int main(int argc, char const *argv[]) {
int batch_size = 40;
int max_epoch = 100;
float learning_rate = 1e-4;
float weight_decay = 1e-4;
auto inception_bn_net = InceptionSymbol(10);
std::map<std::string, NDArray> args_map;
std::map<std::string, NDArray> aux_map;
args_map["data"] = NDArray(Shape(batch_size, 3, 224, 224), Context::gpu());
args_map["data_label"] = NDArray(Shape(batch_size), Context::gpu());
inception_bn_net.InferArgsMap(Context::gpu(), &args_map, args_map);
auto train_iter = MXDataIter("ImageRecordIter")
.SetParam("path_imglist", "./train.lst")
.SetParam("path_imgrec", "./train.rec")
.SetParam("data_shape", Shape(3, 224, 224))
.SetParam("batch_size", batch_size)
.SetParam("shuffle", 1)
.CreateDataIter();
auto val_iter = MXDataIter("ImageRecordIter")
.SetParam("path_imglist", "./val.lst")
.SetParam("path_imgrec", "./val.rec")
.SetParam("data_shape", Shape(3, 224, 224))
.SetParam("batch_size", batch_size)
.CreateDataIter();
Optimizer* opt = OptimizerRegistry::Find("ccsgd");
opt->SetParam("momentum", 0.9)
->SetParam("rescale_grad", 1.0 / batch_size)
->SetParam("clip_gradient", 10)
->SetParam("lr", learning_rate)
->SetParam("wd", weight_decay);
auto *exec = inception_bn_net.SimpleBind(Context::gpu(), args_map);
auto arg_names = inception_bn_net.ListArguments();
for (int iter = 0; iter < max_epoch; ++iter) {
LG << "Epoch: " << iter;
train_iter.Reset();
while (train_iter.Next()) {
auto data_batch = train_iter.GetDataBatch();
data_batch.data.CopyTo(&args_map["data"]);
data_batch.label.CopyTo(&args_map["data_label"]);
NDArray::WaitAll();
exec->Forward(true);
exec->Backward();
// Update parameters
for (size_t i = 0; i < arg_names.size(); ++i) {
if (arg_names[i] == "data" || arg_names[i] == "data_label") continue;
opt->Update(i, exec->arg_arrays[i], exec->grad_arrays[i]);
}
NDArray::WaitAll();
}
Accuracy acu;
val_iter.Reset();
while (val_iter.Next()) {
auto data_batch = val_iter.GetDataBatch();
data_batch.data.CopyTo(&args_map["data"]);
data_batch.label.CopyTo(&args_map["data_label"]);
NDArray::WaitAll();
exec->Forward(false);
NDArray::WaitAll();
acu.Update(data_batch.label, exec->outputs[0]);
}
LG << "Accuracy: " << acu.Get();
}
delete exec;
MXNotifyShutdown();
return 0;
}
int main(int argc, char const *argv[]) {
/*setup basic configs*/
int W = 28;
int H = 28;
int batch_size = 128;
int max_epoch = 100;
float learning_rate = 1e-4;
float weight_decay = 1e-4;
auto lenet = LenetSymbol();
std::map<string, NDArray> args_map;
args_map["data"] = NDArray(Shape(batch_size, 1, W, H), Context::gpu());
args_map["data_label"] = NDArray(Shape(batch_size), Context::gpu());
lenet.InferArgsMap(Context::gpu(), &args_map, args_map);
args_map["fc1_w"] = NDArray(Shape(500, 4 * 4 * 50), Context::gpu());
NDArray::SampleGaussian(0, 1, &args_map["fc1_w"]);
args_map["fc2_b"] = NDArray(Shape(10), Context::gpu());
args_map["fc2_b"] = 0;
auto train_iter = MXDataIter("MNISTIter")
.SetParam("image", "./train-images-idx3-ubyte")
.SetParam("label", "./train-labels-idx1-ubyte")
.SetParam("batch_size", batch_size)
.SetParam("shuffle", 1)
.SetParam("flat", 0)
.CreateDataIter();
auto val_iter = MXDataIter("MNISTIter")
.SetParam("image", "./t10k-images-idx3-ubyte")
.SetParam("label", "./t10k-labels-idx1-ubyte")
.CreateDataIter();
Optimizer opt("ccsgd", learning_rate, weight_decay);
opt.SetParam("momentum", 0.9).SetParam("rescale_grad", 1.0).SetParam(
"clip_gradient", 10);
for (int iter = 0; iter < max_epoch; ++iter) {
LG << "Epoch: " << iter;
train_iter.Reset();
while (train_iter.Next()) {
auto data_batch = train_iter.GetDataBatch();
args_map["data"] = data_batch.data.Copy(Context::gpu());
args_map["data_label"] = data_batch.label.Copy(Context::gpu());
NDArray::WaitAll();
auto *exec = lenet.SimpleBind(Context::gpu(), args_map);
exec->Forward(true);
exec->Backward();
exec->UpdateAll(&opt, learning_rate, weight_decay);
delete exec;
}
Accuracy acu;
val_iter.Reset();
while (val_iter.Next()) {
auto data_batch = val_iter.GetDataBatch();
args_map["data"] = data_batch.data.Copy(Context::gpu());
args_map["data_label"] = data_batch.label.Copy(Context::gpu());
NDArray::WaitAll();
auto *exec = lenet.SimpleBind(Context::gpu(), args_map);
exec->Forward(false);
NDArray::WaitAll();
acu.Update(data_batch.label, exec->outputs[0]);
delete exec;
}
LG << "Accuracy: " << acu.Get();
}
return 0;
}
int main(int argc, char** argv) {
const int image_size = 28;
const std::vector<int> layers{128, 64, 10};
const int batch_size = 100;
const int max_epoch = 10;
const float learning_rate = 0.1;
const float weight_decay = 1e-2;
std::vector<std::string> data_files = { "./data/mnist_data/train-images-idx3-ubyte",
"./data/mnist_data/train-labels-idx1-ubyte",
"./data/mnist_data/t10k-images-idx3-ubyte",
"./data/mnist_data/t10k-labels-idx1-ubyte"
};
auto train_iter = MXDataIter("MNISTIter");
if (!setDataIter(&train_iter, "Train", data_files, batch_size)) {
return 1;
}
auto val_iter = MXDataIter("MNISTIter");
if (!setDataIter(&val_iter, "Label", data_files, batch_size)) {
return 1;
}
TRY
auto net = mlp(layers);
Context ctx = Context::cpu(); // Use CPU for training
std::map<std::string, NDArray> args;
args["X"] = NDArray(Shape(batch_size, image_size*image_size), ctx);
args["label"] = NDArray(Shape(batch_size), ctx);
// Let MXNet infer shapes other parameters such as weights
net.InferArgsMap(ctx, &args, args);
// Initialize all parameters with uniform distribution U(-0.01, 0.01)
auto initializer = Uniform(0.01);
for (auto& arg : args) {
// arg.first is parameter name, and arg.second is the value
initializer(arg.first, &arg.second);
}
// Create sgd optimizer
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();
// Set data and label
data_batch.data.CopyTo(&args["X"]);
data_batch.label.CopyTo(&args["label"]);
// Compute gradients
exec->Forward(true);
exec->Backward();
// Update parameters
for (size_t i = 0; i < arg_names.size(); ++i) {
if (arg_names[i] == "X" || 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();
data_batch.data.CopyTo(&args["X"]);
data_batch.label.CopyTo(&args["label"]);
// Forward pass is enough as no gradient is needed when evaluating
exec->Forward(false);
acc.Update(data_batch.label, 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;
}
int main(int argc, char** argv) {
const int image_size = 28;
const int num_mnist_features = image_size * image_size;
int batch_size = 100;
int max_epoch = 10;
const float learning_rate = 0.1;
const float weight_decay = 1e-2;
bool isGpu = false;
std::string training_set;
std::string test_set;
std::string hidden_units_string;
int index = 1;
while (index < argc) {
if (strcmp("--train", argv[index]) == 0) {
index++;
training_set = argv[index];
} else if (strcmp("--test", argv[index]) == 0) {
index++;
test_set = argv[index];
} else if (strcmp("--epochs", argv[index]) == 0) {
index++;
max_epoch = strtol(argv[index], NULL, 10);
} else if (strcmp("--batch_size", argv[index]) == 0) {
index++;
batch_size = strtol(argv[index], NULL, 10);
} else if (strcmp("--hidden_units", argv[index]) == 0) {
index++;
hidden_units_string = argv[index];
} else if (strcmp("--gpu", argv[index]) == 0) {
isGpu = true;
index++;
} else if (strcmp("--help", argv[index]) == 0) {
printUsage();
return 0;
}
index++;
}
if (training_set.empty() || test_set.empty() || hidden_units_string.empty()) {
std::cout << "ERROR: The mandatory arguments such as path to training and test data or "
<< "number of hidden units for mlp are not specified." << std::endl << std::endl;
printUsage();
return 1;
}
std::vector<int> hidden_units = getLayers(hidden_units_string);
if (hidden_units.empty()) {
std::cout << "ERROR: Number of hidden units are not provided in correct format."
<< "The numbers need to be separated by ' '." << std::endl << std::endl;
printUsage();
return 1;
}
/*
* The MNIST data in CSV format has 785 columns.
* The first column is "Label" and rest of the columns contain data.
* The mnist_train.csv has 60000 records and mnist_test.csv has
* 10000 records.
*/
auto train_iter = MXDataIter("CSVIter")
.SetParam("data_csv", training_set)
.SetParam("data_shape", Shape(num_mnist_features + 1, 1))
.SetParam("batch_size", batch_size)
.SetParam("flat", 1)
.SetParam("shuffle", 0)
.CreateDataIter();
auto val_iter = MXDataIter("CSVIter")
.SetParam("data_csv", test_set)
.SetParam("data_shape", Shape(num_mnist_features + 1, 1))
.SetParam("batch_size", batch_size)
.SetParam("flat", 1)
.SetParam("shuffle", 0)
.CreateDataIter();
TRY
auto net = mlp(hidden_units);
Context ctx = Context::cpu();
if (isGpu) {
ctx = Context::gpu();
}
std::map<std::string, NDArray> args;
args["data"] = NDArray(Shape(batch_size, num_mnist_features), ctx);
args["label"] = NDArray(Shape(batch_size), ctx);
// Let MXNet infer shapes other parameters such as weights
net.InferArgsMap(ctx, &args, args);
// Initialize all parameters with uniform distribution U(-0.01, 0.01)
auto initializer = Uniform(0.01);
for (auto& arg : args) {
// 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;
}
int main(int argc, char const *argv[]) {
/*setup basic configs*/
int W = 28;
int H = 28;
int batch_size = 128;
int max_epoch = 100;
float learning_rate = 1e-4;
float weight_decay = 1e-4;
auto lenet = LenetSymbol();
std::map<string, NDArray> args_map;
args_map["data"] = NDArray(Shape(batch_size, 1, W, H), Context::gpu());
args_map["data_label"] = NDArray(Shape(batch_size), Context::gpu());
lenet.InferArgsMap(Context::gpu(), &args_map, args_map);
args_map["fc1_w"] = NDArray(Shape(500, 4 * 4 * 50), Context::gpu());
NDArray::SampleGaussian(0, 1, &args_map["fc1_w"]);
args_map["fc2_b"] = NDArray(Shape(10), Context::gpu());
args_map["fc2_b"] = 0;
auto train_iter = MXDataIter("MNISTIter")
.SetParam("image", "./mnist_data/train-images-idx3-ubyte")
.SetParam("label", "./mnist_data/train-labels-idx1-ubyte")
.SetParam("batch_size", batch_size)
.SetParam("shuffle", 1)
.SetParam("flat", 0)
.CreateDataIter();
auto val_iter = MXDataIter("MNISTIter")
.SetParam("image", "./mnist_data/t10k-images-idx3-ubyte")
.SetParam("label", "./mnist_data/t10k-labels-idx1-ubyte")
.CreateDataIter();
Optimizer* opt = OptimizerRegistry::Find("ccsgd");
opt->SetParam("momentum", 0.9)
->SetParam("rescale_grad", 1.0)
->SetParam("clip_gradient", 10)
->SetParam("lr", learning_rate)
->SetParam("wd", weight_decay);
auto *exec = lenet.SimpleBind(Context::gpu(), args_map);
auto arg_names = lenet.ListArguments();
// Create metrics
Accuracy train_acc, val_acc;
for (int iter = 0; iter < max_epoch; ++iter) {
int samples = 0;
train_iter.Reset();
train_acc.Reset();
auto tic = chrono::system_clock::now();
while (train_iter.Next()) {
samples += batch_size;
auto data_batch = train_iter.GetDataBatch();
data_batch.data.CopyTo(&args_map["data"]);
data_batch.label.CopyTo(&args_map["data_label"]);
NDArray::WaitAll();
// Compute gradients
exec->Forward(true);
exec->Backward();
// Update parameters
for (size_t i = 0; i < arg_names.size(); ++i) {
if (arg_names[i] == "data" || arg_names[i] == "data_label") continue;
opt->Update(i, exec->arg_arrays[i], exec->grad_arrays[i]);
}
// Update metric
train_acc.Update(data_batch.label, exec->outputs[0]);
}
// one epoch of training is finished
auto toc = chrono::system_clock::now();
float duration = chrono::duration_cast<chrono::milliseconds>(toc - tic).count() / 1000.0;
LG << "Epoch[" << iter << "] " << samples / duration \
<< " samples/sec " << "Train-Accuracy=" << train_acc.Get();;
val_iter.Reset();
val_acc.Reset();
Accuracy acu;
val_iter.Reset();
while (val_iter.Next()) {
auto data_batch = val_iter.GetDataBatch();
data_batch.data.CopyTo(&args_map["data"]);
data_batch.label.CopyTo(&args_map["data_label"]);
NDArray::WaitAll();
// Only forward pass is enough as no gradient is needed when evaluating
exec->Forward(false);
NDArray::WaitAll();
acu.Update(data_batch.label, exec->outputs[0]);
val_acc.Update(data_batch.label, exec->outputs[0]);
}
LG << "Epoch[" << iter << "] Val-Accuracy=" << val_acc.Get();
}
delete exec;
MXNotifyShutdown();
return 0;
}
int main(int argc, char** argv) {
const int image_size = 28;
const vector<int> layers{128, 64, 10};
const int batch_size = 100;
const int max_epoch = 10;
const float learning_rate = 0.1;
const float weight_decay = 1e-2;
auto train_iter = MXDataIter("MNISTIter")
.SetParam("image", "./mnist_data/train-images-idx3-ubyte")
.SetParam("label", "./mnist_data/train-labels-idx1-ubyte")
.SetParam("batch_size", batch_size)
.SetParam("flat", 1)
.CreateDataIter();
auto val_iter = MXDataIter("MNISTIter")
.SetParam("image", "./mnist_data/t10k-images-idx3-ubyte")
.SetParam("label", "./mnist_data/t10k-labels-idx1-ubyte")
.SetParam("batch_size", batch_size)
.SetParam("flat", 1)
.CreateDataIter();
auto net = mlp(layers);
Context ctx = Context::cpu(); // Use CPU for training
std::map<string, NDArray> args;
args["X"] = NDArray(Shape(batch_size, image_size*image_size), ctx);
args["label"] = NDArray(Shape(batch_size), ctx);
// Let MXNet infer shapes other parameters such as weights
net.InferArgsMap(ctx, &args, args);
// Initialize all parameters with uniform distribution U(-0.01, 0.01)
auto initializer = Uniform(0.01);
for (auto& arg : args) {
// arg.first is parameter name, and arg.second is the value
initializer(arg.first, &arg.second);
}
// Create sgd optimizer
Optimizer* opt = OptimizerRegistry::Find("sgd");
opt->SetParam("rescale_grad", 1.0/batch_size);
// Start training
for (int iter = 0; iter < max_epoch; ++iter) {
int samples = 0;
train_iter.Reset();
auto tic = chrono::system_clock::now();
while (train_iter.Next()) {
samples += batch_size;
auto data_batch = train_iter.GetDataBatch();
// Set data and label
args["X"] = data_batch.data;
args["label"] = data_batch.label;
// Create executor by binding parmeters to the model
auto *exec = net.SimpleBind(ctx, args);
// Compute gradients
exec->Forward(true);
exec->Backward();
// Update parameters
exec->UpdateAll(opt, learning_rate, weight_decay);
// Remember to free the memory
delete exec;
}
auto toc = chrono::system_clock::now();
Accuracy acc;
val_iter.Reset();
while (val_iter.Next()) {
auto data_batch = val_iter.GetDataBatch();
args["X"] = data_batch.data;
args["label"] = data_batch.label;
auto *exec = net.SimpleBind(ctx, args);
// Forward pass is enough as no gradient is needed when evaluating
exec->Forward(false);
acc.Update(data_batch.label, exec->outputs[0]);
delete exec;
}
float duration = chrono::duration_cast<chrono::milliseconds>(toc - tic).count() / 1000.0;
LG << "Epoch: " << iter << " " << samples/duration << " samples/sec Accuracy: " << acc.Get();
}
MXNotifyShutdown();
return 0;
}
int main(int argc, char const *argv[]) {
/*setup basic configs*/
int W = 28;
int H = 28;
int batch_size = 128;
int max_epoch = argc > 1 ? strtol(argv[1], NULL, 10) : 100;
float learning_rate = 1e-4;
float weight_decay = 1e-4;
auto dev_ctx = Context::cpu();
int num_gpu;
MXGetGPUCount(&num_gpu);
#if !MXNET_USE_CPU
if (num_gpu > 0) {
dev_ctx = Context::gpu();
}
#endif
auto lenet = LenetSymbol();
std::map<std::string, NDArray> args_map;
const Shape data_shape = Shape(batch_size, 1, H, W),
label_shape = Shape(batch_size);
args_map["data"] = NDArray(data_shape, dev_ctx);
args_map["data_label"] = NDArray(label_shape, dev_ctx);
lenet.InferArgsMap(dev_ctx, &args_map, args_map);
args_map["fc1_w"] = NDArray(Shape(500, 4 * 4 * 50), dev_ctx);
NDArray::SampleGaussian(0, 1, &args_map["fc1_w"]);
args_map["fc2_b"] = NDArray(Shape(10), dev_ctx);
args_map["fc2_b"] = 0;
std::vector<std::string> data_files = { "./data/mnist_data/train-images-idx3-ubyte",
"./data/mnist_data/train-labels-idx1-ubyte",
"./data/mnist_data/t10k-images-idx3-ubyte",
"./data/mnist_data/t10k-labels-idx1-ubyte"
};
auto train_iter = MXDataIter("MNISTIter");
if (!setDataIter(&train_iter, "Train", data_files, batch_size)) {
return 1;
}
auto val_iter = MXDataIter("MNISTIter");
if (!setDataIter(&val_iter, "Label", data_files, batch_size)) {
return 1;
}
Optimizer* opt = OptimizerRegistry::Find("sgd");
opt->SetParam("momentum", 0.9)
->SetParam("rescale_grad", 1.0)
->SetParam("clip_gradient", 10)
->SetParam("lr", learning_rate)
->SetParam("wd", weight_decay);
auto *exec = lenet.SimpleBind(dev_ctx, args_map);
auto arg_names = lenet.ListArguments();
// Create metrics
Accuracy train_acc, val_acc;
for (int iter = 0; iter < max_epoch; ++iter) {
int samples = 0;
train_iter.Reset();
train_acc.Reset();
auto tic = std::chrono::system_clock::now();
while (train_iter.Next()) {
samples += batch_size;
auto data_batch = train_iter.GetDataBatch();
ResizeInput(data_batch.data, data_shape).CopyTo(&args_map["data"]);
data_batch.label.CopyTo(&args_map["data_label"]);
NDArray::WaitAll();
// Compute gradients
exec->Forward(true);
exec->Backward();
// Update parameters
for (size_t i = 0; i < arg_names.size(); ++i) {
if (arg_names[i] == "data" || arg_names[i] == "data_label") continue;
opt->Update(i, exec->arg_arrays[i], exec->grad_arrays[i]);
}
// Update metric
train_acc.Update(data_batch.label, exec->outputs[0]);
}
// one epoch of training is finished
auto toc = std::chrono::system_clock::now();
float duration = std::chrono::duration_cast<std::chrono::milliseconds>
(toc - tic).count() / 1000.0;
LG << "Epoch[" << iter << "] " << samples / duration \
<< " samples/sec " << "Train-Accuracy=" << train_acc.Get();;
val_iter.Reset();
val_acc.Reset();
Accuracy acu;
val_iter.Reset();
while (val_iter.Next()) {
auto data_batch = val_iter.GetDataBatch();
ResizeInput(data_batch.data, data_shape).CopyTo(&args_map["data"]);
data_batch.label.CopyTo(&args_map["data_label"]);
NDArray::WaitAll();
// Only forward pass is enough as no gradient is needed when evaluating
exec->Forward(false);
NDArray::WaitAll();
acu.Update(data_batch.label, exec->outputs[0]);
val_acc.Update(data_batch.label, exec->outputs[0]);
}
LG << "Epoch[" << iter << "] Val-Accuracy=" << val_acc.Get();
}
delete exec;
delete opt;
MXNotifyShutdown();
return 0;
}