def flatten(x, outdim=1):
"""
Reshapes the variable x by keeping
the first outdim-1 dimension size(s) of x the same,
and making the last dimension size of x equal to
the multiplication of its remaining dimension size(s).
Parameters
----------
x : theano.tensor.var.TensorVariable
the variable that should be reshaped.
outdim : int
the number of dimensions of the returned variable
Returns
-------
theano.tensor.var.TensorVariable
the flattend variable with dimensionality of outdim
"""
# Any input variable can be flattened to have outdim of 1,
# even if it's a scalar. Otherwise, outdim must be positive
# and smaller than x.ndim.
if outdim < 1 or (outdim > 1 and outdim > x.ndim):
raise ValueError('outdim %s out of bound [1, %d)'
% (outdim, x.ndim + 1))
if outdim > 1:
dims = tuple(x.shape[:outdim - 1]) + (-1,)
else:
dims = (-1,)
x_reshaped = x.reshape(dims)
bcast_kept_dims = x.broadcastable[:outdim - 1]
bcast_new_dim = python_all(x.broadcastable[outdim - 1:])
broadcastable = bcast_kept_dims + (bcast_new_dim,)
x_reshaped = theano.tensor.addbroadcast(
x_reshaped, *filter(lambda i: broadcastable[i], range(outdim)))
return x_reshaped
# class TileGrad(Op):
# """
# Calculates the gradient of the Tile Op.
# """
# # this is so weird, I can't think of how to make this a general thing.
# def make_node(self, x, reps, g_out):
# return gof.Apply(self, [x, reps, g_out], [x.type()])
#
# def perform(self, node, inp, out):
# x, reps, g_out = inp
# gx, = out
# xsh = x.shape
# if len(reps) == 2 and reps[1] == 1 and len(x.shape) == 1:
# gx[0] = numpy.sum(g_out, axis=0)
# else:
# raise NotImplementedError('x.shape, reps combination not '
# 'supported', (x.shape, reps))
#
# tilegrad = TileGrad()
评论列表
文章目录
