|
/*
|
||
|
|
* 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::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;
|
||
|
|
}
|