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Neuron Net
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Describes the optimization process for the Neuron of convolution layer. More...
Macros | |
| #define | def_k_UpdateWeightsConvMomentum 9 |
| Index of the kernel of the convolution neuron to update weights SGD (UpdateWeightsConvMomentum) More... | |
| #define | def_k_uwcm_matrix_w 0 |
| Weights matrix (m+1)*n, where m - input window and n - output window. More... | |
| #define | def_k_uwcm_matrix_g 1 |
| Tensor of gradients at current layer. More... | |
| #define | def_k_uwcm_matrix_i 2 |
| Inputs tesor. More... | |
| #define | def_k_uwcm_matrix_dw 3 |
| Matrix of delta weights in last correction. More... | |
| #define | def_k_uwcm_inputs 4 |
| Number of inputs. More... | |
| #define | def_k_uwcm_learning_rates 5 |
| Learning rates. More... | |
| #define | def_k_uwcm_momentum 6 |
| Momentum multiplier. More... | |
| #define | def_k_uwcm_window_in 7 |
| Size of input window. More... | |
| #define | def_k_uwcm_window_out 8 |
| Size of output window. More... | |
| #define | def_k_uwcm_step 9 |
| Step size. More... | |
| #define | def_k_UpdateWeightsConvAdam 10 |
| Index of the kernel of the convolution neuron to update weights Adam (UpdateWeightsConvAdam) More... | |
| #define | def_k_uwca_matrix_w 0 |
| Weights matrix (m+1)*n, where m - input window and n - output window. More... | |
| #define | def_k_uwca_matrix_g 1 |
| Tensor of gradients at current layer. More... | |
| #define | def_k_uwca_matrix_i 2 |
| Inputs tesor. More... | |
| #define | def_k_uwca_matrix_m 3 |
| Matrix of first momentum. More... | |
| #define | def_k_uwca_matrix_v 4 |
| Matrix of seconfd momentum. More... | |
| #define | def_k_uwca_inputs 5 |
| Number of inputs. More... | |
| #define | def_k_uwca_l 6 |
| Learning rates. More... | |
| #define | def_k_uwca_b1 7 |
| First momentum multiplier. More... | |
| #define | def_k_uwca_b2 8 |
| Second momentum multiplier. More... | |
| #define | def_k_uwca_window_in 9 |
| Size of input window. More... | |
| #define | def_k_uwca_window_out 10 |
| Size of output window. More... | |
| #define | def_k_uwca_step 11 |
| Step size. More... | |
Functions | |
| __kernel void | UpdateWeightsConvMomentum (__global double *matrix_w, __global double *matrix_g, __global double *matrix_i, __global double *matrix_dw, int inputs, double learning_rates, double momentum, int window_in, int window_out, int step) |
| Describes the process of SGD optimization weights for the Convolution Neuron (CNeuronConvOCL). More... | |
| __kernel void | UpdateWeightsConvAdam (__global double *matrix_w, __global const double *matrix_g, __global const double *matrix_i, __global double *matrix_m, __global double *matrix_v, const int inputs, const double l, const double b1, const double b2, int window_in, int window_out, int step) |
| Describes the process of Adam optimization weights for the Convolution Neuron (CNeuronConvOCL). More... | |
Describes the optimization process for the Neuron of convolution layer.
| #define def_k_UpdateWeightsConvAdam 10 |
Index of the kernel of the convolution neuron to update weights Adam (UpdateWeightsConvAdam)
Definition at line 217 of file NeuroNet.mqh.
| #define def_k_UpdateWeightsConvMomentum 9 |
Index of the kernel of the convolution neuron to update weights SGD (UpdateWeightsConvMomentum)
Definition at line 205 of file NeuroNet.mqh.
| #define def_k_uwca_b1 7 |
First momentum multiplier.
Definition at line 225 of file NeuroNet.mqh.
| #define def_k_uwca_b2 8 |
Second momentum multiplier.
Definition at line 226 of file NeuroNet.mqh.
| #define def_k_uwca_inputs 5 |
Number of inputs.
Definition at line 223 of file NeuroNet.mqh.
| #define def_k_uwca_l 6 |
Learning rates.
Definition at line 224 of file NeuroNet.mqh.
| #define def_k_uwca_matrix_g 1 |
Tensor of gradients at current layer.
Definition at line 219 of file NeuroNet.mqh.
| #define def_k_uwca_matrix_i 2 |
Inputs tesor.
Definition at line 220 of file NeuroNet.mqh.
| #define def_k_uwca_matrix_m 3 |
Matrix of first momentum.
Definition at line 221 of file NeuroNet.mqh.
| #define def_k_uwca_matrix_v 4 |
Matrix of seconfd momentum.
Definition at line 222 of file NeuroNet.mqh.
| #define def_k_uwca_matrix_w 0 |
Weights matrix (m+1)*n, where m - input window and n - output window.
Definition at line 218 of file NeuroNet.mqh.
| #define def_k_uwca_step 11 |
Step size.
Definition at line 229 of file NeuroNet.mqh.
| #define def_k_uwca_window_in 9 |
Size of input window.
Definition at line 227 of file NeuroNet.mqh.
| #define def_k_uwca_window_out 10 |
Size of output window.
Definition at line 228 of file NeuroNet.mqh.
| #define def_k_uwcm_inputs 4 |
Number of inputs.
Definition at line 210 of file NeuroNet.mqh.
| #define def_k_uwcm_learning_rates 5 |
Learning rates.
Definition at line 211 of file NeuroNet.mqh.
| #define def_k_uwcm_matrix_dw 3 |
Matrix of delta weights in last correction.
Definition at line 209 of file NeuroNet.mqh.
| #define def_k_uwcm_matrix_g 1 |
Tensor of gradients at current layer.
Definition at line 207 of file NeuroNet.mqh.
| #define def_k_uwcm_matrix_i 2 |
Inputs tesor.
Definition at line 208 of file NeuroNet.mqh.
| #define def_k_uwcm_matrix_w 0 |
Weights matrix (m+1)*n, where m - input window and n - output window.
Definition at line 206 of file NeuroNet.mqh.
| #define def_k_uwcm_momentum 6 |
Momentum multiplier.
Definition at line 212 of file NeuroNet.mqh.
| #define def_k_uwcm_step 9 |
Step size.
Definition at line 215 of file NeuroNet.mqh.
| #define def_k_uwcm_window_in 7 |
Size of input window.
Definition at line 213 of file NeuroNet.mqh.
| #define def_k_uwcm_window_out 8 |
Size of output window.
Definition at line 214 of file NeuroNet.mqh.
| __kernel void UpdateWeightsConvAdam | ( | __global double * | matrix_w, |
| __global const double * | matrix_g, | ||
| __global const double * | matrix_i, | ||
| __global double * | matrix_m, | ||
| __global double * | matrix_v, | ||
| const int | inputs, | ||
| const double | l, | ||
| const double | b1, | ||
| const double | b2, | ||
| int | window_in, | ||
| int | window_out, | ||
| int | step | ||
| ) |
Describes the process of Adam optimization weights for the Convolution Neuron (CNeuronConvOCL).
| [in,out] | matrix_w | Weights matrix (m+1)*n, where m - input window and n - output window |
| [in] | matrix_g | Tensor of gradients at current layer |
| [in] | matrix_i | Inputs tesor |
| [in] | matrix_m | Matrix of first momentum |
| [in] | matrix_v | Matrix of seconfd momentum |
| inputs | Number of inputs | |
| l | Learning rates | |
| b1 | First momentum multiplier | |
| b2 | Second momentum multiplier | |
| window_in | Size of input window | |
| window_out | Size of output window | |
| step | Step size |
Definition at line 481 of file NeuroNet.cl.
| __kernel void UpdateWeightsConvMomentum | ( | __global double * | matrix_w, |
| __global double * | matrix_g, | ||
| __global double * | matrix_i, | ||
| __global double * | matrix_dw, | ||
| int | inputs, | ||
| double | learning_rates, | ||
| double | momentum, | ||
| int | window_in, | ||
| int | window_out, | ||
| int | step | ||
| ) |
Describes the process of SGD optimization weights for the Convolution Neuron (CNeuronConvOCL).
| [in,out] | matrix_w | Weights matrix (m+1)*n, where m - input window and n - output window |
| [in] | matrix_g | Tensor of gradients at current layer |
| [in] | matrix_i | Inputs tesor |
| [in,out] | matrix_dw | Matrix of delta weights in last correction |
| inputs | Number of inputs | |
| learning_rates | Learning rates | |
| momentum | Momentum multiplier | |
| window_in | Size of input window | |
| window_out | Size of output window | |
| step | Step size |
Definition at line 449 of file NeuroNet.cl.