def make_node(self, axis, *tensors):
node = Join.make_node(self, axis, *tensors)
return Apply(self, [node.inputs[0]] + map(as_gpuarray_variable,
tensors),
[GpuArrayType(broadcastable=node.outputs[0].broadcastable,
dtype=node.outputs[0].dtype)()])
def make_node(self, axis, *tensors):
node = Join.make_node(self, axis, *tensors)
return Apply(self, [node.inputs[0]] + list(map(as_gpuarray_variable,
tensors)),
[GpuArrayType(broadcastable=node.outputs[0].broadcastable,
dtype=node.outputs[0].dtype)()])
def make_node(self, axis, *tensors):
node = Join.make_node(self, axis, *tensors)
ctx_name = infer_context_name(*tensors)
def agv(v):
return as_gpuarray_variable(v, context_name=ctx_name)
return Apply(self, [node.inputs[0]] + list(map(agv, tensors)),
[GpuArrayType(broadcastable=node.outputs[0].broadcastable,
dtype=node.outputs[0].dtype,
context_name=ctx_name)()])
def make_node(self, axis, *tensors):
node = Join.make_node(self, axis, *tensors)
ctx_name = infer_context_name(*tensors)
def agv(v):
return as_gpuarray_variable(v, context_name=ctx_name)
return Apply(self, [node.inputs[0]] + list(map(agv, tensors)),
[GpuArrayType(broadcastable=node.outputs[0].broadcastable,
dtype=node.outputs[0].dtype,
context_name=ctx_name)()])
def make_node(self, *tensors):
# Neet to check ndim and shape of all input tensors!
for x in tensors:
assert x.type.ndim == 4
node = Join.make_node(self, 1, *tensors)
def agv(v):
return as_tensor_variable(v)
return Apply(self, list(map(agv, tensors)),
[TensorType(dtype=node.outputs[0].dtype,
broadcastable=node.outputs[0].broadcastable)()])
def make_node(self, *axis_and_tensors):
return Join.make_node(self, *axis_and_tensors)
def make_node(self, *axis_and_tensors):
return Join.make_node(self, *axis_and_tensors)
