cpp-package/example/lenet.cpp - mxnet - Git at Google

 /*!
  * Copyright (c) 2015 by Contributors
  */
 #include <iostream>
 #include <fstream>
 #include <map>
 #include <string>
 #include <vector>
 #include "mxnet-cpp/MxNetCpp.h"
 // Allow IDE to parse the types
 #include "../include/mxnet-cpp/op.h"

 using namespace std;
 using namespace mxnet::cpp;

 class Lenet {
  public:
   Lenet()
       : ctx_cpu(Context(DeviceType::kCPU, 0)),
         ctx_dev(Context(DeviceType::kGPU, 0)) {}
   void Run() {
     /*
      * 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;
     int max_epoch = 100000;
     float learning_rate = 1e-4;
     float weight_decay = 1e-4;

     /*prepare the data*/
     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);

     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();

         Executor *exe = lenet.SimpleBind(ctx_dev, args_map);
         exe->Forward(true);
         exe->Backward();
         exe->UpdateAll(opt, learning_rate, weight_decay);

         delete exe;
       }

       LG << "Iter " << ITER
          << ", accuracy: " << ValAccuracy(batch_size * 10, lenet);
     }
   }

  private:
   Context ctx_cpu;
   Context ctx_dev;
   map<string, NDArray> args_map;
   NDArray train_data;
   NDArray train_label;
   NDArray val_data;
   NDArray val_label;

   size_t GetData(vector<float> *data, vector<float> *label) {
     const char *train_data_path = "./train.csv";
     ifstream inf(train_data_path);
     string line;
     inf >> line;  // ignore the header
     size_t _N = 0;
     while (inf >> line) {
       for (auto &c : line) c = (c == ',') ? ' ' : c;
       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[]) {
   Lenet lenet;
   lenet.Run();
   MXNotifyShutdown();
   return 0;
 }
	/*!
	* Copyright (c) 2015 by Contributors
	*/
	#include <iostream>
	#include <fstream>
	#include <map>
	#include <string>
	#include <vector>
	#include "mxnet-cpp/MxNetCpp.h"
	// Allow IDE to parse the types
	#include "../include/mxnet-cpp/op.h"

	using namespace std;
	using namespace mxnet::cpp;

	class Lenet {
	public:
	Lenet()
	: ctx_cpu(Context(DeviceType::kCPU, 0)),
	ctx_dev(Context(DeviceType::kGPU, 0)) {}
	void Run() {
	/*
	* 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;
	int max_epoch = 100000;
	float learning_rate = 1e-4;
	float weight_decay = 1e-4;

	/prepare the data/
	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);

	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();

	Executor *exe = lenet.SimpleBind(ctx_dev, args_map);
	exe->Forward(true);
	exe->Backward();
	exe->UpdateAll(opt, learning_rate, weight_decay);

	delete exe;
	}

	LG << "Iter " << ITER
	<< ", accuracy: " << ValAccuracy(batch_size * 10, lenet);
	}
	}

	private:
	Context ctx_cpu;
	Context ctx_dev;
	map<string, NDArray> args_map;
	NDArray train_data;
	NDArray train_label;
	NDArray val_data;
	NDArray val_label;

	size_t GetData(vector<float> data, vector<float> label) {
	const char *train_data_path = "./train.csv";
	ifstream inf(train_data_path);
	string line;
	inf >> line; // ignore the header
	size_t _N = 0;
	while (inf >> line) {
	for (auto &c : line) c = (c == ',') ? ' ' : c;
	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[]) {
	Lenet lenet;
	lenet.Run();
	MXNotifyShutdown();
	return 0;
	}