本文整理汇总了C++中NDArray::CopyTo方法的典型用法代码示例。如果您正苦于以下问题：C++ NDArray::CopyTo方法的具体用法？C++ NDArray::CopyTo怎么用？C++ NDArray::CopyTo使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类NDArray的用法示例。
在下文中一共展示了NDArray::CopyTo方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: main
//.........这里部分代码省略.........
// arg.first is parameter name, and arg.second is the value
initializer(arg.first, &arg.second);
}
// Create sgd optimiz er
Optimizer* opt = OptimizerRegistry::Find("sgd");
opt->SetParam("rescale_grad", 1.0/batch_size)
->SetParam("lr", learning_rate)
->SetParam("wd", weight_decay);
// Create executor by binding parameters to the model
auto *exec = net.SimpleBind(ctx, args);
auto arg_names = net.ListArguments();
// Start training
for (int iter = 0; iter < max_epoch; ++iter) {
int samples = 0;
train_iter.Reset();
auto tic = std::chrono::system_clock::now();
while (train_iter.Next()) {
samples += batch_size;
auto data_batch = train_iter.GetDataBatch();
/*
* The shape of data_batch.data is (batch_size, (num_mnist_features + 1))
* Need to reshape this data so that label column can be extracted from this data.
*/
NDArray reshapedData = data_batch.data.Reshape(Shape((num_mnist_features + 1),
batch_size));
/*
* Extract the label data by slicing the first column of the data and
* copy it to "label" arg.
*/
reshapedData.Slice(0, 1).Reshape(Shape(batch_size)).CopyTo(&args["label"]);
/*
* Extract the feature data by slicing the columns 1 to 785 of the data and
* copy it to "data" arg.
*/
reshapedData.Slice(1, (num_mnist_features + 1)).Reshape(Shape(batch_size,
num_mnist_features))
.CopyTo(&args["data"]);
exec->Forward(true);
// Compute gradients
exec->Backward();
// Update parameters
for (size_t i = 0; i < arg_names.size(); ++i) {
if (arg_names[i] == "data" || arg_names[i] == "label") continue;
opt->Update(i, exec->arg_arrays[i], exec->grad_arrays[i]);
}
}
auto toc = std::chrono::system_clock::now();
Accuracy acc;
val_iter.Reset();
while (val_iter.Next()) {
auto data_batch = val_iter.GetDataBatch();
/*
* The shape of data_batch.data is (batch_size, (num_mnist_features + 1))
* Need to reshape this data so that label column can be extracted from this data.
*/
NDArray reshapedData = data_batch.data.Reshape(Shape((num_mnist_features + 1),
batch_size));
/*
* Extract the label data by slicing the first column of the data and
* copy it to "label" arg.
*/
NDArray labelData = reshapedData.Slice(0, 1).Reshape(Shape(batch_size));
labelData.CopyTo(&args["label"]);
/*
* Extract the feature data by slicing the columns 1 to 785 of the data and
* copy it to "data" arg.
*/
reshapedData.Slice(1, (num_mnist_features + 1)).Reshape(Shape(batch_size,
num_mnist_features))
.CopyTo(&args["data"]);
// Forward pass is enough as no gradient is needed when evaluating
exec->Forward(false);
acc.Update(labelData, exec->outputs[0]);
}
float duration = std::chrono::duration_cast<std::chrono::milliseconds>
(toc - tic).count() / 1000.0;
LG << "Epoch[" << iter << "] " << samples/duration << " samples/sec Accuracy: "
<< acc.Get();
}
delete exec;
delete opt;
MXNotifyShutdown();
CATCH
return 0;
}