def __init__(self):
super(NetReluGrad, self).__init__()
self.rekuGrad = G.ReluGrad()
self.x = Parameter(initializer(Tensor(np.array([[[[-1, 1, 1],
[1, -1, 1],
[1, 1, -1]]]]).astype(np.float32)), [1, 1, 3, 3]), name='x')
self.dy = Parameter(initializer(Tensor(np.array([[[[1, 0, 1],
[0, 1, 0],
[1, 1, 1]]]]).astype(np.float32)), [1, 1, 3, 3]), name='dy')
'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']}),
('Sigmoid', {
'block': P.Sigmoid(),
'desc_inputs': [[1, 3, 4, 4]],
'desc_bprop': [[1, 3, 4, 4]]}),
def __init__(self):
super(Net, self).__init__()
self.relu_grad = G.ReluGrad()
def __init__(self):
super(NetReluGrad, self).__init__()
self.rekuGrad = G.ReluGrad()
def __init__(self):
super(AddReluNet, self).__init__()
self.add = P.TensorAdd()
self.relu = P.ReLU()
self.relu_grad = G.ReluGrad()
def __init__(self):
super(ReluNet, self).__init__()
self.relu = P.ReLU()
self.relu_grad = G.ReluGrad()
