Proton IDE V1.0.4.6 Full Version
I've been trying to train a simple multilayer perceptron (MLP) neural network from scratch, but it's not working.
my code is this:
data = [x_train, x_test]
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import PolynomialFeatures
from sklearn.model_selection import KFold
scaler = StandardScaler()
scaler.fit(x_train)
x_train_standardized = scaler.transform(x_train)
x_test_standardized = scaler.transform(x_test)
def make_model():
    model = MLPRegressor(max_iter=10000, learning_rate=1, n_iter_no_change=10, tol=0.01, penalty='l1')
    model.fit(x_train_standardized, y_train)
    return model
model = make_model()
score = model.score(x_test_standardized, y_test)
Can anyone give me some insights on what might be going wrong?
A:
You have 3 problems here :
You are giving wrong input to MLP, in your input format you can only give 1 feature vector, try to add one more feature column like 'Gender' and then you have to train your model with this additional column, else you will get your error message, i.e. error :
ValueError: Found input variables with inconsistent numbers of samples: [18, 1], [18, 0]
Secondly, your X matrix are same for training and test data and thirdly you are dividing your X matrix into training and test data, in your implementation you are making test dataset as your input to your neural net. Make sure that your test dataset is not the same as your training dataset and then you are able to run your model.
Hope it helps you to run your code.
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