def test_spares_lazy_adam():
""" test sparse adam"""
indices = Tensor(np.array([0, 1]).astype(np.int32))
label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
net = NetWithSparseGatherV2()
net.set_train()
optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
optimizer.target = 'Ascend'
train_network = TrainOneStepCell(net, optimizer)
_executor.compile(train_network, indices, label)
def test_lazy_adam_net():
indices = Tensor(np.array([0, 0, 1]).astype(np.int32))
label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
net = NetWithSparseGatherV2()
optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
optimizer.target = 'Ascend'
train_network = TrainOneStepCell(net, optimizer)
output = train_network(indices, label)
np.allclose(output.asnumpy(), np.array([[[2, 2]], [[2, 2]], [[2, 2]]]))
np.allclose(net.weight1.asnumpy(), np.array([[[0.9, 0.9]], [[0.9, 0.9]], [[1.0, 1.0]]]))
np.allclose(net.weight2.asnumpy(), np.array([[[0.9, 0.9]], [[0.9, 0.9]], [[0.9, 0.9]]]))
