وعلي فكرة توجد مواضيع بمواقع أجنبية تحتاج أن يتفرغ لها واحد بأقرب فرصة لعل يكون هناك طريقة ممكن عمل بها أكسبرت ناجح ونحن لا ندري
فوجدت مثلا موضوع يتكلم عن التوقع بأستعمال الشبكات العصبية ومعه أكواد جاهزة بلغة السي يعني وفروا حتي وقت البرمجة وأنا ليس لدي فكرة عن أستعمال الشبكة للتوقع وكل ماعملته هو أني قرأت كتاب بالعربي عن المعادلات التي تستعمل بشبكة بها 3 طبقات وحولتها للغة سي مع العلم بأنه يوجد أنواع كثيرة وأنا أستعملت أبسط شبكة
وهذا مثال عن ما وجدت
Backpropagation Network
Time-Series Forecasting
Prediction of the Annual Number of Sunspots
This program implements the now classic multi-layer backpropagation network with bias terms and momentum. It is used to detect structure in time-series, which is presented to the network using a simple tapped delay-line memory. The program learns to predict future sunspot activity from historical data collected over the past three centuries. To avoid overfitting, the termination of the learning procedure is controlled by the so-called stopped training method.
وبعد ما قمت بتطبيق هذا البرنامج أظهرلي ملف مكتوب به النتائج ويحتاج واحد يتفرغ بأقرب فرصة ويفهمه وحدة وحدةكود:
--------------------------------------------------------------------------------
The Backpropagation Network
--------------------------------------------------------------------------------
This program is copyright (c) 1996 by the author. It is made available as is,
and no warranty - about the program, its performance, or its conformity to any
specification - is given or implied. It may be used, modified, and distributed
freely for private and commercial purposes, as long as the original author is
credited as part of the final work.
Backpropagation Network Simulator
/******************************************************************************
====================================================
Network: Backpropagation Network with Bias Terms and Momentum
====================================================
Application: Time-Series Forecasting
Prediction of the Annual Number of Sunspots
Author: Karsten Kutza
Date: 17.4.96
Reference: D.E. Rumelhart, G.E. Hinton, R.J. Williams
Learning Internal Representations by Error Propagation
in:
D.E. Rumelhart, J.L. McClelland (Eds.)
Parallel Distributed Processing, Volume 1
MIT Press, Cambridge, MA, pp. 318-362, 1986
******************************************************************************/
/******************************************************************************
D E C L A R A T I O N S
******************************************************************************/
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
typedef int BOOL;
typedef int INT;
typedef double REAL;
#define FALSE 0
#define TRUE 1
#define NOT !
#define AND &&
#define OR ||
#define MIN_REAL -HUGE_VAL
#define MAX_REAL +HUGE_VAL
#define MIN(x,y) ((x)<(y) ? (x) : (y))
#define MAX(x,y) ((x)>(y) ? (x) : (y))
#define LO 0.1
#define HI 0.9
#define BIAS 1
#define sqr(x) ((x)*(x))
typedef struct { /* A LAYER OF A NET: */
INT Units; /* - number of units in this layer */
REAL* Output; /* - output of ith unit */
REAL* Error; /* - error term of ith unit */
REAL** Weight; /* - connection weights to ith unit */
REAL** WeightSave; /* - saved weights for stopped training */
REAL** dWeight; /* - last weight deltas for momentum */
} LAYER;
typedef struct { /* A NET: */
LAYER** Layer; /* - layers of this net */
LAYER* InputLayer; /* - input layer */
LAYER* OutputLayer; /* - output layer */
REAL Alpha; /* - momentum factor */
REAL Eta; /* - learning rate */
REAL Gain; /* - gain of sigmoid function */
REAL Error; /* - total net error */
} NET;
/******************************************************************************
R A N D O M S D R A W N F R O M D I S T R I B U T I O N S
******************************************************************************/
void InitializeRandoms()
{
srand(4711);
}
INT RandomEqualINT(INT Low, INT High)
{
return rand() % (High-Low+1) + Low;
}
REAL RandomEqualREAL(REAL Low, REAL High)
{
return ((REAL) rand() / RAND_MAX) * (High-Low) + Low;
}
/******************************************************************************
A P P L I C A T I O N - S P E C I F I C C O D E
******************************************************************************/
#define NUM_LAYERS 3
#define N 30
#define M 1
INT Units[NUM_LAYERS] = {N, 10, M};
#define FIRST_YEAR 1700
#define NUM_YEARS 280
#define TRAIN_LWB (N)
#define TRAIN_UPB (179)
#define TRAIN_YEARS (TRAIN_UPB - TRAIN_LWB + 1)
#define TEST_LWB (180)
#define TEST_UPB (259)
#define TEST_YEARS (TEST_UPB - TEST_LWB + 1)
#define EVAL_LWB (260)
#define EVAL_UPB (NUM_YEARS - 1)
#define EVAL_YEARS (EVAL_UPB - EVAL_LWB + 1)
REAL Sunspots_[NUM_YEARS];
REAL Sunspots [NUM_YEARS] = {
0.0262, 0.0575, 0.0837, 0.1203, 0.1883, 0.3033,
0.1517, 0.1046, 0.0523, 0.0418, 0.0157, 0.0000,
0.0000, 0.0105, 0.0575, 0.1412, 0.2458, 0.3295,
0.3138, 0.2040, 0.1464, 0.1360, 0.1151, 0.0575,
0.1098, 0.2092, 0.4079, 0.6381, 0.5387, 0.3818,
0.2458, 0.1831, 0.0575, 0.0262, 0.0837, 0.1778,
0.3661, 0.4236, 0.5805, 0.5282, 0.3818, 0.2092,
0.1046, 0.0837, 0.0262, 0.0575, 0.1151, 0.2092,
0.3138, 0.4231, 0.4362, 0.2495, 0.2500, 0.1606,
0.0638, 0.0502, 0.0534, 0.1700, 0.2489, 0.2824,
0.3290, 0.4493, 0.3201, 0.2359, 0.1904, 0.1093,
0.0596, 0.1977, 0.3651, 0.5549, 0.5272, 0.4268,
0.3478, 0.1820, 0.1600, 0.0366, 0.1036, 0.4838,
0.8075, 0.6585, 0.4435, 0.3562, 0.2014, 0.1192,
0.0534, 0.1260, 0.4336, 0.6904, 0.6846, 0.6177,
0.4702, 0.3483, 0.3138, 0.2453, 0.2144, 0.1114,
0.0837, 0.0335, 0.0214, 0.0356, 0.0758, 0.1778,
0.2354, 0.2254, 0.2484, 0.2207, 0.1470, 0.0528,
0.0424, 0.0131, 0.0000, 0.0073, 0.0262, 0.0638,
0.0727, 0.1851, 0.2395, 0.2150, 0.1574, 0.1250,
0.0816, 0.0345, 0.0209, 0.0094, 0.0445, 0.0868,
0.1898, 0.2594, 0.3358, 0.3504, 0.3708, 0.2500,
0.1438, 0.0445, 0.0690, 0.2976, 0.6354, 0.7233,
0.5397, 0.4482, 0.3379, 0.1919, 0.1266, 0.0560,
0.0785, 0.2097, 0.3216, 0.5152, 0.6522, 0.5036,
0.3483, 0.3373, 0.2829, 0.2040, 0.1077, 0.0350,
0.0225, 0.1187, 0.2866, 0.4906, 0.5010, 0.4038,
0.3091, 0.2301, 0.2458, 0.1595, 0.0853, 0.0382,
0.1966, 0.3870, 0.7270, 0.5816, 0.5314, 0.3462,
0.2338, 0.0889, 0.0591, 0.0649, 0.0178, 0.0314,
0.1689, 0.2840, 0.3122, 0.3332, 0.3321, 0.2730,
0.1328, 0.0685, 0.0356, 0.0330, 0.0371, 0.1862,
0.3818, 0.4451, 0.4079, 0.3347, 0.2186, 0.1370,
0.1396, 0.0633, 0.0497, 0.0141, 0.0262, 0.1276,
0.2197, 0.3321, 0.2814, 0.3243, 0.2537, 0.2296,
0.0973, 0.0298, 0.0188, 0.0073, 0.0502, 0.2479,
0.2986, 0.5434, 0.4215, 0.3326, 0.1966, 0.1365,
0.0743, 0.0303, 0.0873, 0.2317, 0.3342, 0.3609,
0.4069, 0.3394, 0.1867, 0.1109, 0.0581, 0.0298,
0.0455, 0.1888, 0.4168, 0.5983, 0.5732, 0.4644,
0.3546, 0.2484, 0.1600, 0.0853, 0.0502, 0.1736,
0.4843, 0.7929, 0.7128, 0.7045, 0.4388, 0.3630,
0.1647, 0.0727, 0.0230, 0.1987, 0.7411, 0.9947,
0.9665, 0.8316, 0.5873, 0.2819, 0.1961, 0.1459,
0.0534, 0.0790, 0.2458, 0.4906, 0.5539, 0.5518,
0.5465, 0.3483, 0.3603, 0.1987, 0.1804, 0.0811,
0.0659, 0.1428, 0.4838, 0.8127
};
REAL Mean;
REAL TrainError;
REAL TrainErrorPredictingMean;
REAL TestError;
REAL TestErrorPredictingMean;
FILE* f;
void NormalizeSunspots()
{
INT Year;
REAL Min, Max;
Min = MAX_REAL;
Max = MIN_REAL;
for (Year=0; Year<NUM_YEARS; Year++) {
Min = MIN(Min, Sunspots[Year]);
Max = MAX(Max, Sunspots[Year]);
}
Mean = 0;
for (Year=0; Year<NUM_YEARS; Year++) {
Sunspots_[Year] =
Sunspots [Year] = ((Sunspots[Year]-Min) / (Max-Min)) * (HI-LO) + LO;
Mean += Sunspots[Year] / NUM_YEARS;
}
}
void InitializeApplication(NET* Net)
{
INT Year, i;
REAL Out, Err;
Net->Alpha = 0.5;
Net->Eta = 0.05;
Net->Gain = 1;
NormalizeSunspots();
TrainErrorPredictingMean = 0;
for (Year=TRAIN_LWB; Year<=TRAIN_UPB; Year++) {
for (i=0; i<M; i++) {
Out = Sunspots[Year+i];
Err = Mean - Out;
TrainErrorPredictingMean += 0.5 * sqr(Err);
}
}
TestErrorPredictingMean = 0;
for (Year=TEST_LWB; Year<=TEST_UPB; Year++) {
for (i=0; i<M; i++) {
Out = Sunspots[Year+i];
Err = Mean - Out;
TestErrorPredictingMean += 0.5 * sqr(Err);
}
}
f = fopen("BPN.txt", "w");
}
void FinalizeApplication(NET* Net)
{
fclose(f);
}
/******************************************************************************
I N I T I A L I Z A T I O N
******************************************************************************/
void GenerateNetwork(NET* Net)
{
INT l,i;
Net->Layer = (LAYER**) calloc(NUM_LAYERS, sizeof(LAYER*));
for (l=0; l<NUM_LAYERS; l++) {
Net->Layer[l] = (LAYER*) malloc(sizeof(LAYER));
Net->Layer[l]->Units = Units[l];
Net->Layer[l]->Output = (REAL*) calloc(Units[l]+1, sizeof(REAL));
Net->Layer[l]->Error = (REAL*) calloc(Units[l]+1, sizeof(REAL));
Net->Layer[l]->Weight = (REAL**) calloc(Units[l]+1, sizeof(REAL*));
Net->Layer[l]->WeightSave = (REAL**) calloc(Units[l]+1, sizeof(REAL*));
Net->Layer[l]->dWeight = (REAL**) calloc(Units[l]+1, sizeof(REAL*));
Net->Layer[l]->Output[0] = BIAS;
if (l != 0) {
for (i=1; i<=Units[l]; i++) {
Net->Layer[l]->Weight[i] = (REAL*) calloc(Units[l-1]+1,
sizeof(REAL));
Net->Layer[l]->WeightSave[i] = (REAL*) calloc(Units[l-1]+1,
sizeof(REAL));
Net->Layer[l]->dWeight[i] = (REAL*) calloc(Units[l-1]+1,
sizeof(REAL));
}
}
}
Net->InputLayer = Net->Layer[0];
Net->OutputLayer = Net->Layer[NUM_LAYERS - 1];
Net->Alpha = 0.9;
Net->Eta = 0.25;
Net->Gain = 1;
}
void RandomWeights(NET* Net)
{
INT l,i,j;
for (l=1; l<NUM_LAYERS; l++) {
for (i=1; i<=Net->Layer[l]->Units; i++) {
for (j=0; j<=Net->Layer[l-1]->Units; j++) {
Net->Layer[l]->Weight[i][j] = RandomEqualREAL(-0.5, 0.5);
}
}
}
}
void SetInput(NET* Net, REAL* Input)
{
INT i;
for (i=1; i<=Net->InputLayer->Units; i++) {
Net->InputLayer->Output[i] = Input[i-1];
}
}
void GetOutput(NET* Net, REAL* Output)
{
INT i;
for (i=1; i<=Net->OutputLayer->Units; i++) {
Output[i-1] = Net->OutputLayer->Output[i];
}
}
/******************************************************************************
S U P P O R T F O R S T O P P E D T R A I N I N G
******************************************************************************/
void SaveWeights(NET* Net)
{
INT l,i,j;
for (l=1; l<NUM_LAYERS; l++) {
for (i=1; i<=Net->Layer[l]->Units; i++) {
for (j=0; j<=Net->Layer[l-1]->Units; j++) {
Net->Layer[l]->WeightSave[i][j] = Net->Layer[l]->Weight[i][j];
}
}
}
}
void RestoreWeights(NET* Net)
{
INT l,i,j;
for (l=1; l<NUM_LAYERS; l++) {
for (i=1; i<=Net->Layer[l]->Units; i++) {
for (j=0; j<=Net->Layer[l-1]->Units; j++) {
Net->Layer[l]->Weight[i][j] = Net->Layer[l]->WeightSave[i][j];
}
}
}
}
/******************************************************************************
P R O P A G A T I N G S I G N A L S
******************************************************************************/
void PropagateLayer(NET* Net, LAYER* Lower, LAYER* Upper)
{
INT i,j;
REAL Sum;
for (i=1; i<=Upper->Units; i++) {
Sum = 0;
for (j=0; j<=Lower->Units; j++) {
Sum += Upper->Weight[i][j] * Lower->Output[j];
}
Upper->Output[i] = 1 / (1 + exp(-Net->Gain * Sum));
}
}
void PropagateNet(NET* Net)
{
INT l;
for (l=0; l<NUM_LAYERS-1; l++) {
PropagateLayer(Net, Net->Layer[l], Net->Layer[l+1]);
}
}
/******************************************************************************
B A C K P R O P A G A T I N G E R R O R S
******************************************************************************/
void ComputeOutputError(NET* Net, REAL* Target)
{
INT i;
REAL Out, Err;
Net->Error = 0;
for (i=1; i<=Net->OutputLayer->Units; i++) {
Out = Net->OutputLayer->Output[i];
Err = Target[i-1]-Out;
Net->OutputLayer->Error[i] = Net->Gain * Out * (1-Out) * Err;
Net->Error += 0.5 * sqr(Err);
}
}
void BackpropagateLayer(NET* Net, LAYER* Upper, LAYER* Lower)
{
INT i,j;
REAL Out, Err;
for (i=1; i<=Lower->Units; i++) {
Out = Lower->Output[i];
Err = 0;
for (j=1; j<=Upper->Units; j++) {
Err += Upper->Weight[j][i] * Upper->Error[j];
}
Lower->Error[i] = Net->Gain * Out * (1-Out) * Err;
}
}
void BackpropagateNet(NET* Net)
{
INT l;
for (l=NUM_LAYERS-1; l>1; l--) {
BackpropagateLayer(Net, Net->Layer[l], Net->Layer[l-1]);
}
}
void AdjustWeights(NET* Net)
{
INT l,i,j;
REAL Out, Err, dWeight;
for (l=1; l<NUM_LAYERS; l++) {
for (i=1; i<=Net->Layer[l]->Units; i++) {
for (j=0; j<=Net->Layer[l-1]->Units; j++) {
Out = Net->Layer[l-1]->Output[j];
Err = Net->Layer[l]->Error[i];
dWeight = Net->Layer[l]->dWeight[i][j];
Net->Layer[l]->Weight[i][j] += Net->Eta * Err * Out + Net->Alpha *
dWeight;
Net->Layer[l]->dWeight[i][j] = Net->Eta * Err * Out;
}
}
}
}
/******************************************************************************
S I M U L A T I N G T H E N E T
******************************************************************************/
void SimulateNet(NET* Net, REAL* Input, REAL* Output, REAL* Target, BOOL
Training)
{
SetInput(Net, Input);
PropagateNet(Net);
GetOutput(Net, Output);
ComputeOutputError(Net, Target);
if (Training) {
BackpropagateNet(Net);
AdjustWeights(Net);
}
}
void TrainNet(NET* Net, INT Epochs)
{
INT Year, n;
REAL Output[M];
for (n=0; n<Epochs*TRAIN_YEARS; n++) {
Year = RandomEqualINT(TRAIN_LWB, TRAIN_UPB);
SimulateNet(Net, &(Sunspots[Year-N]), Output, &(Sunspots[Year]), TRUE);
}
}
void TestNet(NET* Net)
{
INT Year;
REAL Output[M];
TrainError = 0;
for (Year=TRAIN_LWB; Year<=TRAIN_UPB; Year++) {
SimulateNet(Net, &(Sunspots[Year-N]), Output, &(Sunspots[Year]), FALSE);
TrainError += Net->Error;
}
TestError = 0;
for (Year=TEST_LWB; Year<=TEST_UPB; Year++) {
SimulateNet(Net, &(Sunspots[Year-N]), Output, &(Sunspots[Year]), FALSE);
TestError += Net->Error;
}
fprintf(f, "\nNMSE is %0.3f on Training Set and %0.3f on Test Set",
TrainError / TrainErrorPredictingMean,
TestError / TestErrorPredictingMean);
}
void EvaluateNet(NET* Net)
{
INT Year;
REAL Output [M];
REAL Output_[M];
fprintf(f, "\n\n\n");
fprintf(f, "Year Sunspots Open-Loop Prediction Closed-Loop
Prediction\n");
fprintf(f, "\n");
for (Year=EVAL_LWB; Year<=EVAL_UPB; Year++) {
SimulateNet(Net, &(Sunspots [Year-N]), Output, &(Sunspots [Year]), FALSE);
SimulateNet(Net, &(Sunspots_[Year-N]), Output_, &(Sunspots_[Year]), FALSE);
Sunspots_[Year] = Output_[0];
fprintf(f, "%d %0.3f %0.3f
%0.3f\n",
FIRST_YEAR + Year,
Sunspots[Year],
Output [0],
Output_[0]);
}
}
/******************************************************************************
M A I N
******************************************************************************/
void main()
{
NET Net;
BOOL Stop;
REAL MinTestError;
InitializeRandoms();
GenerateNetwork(&Net);
RandomWeights(&Net);
InitializeApplication(&Net);
Stop = FALSE;
MinTestError = MAX_REAL;
do {
TrainNet(&Net, 10);
TestNet(&Net);
if (TestError < MinTestError) {
fprintf(f, " - saving Weights ...");
MinTestError = TestError;
SaveWeights(&Net);
}
else if (TestError > 1.2 * MinTestError) {
fprintf(f, " - stopping Training and restoring Weights ...");
Stop = TRUE;
RestoreWeights(&Net);
}
} while (NOT Stop);
TestNet(&Net);
EvaluateNet(&Net);
FinalizeApplication(&Net);
}
Simulator Output for the Time-Series Forecasting Application
NMSE is 0.879 on Training Set and 0.834 on Test Set - saving Weights ...
NMSE is 0.818 on Training Set and 0.783 on Test Set - saving Weights ...
NMSE is 0.749 on Training Set and 0.693 on Test Set - saving Weights ...
NMSE is 0.691 on Training Set and 0.614 on Test Set - saving Weights ...
NMSE is 0.622 on Training Set and 0.555 on Test Set - saving Weights ...
NMSE is 0.569 on Training Set and 0.491 on Test Set - saving Weights ...
NMSE is 0.533 on Training Set and 0.467 on Test Set - saving Weights ...
NMSE is 0.490 on Training Set and 0.416 on Test Set - saving Weights ...
NMSE is 0.470 on Training Set and 0.401 on Test Set - saving Weights ...
NMSE is 0.441 on Training Set and 0.361 on Test Set - saving Weights ...
.
.
.
NMSE is 0.142 on Training Set and 0.143 on Test Set
NMSE is 0.142 on Training Set and 0.146 on Test Set
NMSE is 0.141 on Training Set and 0.143 on Test Set
NMSE is 0.146 on Training Set and 0.141 on Test Set
NMSE is 0.144 on Training Set and 0.141 on Test Set
NMSE is 0.140 on Training Set and 0.142 on Test Set
NMSE is 0.144 on Training Set and 0.148 on Test Set
NMSE is 0.140 on Training Set and 0.139 on Test Set - saving Weights ...
NMSE is 0.140 on Training Set and 0.140 on Test Set
NMSE is 0.141 on Training Set and 0.138 on Test Set - saving Weights ...
.
.
.
NMSE is 0.104 on Training Set and 0.154 on Test Set
NMSE is 0.102 on Training Set and 0.160 on Test Set
NMSE is 0.102 on Training Set and 0.160 on Test Set
NMSE is 0.100 on Training Set and 0.157 on Test Set
NMSE is 0.105 on Training Set and 0.153 on Test Set
NMSE is 0.100 on Training Set and 0.155 on Test Set
NMSE is 0.101 on Training Set and 0.154 on Test Set
NMSE is 0.100 on Training Set and 0.158 on Test Set
NMSE is 0.107 on Training Set and 0.170 on Test Set - stopping Training
and restoring Weights ...
NMSE is 0.141 on Training Set and 0.138 on Test Set
Year Sunspots Open-Loop Prediction Closed-Loop Prediction
1960 0.572 0.532 0.532
1961 0.327 0.334 0.301
1962 0.258 0.158 0.146
1963 0.217 0.156 0.098
1964 0.143 0.236 0.149
1965 0.164 0.230 0.273
1966 0.298 0.263 0.405
1967 0.495 0.454 0.552
1968 0.545 0.615 0.627
1969 0.544 0.550 0.589
1970 0.540 0.474 0.464
1971 0.380 0.455 0.305
1972 0.390 0.270 0.191
1973 0.260 0.275 0.139
1974 0.245 0.211 0.158
1975 0.165 0.181 0.170
1976 0.153 0.128 0.175
1977 0.215 0.151 0.193
1978 0.489 0.316 0.274
1979 0.754 0.622 0.373
النتائج
كود:
NMSE is 0.879 on Training Set and 0.834 on Test Set - saving Weights ...
NMSE is 0.818 on Training Set and 0.783 on Test Set - saving Weights ...
NMSE is 0.749 on Training Set and 0.693 on Test Set - saving Weights ...
NMSE is 0.691 on Training Set and 0.614 on Test Set - saving Weights ...
NMSE is 0.622 on Training Set and 0.555 on Test Set - saving Weights ...
NMSE is 0.569 on Training Set and 0.491 on Test Set - saving Weights ...
NMSE is 0.533 on Training Set and 0.467 on Test Set - saving Weights ...
NMSE is 0.490 on Training Set and 0.416 on Test Set - saving Weights ...
NMSE is 0.470 on Training Set and 0.401 on Test Set - saving Weights ...
NMSE is 0.441 on Training Set and 0.361 on Test Set - saving Weights ...
NMSE is 0.423 on Training Set and 0.345 on Test Set - saving Weights ...
NMSE is 0.402 on Training Set and 0.329 on Test Set - saving Weights ...
NMSE is 0.385 on Training Set and 0.319 on Test Set - saving Weights ...
NMSE is 0.381 on Training Set and 0.323 on Test Set
NMSE is 0.356 on Training Set and 0.292 on Test Set - saving Weights ...
NMSE is 0.350 on Training Set and 0.279 on Test Set - saving Weights ...
NMSE is 0.333 on Training Set and 0.272 on Test Set - saving Weights ...
NMSE is 0.322 on Training Set and 0.258 on Test Set - saving Weights ...
NMSE is 0.318 on Training Set and 0.250 on Test Set - saving Weights ...
NMSE is 0.303 on Training Set and 0.244 on Test Set - saving Weights ...
NMSE is 0.303 on Training Set and 0.235 on Test Set - saving Weights ...
NMSE is 0.290 on Training Set and 0.238 on Test Set
NMSE is 0.281 on Training Set and 0.224 on Test Set - saving Weights ...
NMSE is 0.275 on Training Set and 0.215 on Test Set - saving Weights ...
NMSE is 0.275 on Training Set and 0.208 on Test Set - saving Weights ...
NMSE is 0.264 on Training Set and 0.209 on Test Set
NMSE is 0.259 on Training Set and 0.207 on Test Set - saving Weights ...
NMSE is 0.255 on Training Set and 0.203 on Test Set - saving Weights ...
NMSE is 0.251 on Training Set and 0.202 on Test Set - saving Weights ...
NMSE is 0.247 on Training Set and 0.198 on Test Set - saving Weights ...
NMSE is 0.246 on Training Set and 0.203 on Test Set
NMSE is 0.243 on Training Set and 0.191 on Test Set - saving Weights ...
NMSE is 0.241 on Training Set and 0.198 on Test Set
NMSE is 0.233 on Training Set and 0.185 on Test Set - saving Weights ...
NMSE is 0.235 on Training Set and 0.184 on Test Set - saving Weights ...
NMSE is 0.232 on Training Set and 0.192 on Test Set
NMSE is 0.224 on Training Set and 0.183 on Test Set - saving Weights ...
NMSE is 0.222 on Training Set and 0.179 on Test Set - saving Weights ...
NMSE is 0.219 on Training Set and 0.179 on Test Set - saving Weights ...
NMSE is 0.225 on Training Set and 0.188 on Test Set
NMSE is 0.213 on Training Set and 0.172 on Test Set - saving Weights ...
NMSE is 0.217 on Training Set and 0.171 on Test Set - saving Weights ...
NMSE is 0.208 on Training Set and 0.169 on Test Set - saving Weights ...
NMSE is 0.207 on Training Set and 0.170 on Test Set
NMSE is 0.204 on Training Set and 0.167 on Test Set - saving Weights ...
NMSE is 0.203 on Training Set and 0.172 on Test Set
NMSE is 0.200 on Training Set and 0.170 on Test Set
NMSE is 0.199 on Training Set and 0.172 on Test Set
NMSE is 0.201 on Training Set and 0.165 on Test Set - saving Weights ...
NMSE is 0.195 on Training Set and 0.164 on Test Set - saving Weights ...
NMSE is 0.200 on Training Set and 0.162 on Test Set - saving Weights ...
NMSE is 0.193 on Training Set and 0.164 on Test Set
NMSE is 0.195 on Training Set and 0.167 on Test Set
NMSE is 0.190 on Training Set and 0.162 on Test Set - saving Weights ...
NMSE is 0.188 on Training Set and 0.155 on Test Set - saving Weights ...
NMSE is 0.189 on Training Set and 0.161 on Test Set
NMSE is 0.189 on Training Set and 0.163 on Test Set
NMSE is 0.184 on Training Set and 0.155 on Test Set - saving Weights ...
NMSE is 0.196 on Training Set and 0.175 on Test Set
NMSE is 0.183 on Training Set and 0.160 on Test Set
NMSE is 0.184 on Training Set and 0.164 on Test Set
NMSE is 0.181 on Training Set and 0.161 on Test Set
NMSE is 0.180 on Training Set and 0.159 on Test Set
NMSE is 0.187 on Training Set and 0.155 on Test Set
NMSE is 0.178 on Training Set and 0.159 on Test Set
NMSE is 0.176 on Training Set and 0.153 on Test Set - saving Weights ...
NMSE is 0.177 on Training Set and 0.160 on Test Set
NMSE is 0.177 on Training Set and 0.153 on Test Set - saving Weights ...
NMSE is 0.179 on Training Set and 0.152 on Test Set - saving Weights ...
NMSE is 0.173 on Training Set and 0.152 on Test Set - saving Weights ...
NMSE is 0.171 on Training Set and 0.155 on Test Set
NMSE is 0.174 on Training Set and 0.152 on Test Set - saving Weights ...
NMSE is 0.170 on Training Set and 0.154 on Test Set
NMSE is 0.170 on Training Set and 0.156 on Test Set
NMSE is 0.170 on Training Set and 0.156 on Test Set
NMSE is 0.169 on Training Set and 0.151 on Test Set - saving Weights ...
NMSE is 0.167 on Training Set and 0.152 on Test Set
NMSE is 0.167 on Training Set and 0.152 on Test Set
NMSE is 0.166 on Training Set and 0.156 on Test Set
NMSE is 0.172 on Training Set and 0.153 on Test Set
NMSE is 0.166 on Training Set and 0.155 on Test Set
NMSE is 0.172 on Training Set and 0.154 on Test Set
NMSE is 0.168 on Training Set and 0.151 on Test Set
NMSE is 0.164 on Training Set and 0.148 on Test Set - saving Weights ...
NMSE is 0.164 on Training Set and 0.152 on Test Set
NMSE is 0.162 on Training Set and 0.148 on Test Set - saving Weights ...
NMSE is 0.165 on Training Set and 0.154 on Test Set
NMSE is 0.161 on Training Set and 0.146 on Test Set - saving Weights ...
NMSE is 0.160 on Training Set and 0.149 on Test Set
NMSE is 0.160 on Training Set and 0.149 on Test Set
NMSE is 0.159 on Training Set and 0.146 on Test Set - saving Weights ...
NMSE is 0.160 on Training Set and 0.148 on Test Set
NMSE is 0.158 on Training Set and 0.145 on Test Set - saving Weights ...
NMSE is 0.157 on Training Set and 0.144 on Test Set - saving Weights ...
NMSE is 0.160 on Training Set and 0.141 on Test Set - saving Weights ...
NMSE is 0.157 on Training Set and 0.144 on Test Set
NMSE is 0.159 on Training Set and 0.150 on Test Set
NMSE is 0.157 on Training Set and 0.144 on Test Set
NMSE is 0.157 on Training Set and 0.150 on Test Set
NMSE is 0.156 on Training Set and 0.150 on Test Set
NMSE is 0.154 on Training Set and 0.144 on Test Set
NMSE is 0.154 on Training Set and 0.146 on Test Set
NMSE is 0.155 on Training Set and 0.149 on Test Set
NMSE is 0.154 on Training Set and 0.148 on Test Set
NMSE is 0.152 on Training Set and 0.144 on Test Set
NMSE is 0.153 on Training Set and 0.145 on Test Set
NMSE is 0.151 on Training Set and 0.143 on Test Set
NMSE is 0.151 on Training Set and 0.145 on Test Set
NMSE is 0.152 on Training Set and 0.143 on Test Set
NMSE is 0.152 on Training Set and 0.147 on Test Set
NMSE is 0.151 on Training Set and 0.141 on Test Set - saving Weights ...
NMSE is 0.154 on Training Set and 0.141 on Test Set
NMSE is 0.152 on Training Set and 0.147 on Test Set
NMSE is 0.150 on Training Set and 0.146 on Test Set
NMSE is 0.149 on Training Set and 0.146 on Test Set
NMSE is 0.150 on Training Set and 0.150 on Test Set
NMSE is 0.148 on Training Set and 0.143 on Test Set
NMSE is 0.150 on Training Set and 0.147 on Test Set
NMSE is 0.147 on Training Set and 0.144 on Test Set
NMSE is 0.153 on Training Set and 0.142 on Test Set
NMSE is 0.147 on Training Set and 0.144 on Test Set
NMSE is 0.146 on Training Set and 0.144 on Test Set
NMSE is 0.147 on Training Set and 0.141 on Test Set
NMSE is 0.145 on Training Set and 0.141 on Test Set - saving Weights ...
NMSE is 0.145 on Training Set and 0.141 on Test Set - saving Weights ...
NMSE is 0.145 on Training Set and 0.140 on Test Set - saving Weights ...
NMSE is 0.145 on Training Set and 0.142 on Test Set
NMSE is 0.147 on Training Set and 0.140 on Test Set - saving Weights ...
NMSE is 0.150 on Training Set and 0.140 on Test Set
NMSE is 0.150 on Training Set and 0.141 on Test Set
NMSE is 0.143 on Training Set and 0.144 on Test Set
NMSE is 0.143 on Training Set and 0.142 on Test Set
NMSE is 0.142 on Training Set and 0.141 on Test Set
NMSE is 0.142 on Training Set and 0.143 on Test Set
NMSE is 0.142 on Training Set and 0.143 on Test Set
NMSE is 0.142 on Training Set and 0.146 on Test Set
NMSE is 0.141 on Training Set and 0.143 on Test Set
NMSE is 0.146 on Training Set and 0.141 on Test Set
NMSE is 0.144 on Training Set and 0.141 on Test Set
NMSE is 0.140 on Training Set and 0.142 on Test Set
NMSE is 0.144 on Training Set and 0.148 on Test Set
NMSE is 0.140 on Training Set and 0.139 on Test Set - saving Weights ...
NMSE is 0.140 on Training Set and 0.140 on Test Set
NMSE is 0.141 on Training Set and 0.138 on Test Set - saving Weights ...
NMSE is 0.139 on Training Set and 0.140 on Test Set
NMSE is 0.138 on Training Set and 0.141 on Test Set
NMSE is 0.138 on Training Set and 0.140 on Test Set
NMSE is 0.138 on Training Set and 0.141 on Test Set
NMSE is 0.137 on Training Set and 0.141 on Test Set
NMSE is 0.139 on Training Set and 0.140 on Test Set
NMSE is 0.140 on Training Set and 0.140 on Test Set
NMSE is 0.137 on Training Set and 0.142 on Test Set
NMSE is 0.137 on Training Set and 0.144 on Test Set
NMSE is 0.137 on Training Set and 0.141 on Test Set
NMSE is 0.144 on Training Set and 0.142 on Test Set
NMSE is 0.140 on Training Set and 0.151 on Test Set
NMSE is 0.135 on Training Set and 0.142 on Test Set
NMSE is 0.138 on Training Set and 0.142 on Test Set
NMSE is 0.135 on Training Set and 0.144 on Test Set
NMSE is 0.134 on Training Set and 0.144 on Test Set
NMSE is 0.134 on Training Set and 0.141 on Test Set
NMSE is 0.134 on Training Set and 0.145 on Test Set
NMSE is 0.134 on Training Set and 0.142 on Test Set
NMSE is 0.134 on Training Set and 0.144 on Test Set
NMSE is 0.133 on Training Set and 0.142 on Test Set
NMSE is 0.133 on Training Set and 0.143 on Test Set
NMSE is 0.132 on Training Set and 0.145 on Test Set
NMSE is 0.133 on Training Set and 0.146 on Test Set
NMSE is 0.132 on Training Set and 0.146 on Test Set
NMSE is 0.132 on Training Set and 0.144 on Test Set
NMSE is 0.132 on Training Set and 0.147 on Test Set
NMSE is 0.132 on Training Set and 0.149 on Test Set
NMSE is 0.132 on Training Set and 0.148 on Test Set
NMSE is 0.136 on Training Set and 0.154 on Test Set
NMSE is 0.131 on Training Set and 0.143 on Test Set
NMSE is 0.132 on Training Set and 0.148 on Test Set
NMSE is 0.137 on Training Set and 0.141 on Test Set
NMSE is 0.129 on Training Set and 0.143 on Test Set
NMSE is 0.130 on Training Set and 0.142 on Test Set
NMSE is 0.129 on Training Set and 0.147 on Test Set
NMSE is 0.129 on Training Set and 0.146 on Test Set
NMSE is 0.129 on Training Set and 0.148 on Test Set
NMSE is 0.128 on Training Set and 0.145 on Test Set
NMSE is 0.131 on Training Set and 0.149 on Test Set
NMSE is 0.128 on Training Set and 0.142 on Test Set
NMSE is 0.134 on Training Set and 0.142 on Test Set
NMSE is 0.128 on Training Set and 0.146 on Test Set
NMSE is 0.127 on Training Set and 0.145 on Test Set
NMSE is 0.127 on Training Set and 0.144 on Test Set
NMSE is 0.127 on Training Set and 0.146 on Test Set
NMSE is 0.126 on Training Set and 0.145 on Test Set
NMSE is 0.126 on Training Set and 0.148 on Test Set
NMSE is 0.131 on Training Set and 0.156 on Test Set
NMSE is 0.126 on Training Set and 0.148 on Test Set
NMSE is 0.125 on Training Set and 0.147 on Test Set
NMSE is 0.138 on Training Set and 0.147 on Test Set
NMSE is 0.128 on Training Set and 0.154 on Test Set
NMSE is 0.124 on Training Set and 0.146 on Test Set
NMSE is 0.126 on Training Set and 0.144 on Test Set
NMSE is 0.125 on Training Set and 0.150 on Test Set
NMSE is 0.130 on Training Set and 0.146 on Test Set
NMSE is 0.125 on Training Set and 0.145 on Test Set
NMSE is 0.123 on Training Set and 0.146 on Test Set
NMSE is 0.123 on Training Set and 0.147 on Test Set
NMSE is 0.124 on Training Set and 0.150 on Test Set
NMSE is 0.128 on Training Set and 0.146 on Test Set
NMSE is 0.123 on Training Set and 0.146 on Test Set
NMSE is 0.124 on Training Set and 0.151 on Test Set
NMSE is 0.122 on Training Set and 0.146 on Test Set
NMSE is 0.131 on Training Set and 0.146 on Test Set
NMSE is 0.121 on Training Set and 0.146 on Test Set
NMSE is 0.129 on Training Set and 0.159 on Test Set
NMSE is 0.121 on Training Set and 0.146 on Test Set
NMSE is 0.121 on Training Set and 0.144 on Test Set
NMSE is 0.122 on Training Set and 0.151 on Test Set
NMSE is 0.120 on Training Set and 0.147 on Test Set
NMSE is 0.120 on Training Set and 0.147 on Test Set
NMSE is 0.120 on Training Set and 0.147 on Test Set
NMSE is 0.119 on Training Set and 0.147 on Test Set
NMSE is 0.120 on Training Set and 0.152 on Test Set
NMSE is 0.119 on Training Set and 0.148 on Test Set
NMSE is 0.120 on Training Set and 0.148 on Test Set
NMSE is 0.121 on Training Set and 0.156 on Test Set
NMSE is 0.120 on Training Set and 0.148 on Test Set
NMSE is 0.119 on Training Set and 0.146 on Test Set
NMSE is 0.119 on Training Set and 0.152 on Test Set
NMSE is 0.119 on Training Set and 0.152 on Test Set
NMSE is 0.118 on Training Set and 0.153 on Test Set
NMSE is 0.122 on Training Set and 0.146 on Test Set
NMSE is 0.120 on Training Set and 0.146 on Test Set
NMSE is 0.117 on Training Set and 0.152 on Test Set
NMSE is 0.118 on Training Set and 0.148 on Test Set
NMSE is 0.118 on Training Set and 0.155 on Test Set
NMSE is 0.117 on Training Set and 0.148 on Test Set
NMSE is 0.117 on Training Set and 0.148 on Test Set
NMSE is 0.117 on Training Set and 0.148 on Test Set
NMSE is 0.118 on Training Set and 0.156 on Test Set
NMSE is 0.118 on Training Set and 0.157 on Test Set
NMSE is 0.115 on Training Set and 0.153 on Test Set
NMSE is 0.115 on Training Set and 0.150 on Test Set
NMSE is 0.118 on Training Set and 0.157 on Test Set
NMSE is 0.117 on Training Set and 0.157 on Test Set
NMSE is 0.114 on Training Set and 0.149 on Test Set
NMSE is 0.115 on Training Set and 0.148 on Test Set
NMSE is 0.115 on Training Set and 0.149 on Test Set
NMSE is 0.117 on Training Set and 0.151 on Test Set
NMSE is 0.115 on Training Set and 0.151 on Test Set
NMSE is 0.113 on Training Set and 0.150 on Test Set
NMSE is 0.114 on Training Set and 0.155 on Test Set
NMSE is 0.113 on Training Set and 0.149 on Test Set
NMSE is 0.113 on Training Set and 0.151 on Test Set
NMSE is 0.115 on Training Set and 0.148 on Test Set
NMSE is 0.114 on Training Set and 0.148 on Test Set
NMSE is 0.112 on Training Set and 0.152 on Test Set
NMSE is 0.112 on Training Set and 0.152 on Test Set
NMSE is 0.113 on Training Set and 0.154 on Test Set
NMSE is 0.112 on Training Set and 0.151 on Test Set
NMSE is 0.111 on Training Set and 0.150 on Test Set
NMSE is 0.112 on Training Set and 0.153 on Test Set
NMSE is 0.113 on Training Set and 0.148 on Test Set
NMSE is 0.112 on Training Set and 0.149 on Test Set
NMSE is 0.111 on Training Set and 0.149 on Test Set
NMSE is 0.111 on Training Set and 0.149 on Test Set
NMSE is 0.113 on Training Set and 0.155 on Test Set
NMSE is 0.110 on Training Set and 0.151 on Test Set
NMSE is 0.112 on Training Set and 0.158 on Test Set
NMSE is 0.112 on Training Set and 0.149 on Test Set
NMSE is 0.115 on Training Set and 0.163 on Test Set
NMSE is 0.109 on Training Set and 0.152 on Test Set
NMSE is 0.112 on Training Set and 0.150 on Test Set
NMSE is 0.109 on Training Set and 0.154 on Test Set
NMSE is 0.113 on Training Set and 0.160 on Test Set
NMSE is 0.109 on Training Set and 0.157 on Test Set
NMSE is 0.109 on Training Set and 0.155 on Test Set
NMSE is 0.108 on Training Set and 0.154 on Test Set
NMSE is 0.112 on Training Set and 0.161 on Test Set
NMSE is 0.107 on Training Set and 0.152 on Test Set
NMSE is 0.108 on Training Set and 0.154 on Test Set
NMSE is 0.107 on Training Set and 0.154 on Test Set
NMSE is 0.110 on Training Set and 0.157 on Test Set
NMSE is 0.109 on Training Set and 0.158 on Test Set
NMSE is 0.107 on Training Set and 0.152 on Test Set
NMSE is 0.107 on Training Set and 0.155 on Test Set
NMSE is 0.106 on Training Set and 0.155 on Test Set
NMSE is 0.106 on Training Set and 0.154 on Test Set
NMSE is 0.106 on Training Set and 0.151 on Test Set
NMSE is 0.108 on Training Set and 0.157 on Test Set
NMSE is 0.106 on Training Set and 0.154 on Test Set
NMSE is 0.111 on Training Set and 0.150 on Test Set
NMSE is 0.110 on Training Set and 0.161 on Test Set
NMSE is 0.105 on Training Set and 0.153 on Test Set
NMSE is 0.106 on Training Set and 0.151 on Test Set
NMSE is 0.106 on Training Set and 0.151 on Test Set
NMSE is 0.105 on Training Set and 0.155 on Test Set
NMSE is 0.105 on Training Set and 0.155 on Test Set
NMSE is 0.105 on Training Set and 0.154 on Test Set
NMSE is 0.105 on Training Set and 0.152 on Test Set
NMSE is 0.105 on Training Set and 0.155 on Test Set
NMSE is 0.105 on Training Set and 0.153 on Test Set
NMSE is 0.107 on Training Set and 0.152 on Test Set
NMSE is 0.108 on Training Set and 0.152 on Test Set
NMSE is 0.104 on Training Set and 0.156 on Test Set
NMSE is 0.104 on Training Set and 0.153 on Test Set
NMSE is 0.104 on Training Set and 0.157 on Test Set
NMSE is 0.106 on Training Set and 0.155 on Test Set
NMSE is 0.103 on Training Set and 0.158 on Test Set
NMSE is 0.104 on Training Set and 0.154 on Test Set
NMSE is 0.102 on Training Set and 0.157 on Test Set
NMSE is 0.109 on Training Set and 0.155 on Test Set
NMSE is 0.102 on Training Set and 0.156 on Test Set
NMSE is 0.103 on Training Set and 0.152 on Test Set
NMSE is 0.102 on Training Set and 0.156 on Test Set
NMSE is 0.104 on Training Set and 0.152 on Test Set
NMSE is 0.102 on Training Set and 0.157 on Test Set
NMSE is 0.103 on Training Set and 0.162 on Test Set
NMSE is 0.101 on Training Set and 0.158 on Test Set
NMSE is 0.102 on Training Set and 0.160 on Test Set
NMSE is 0.102 on Training Set and 0.161 on Test Set
NMSE is 0.104 on Training Set and 0.154 on Test Set
NMSE is 0.102 on Training Set and 0.160 on Test Set
NMSE is 0.102 on Training Set and 0.160 on Test Set
NMSE is 0.100 on Training Set and 0.157 on Test Set
NMSE is 0.105 on Training Set and 0.153 on Test Set
NMSE is 0.100 on Training Set and 0.155 on Test Set
NMSE is 0.101 on Training Set and 0.154 on Test Set
NMSE is 0.100 on Training Set and 0.158 on Test Set
NMSE is 0.107 on Training Set and 0.170 on Test Set - stopping Training and restoring Weights ...
NMSE is 0.141 on Training Set and 0.138 on Test Set
Year Sunspots Open-Loop Prediction Closed-LoopPrediction
1960 0.572 0.5320.532
1961 0.327 0.3340.301
1962 0.258 0.1580.146
1963 0.217 0.1560.098
1964 0.143 0.2360.149
1965 0.164 0.2300.273
1966 0.298 0.2630.405
1967 0.495 0.4540.552
1968 0.545 0.6150.627
1969 0.544 0.5500.589
1970 0.540 0.4740.464
1971 0.380 0.4550.305
1972 0.390 0.2700.191
1973 0.260 0.2750.139
1974 0.245 0.2110.158
1975 0.165 0.1810.170
1976 0.153 0.1280.175
1977 0.215 0.1510.193
1978 0.489 0.3160.274
1979 0.754 0.6220.373
