Add TensorVariable.zeros_like() and SparseVariable.zeros_like()

theano/sparse/basic.py

@@ -137,6 +137,11 @@ def sp_ones_like(x):
     data, indices, indptr, shape = csm_properties(x) #TODO: don't restrict to CSM formats
     return CSM(format=x.format)(tensor.ones_like(data), indices, indptr, shape)
 
+def sp_zeros_like(x):
+    _, _, indptr, shape = csm_properties(x) #TODO: don't restrict to CSM formats
+    return CSM(format=x.format)(numpy.array([], dtype=x.type.dtype), numpy.array([]), tensor.zeros_like(indptr), shape)
+
+
 class _sparse_py_operators:
     T = property(lambda self: transpose(self), doc = "Return aliased transpose of self (read-only)")
     def __neg__(self): return neg(self)
@@ -184,6 +189,10 @@ class SparseVariable(gof.Variable, _sparse_py_operators):
     def __repr__(self):
         return str(self)
 
+    def zeros_like(model, dtype=None):
+        # TODO: don't ignore dtype
+        return sp_zeros_like(model)
+
 class SparseConstantSignature(tuple):
     def __eq__(self, other):
         (a, b), (x,y) = self, other

theano/sparse/tests/test_basic.py

@@ -820,6 +820,17 @@ class UsmmTests(unittest.TestCase):
                         for node in topo]) == nb
 
 
+class test_zeros_like(unittest.TestCase):
+    def test(self):
+        x = theano.sparse.csr_matrix()
+        f = theano.function([x], theano.sparse.sp_zeros_like(x))
+        vx = scipy.sparse.csr_matrix(numpy.asarray(numpy.random.binomial(1, 0.5, (100, 100)), dtype=theano.config.floatX))
+        
+        fx = f(vx)
+        
+        assert fx.nnz == 0
+        assert fx.shape == vx.shape
+
 def test_shape_i():
     sparse_dtype = 'float32'
 

theano/tensor/basic.py

@@ -1454,7 +1454,12 @@ class _tensor_py_operators:
 
 class TensorVariable(_tensor_py_operators, Variable):
     """Subclass to add the tensor operators to the basic `Variable` class."""
-
+    def zeros_like(model, dtype=None):
+        "Used for grad, Lop and Rop"
+        # Tested through the zeros_like method below
+        if dtype is None:
+            dtype = model.type.dtype
+        return fill(model, constant(0.0, dtype=dtype))
 
 TensorType.Variable = TensorVariable
 
@@ -2364,10 +2369,7 @@ def ones_like(model, dtype=None):
 @constructor
 def zeros_like(model, dtype=None):
     """equivalent of numpy.zeros_like"""
-    if dtype is None:
-        dtype = model.type.dtype
-    return fill(model, constant(0.0, dtype=dtype))
-
+    return TensorVariable.zeros_like(model, dtype=None)
 
 def zeros(shape, dtype=config.floatX):
     """