Hi
I am trying to train a network with input of shape (174,1) which is a smoothed time series signal and output a signal label(174,1). I have tried some networks from linear regression, CNN1D, but failed to train. The network does not seem to learn. I have pasted the input and the label time series. This would be inputted and outputted as (174,1). I appreciate help
salman
Input
0
4.18253e-07
6.25848e-07
5.84575e-07
4.14491e-07
2.33669e-07
1.03124e-07
3.6766e-08
2.25682e-08
3.67369e-08
5.46634e-08
5.60218e-08
3.35225e-08
-3.82534e-09
-3.7432e-08
-5.2614e-08
-4.95902e-08
-3.9658e-08
-3.29841e-08
-3.01247e-08
-2.43349e-08
-1.01856e-08
1.02387e-08
2.85015e-08
3.66587e-08
3.28471e-08
2.14525e-08
9.00486e-09
-2.82683e-10
-6.21392e-09
-1.12802e-08
-1.81336e-08
-2.76227e-08
-3.81269e-08
-4.64072e-08
-4.93542e-08
-4.56265e-08
-3.61518e-08
-2.34187e-08
-1.02873e-08
1.07397e-09
9.64328e-09
1.53799e-08
1.87336e-08
2.03344e-08
2.09435e-08
2.15068e-08
2.30533e-08
2.63472e-08
3.14275e-08
3.73646e-08
4.24657e-08
4.48938e-08
4.32527e-08
3.69056e-08
2.60382e-08
1.14924e-08
-5.474e-09
-2.33905e-08
-4.07472e-08
-5.60986e-08
-6.81632e-08
-7.58616e-08
-7.84307e-08
-7.54817e-08
-6.71011e-08
-5.37653e-08
-3.64516e-08
-1.657e-08
4.19623e-09
2.40891e-08
4.14968e-08
5.52465e-08
6.46469e-08
6.94281e-08
6.96969e-08
6.58246e-08
5.84126e-08
4.82109e-08
3.60195e-08
2.26083e-08
8.78161e-09
-4.67181e-09
-1.70447e-08
-2.77568e-08
-3.63561e-08
-4.25335e-08
-4.60825e-08
-4.69534e-08
-4.52663e-08
-4.13058e-08
-3.55161e-08
-2.84493e-08
-2.07163e-08
-1.29426e-08
-5.68107e-09
6.44103e-10
5.74641e-09
9.46534e-09
1.17695e-08
1.27583e-08
1.26316e-08
1.1701e-08
1.03009e-08
8.75454e-09
7.35068e-09
6.33561e-09
5.90387e-09
6.18461e-09
7.23588e-09
9.03935e-09
1.14898e-08
1.4412e-08
1.75921e-08
2.07809e-08
2.37243e-08
2.6182e-08
2.79273e-08
2.87658e-08
2.85709e-08
2.73503e-08
2.5218e-08
2.23534e-08
1.89692e-08
1.52876e-08
1.15147e-08
7.82862e-09
4.37359e-09
1.25552e-09
-1.45517e-09
-3.72149e-09
-5.53915e-09
-6.91896e-09
-7.88362e-09
-8.46514e-09
-8.69505e-09
-8.61432e-09
-8.27313e-09
-7.7298e-09
-7.04839e-09
-6.29663e-09
-5.5376e-09
-4.82532e-09
-4.20149e-09
-3.69277e-09
-3.31181e-09
-3.05476e-09
-2.90152e-09
-2.81823e-09
-2.76388e-09
-2.69684e-09
-2.58118e-09
-2.39284e-09
-2.1232e-09
-1.78112e-09
-1.39058e-09
-9.87143e-10
-6.07919e-10
-2.83052e-10
-3.07821e-11
1.4294e-10
2.43158e-10
2.8209e-10
2.74077e-10
2.35004e-10
1.80104e-10
1.21749e-10
6.8588e-11
2.66791e-11
-8.76295e-13
-1.40839e-11
-1.54374e-11
-9.27075e-12
0
Label
0
-1.71736e-08
-1.53378e-08
-9.79676e-10
7.9377e-09
5.7047e-09
-1.62103e-09
-5.58527e-09
-2.47281e-09
3.47859e-09
6.07766e-09
4.55817e-09
4.09875e-10
-5.31704e-09
-8.80423e-09
-8.93366e-09
-7.01059e-09
-1.53077e-09
3.48999e-09
2.5344e-09
1.21082e-09
6.47833e-09
8.45462e-09
-1.26533e-09
-2.07172e-08
-3.72516e-08
-3.9911e-08
-2.72364e-08
-6.38585e-09
1.03922e-08
1.74559e-08
1.78949e-08
1.54622e-08
1.21602e-08
8.3302e-09
5.14523e-09
5.24475e-09
1.04539e-08
1.91552e-08
2.40073e-08
1.76475e-08
-6.3008e-10
-2.16299e-08
-3.39414e-08
-3.16058e-08
-1.83078e-08
-5.60728e-09
-2.16836e-09
-8.46477e-09
-1.69005e-08
-1.90525e-08
-1.03817e-08
6.78319e-09
2.46788e-08
3.25951e-08
2.44597e-08
4.44593e-09
-1.7565e-08
-3.49127e-08
-4.28466e-08
-3.89797e-08
-2.38185e-08
-4.64603e-10
2.61788e-08
5.02617e-08
6.66615e-08
7.30009e-08
6.85348e-08
5.44703e-08
3.29172e-08
7.4654e-09
-1.81527e-08
-4.08275e-08
-5.84084e-08
-6.94242e-08
-7.3454e-08
-7.08332e-08
-6.25175e-08
-5.01053e-08
-3.62456e-08
-2.17525e-08
-7.28551e-09
6.51989e-09
1.90262e-08
2.95807e-08
3.77014e-08
4.28397e-08
4.32941e-08
3.91273e-08
3.11639e-08
2.08262e-08
9.14455e-09
-2.88418e-09
-1.40598e-08
-2.3266e-08
-2.96125e-08
-3.2468e-08
-3.16208e-08
-2.7366e-08
-2.0506e-08
-1.22422e-08
-3.97034e-09
3.18908e-09
8.0484e-09
9.89044e-09
8.5951e-09
4.50164e-09
-1.73457e-09
-8.98272e-09
-1.59889e-08
-2.20489e-08
-2.65539e-08
-2.88968e-08
-2.81232e-08
-2.40126e-08
-1.68868e-08
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3.7811e-09
1.51694e-08
2.59994e-08
3.4746e-08
4.04234e-08
4.35002e-08
4.24569e-08
3.81165e-08
3.08343e-08
2.20616e-08
1.33623e-08
5.463e-09
-1.09135e-09
-6.00362e-09
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-1.0991e-08
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-1.14864e-08
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-1.03193e-08
-9.62962e-09
-8.89446e-09
-8.07276e-09
-7.092e-09
-5.89186e-09
-4.45678e-09
-2.83063e-09
-1.11372e-09
5.55398e-10
2.02741e-09
3.21473e-09
4.04171e-09
4.41438e-09
4.27703e-09
3.68908e-09
2.81198e-09
1.81048e-09
8.35732e-10
-8.06214e-12
-6.60893e-10
-1.07511e-09
-1.27033e-09
-1.28195e-09
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-8.9583e-10
-6.28419e-10
-3.77294e-10
-1.62466e-10
3.35482e-12
1.18056e-10
1.83718e-10
2.09035e-10
2.03711e-10
1.67221e-10
1.1468e-10
5.73629e-11
0