simple_net_keras.py

import keras
import numpy as np

model = keras.Sequential()
model.add(keras.layers.Dense(5, activation='sigmoid', input_shape=(1,)))
model.add(keras.layers.Dense(1, activation='sigmoid'))
model.compile('adadelta', 'mse', metrics=['accuracy'])

xs = np.array(range(1, 10))
ys = np.array([[0] for _ in range(5)] + [[1] for _ in range(4)])

model.fit(xs, ys, epochs=2000)
print(model.predict(xs))

# 9/9 [==============================] - 0s 82us/step - loss: 0.0155 - acc: 1.0000
# [[0.00141504]
#  [0.00251564]
#  [0.00749135]
#  [0.0397768 ]
#  [0.26316088]
#  [0.74775267]
#  [0.9371815 ]
#  [0.9762724 ]
#  [0.986349  ]]