simple logistic regression in keras

import seaborn as sns
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LogisticRegressionCV
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.utils import np_utils

# Load Data from Keras
iris = sns.load_dataset("iris")
X = iris.values[:, 0:4]
y = iris.values[:, 4]

# Create tain and test data
train_X, test_X, train_y, test_y = train_test_split(X, y, train_size=0.5, random_state=0)

# Make one -hot encoder
def one_hot_encode_object_array(arr):
      
      '''One hot encode a numpy array of objects (e.g. strings)'''
      
       uniques, ids = np.unique(arr, return_inverse=True)
       return np_utils.to_categorical(ids, len(uniques))

train_y_ohe = one_hot_encode_object_array(train_y)
test_y_ohe = one_hot_encode_object_array(test_y)

model = Sequential()
model.add(Dense(16, input_shape=(4,)))
model.add(Activation('sigmoid'))
model.add(Dense(3))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')

# Actual modelling
model.fit(train_X, train_y_ohe, verbose=0, batch_size=1)
score, accuracy = model.evaluate(test_X, test_y_ohe, batch_size=16, verbose=0)
print("Test fraction correct (NN-Score) = {:.2f}".format(score))
print("Test fraction correct (NN-Accuracy) = {:.2f}".format(accuracy))