def test_spares_proximal_ada_grad_compile():
""" test sparse proximal_ada_grad compile """
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 = ProximalAdagrad(net.trainable_params(), weight_decay=0.9, loss_scale=1024.0)
optimizer.target = 'CPU'
train_network = TrainOneStepCell(net, optimizer)
_executor.compile(train_network, indices, label)
def test_proximal_target():
""" test_proximal_target """
net = NetWithSparseGatherV2()
net.set_train()
optimizer = ProximalAdagrad(net.trainable_params(), weight_decay=0.9, loss_scale=1024.0)
if optimizer.target not in ('CPU', 'Ascend'):
raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(optimizer.target))
def test_proximal_ada_grad():
""" test_proximal_ada_grad """
inputs = Tensor(np.ones([1, 64]).astype(np.float32))
label = Tensor(np.zeros([1, 10]).astype(np.float32))
net = Net()
net.set_train()
loss = nn.SoftmaxCrossEntropyWithLogits()
optimizer = ProximalAdagrad(net.trainable_params())
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)