multi_input.py

from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
import numpy as np

x_data = [[0, 0, 0],
          [1, 0, 0],
          [0, 1, 0],
          [0, 0, 1],
          [1, 1, 0],
          [1, 0, 1]]
y_data = [[0],
          [1],
          [0],
          [0],
          [1],
          [1]]

model = Sequential()
model.add(Dense(1, input_dim=3, activation='sigmoid'))

sgd = SGD(lr=0.1)
model.compile(loss='binary_crossentropy', optimizer=sgd)

model.summary()
model.fit(x_data, y_data, epochs=1000)

print("[1, 1, 1]", model.predict_classes(np.array([[1, 1, 1]])))
print("[0, 1, 1]", model.predict_classes(np.array([[0, 1, 1]])))