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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.
*/
/*!
*/
#include <fstream>
#include <map>
#include <string>
#include <vector>
#include <cstdlib>
#include "mxnet-cpp/MxNetCpp.h"
#include "utils.h"
using namespace mxnet::cpp;
class Lenet {
public:
Lenet()
: ctx_cpu(Context(DeviceType::kCPU, 0)),
#if !MXNET_USE_CUDA
ctx_dev(Context(DeviceType::kCPU, 0))
#else
ctx_dev(Context(DeviceType::kGPU, 0))
#endif
{}
void Run(int max_epoch) {
/*
* LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick Haffner.
* "Gradient-based learning applied to document recognition."
* Proceedings of the IEEE (1998)
* */
/*define the symbolic net*/
Symbol data = Symbol::Variable("data");
Symbol data_label = Symbol::Variable("data_label");
Symbol conv1_w("conv1_w"), conv1_b("conv1_b");
Symbol conv2_w("conv2_w"), conv2_b("conv2_b");
Symbol conv3_w("conv3_w"), conv3_b("conv3_b");
Symbol fc1_w("fc1_w"), fc1_b("fc1_b");
Symbol fc2_w("fc2_w"), fc2_b("fc2_b");
Symbol conv1 =
Convolution("conv1", data, conv1_w, conv1_b, Shape(5, 5), 20);
Symbol tanh1 = Activation("tanh1", conv1, ActivationActType::kTanh);
Symbol pool1 = Pooling("pool1", tanh1, Shape(2, 2), PoolingPoolType::kMax,
false, false, PoolingPoolingConvention::kValid, Shape(2, 2));
Symbol conv2 = Convolution("conv2", pool1, conv2_w, conv2_b,
Shape(5, 5), 50);
Symbol tanh2 = Activation("tanh2", conv2, ActivationActType::kTanh);
Symbol pool2 = Pooling("pool2", tanh2, Shape(2, 2), PoolingPoolType::kMax,
false, false, PoolingPoolingConvention::kValid, Shape(2, 2));
Symbol conv3 = Convolution("conv3", pool2, conv3_w, conv3_b,
Shape(2, 2), 500);
Symbol tanh3 = Activation("tanh3", conv3, ActivationActType::kTanh);
Symbol pool3 = Pooling("pool3", tanh3, Shape(2, 2), PoolingPoolType::kMax,
false, false, PoolingPoolingConvention::kValid, Shape(1, 1));
Symbol flatten = Flatten("flatten", pool3);
Symbol fc1 = FullyConnected("fc1", flatten, fc1_w, fc1_b, 500);
Symbol tanh4 = Activation("tanh4", fc1, ActivationActType::kTanh);
Symbol fc2 = FullyConnected("fc2", tanh4, fc2_w, fc2_b, 10);
Symbol lenet = SoftmaxOutput("softmax", fc2, data_label);
for (auto s : lenet.ListArguments()) {
LG << s;
}
/*setup basic configs*/
int val_fold = 1;
int W = 28;
int H = 28;
int batch_size = 42;
float learning_rate = 1e-4;
float weight_decay = 1e-4;
/*prepare the data*/
std::vector<float> data_vec, label_vec;
size_t data_count = GetData(&data_vec, &label_vec);
const float *dptr = data_vec.data();
const float *lptr = label_vec.data();
NDArray data_array = NDArray(Shape(data_count, 1, W, H), ctx_cpu,
false); // store in main memory, and copy to
// device memory while training
NDArray label_array =
NDArray(Shape(data_count), ctx_cpu,
false); // it's also ok if just store them all in device memory
data_array.SyncCopyFromCPU(dptr, data_count * W * H);
label_array.SyncCopyFromCPU(lptr, data_count);
data_array.WaitToRead();
label_array.WaitToRead();
size_t train_num = data_count * (1 - val_fold / 10.0);
train_data = data_array.Slice(0, train_num);
train_label = label_array.Slice(0, train_num);
val_data = data_array.Slice(train_num, data_count);
val_label = label_array.Slice(train_num, data_count);
LG << "here read fin";
/*init some of the args*/
// map<string, NDArray> args_map;
args_map["data"] = data_array.Slice(0, batch_size).Copy(ctx_dev);
args_map["data_label"] = label_array.Slice(0, batch_size).Copy(ctx_dev);
NDArray::WaitAll();
LG << "here slice fin";
/*
* we can also feed in some of the args other than the input all by
* ourselves,
* fc2-w , fc1-b for example:
* */
// args_map["fc2_w"] =
// NDArray(mshadow::Shape2(500, 4 * 4 * 50), ctx_dev, false);
// NDArray::SampleGaussian(0, 1, &args_map["fc2_w"]);
// args_map["fc1_b"] = NDArray(mshadow::Shape1(10), ctx_dev, false);
// args_map["fc1_b"] = 0;
lenet.InferArgsMap(ctx_dev, &args_map, args_map);
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);
Executor *exe = lenet.SimpleBind(ctx_dev, args_map);
auto arg_names = lenet.ListArguments();
for (int ITER = 0; ITER < max_epoch; ++ITER) {
size_t start_index = 0;
while (start_index < train_num) {
if (start_index + batch_size > train_num) {
start_index = train_num - batch_size;
}
args_map["data"] =
train_data.Slice(start_index, start_index + batch_size)
.Copy(ctx_dev);
args_map["data_label"] =
train_label.Slice(start_index, start_index + batch_size)
.Copy(ctx_dev);
start_index += batch_size;
NDArray::WaitAll();
exe->Forward(true);
exe->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, exe->arg_arrays[i], exe->grad_arrays[i]);
}
}
LG << "Iter " << ITER
<< ", accuracy: " << ValAccuracy(batch_size * 10, lenet);
}
delete exe;
delete opt;
}
private:
Context ctx_cpu;
Context ctx_dev;
std::map<std::string, NDArray> args_map;
NDArray train_data;
NDArray train_label;
NDArray val_data;
NDArray val_label;
size_t GetData(std::vector<float> *data, std::vector<float> *label) {
const char *train_data_path = "./data/mnist_data/mnist_train.csv";
std::ifstream inf(train_data_path);
std::string line;
inf >> line; // ignore the header
size_t _N = 0;
while (inf >> line) {
for (auto &c : line) c = (c == ',') ? ' ' : c;
std::stringstream ss;
ss << line;
float _data;
ss >> _data;
label->push_back(_data);
while (ss >> _data) data->push_back(_data / 256.0);
_N++;
}
inf.close();
return _N;
}
float ValAccuracy(int batch_size, Symbol lenet) {
size_t val_num = val_data.GetShape()[0];
size_t correct_count = 0;
size_t all_count = 0;
size_t start_index = 0;
while (start_index < val_num) {
if (start_index + batch_size > val_num) {
start_index = val_num - batch_size;
}
args_map["data"] =
val_data.Slice(start_index, start_index + batch_size).Copy(ctx_dev);
args_map["data_label"] =
val_label.Slice(start_index, start_index + batch_size).Copy(ctx_dev);
start_index += batch_size;
NDArray::WaitAll();
Executor *exe = lenet.SimpleBind(ctx_dev, args_map);
exe->Forward(false);
const auto &out = exe->outputs;
NDArray out_cpu = out[0].Copy(ctx_cpu);
NDArray label_cpu =
val_label.Slice(start_index - batch_size, start_index).Copy(ctx_cpu);
NDArray::WaitAll();
const mx_float *dptr_out = out_cpu.GetData();
const mx_float *dptr_label = label_cpu.GetData();
for (int i = 0; i < batch_size; ++i) {
float label = dptr_label[i];
int cat_num = out_cpu.GetShape()[1];
float p_label = 0, max_p = dptr_out[i * cat_num];
for (int j = 0; j < cat_num; ++j) {
float p = dptr_out[i * cat_num + j];
if (max_p < p) {
p_label = j;
max_p = p;
}
}
if (label == p_label) correct_count++;
}
all_count += batch_size;
delete exe;
}
return correct_count * 1.0 / all_count;
}
};
int main(int argc, char const *argv[]) {
TRY
Lenet lenet;
lenet.Run(argc > 1 ? strtol(argv[1], nullptr, 10) : 100000);
MXNotifyShutdown();
CATCH
return 0;
}