import plaidml.keras
plaidml.keras.install_backend()

import math
import numpy as np
from keras.models import Sequential
from keras.layers import Dense

batch_size = 1024

def genxy():
    pi2 = math.pi * 2
    while True:
        x = np.random.rand(batch_size)
        y = np.sin(x * pi2)
        yield (x, y)

model = Sequential()
model.add(Dense(2, activation='relu', input_shape=(1, )))
model.add(Dense(4, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(16, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1, name='output'))
model.compile(loss='mean_squared_error', optimizer='nadam')
model.fit_generator(
    genxy(),
    1024,
    4,
    verbose=2,
    validation_data=genxy(),
    validation_steps=1,
    )