486 lines
37 KiB
MQL5
486 lines
37 KiB
MQL5
//+------------------------------------------------------------------+
|
|
//| NeuronConv.mqh |
|
|
//| Copyright 2021, MetaQuotes Ltd. |
|
|
//| https://www.mql5.com |
|
|
//+------------------------------------------------------------------+
|
|
#property copyright "Copyright 2021, MetaQuotes Ltd."
|
|
#property link "https://www.mql5.com"
|
|
//+------------------------------------------------------------------+
|
|
//| Connect libraries |
|
|
//+------------------------------------------------------------------+
|
|
#include "neuronproof.mqh"
|
|
//+------------------------------------------------------------------+
|
|
//| Class CNeuronConv |
|
|
//| Purpose: Class organizing the convolutional layer |
|
|
//+------------------------------------------------------------------+
|
|
class CNeuronConv : public CNeuronProof
|
|
{
|
|
protected:
|
|
bool m_bTransposedOutput;
|
|
public:
|
|
CNeuronConv(void) {m_bTransposedOutput = false;};
|
|
~CNeuronConv(void) {};
|
|
//---
|
|
virtual bool Init(const CLayerDescription *desc) override;
|
|
virtual bool FeedForward(CNeuronBase *prevLayer) override;
|
|
virtual bool CalcHiddenGradient(CNeuronBase *prevLayer) override;
|
|
virtual bool CalcDeltaWeights(CNeuronBase *prevLayer, bool read) override;
|
|
virtual bool UpdateWeights(int batch_size, TYPE learningRate,
|
|
VECTOR &Beta, VECTOR &Lambda) override
|
|
{
|
|
return CNeuronBase::UpdateWeights(batch_size,
|
|
learningRate,
|
|
Beta,
|
|
Lambda);
|
|
}
|
|
//---
|
|
virtual CBufferType* GetWeights(void) override const { return(m_cWeights); }
|
|
virtual CBufferType* GetDeltaWeights(void) override const { return(m_cDeltaWeights);}
|
|
bool SetTransposedOutput(const bool value);
|
|
//--- file handling methods
|
|
virtual bool Save(const int file_handle) override;
|
|
virtual bool Load(const int file_handle) override;
|
|
//--- object identification method
|
|
virtual int Type(void) override const { return(defNeuronConv); }
|
|
};
|
|
//+------------------------------------------------------------------+
|
|
//| Class initialization method |
|
|
//+------------------------------------------------------------------+
|
|
bool CNeuronConv::Init(const CLayerDescription *desc)
|
|
{
|
|
//--- control block
|
|
if(!desc || desc.type != Type() || desc.count <= 0 || desc.window <= 0)
|
|
return false;
|
|
//--- save constants
|
|
m_iWindow = desc.window;
|
|
m_iStep = desc.step;
|
|
m_iWindowOut = desc.window_out;
|
|
m_iNeurons = desc.count;
|
|
//--- save the parameter optimization method and the result tensor transposition flag
|
|
m_eOptimization = desc.optimization;
|
|
m_bTransposedOutput = (desc.probability != 0);
|
|
//--- initialize the results buffer
|
|
if(!m_cOutputs)
|
|
if(!(m_cOutputs = new CBufferType()))
|
|
return false;
|
|
//--- initialize the error gradient buffer
|
|
if(!m_cGradients)
|
|
if(!(m_cGradients = new CBufferType()))
|
|
return false;
|
|
if(m_bTransposedOutput)
|
|
{
|
|
if(!m_cOutputs.BufferInit(m_iNeurons, m_iWindowOut, 0))
|
|
return false;
|
|
if(!m_cGradients.BufferInit(m_iNeurons, m_iWindowOut, 0))
|
|
return false;
|
|
}
|
|
else
|
|
{
|
|
if(!m_cOutputs.BufferInit(m_iWindowOut, m_iNeurons, 0))
|
|
return false;
|
|
if(!m_cGradients.BufferInit(m_iWindowOut, m_iNeurons, 0))
|
|
return false;
|
|
}
|
|
//--- initialize the activation function class
|
|
VECTOR params = desc.activation_params;
|
|
if(!SetActivation(desc.activation, params))
|
|
return false;
|
|
//--- initialize the weight matrix buffer
|
|
if(!m_cWeights)
|
|
if(!(m_cWeights = new CBufferType()))
|
|
return false;
|
|
if(!m_cWeights.BufferInit(desc.window_out, desc.window + 1))
|
|
return false;
|
|
double weights[];
|
|
double sigma = desc.activation == AF_LRELU ?
|
|
2.0 / (double)(MathPow(1 + desc.activation_params[0], 2) * desc.window) :
|
|
1.0 / (double)desc.window;
|
|
if(!MathRandomNormal(0, MathSqrt(sigma), (uint)m_cWeights.Total(), weights))
|
|
return false;
|
|
for(uint i = 0; i < m_cWeights.Total(); i++)
|
|
if(!m_cWeights.m_mMatrix.Flat(i, (TYPE)weights[i]))
|
|
return false;
|
|
//--- initialize the gradient buffer at the weight matrix level
|
|
if(!m_cDeltaWeights)
|
|
if(!(m_cDeltaWeights = new CBufferType()))
|
|
return false;
|
|
if(!m_cDeltaWeights.BufferInit(desc.window_out, desc.window + 1, 0))
|
|
return false;
|
|
//--- initialize moment buffers
|
|
switch(desc.optimization)
|
|
{
|
|
case None:
|
|
case SGD:
|
|
for(int i = 0; i < 2; i++)
|
|
if(m_cMomenum[i])
|
|
delete m_cMomenum[i];
|
|
break;
|
|
case MOMENTUM:
|
|
case AdaGrad:
|
|
case RMSProp:
|
|
if(!m_cMomenum[0])
|
|
if(!(m_cMomenum[0] = new CBufferType()))
|
|
return false;
|
|
if(!m_cMomenum[0].BufferInit(desc.window_out, desc.window + 1, 0))
|
|
return false;
|
|
if(m_cMomenum[1])
|
|
delete m_cMomenum[1];
|
|
break;
|
|
case AdaDelta:
|
|
case Adam:
|
|
for(int i = 0; i < 2; i++)
|
|
{
|
|
if(!m_cMomenum[i])
|
|
if(!(m_cMomenum[i] = new CBufferType()))
|
|
return false;
|
|
if(!m_cMomenum[i].BufferInit(desc.window_out, desc.window + 1, 0))
|
|
return false;
|
|
}
|
|
break;
|
|
default:
|
|
return false;
|
|
break;
|
|
}
|
|
return true;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| Feed-forward method |
|
|
//+------------------------------------------------------------------+
|
|
bool CNeuronConv::FeedForward(CNeuronBase *prevLayer)
|
|
{
|
|
//--- control block
|
|
if(!prevLayer || !m_cOutputs || !m_cWeights || !prevLayer.GetOutputs())
|
|
return false;
|
|
CBufferType *input_data = prevLayer.GetOutputs();
|
|
ulong total = input_data.Total();
|
|
//--- branching of the algorithm depending on the device used for performing operations
|
|
if(!m_cOpenCL)
|
|
{
|
|
MATRIX m;
|
|
if(m_iWindow == m_iStep && total == (m_iNeurons * m_iWindow))
|
|
{
|
|
m = input_data.m_mMatrix;
|
|
if(!m.Reshape(m_iNeurons, m_iWindow))
|
|
return false;
|
|
}
|
|
else
|
|
{
|
|
if(!m.Init(m_iNeurons, m_iWindow))
|
|
return false;
|
|
for(ulong r = 0; r < m_iNeurons; r++)
|
|
{
|
|
ulong shift = r * m_iStep;
|
|
for(ulong c = 0; c < m_iWindow; c++)
|
|
{
|
|
ulong k = shift + c;
|
|
m[r, c] = (k < total ? input_data.At((uint)k) : 0);
|
|
}
|
|
}
|
|
}
|
|
//--- add a bias column
|
|
if(!m.Resize(m.Rows(), m_iWindow + 1) ||
|
|
!m.Col(VECTOR::Ones(m_iNeurons), m_iWindow))
|
|
return false;
|
|
//--- Calculate the weighted sum of elements of the input window
|
|
if(m_bTransposedOutput)
|
|
m = m.MatMul(m_cWeights.m_mMatrix.Transpose());
|
|
else
|
|
m = m_cWeights.m_mMatrix.MatMul(m.Transpose());
|
|
m_cOutputs.m_mMatrix = m;
|
|
}
|
|
else
|
|
{
|
|
//--- check data buffers
|
|
if(input_data.GetIndex() < 0)
|
|
return false;
|
|
if(m_cWeights.GetIndex() < 0)
|
|
return false;
|
|
if(m_cOutputs.GetIndex() < 0)
|
|
return false;
|
|
//--- pass arguments to the kernel
|
|
if(!m_cOpenCL.SetArgumentBuffer(def_k_ConvolutionFeedForward, def_cff_inputs, input_data.GetIndex()))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgumentBuffer(def_k_ConvolutionFeedForward, def_cff_weights, m_cWeights.GetIndex()))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgumentBuffer(def_k_ConvolutionFeedForward, def_cff_outputs, m_cOutputs.GetIndex()))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgument(def_k_ConvolutionFeedForward, def_cff_inputs_total, (int)input_data.Total()))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgument(def_k_ConvolutionFeedForward, def_cff_window, m_iWindow))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgument(def_k_ConvolutionFeedForward, def_cff_step, m_iStep))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgument(def_k_ConvolutionFeedForward, def_cff_window_out, m_iWindowOut))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgument(def_k_ConvolutionFeedForward, def_cff_transposed_out, (int)m_bTransposedOutput))
|
|
return false;
|
|
//--- place kernel to the execution queue
|
|
int off_set[] = {0};
|
|
int NDRange[] = {(int)m_iNeurons};
|
|
if(!m_cOpenCL.Execute(def_k_ConvolutionFeedForward, 1, off_set, NDRange))
|
|
return false;
|
|
}
|
|
if(!m_cActivation.Activation(m_cOutputs))
|
|
return false;
|
|
//---
|
|
return true;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| Method for propagating error gradient through hidden layer |
|
|
//+------------------------------------------------------------------+
|
|
bool CNeuronConv::CalcHiddenGradient(CNeuronBase *prevLayer)
|
|
{
|
|
//--- control block
|
|
if(!prevLayer || !prevLayer.GetOutputs() || !prevLayer.GetGradients() || !m_cGradients || !m_cWeights)
|
|
return false;
|
|
//--- adjust error gradients to the derivative of the activation function
|
|
if(m_cActivation)
|
|
{
|
|
if(!m_cActivation.Derivative(m_cGradients))
|
|
return false;
|
|
}
|
|
//--- branching of the algorithm depending on the device used for performing operations
|
|
CBufferType* input_gradient = prevLayer.GetGradients();
|
|
if(!m_cOpenCL)
|
|
{
|
|
MATRIX g = m_cGradients.m_mMatrix;
|
|
if(m_bTransposedOutput)
|
|
{
|
|
if(!g.Reshape(m_iNeurons, m_iWindowOut))
|
|
return false;
|
|
}
|
|
else
|
|
{
|
|
if(!g.Reshape(m_iWindowOut, m_iNeurons))
|
|
return false;
|
|
g = g.Transpose();
|
|
}
|
|
g = g.MatMul(m_cWeights.m_mMatrix);
|
|
if(!g.Resize(m_iNeurons, m_iWindow))
|
|
return false;
|
|
if(m_iWindow == m_iStep && input_gradient.Total() == (m_iNeurons * m_iWindow))
|
|
{
|
|
if(!g.Reshape(input_gradient.Rows(), input_gradient.Cols()))
|
|
return false;
|
|
input_gradient.m_mMatrix = g;
|
|
}
|
|
else
|
|
{
|
|
input_gradient.m_mMatrix.Fill(0);
|
|
ulong total = input_gradient.Total();
|
|
for(ulong r = 0; r < m_iNeurons; r++)
|
|
{
|
|
ulong shift = r * m_iStep;
|
|
for(ulong c = 0; c < m_iWindow; c++)
|
|
{
|
|
ulong k = shift + c;
|
|
if(k >= total)
|
|
break;
|
|
if(!input_gradient.m_mMatrix.Flat(k, input_gradient.m_mMatrix.Flat(k) + g[r, c]))
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else // OpenCL operations block
|
|
{
|
|
//--- check data buffers
|
|
if(m_cWeights.GetIndex() < 0)
|
|
return false;
|
|
if(input_gradient.GetIndex() < 0)
|
|
return false;
|
|
if(m_cGradients.GetIndex() < 0)
|
|
return false;
|
|
//--- pass arguments to the kernel
|
|
if(!m_cOpenCL.SetArgumentBuffer(def_k_ConvolutionHiddenGradients, def_convhgr_gradient_inputs, input_gradient.GetIndex()))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgumentBuffer(def_k_ConvolutionHiddenGradients, def_convhgr_weights, m_cWeights.GetIndex()))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgumentBuffer(def_k_ConvolutionHiddenGradients, def_convhgr_gradients, m_cGradients.GetIndex()))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgument(def_k_ConvolutionHiddenGradients, def_convhgr_neurons, m_iNeurons))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgument(def_k_ConvolutionHiddenGradients, def_convhgr_window, m_iWindow))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgument(def_k_ConvolutionHiddenGradients, def_convhgr_step, m_iStep))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgument(def_k_ConvolutionHiddenGradients, def_convhgr_window_out, m_iWindowOut))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgument(def_k_ConvolutionHiddenGradients, def_convhgr_transposed_out, (int)m_bTransposedOutput))
|
|
return false;
|
|
//--- place kernel to the execution queue
|
|
int NDRange[] = {(int)input_gradient.Total()};
|
|
int off_set[] = {0};
|
|
if(!m_cOpenCL.Execute(def_k_ConvolutionHiddenGradients, 1, off_set, NDRange))
|
|
return false;
|
|
}
|
|
//---
|
|
return true;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| Method for propagating the error gradient to the weight matrix |
|
|
//+------------------------------------------------------------------+
|
|
bool CNeuronConv::CalcDeltaWeights(CNeuronBase *prevLayer, bool read)
|
|
{
|
|
//--- control block
|
|
if(!prevLayer || !prevLayer.GetOutputs() || !m_cGradients || !m_cDeltaWeights)
|
|
return false;
|
|
//--- branching of the algorithm depending on the device used for performing operations
|
|
CBufferType *input_data = prevLayer.GetOutputs();
|
|
if(!m_cOpenCL)
|
|
{
|
|
MATRIX inp;
|
|
ulong input_total = input_data.Total();
|
|
if(m_iWindow == m_iStep && input_total == (m_iNeurons * m_iWindow))
|
|
{
|
|
inp = input_data.m_mMatrix;
|
|
if(!inp.Reshape(m_iNeurons, m_iWindow))
|
|
return false;
|
|
}
|
|
else
|
|
{
|
|
if(!inp.Init(m_iNeurons, m_iWindow))
|
|
return false;
|
|
for(ulong r = 0; r < m_iNeurons; r++)
|
|
{
|
|
ulong shift = r * m_iStep;
|
|
for(ulong c = 0; c < m_iWindow; c++)
|
|
{
|
|
ulong k = shift + c;
|
|
inp[r, c] = (k < input_total ? input_data.At((uint)k) : 0);
|
|
}
|
|
}
|
|
}
|
|
//--- add a bias column
|
|
if(!inp.Resize(inp.Rows(), m_iWindow + 1) ||
|
|
!inp.Col(VECTOR::Ones(m_iNeurons), m_iWindow))
|
|
return false;
|
|
//---
|
|
MATRIX g = m_cGradients.m_mMatrix;
|
|
if(m_bTransposedOutput)
|
|
{
|
|
if(!g.Reshape(m_iNeurons, m_iWindowOut))
|
|
return false;
|
|
g = g.Transpose();
|
|
}
|
|
else
|
|
{
|
|
if(!g.Reshape(m_iWindowOut, m_iNeurons))
|
|
return false;
|
|
}
|
|
m_cDeltaWeights.m_mMatrix += g.MatMul(inp);
|
|
}
|
|
else // OpenCL operations block
|
|
{
|
|
//--- check data buffers
|
|
if(m_cGradients.GetIndex() < 0)
|
|
return false;
|
|
if(m_cDeltaWeights.GetIndex() < 0)
|
|
return false;
|
|
if(input_data.GetIndex() < 0)
|
|
return false;
|
|
//--- pass arguments to the kernel
|
|
if(!m_cOpenCL.SetArgumentBuffer(def_k_ConvolutionDeltaWeights, def_convdelt_delta_weights, m_cDeltaWeights.GetIndex()))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgumentBuffer(def_k_ConvolutionDeltaWeights, def_convdelt_inputs, input_data.GetIndex()))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgumentBuffer(def_k_ConvolutionDeltaWeights, def_convdelt_gradients, m_cGradients.GetIndex()))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgument(def_k_ConvolutionDeltaWeights, def_convdelt_inputs_total, (int)input_data.Total()))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgument(def_k_ConvolutionDeltaWeights, def_convdelt_neurons, m_iNeurons))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgument(def_k_ConvolutionDeltaWeights, def_convdelt_step, m_iStep))
|
|
return false;
|
|
if(!m_cOpenCL.SetArgument(def_k_ConvolutionDeltaWeights, def_convdelt_transposed_out, (int)m_bTransposedOutput))
|
|
return false;
|
|
//--- place kernel to the execution queue
|
|
uint NDRange[] = {m_iWindow + 1, m_iWindowOut};
|
|
uint off_set[] = {0, 0};
|
|
if(!m_cOpenCL.Execute(def_k_ConvolutionDeltaWeights, 2, off_set, NDRange))
|
|
return false;
|
|
if(read && !m_cDeltaWeights.BufferRead())
|
|
return false;
|
|
}
|
|
//---
|
|
return true;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| Method for saving class elements to a file |
|
|
//+------------------------------------------------------------------+
|
|
bool CNeuronConv::Save(const int file_handle)
|
|
{
|
|
//--- call of the method of the parent class
|
|
if(!CNeuronBase::Save(file_handle))
|
|
return false;
|
|
//--- save constant values
|
|
if(FileWriteInteger(file_handle, (int)m_iWindow) <= 0)
|
|
return false;
|
|
if(FileWriteInteger(file_handle, (int)m_iStep) <= 0)
|
|
return false;
|
|
if(FileWriteInteger(file_handle, (int)m_iWindowOut) <= 0)
|
|
return false;
|
|
if(FileWriteInteger(file_handle, (int)m_iNeurons) <= 0)
|
|
return false;
|
|
if(FileWriteInteger(file_handle, (int)m_bTransposedOutput) <= 0)
|
|
return false;
|
|
//---
|
|
return true;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| Method for restoring the class from a file |
|
|
//+------------------------------------------------------------------+
|
|
bool CNeuronConv::Load(const int file_handle)
|
|
{
|
|
//--- call of the method of the parent class
|
|
if(!CNeuronBase::Load(file_handle))
|
|
return false;
|
|
//--- read constant values
|
|
m_iWindow = (uint)FileReadInteger(file_handle);
|
|
m_iStep = (uint)FileReadInteger(file_handle);
|
|
m_iWindowOut = (uint)FileReadInteger(file_handle);
|
|
m_iNeurons = (uint)FileReadInteger(file_handle);
|
|
m_bTransposedOutput = (bool)FileReadInteger(file_handle);
|
|
m_eActivation = -1;
|
|
//---
|
|
if(m_bTransposedOutput)
|
|
{
|
|
if(!m_cOutputs.Reshape(m_iNeurons, m_iWindowOut))
|
|
return false;
|
|
if(!m_cGradients.Reshape(m_iNeurons, m_iWindowOut))
|
|
return false;
|
|
}
|
|
else
|
|
{
|
|
if(!m_cOutputs.Reshape(m_iWindowOut, m_iNeurons))
|
|
return false;
|
|
if(!m_cGradients.Reshape(m_iWindowOut, m_iNeurons))
|
|
return false;
|
|
}
|
|
//---
|
|
return true;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
bool CNeuronConv::SetTransposedOutput(const bool value)
|
|
{
|
|
m_bTransposedOutput = value;
|
|
if(value)
|
|
{
|
|
if(!m_cOutputs.BufferInit(m_iNeurons, m_iWindowOut, 0))
|
|
return false;
|
|
if(!m_cGradients.BufferInit(m_iNeurons, m_iWindowOut, 0))
|
|
return false;
|
|
}
|
|
else
|
|
{
|
|
if(!m_cOutputs.BufferInit(m_iWindowOut, m_iNeurons, 0))
|
|
return false;
|
|
if(!m_cGradients.BufferInit(m_iWindowOut, m_iNeurons, 0))
|
|
return false;
|
|
}
|
|
//---
|
|
return true;
|
|
}
|
|
//+------------------------------------------------------------------+
|