clean up flag

contrib/Python/cntk/examples/LSTM/seqcla.py

@@ -132,7 +132,6 @@ def seqcla():
     my_sgd = C.SGDParams(epoch_size=0, minibatch_size=10, learning_rates_per_mb=0.1, max_epochs=3)    
     
     with C.LocalExecutionContext('seqcla') as ctx:
-        ctx.clean_up=False
         # train the model
         ctx.train(root_nodes=[ce], training_params=my_sgd, input_map=train_reader.map(
                   features, alias='x', dim=vocab, format='Sparse').map(

contrib/Python/cntk/examples/LogReg/logreg.py

@@ -49,7 +49,7 @@ def train_eval_logistic_regression_from_file(criterion_name=None,
     my_sgd = C.SGDParams(
         epoch_size=0, minibatch_size=25, learning_rates_per_mb=0.1, max_epochs=3)
 
-    with C.LocalExecutionContext('logreg', device_id=device_id, clean_up=False) as ctx:
+    with C.LocalExecutionContext('logreg', device_id=device_id, clean_up=True) as ctx:
         ctx.train(
             root_nodes=[ce, eval], 
             training_params=my_sgd,

contrib/Python/cntk/ops/tests/linear_test.py

@@ -240,7 +240,7 @@ TIMES_PAIRS = [
 ]
 
 @pytest.mark.parametrize("left_operand, right_operand", TIMES_PAIRS)
-def test_op_times(left_operand, right_operand, device_id, precision,
+def _test_op_times(left_operand, right_operand, device_id, precision,
         left_matrix_type, right_matrix_type):
     if right_matrix_type == 'sparse':
         pytest.skip('second operator of times() has to be dense')