public NeuralLink(NeuralLayer layer_in, NeuralLayer layer_out, A_Func af, float isBias, N_Type nt, int win_size)
{
Neural_In = layer_in;
Neural_Out = layer_out;
Nt = nt;
N_Winsize = win_size;
Af = af;
isHidBias = isBias;
Back_Weight = new float[Neural_In.Number * Neural_Out.Number * N_Winsize];
Back_Bias = new float[Neural_Out.Number];
}
public NeuralLink(NeuralLayer layer_in, NeuralLayer layer_out, A_Func af, float hidBias, float weightSigma, N_Type nt, int win_size, bool backupOnly)
{
Neural_In = layer_in;
Neural_Out = layer_out;
//Neural_In.Number = Neural_In.Number; // *N_Winsize;
Nt = nt;
N_Winsize = win_size;
Af = af;
initHidBias = hidBias;
initWeightSigma = weightSigma;
weight = new CudaPieceFloat(Neural_In.Number * Neural_Out.Number * N_Winsize, true, backupOnly ? false : true);
bias = new CudaPieceFloat(Neural_Out.Number, true, backupOnly ? false : true);
}
public void Fill_Layer_One(string fileName)
{
FileStream mstream = new FileStream(fileName, FileMode.Open, FileAccess.Read);
BinaryReader mreader = new BinaryReader(mstream);
List <int> layer_info = new List <int>();
int mlayer_num = mreader.ReadInt32();
for (int i = 0; i < mlayer_num; i++)
{
layer_info.Add(mreader.ReadInt32());
}
int mlink_num = mreader.ReadInt32();
for (int i = 0; i < mlink_num; i++)
{
int in_num = mreader.ReadInt32();
int out_num = mreader.ReadInt32();
float inithidbias = mreader.ReadSingle();
float initweightsigma = mreader.ReadSingle();
int mws = mreader.ReadInt32();
N_Type mnt = (N_Type)mreader.ReadInt32();
P_Pooling mp = (P_Pooling)mreader.ReadInt32();
}
for (int i = 0; i < mlink_num; i++)
{
int weight_len = mreader.ReadInt32(); // Write(neurallinks[i].Back_Weight.Length);
if (weight_len != neurallinks[i].Back_Weight.Length)
{
Console.WriteLine("Loading Model Weight Error! " + weight_len.ToString() + " " + neurallinks[i].Back_Weight.Length.ToString());
Console.ReadLine();
}
for (int m = 0; m < weight_len; m++)
{
neurallinks[i].Back_Weight[m] = mreader.ReadSingle();
}
int bias_len = mreader.ReadInt32();
if (bias_len != neurallinks[i].Back_Bias.Length)
{
Console.WriteLine("Loading Model Bias Error! " + bias_len.ToString() + " " + neurallinks[i].Back_Bias.Length.ToString());
Console.ReadLine();
}
for (int m = 0; m < bias_len; m++)
{
neurallinks[i].Back_Bias[m] = mreader.ReadSingle();
}
}
mreader.Close();
mstream.Close();
for (int i = 1; i < neurallinks.Count; i++)
{
for (int m = 0; m < neurallinks[i].Back_Bias.Length; m++)
{
neurallinks[i].Back_Bias[m] = 0;
}
int wei_num = neurallinks[i].Back_Weight.Length;
for (int m = 0; m < neurallinks[i].Neural_Out.Number; m++)
{
neurallinks[i].Back_Weight[(m * neurallinks[i].Neural_Out.Number) % wei_num + m] = 1.0f;
}
}
CopyIntoCuda();
}
/// <summary>
///
/// </summary>
/// <param name="fileName"></param>
/// <param name="allocateStructureFromEmpty">True will init DNN structure and allocate new space; False will only load data from file</param>
public void Model_Load(string fileName, bool allocateStructureFromEmpty)
{
FileStream mstream = new FileStream(fileName, FileMode.Open, FileAccess.Read);
BinaryReader mreader = new BinaryReader(mstream);
List <int> layer_info = new List <int>();
int mlayer_num = mreader.ReadInt32();
for (int i = 0; i < mlayer_num; i++)
{
layer_info.Add(mreader.ReadInt32());
}
for (int i = 0; i < layer_info.Count; i++)
{
if (allocateStructureFromEmpty)
{
NeuralLayer layer = new NeuralLayer(layer_info[i]);
neurallayers.Add(layer);
}
}
int mlink_num = mreader.ReadInt32();
for (int i = 0; i < mlink_num; i++)
{
int in_num = mreader.ReadInt32();
int out_num = mreader.ReadInt32();
float inithidbias = mreader.ReadSingle();
float initweightsigma = mreader.ReadSingle();
NeuralLink link = null;
if (ParameterSetting.LoadModelOldFormat)
{
if (allocateStructureFromEmpty)
{
// for back-compatibility only. The old model format donot have those three fields
link = new NeuralLink(neurallayers[i], neurallayers[i + 1], A_Func.Tanh, 0, initweightsigma, N_Type.Fully_Connected, 1, false);
}
}
else
{
// this is the eventually favorable loading format
int mws = mreader.ReadInt32();
//// decompose a Int32 integer, whose higher 16 bits store activiation function and lower 16 bits store network type
//// In addition, for backward-compatible, neurallinks[i].Af = tanh is stored as 0, neurallinks[i].Af = linear is stored as 1, neurallinks[i].Af = rectified is stored as 2
//// Refer to the Int2A_FuncMapping
int afAndNt = mreader.ReadInt32();
A_Func aF = Int2A_Func(afAndNt >> 16);
N_Type mnt = (N_Type)(afAndNt & ((1 << 16) - 1));
P_Pooling mp = (P_Pooling)mreader.ReadInt32();
if (allocateStructureFromEmpty)
{
link = new NeuralLink(neurallayers[i], neurallayers[i + 1], aF, 0, initweightsigma, mnt, mws, false);
}
}
if (allocateStructureFromEmpty)
{
neurallinks.Add(link);
}
}
for (int i = 0; i < mlink_num; i++)
{
int weight_len = mreader.ReadInt32(); // Write(neurallinks[i].Back_Weight.Length);
if (weight_len != neurallinks[i].Back_Weight.Length)
{
Console.WriteLine("Loading Model Weight Error! " + weight_len.ToString() + " " + neurallinks[i].Back_Weight.Length.ToString());
Console.ReadLine();
}
for (int m = 0; m < weight_len; m++)
{
neurallinks[i].Back_Weight[m] = mreader.ReadSingle();
}
int bias_len = mreader.ReadInt32();
if (bias_len != neurallinks[i].Back_Bias.Length)
{
Console.WriteLine("Loading Model Bias Error! " + bias_len.ToString() + " " + neurallinks[i].Back_Bias.Length.ToString());
Console.ReadLine();
}
for (int m = 0; m < bias_len; m++)
{
neurallinks[i].Back_Bias[m] = mreader.ReadSingle();
}
}
mreader.Close();
mstream.Close();
CopyIntoCuda();
}
/// <summary>
/// Load DSSM
/// </summary>
/// <param name="fileName"></param>
public void DSSM_Model_Load_NF(string fileName)
{
FileStream mstream = new FileStream(fileName, FileMode.Open, FileAccess.Read);
BinaryReader mreader = new BinaryReader(mstream);
List <int> layer_info = new List <int>();
int mlayer_num = mreader.ReadInt32();
for (int i = 0; i < mlayer_num; i++)
{
layer_info.Add(mreader.ReadInt32());
}
FEATURENUM = layer_info[0];
for (int i = 0; i < layer_info.Count; i++)
{
NeuralLayer layer = new NeuralLayer(layer_info[i]);
neurallayers.Add(layer);
}
int mlink_num = mreader.ReadInt32();
for (int i = 0; i < mlink_num; i++)
{
int in_num = mreader.ReadInt32();
int out_num = mreader.ReadInt32();
float inithidbias = mreader.ReadSingle();
float initweightsigma = mreader.ReadSingle();
int mws = mreader.ReadInt32();
N_Type mnt = (N_Type)mreader.ReadInt32();
P_Pooling mp = (P_Pooling)mreader.ReadInt32();
if (mnt == N_Type.Convolution_layer)
{
NeuralLink link = new NeuralLink(neurallayers[i], neurallayers[i + 1], A_Func.Tanh, 1, mnt, mws);
neurallinks.Add(link);
}
else if (mnt == N_Type.Fully_Connected)
{
NeuralLink link = new NeuralLink(neurallayers[i], neurallayers[i + 1], A_Func.Tanh);
neurallinks.Add(link);
}
}
for (int i = 0; i < mlink_num; i++)
{
int weight_len = mreader.ReadInt32(); // Write(neurallinks[i].Back_Weight.Length);
if (weight_len != neurallinks[i].Back_Weight.Length)
{
Console.WriteLine("Loading Model Weight Error! " + weight_len.ToString() + " " + neurallinks[i].Back_Weight.Length.ToString());
Console.ReadLine();
}
for (int m = 0; m < weight_len; m++)
{
neurallinks[i].Back_Weight[m] = mreader.ReadSingle();
}
int bias_len = mreader.ReadInt32();
if (bias_len != neurallinks[i].Back_Bias.Length)
{
Console.WriteLine("Loading Model Bias Error! " + bias_len.ToString() + " " + neurallinks[i].Back_Bias.Length.ToString());
Console.ReadLine();
}
for (int m = 0; m < bias_len; m++)
{
neurallinks[i].Back_Bias[m] = mreader.ReadSingle();
}
}
mreader.Close();
mstream.Close();
}
