def make_node(self, value, *shape):
value = as_gpuarray_variable(value, context_name=self.context_name)
sh, bcast = alloc_validate_shape(shape)
if value.ndim > len(sh):
TypeError("The GpuAlloc value to use has more dimensions "
"than the specified shape", value.ndim, len(sh))
otype = value.type.clone(broadcastable=bcast)
return Apply(self, [value] + sh, [otype()])
def make_node(self, value, *shape):
value = as_gpuarray_variable(value, context_name=self.context_name)
sh, bcast = alloc_validate_shape(shape)
if value.ndim > len(sh):
TypeError("The GpuAlloc value to use has more dimensions "
"than the specified shape", value.ndim, len(sh))
otype = value.type.clone(broadcastable=bcast)
return Apply(self, [value] + sh, [otype()])
def make_node(self, a, *shape):
a = basic.as_tensor_variable(a)
shape = basic.as_tensor_variable(shape, ndim=1)
shape, bcast = basic.alloc_validate_shape(shape)
out = type(a.type)(dtype=a.type.dtype, broadcastable=bcast)()
return theano.Apply(self, [a] + shape, [out])
def make_node(self, *shape):
sh, bcast = alloc_validate_shape(shape)
output = GpuArrayType(dtype=self.dtype, broadcastable=bcast,
context_name=self.context_name)()
output.tag.values_eq_approx = tensor.type.values_eq_approx_always_true
# The outut can contain nan/inf.
output.type.filter_checks_isfinite = False
output.tag.nan_guard_mode_check = False
return Apply(self, sh, [output])
def make_node(self, *shape):
sh, bcast = alloc_validate_shape(shape)
output = GpuArrayType(dtype=self.dtype, broadcastable=bcast,
context_name=self.context_name)()
output.tag.values_eq_approx = tensor.type.values_eq_approx_always_true
# The outut can contain nan/inf.
output.type.filter_checks_isfinite = False
output.tag.nan_guard_mode_check = False
return Apply(self, sh, [output])
def grad(self, inputs, outputs_gradients):
a, *shape = inputs
(dout, ) = outputs_gradients
# Determine the dimensions that were added by broadcasting
new_dims = list(range(dout.ndim - a.ndim))
d_wrt_a = broadcast_to(dout, shape).sum(axis=new_dims)
# Determine the dimensions that were broadcast
_, shape_bcast = basic.alloc_validate_shape(shape)
bcast_sums = [
i for i, (
a_b,
s_b) in enumerate(zip(a.broadcastable, shape_bcast[-a.ndim:]))
if a_b and not s_b
]
if bcast_sums:
d_wrt_a = d_wrt_a.sum(axis=bcast_sums, keepdims=True)
return [d_wrt_a] + [
grad_undefined(self, i, shp) for i, shp in enumerate(shape, 1)
]