+        n_test_batches  = test_set_x.get_value(borrow=True).shape[0]  / batch_size
 
         index   = T.lscalar('index')    # index to a [mini]batch