from keras.models import Sequential
from keras.layers import Activation, Dense

# xor model
model = Sequential()
model.add(Dense(4, input_dim=2, activation="relu"))
model.add(Dense(2, activation="softmax"))
model.compile(optimizer="sgd", loss="categorical_crossentropy")

# training data
import numpy as np

x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
y1 = np.logical_xor(x[:, 0], x[:, 1])
y0 = np.logical_not(y1)
y = np.array([y0, y1]).T.astype(np.float32)

# train
model.fit(x, y, nb_epoch=15000)

print("\n[weight and bias]")
for layer in model.layers:
    print(layer.get_weights())
    pass


# predict
pred_x = x[0:4]
pred_y = model.predict(pred_x)

print("\n[predicate]")
print([pred_x, pred_y]) # => [[1, 0], [0, 1], [0, 1], [1, 0]]?