def __init__(self):
super(Net, self).__init__()
self.relu_v2 = P.ReLUV2()
'desc_bprop': [[1, 3, 4, 4]]}),
('TanhGrad', {
'block': G.TanhGrad(),
'desc_inputs': [[1, 3, 4, 4], [1, 3, 4, 4]],
'desc_bprop': [[1, 3, 4, 4]],
'skip': ['backward']}),
('ReLU', {
'block': P.ReLU(),
'desc_inputs': [[1, 3, 4, 4]],
'desc_bprop': [[1, 3, 4, 4]]}),
('ReLU6', {
'block': P.ReLU6(),
'desc_inputs': [[1, 3, 4, 4]],
'desc_bprop': [[1, 3, 4, 4]]}),
('ReLUV2', {
'block': P.ReLUV2(),
'desc_inputs': [[1, 3, 4, 4]],
'desc_bprop': [[1, 3, 4, 4], [1, 3, 4, 4]]}),
('ReLUGrad', {
'block': G.ReluGrad(),
'desc_inputs': [[1, 3, 4, 4], [1, 3, 4, 4]],
'skip': ['backward']}),
('Elu', {
'block': P.Elu(),
'desc_inputs': [[2, 3, 4]],
'desc_bprop': [[2, 3, 4]]}),
('EluGrad', {
'block': G.EluGrad(),
'desc_inputs': [[2, 3, 4], [2, 3, 4]],
'desc_bprop': [[2, 3, 4]],
'skip': ['backward']}),
def __init__(self, mul_weight, strategy=None):
super(Net, self).__init__()
self.reluv2 = P.ReLUV2().shard(strategy)
self.mul = P.Mul()
self.weight = Parameter(mul_weight, "w1")
