| @@ -83,7 +83,7 @@ class Model: | |||||
| >>> return out | >>> return out | ||||
| >>> | >>> | ||||
| >>> net = Net() | >>> net = Net() | ||||
| >>> loss = nn.SoftmaxCrossEntropyWithLogits() | |||||
| >>> loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) | |||||
| >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) | >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) | ||||
| >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None) | >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None) | ||||
| >>> dataset = get_dataset() | >>> dataset = get_dataset() | ||||
| @@ -400,7 +400,7 @@ class Model: | |||||
| Examples: | Examples: | ||||
| >>> dataset = get_dataset() | >>> dataset = get_dataset() | ||||
| >>> net = Net() | >>> net = Net() | ||||
| >>> loss = nn.SoftmaxCrossEntropyWithLogits() | |||||
| >>> loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) | |||||
| >>> loss_scale_manager = FixedLossScaleManager() | >>> loss_scale_manager = FixedLossScaleManager() | ||||
| >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) | >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) | ||||
| >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None, loss_scale_manager=loss_scale_manager) | >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None, loss_scale_manager=loss_scale_manager) | ||||
| @@ -523,7 +523,7 @@ class Model: | |||||
| Examples: | Examples: | ||||
| >>> dataset = get_dataset() | >>> dataset = get_dataset() | ||||
| >>> net = Net() | >>> net = Net() | ||||
| >>> loss = nn.SoftmaxCrossEntropyWithLogits() | |||||
| >>> loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) | |||||
| >>> model = Model(net, loss_fn=loss, optimizer=None, metrics={'acc'}) | >>> model = Model(net, loss_fn=loss, optimizer=None, metrics={'acc'}) | ||||
| >>> model.eval(dataset) | >>> model.eval(dataset) | ||||
| """ | """ | ||||