CODEWITHSUNDEEP
tensorflowkerasloss-function
SOJA
SOJA

Reputation: 721

scaling back data in customized keras training loss function

I define a customized loss function for my LSTM model (RMSE function) to be as follows: 

def RMSE(y_true, y_pred):
        return K.sqrt(K.mean(K.square(y_pred - y_true)))

everything good so far, but the issue is that I scale my input data to be in the range of [-1, 1], so the reported loss will be associated with this scale, I want the model to report the training loss in the range of my original data, for example by applying the scaler.inverse_transform function on the y_true and y_pred somehow, but no luck doing it... as they are tensor and the scaler.inverse_transform requires numpy array....

any idea how to force re-scaling the data and reporting the loss values in the right scale?

Upvotes: 1

Views: 1335

Answers (1)

giser_yugang
giser_yugang

Reputation: 6176

scaler.inverse_transform essentially uses scaler.min_ and scaler.scale_ parameters to convert data in sklearn.preprocessing.minmaxscaler. An example:

from sklearn.preprocessing import MinMaxScaler
import numpy as np

data = np.array([[-1, 2], [-0.5, 6], [0, 10], [1, 18]])
scaler = MinMaxScaler()
data_trans = scaler.fit_transform(data)
print('transform:\n',data_trans)

data_inverse = (data_trans - scaler.min_)/scaler.scale_
print('inverse transform:\n',data_inverse)

# print
transform:
 [[0.   0.  ]
 [0.25 0.25]
 [0.5  0.5 ]
 [1.   1.  ]]
inverse transform:
 [[-1.   2. ]
 [-0.5  6. ]
 [ 0.  10. ]
 [ 1.  18. ]]

So you just need to use them to achieve your goals in RMSE function.

def RMSE_inverse(y_true, y_pred):
    y_true = (y_true - K.constant(scaler.min_)) / K.constant(scaler.scale_)
    y_pred = (y_pred - K.constant(scaler.min_)) / K.constant(scaler.scale_)
    return K.sqrt(K.mean(K.square(y_pred - y_true)))

Upvotes: 3

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