NAN in keras neural network results

I am creating a neural network simple architecture. But I keep getting NAN in result, cant figure out why, below is my code.

import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import Pipeline
from collections import Counter
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import StandardScaler
#from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense

X, Y = make_classification(n_samples=10, n_features=2000, n_informative=15, n_redundant=5, random_state=3)

# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# convert integers to dummy variables (i.e. one hot encoded)
dummy_y = np_utils.to_categorical(encoded_Y)

# define baseline model
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(10, input_dim=10, activation='relu'))
    model.add(Dense(3, activation='softmax'))
    # Compile model
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

estimator = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=500, verbose=0)
kfold = KFold(n_splits=10, shuffle=True)
results = cross_val_score(estimator, X, dummy_y, cv=kfold)
print(Baseline: %.2f%% (%.2f%%) % (results.mean()*100, results.std()*100))

I get the following

Baseline: nan% (nan%)

Can't understand why. Also if there are ways this can be improved ?