create dot function to call the new tensordot function for inputs with more than 2 dimensions

theano/tensor/basic.py

@@ -7099,11 +7099,47 @@ class Dot(Op):
 
     def __str__(self):
         return "dot"
-dot = Dot()
 
+_dot = Dot()
+pprint.assign(_dot, printing.OperatorPrinter(printing.special['middle_dot'],
+                                             -1, 'left'))
+
+def dot(a, b):
+    """
+    Computes the dot product of two variables. For two matrices, this is
+    equivalent to matrix multiplication. For two vectors, this is the inner
+    product. When one variable is a scalar, it is like elementwise
+    multiplication.  For N dimensions, it is a sum product over the last axis
+    of the first array and the second-to-last axis of the second array:
+
+        dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
+
+    Note that this dot function will do one of three things, in this sequence:
+        1. If either a or b is scalar, it returns the elementwise product
+           without calling the Dot op.
+
+        2. If either a or b has more than 2 dimensions, it calls the tensordot
+           function instead of the Dot op. Tensordot expresses high-dimensional
+           dot products as matrix multiplication and is faster than using a
+           high-dimensional Dot op.
+
+        3. Otherwise, calls the Dot op on a and b.
+
+    :note: matrix-matrix products are sometimes optimized to Dot22 ops
+        (see tensor.blas)
+
+    :note: non matrix-matrix products (including matrix-vector
+        products) are handled by numpy.  Ensure that you have linked numpy
+        with a fast BLAS.
+
+    """
+    if a.ndim == 0 or b.ndim == 0:
+        return a * b
+    elif a.ndim > 2 or b.ndim > 2:
+        return tensordot(a, b, [[a.ndim - 1], [b.ndim - 2]])
+    else:
+        return _dot(a, b)
 
-pprint.assign(dot, printing.OperatorPrinter(printing.special['middle_dot'],
-                                            -1, 'left'))
 
 
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