add get_rank

contrib/Python/cntk/ops/__init__.py

@@ -4,7 +4,7 @@
 # ==============================================================================
 
 import numpy as np
-from ..utils import wrap_numpy_arrays
+from ..utils import wrap_numpy_arrays, get_rank
 
 ################################################################################
 # convolution ops
@@ -833,7 +833,7 @@ def future_value(shape, x, time_step=1, default_hidden_activation=0.1, name=None
     from cntk.ops.cntk1 import FutureValue
     op = FutureValue(shape, x, time_step, default_hidden_activation, name = name)
     wrap_numpy_arrays(op)            
-    op.rank = 0 if np.isscalar(shape) else len(shape)
+    op.rank = get_rank(shape)
     return op
     
 def past_value(shape, x, time_step=1, default_hidden_activation=0.1, name=None):
@@ -868,7 +868,7 @@ def past_value(shape, x, time_step=1, default_hidden_activation=0.1, name=None):
     from cntk.ops.cntk1 import PastValue
     op = PastValue(shape, x, time_step, default_hidden_activation, name = name)
     wrap_numpy_arrays(op)  
-    op.rank = 0 if np.isscalar(shape) else len(shape)
+    op.rank = get_rank(shape)
     return op
 
 ################################################################################
@@ -903,7 +903,7 @@ def reshape(x, shape, name=None):
         shape = tuple(reversed(shape))    
     op = NewReshape(x, shape, 0, 0, name = name)
     wrap_numpy_arrays(op)            
-    op.rank = 0 if np.isscalar(shape) else len(shape)
+    op.rank = get_rank(shape)
     return op
     
 def transpose_dimensions(x, axis1, axis2, name=None):
@@ -1079,7 +1079,7 @@ def input(shape, dynamic_axis='', name=None):
         shape = tuple(reversed(shape))
     op = Input(shape, dynamicAxis=dynamic_axis, name=name)
     
-    op.rank = 0 if np.isscalar(shape) else len(shape)
+    op.rank = get_rank(shape)
     return op
 
 def sparse_input_numpy(indices, values, shape, alias=None, dynamic_axis='', name=None):
@@ -1153,7 +1153,7 @@ def sparse_input(shape, dynamic_axis='', name=None):
         # cntk uses column major, thus we reverse the shape    
         shape = tuple(reversed(shape))
     op = SparseInput(shape, dynamicAxis=dynamic_axis, name=name)
-    op.rank = 0 if np.isscalar(shape) else len(shape)
+    op.rank = get_rank(shape)
     return op
 
 def parameter(shape=None, value=None, learning_rate_multiplier=1.0,
@@ -1190,7 +1190,7 @@ def parameter(shape=None, value=None, learning_rate_multiplier=1.0,
                     learningRateMultiplier=learning_rate_multiplier,
                     name=name)
             
-        op.rank = 0 if np.isscalar(shape) else len(shape)
+        op.rank = get_rank(shape)
         return op
         
     """
@@ -1232,7 +1232,7 @@ def parameter(shape=None, value=None, learning_rate_multiplier=1.0,
         init='fromLiteral',
         initFromLiteral=s.getvalue().decode())
 
-    op.rank = 0 if np.isscalar(param_shape) else len(param_shape)
+    op.rank = get_rank(param_shape)
     return op
 
 def constant(value, name=None):

contrib/Python/cntk/utils/__init__.py

@@ -225,6 +225,24 @@ def get_temp_filename(directory=None):
 
     return tf.name
 
+def get_rank(shape):
+    '''
+    computes the rank of a tensor.
+    
+    Args:
+        shape: it is either a tuple or an integer.
+        
+    Returns: the rank of the tensor.
+    
+    '''
+    if np.isscalar(shape):
+        if shape == 1:
+            return 0
+        else:
+            return 1
+    else:
+        return len(shape)
+        
 def wrap_numpy_arrays(node):
     '''
     for a given computation node, wrapes its tensor inputs that are numpy arrays