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I have a dataset with multiple features and a target. I am using ANN to predict. When I scale the features using MInMax, everything works fine and during the compile and fix I get good loss and accuracy.
HOWEVER, when I use StandardScaler(). The prediction is off and then I noticed during the Compile/fit I get acc:0.00000 through out the whole epoch.
Just wondering why. I thought I can use any (specially StandardScaler).
Any suggestion?
Thank you

What I have tried:

Here is the code
dataset = pd.read_csv('somedata.csv')

from sklearn.preprocessing import StandardScaler
sc2= StandardScaler()
X= sc2.fit_transform(X)
y= y.reshape(-1,1)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

from keras import Sequential
from keras.layers import Dense

regressor = Sequential()
regressor.add(Dense(units=13, input_dim=13))
regressor.compile(optimizer='adam', loss='mean_squared_error',  metrics=['accuracy']),y_train, epochs=100, batch_size=32, verbose=1)
Gerry Schmitz 23-May-19 9:18am
Maybe it's the "data".
jameskm69 23-May-19 10:01am
I don't think so BC it works fine/accurate for MinMax()

This content, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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