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Constant-Cranberry29 OP t1_iwnx2yp wrote

Reply to comment by Hamster729 in How to normalize data which contain positive and negative numbers into 0 and 1 by Constant-Cranberry29

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

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Hamster729 t1_iwny1c8 wrote

Can I see the code that does the reverse transform, in the case without abs?

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Constant-Cranberry29 OP t1_iwnyhip wrote

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])

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Constant-Cranberry29 OP t1_iwnz3zj wrote

I have edit the pictures which contain normalization data

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Hamster729 t1_iwo4ma4 wrote

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, the model 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.

P.S. There are other things you could try. Here's an easy one. Drop the abs, drop the normalization, and change the last layer to: model.add(Dense(1, activation=None, use_bias=False))

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Constant-Cranberry29 OP t1_iwo6ukg wrote

>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

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Constant-Cranberry29 OP t1_iwo6vm1 wrote

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)],

)

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Hamster729 t1_iwo99fy wrote

That's a very odd looking time decay rule, and I'm almost certain that it does not do what you expect it to do.

Try:

def lr_time_based_decay(epoch, lr):    
   return lr*0.95

(also see my suggestion from the edit to my previous post)

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Constant-Cranberry29 OP t1_iwoc176 wrote

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

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