fizzbuzz.py

import numpy as np
import tensorflow as tf
import tensorflow.lite as lite

from tensorflow.keras.models import Sequential, model_from_json
from tensorflow.keras.layers import Dense, Dropout, Activation
from tensorflow.keras.optimizers import SGD, Adam

def fizzbuzz(i):
    if i % 15 == 0:
        return np.array([0, 0, 0, 1])
    elif i % 5  == 0:
        return np.array([0, 0, 1, 0])
    elif i % 3  == 0:
        return np.array([0, 1, 0, 0])
    else:
        return np.array([1, 0, 0, 0])

def bin(i, num_digits):
    return np.array([i >> d & 1 for d in range(num_digits)])

NUM_DIGITS = 7
trX = np.array([bin(i, NUM_DIGITS) for i in range(1, 101)])
trY = np.array([fizzbuzz(i) for i in range(1, 101)])
model = Sequential()
model.add(Dense(64, input_dim = 7))
model.add(Activation('tanh'))
model.add(Dense(4, input_dim = 64))
model.add(Activation('softmax'))
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
model.fit(trX, trY, epochs = 3600, batch_size = 64)
model.save('fizzbuzz_model.h5')

converter = lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open('fizzbuzz_model.tflite', 'wb').write(tflite_model)