Merge pull request #6315 from gvtulder/f-tensor6-tensor7

doc/generate_dtype_tensor_table.py

@@ -18,17 +18,23 @@ shapes = [
         ('matrix', (False,False)),
         ('tensor3', (False,False,False)),
         ('tensor4', (False,False,False,False)),
-        ('tensor5', (False,False,False,False,False)),]
+        ('tensor5', (False,False,False,False,False)),
+        ('tensor6', (False,) * 6),
+        ('tensor7', (False,) * 7),]
 
-hdr = '============ =========== ==== ============ ==================================='
+hdr = '============ =========== ==== ================ ==================================='
 print(hdr)
-print('Constructor  dtype       ndim shape        broadcastable')
+print('Constructor  dtype       ndim shape            broadcastable')
 print(hdr)
 for letter in letters:
     for shape in shapes:
         suff = ',)' if len(shape[1])==1 else ')'
         s = '(' + ','.join('1' if b else '?' for b in shape[1]) + suff
-        print('%s%-10s  %-10s  %-4s %-11s  %-20s' %(
-                letter[0], shape[0], letter[1], len(shape[1]), s, shape[1]
+        if len(shape[1]) < 6 or len(set(shape[1])) > 1:
+            broadcastable_str = str(shape[1])
+        else:
+            broadcastable_str = '(%s,) * %d' % (str(shape[1][0]), len(shape[1]))
+        print('%s%-10s  %-10s  %-4s %-15s  %-20s' %(
+                letter[0], shape[0], letter[1], len(shape[1]), s, broadcastable_str
                 ))
 print(hdr)

doc/tutorial/adding.txt

@@ -175,13 +175,13 @@ by :ref:`broadcasting <libdoc_tensor_broadcastable>`.
 
 The following types are available:
 
-* **byte**: ``bscalar, bvector, bmatrix, brow, bcol, btensor3, btensor4, btensor5``
-* **16-bit integers**: ``wscalar, wvector, wmatrix, wrow, wcol, wtensor3, wtensor4, wtensor5``
-* **32-bit integers**: ``iscalar, ivector, imatrix, irow, icol, itensor3, itensor4, itensor5``
-* **64-bit integers**: ``lscalar, lvector, lmatrix, lrow, lcol, ltensor3, ltensor4, ltensor5``
-* **float**: ``fscalar, fvector, fmatrix, frow, fcol, ftensor3, ftensor4, ftensor5``
-* **double**: ``dscalar, dvector, dmatrix, drow, dcol, dtensor3, dtensor4, dtensor5``
-* **complex**: ``cscalar, cvector, cmatrix, crow, ccol, ctensor3, ctensor4, ctensor5``
+* **byte**: ``bscalar, bvector, bmatrix, brow, bcol, btensor3, btensor4, btensor5, btensor6, btensor7``
+* **16-bit integers**: ``wscalar, wvector, wmatrix, wrow, wcol, wtensor3, wtensor4, wtensor5, wtensor6, wtensor7``
+* **32-bit integers**: ``iscalar, ivector, imatrix, irow, icol, itensor3, itensor4, itensor5, itensor6, itensor7``
+* **64-bit integers**: ``lscalar, lvector, lmatrix, lrow, lcol, ltensor3, ltensor4, ltensor5, ltensor6, ltensor7``
+* **float**: ``fscalar, fvector, fmatrix, frow, fcol, ftensor3, ftensor4, ftensor5, ftensor6, ftensor7``
+* **double**: ``dscalar, dvector, dmatrix, drow, dcol, dtensor3, dtensor4, dtensor5, dtensor6, dtensor7``
+* **complex**: ``cscalar, cvector, cmatrix, crow, ccol, ctensor3, ctensor4, ctensor5, ctensor6, ctensor7``
 
 The previous list is not exhaustive and a guide to all types compatible
 with NumPy arrays may be found here: :ref:`tensor creation<libdoc_tensor_creation>`.

theano/gpuarray/tests/test_dnn.py

@@ -1566,10 +1566,8 @@ def test_dnn_batchnorm_train():
         raise SkipTest(dnn.dnn_available.msg)
     utt.seed_rng()
 
-    tensor6 = T.TensorType(theano.config.floatX, (False,) * 6)
-
     for mode in ('per-activation', 'spatial'):
-        for vartype in (tensor6, T.tensor5, T.tensor4, T.tensor3, T.matrix, T.vector):
+        for vartype in (T.tensor6, T.tensor5, T.tensor4, T.tensor3, T.matrix, T.vector):
             x, scale, bias, running_mean, running_var = (vartype(n)
                                                          for n in ('x', 'scale', 'bias',
                                                                    'running_mean',
@@ -1804,10 +1802,8 @@ def test_batchnorm_inference():
         raise SkipTest(dnn.dnn_available.msg)
     utt.seed_rng()
 
-    tensor6 = T.TensorType(theano.config.floatX, (False,) * 6)
-
     for mode in ('per-activation', 'spatial'):
-        for vartype in (tensor6, T.tensor5, T.tensor4, T.tensor3, T.matrix, T.vector):
+        for vartype in (T.tensor6, T.tensor5, T.tensor4, T.tensor3, T.matrix, T.vector):
             x, scale, bias, mean, var = (vartype(n)
                                          for n in ('x', 'scale', 'bias', 'mean', 'var'))
             ndim = x.ndim

theano/tensor/basic.py

@@ -901,6 +901,62 @@ def tensor5(name=None, dtype=None):
 tensor5s, ftensor5s, dtensor5s, itensor5s, ltensor5s = _multi(
     tensor5, ftensor5, dtensor5, itensor5, ltensor5)
 
+ctensor6 = TensorType('complex64', ((False,) * 6))
+ztensor6 = TensorType('complex128', ((False,) * 6))
+ftensor6 = TensorType('float32', ((False,) * 6))
+dtensor6 = TensorType('float64', ((False,) * 6))
+btensor6 = TensorType('int8', ((False,) * 6))
+wtensor6 = TensorType('int16', ((False,) * 6))
+itensor6 = TensorType('int32', ((False,) * 6))
+ltensor6 = TensorType('int64', ((False,) * 6))
+
+
+def tensor6(name=None, dtype=None):
+    """Return a symbolic 6-D variable.
+
+    Parameters
+    ----------
+    dtype: numeric type
+        None means to use theano.config.floatX.
+    name
+        A name to attach to this variable.
+
+    """
+    if dtype is None:
+        dtype = config.floatX
+    type = TensorType(dtype, (False,) * 6)
+    return type(name)
+tensor6s, ftensor6s, dtensor6s, itensor6s, ltensor6s = _multi(
+    tensor6, ftensor6, dtensor6, itensor6, ltensor6)
+
+ctensor7 = TensorType('complex64', ((False,) * 7))
+ztensor7 = TensorType('complex128', ((False,) * 7))
+ftensor7 = TensorType('float32', ((False,) * 7))
+dtensor7 = TensorType('float64', ((False,) * 7))
+btensor7 = TensorType('int8', ((False,) * 7))
+wtensor7 = TensorType('int16', ((False,) * 7))
+itensor7 = TensorType('int32', ((False,) * 7))
+ltensor7 = TensorType('int64', ((False,) * 7))
+
+
+def tensor7(name=None, dtype=None):
+    """Return a symbolic 7-D variable.
+
+    Parameters
+    ----------
+    dtype: numeric type
+        None means to use theano.config.floatX.
+    name
+        A name to attach to this variable.
+
+    """
+    if dtype is None:
+        dtype = config.floatX
+    type = TensorType(dtype, (False,) * 7)
+    return type(name)
+tensor7s, ftensor7s, dtensor7s, itensor7s, ltensor7s = _multi(
+    tensor7, ftensor7, dtensor7, itensor7, ltensor7)
+
 
 Tensor = TensorType