Standardscaler is not working for ANN but minmax does

See more:

Python

machine-learning

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')

X=dataset.iloc[:,0:13]
y=dataset.iloc[:,13].values
#StandardScaler
from sklearn.preprocessing import StandardScaler
sc2= StandardScaler()
X= sc2.fit_transform(X)
y= y.reshape(-1,1)
y=sc2.fit_transform(y)

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.add(Dense(units=1))
regressor.compile(optimizer='adam', loss='mean_squared_error',  metrics=['accuracy'])
regressor.fit(X_train,y_train, epochs=100, batch_size=32, verbose=1)