def expand_dims(input, axis, name=None):
"""
Inserts a dimension of 1 into a tensor's shape.
Given a tensor input, this operation inserts a dimension \
of 1 at the dimension index axis of input's shape. \
The dimension index axis starts at zero; \
if you specify a negative number for axis it is counted backward from the end.
Args:
input: A Tensor.
"""
shape = input.shape
rank = bt.get_rank(shape)
axis = util.to_axis(axis, rank)[0]
new_shape = []
new_shape.extend(shape[:axis])
new_shape.append(1)
new_shape.extend(shape[axis:])
if axis == rank:
return _lazy_reshape(input, new_shape)
return bt._View(input, shape=new_shape, name=name)
def expand_dims(input, axis, name=None):
shape = input.shape
rank = len(shape)
axis = util.to_axis(axis, rank)[0]
new_shape = list(shape[:axis]) + [1] + list(shape[axis:])
return input.reshape(new_shape)
def __init__(self, input_tensor,
axis=None, keep_dims=False, dtype=None, name=None, par=1):
rank = bt.get_rank(input_tensor.shape)
axis = util.to_axis(axis, rank)
shape = util.to_reduce_shape(input_tensor.shape, axis, keep_dims)
bt._ReductionOperator.__init__(self, input_tensor,
dtype=dtype, shape=shape, name=name,
axis=axis, keep_dims=keep_dims, par=par)
def __init__(self, values, axis, dtype=None, name=None):
rank = bt.get_rank(values[0].shape)
_dtype = values[0].dtype
for value in values:
r = bt.get_rank(value.shape)
if r != rank:
raise ValueError('all values must have a same rank: %d != %d' %
(r, rank))
rank = r
d = value.dtype
if d != _dtype:
raise ValueError(
'all values must have a same dtype: %s != %s' %
(d, _dtype))
_dtype = d
if isinstance(axis, (tuple, list)):
raise TypeError('axis must be int, not tuple or list.')
axis = util.to_axis(axis, rank)[0]
shape = []
for i in range(rank):
size = 0
for value in values:
if i == axis:
size += value.shape[i]
else:
if size != 0 and size != value.shape[i]:
raise ValueError(
'all values must have a same shape, excluding axis: %d != %d'
% (size, value.shape[i]))
size = max(size, value.shape[i])
shape.append(size)
shape = tuple(shape)
bt._Operator.__init__(self,
*values,
dtype=dtype,
shape=shape,
name=name)
self.axis = axis