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* Licensed to the Apache Software Foundation (ASF) under one

* or more contributor license agreements. See the NOTICE file

* distributed with this work for additional information

* regarding copyright ownership. The ASF licenses this file

* to you under the Apache License, Version 2.0 (the

* "License"); you may not use this file except in compliance

* with the License. You may obtain a copy of the License at

* http://www.apache.org/licenses/LICENSE-2.0

* Unless required by applicable law or agreed to in writing,

* software distributed under the License is distributed on an

* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY

* KIND, either express or implied. See the License for the

* specific language governing permissions and limitations

* under the License.

/*!

* Xin Li yakumolx@gmail.com

#include <chrono>

#include "utils.h"

#include "mxnet-cpp/MxNetCpp.h"

using namespace mxnet::cpp;

Symbol mlp(const std::vector<int> &layers) {

auto x = Symbol::Variable("X");

auto label = Symbol::Variable("label");

std::vector<Symbol> weights(layers.size());

std::vector<Symbol> biases(layers.size());

std::vector<Symbol> outputs(layers.size());

for (size_t i = 0; i < layers.size(); ++i) {

weights[i] = Symbol::Variable("w" + std::to_string(i));

biases[i] = Symbol::Variable("b" + std::to_string(i));

Symbol fc = FullyConnected(

i == 0? x : outputs[i-1], // data

weights[i],

biases[i],

layers[i]);

outputs[i] = i == layers.size()-1 ? fc : Activation(fc, ActivationActType::kRelu);

}

return SoftmaxOutput(outputs.back(), label);

}

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::gpu(); // Use GPU 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 of 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);

std::unique_ptr<LRScheduler> lr_sch(new FactorScheduler(5000, 0.1));

opt->SetLRScheduler(std::move(lr_sch));

// Create executor by binding parameters to the model

auto *exec = net.SimpleBind(ctx, args);

auto arg_names = net.ListArguments();

// Create metrics

Accuracy train_acc, val_acc;

// Start training

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();

// Data provided by DataIter are stored in memory, should be copied to GPU first.

data_batch.data.CopyTo(&args["X"]);

data_batch.label.CopyTo(&args["label"]);

// CopyTo is imperative, need to wait for it to complete.

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] == "X" || arg_names[i] == "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();

while (val_iter.Next()) {

auto data_batch = val_iter.GetDataBatch();

data_batch.data.CopyTo(&args["X"]);

data_batch.label.CopyTo(&args["label"]);

NDArray::WaitAll();

// Only forward pass is enough as no gradient is needed when evaluating

exec->Forward(false);

val_acc.Update(data_batch.label, exec->outputs[0]);

}

LG << "Epoch[" << iter << "] Val-Accuracy=" << val_acc.Get();

}

delete exec;

delete opt;

MXNotifyShutdown();

CATCH

return 0;

}

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