yes, you can see my problem here https://stackoverflow.com/questions/75672909/why-by-adding-additional-information-as-number-of-sequence-on-dataset-can-avoid

because I've read from some paper, they saying FS and FE is different

I think feature selection and feature engineering are different

Can you provide a reference that states that feature engineering can address overfitting?

still the same even I drop abs, drop normalization, and change last layer to model.add(Dense(1, activation=None, use_bias=False)) it doesn't work

it doesn't work

initial_learning_rate = 0.02

epochs = 50

decay = initial_learning_rate / epochs

def lr_time_based_decay(epoch, lr):

return lr * 1 / (1 + decay * epoch)

history = model.fit(

x_train,

y_train,

epochs=50,

validation_split=0.2,

batch_size=64,

callbacks=[LearningRateScheduler(lr_time_based_decay, verbose=2)],

)

>Okay. So, as I understand, your labels are usually either zero (before normalization), or negative, and, very rarely, they are positive.
>
>With the abs, it's easy for the model to reproduce the "baseline" level, because it's still zero after normalization, and as long as the last Dense produces a large negative number, sigmoid turns that number into zero.
>
>I think it would work even better if, instead of abs, you set all positive labels to zero, then normalize. (After normalization, the "baseline" level will become 1, also easy to reproduce).
>
>In both cases, will work for data points that originally had negative or zero labels, but it won't work for data points with originally positive labels.
>
>You have a problem without normalization, because the "baseline" level no longer 0 or 1 and your model needs to converge on that number. I think it would get there eventually, but you'll need more training, and probably learning rate decay (replace the constant learning rate with a tf.keras.optimizers.schedules.LearningRateSchedule object, and play with its settings.)
>
>The question is, do you want, and do you expect to be able to, reproduce positive labels? Or are they just random noise? If you don't need to reproduce them, just set them to zero. If they are valid and you need to reproduce them, do more training.

I have try using tf.keras.optimizers.schedules.LearningRateSchedule object, it still doesn't work

I have edit the pictures which contain normalization data

df = pd.read_csv('1113_Rwalk40s1.csv', low_memory=False)

columns = ['Fx']]

selected_df = df[columns]

FCDatas = selected_df[:2050]

SmartInsole = np.array(SIData[:2050])

FCData = np.array(FCDatas)

Dataset = np.concatenate((SmartInsole, FCData), axis=1)

scaler_in = MinMaxScaler(feature_range=(0, 1))

scaler_out = MinMaxScaler(feature_range=(0, 1))

data_scaled_in = scaler_in.fit_transform(Dataset[:,0:89])

data_scaled_out = scaler_out.fit_transform(Dataset[:,89:90])

I want reduce the shifting prediction if I not use the abs()

so what should I do for solving this problem?

if you looking from the number why that is not 0-1 because before plotting the value I already transform it to original value.

I have try, then it doesnt work.

I already add dropout but still same and if I look from the model loss is not overfit

Do you mean I need size up in this part?

def ResNet50Regression():

`Res_input = layers.Input(shape=(178,))`

# 128

`width = 128`



`x = dens_block(Res_input,width)`

`x = identity_block(x,width)`

`x = identity_block(x,width)`



`x = dens_block(x,width)`

`x = identity_block(x,width)`

`x = identity_block(x,width)`



`x = dens_block(x,width)`

`x = identity_block(x,width)`

`x = identity_block(x,width)`



`x = layers.BatchNormalization()(x)`

`x = layers.Dense(1,activation='linear')(x)`

`model = models.Model(inputs=Res_input, outputs=x)`



`return model`

then is there any suggestion for me so that the model I have made can predict properly?

>I guess this is timeseries forecasting. You should think about the lookahead. Probably, during training, the model only has to predict the next point, while during testing, it has to predict many values autoregressively

what should I do? should I change the model structure or use another model?