From 8ba7b6c693a167f218e6f8f03918889cd744f4f6 Mon Sep 17 00:00:00 2001 From: lilei Date: Wed, 16 Dec 2020 11:08:19 +0800 Subject: [PATCH] Modify Note --- mindspore/nn/wrap/cell_wrapper.py | 2 +- mindspore/nn/wrap/loss_scale.py | 14 +++++++++----- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/mindspore/nn/wrap/cell_wrapper.py b/mindspore/nn/wrap/cell_wrapper.py index 0b5c874ca2..df9c274e03 100644 --- a/mindspore/nn/wrap/cell_wrapper.py +++ b/mindspore/nn/wrap/cell_wrapper.py @@ -412,7 +412,7 @@ class ParameterUpdate(Cell): >>> update = nn.ParameterUpdate(param) >>> update.phase = "update_param" >>> weight = Tensor(np.arange(12).reshape((4, 3)), mindspore.float32) - >>> network_updata = update(weight) + >>> output = update(weight) """ def __init__(self, param): diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py index 80c3276395..59f4652423 100644 --- a/mindspore/nn/wrap/loss_scale.py +++ b/mindspore/nn/wrap/loss_scale.py @@ -102,7 +102,9 @@ class DynamicLossScaleUpdateCell(Cell): >>> net_with_loss = WithLossCell(net, loss) >>> manager = nn.DynamicLossScaleUpdateCell(loss_scale_value=2**12, scale_factor=2, scale_window=1000) >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=manager) - >>> train_network.set_train() + >>> input = Tensor(np.ones([out_features, in_features]), mindspore.float32) + >>> labels = Tensor(np.ones([out_features,]), mindspore.float32) + >>> output = train_network(input, labels) """ def __init__(self, @@ -185,7 +187,9 @@ class FixedLossScaleUpdateCell(Cell): >>> net_with_loss = WithLossCell(net, loss) >>> manager = nn.FixedLossScaleUpdateCell(loss_scale_value=2**12) >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=manager) - >>> train_network.set_train() + >>> input = Tensor(np.ones([out_features, in_features]), mindspore.float32) + >>> labels = Tensor(np.ones([out_features,]), mindspore.float32) + >>> output = train_network(input, labels) """ def __init__(self, loss_scale_value): @@ -255,7 +259,9 @@ class TrainOneStepWithLossScaleCell(TrainOneStepCell): >>> net_with_loss = WithLossCell(net, loss) >>> manager = nn.DynamicLossScaleUpdateCell(loss_scale_value=2**12, scale_factor=2, scale_window=1000) >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=manager) - >>> train_network.set_train() + >>> input = Tensor(np.ones([out_features, in_features]), mindspore.float32) + >>> labels = Tensor(np.ones([out_features,]), mindspore.float32) + >>> output = train_network(input, labels) >>> >>> #2) when the type of scale_sense is Tensor: >>> net = Net(in_features, out_features) @@ -267,8 +273,6 @@ class TrainOneStepWithLossScaleCell(TrainOneStepCell): >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mstype.float32) >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=scaling_sens) >>> output = train_network(inputs, label) - >>> print(output[0]) - 256.0 """ def __init__(self, network, optimizer, scale_sense): super(TrainOneStepWithLossScaleCell, self).__init__(network, optimizer, sens=None)