pytorch实现FizzBuzz小游戏

FizzBuzz是一个简单的小游戏。游戏规则如下：从1开始往上数数，当遇到3的倍数的时候，说fizz，当遇到5的倍数，说buzz，当遇到15的倍数，就说fizzbuzz，其他情况下则正常数数。

我们可以写一个简单的小程序来决定要返回正常数值还是fizz, buzz 或者 fizzbuzz。

# One-hot encode the desired outputs: [number, "fizz", "buzz", "fizzbuzz"]
def fizz_buzz_encode(i):
    if   i % 15 == 0: return 3
    elif i % 5  == 0: return 2
    elif i % 3  == 0: return 1
    else:             return 0
    
def fizz_buzz_decode(i, prediction):
    return [str(i), "fizz", "buzz", "fizzbuzz"][prediction]

print(fizz_buzz_decode(1, fizz_buzz_encode(1)))
print(fizz_buzz_decode(2, fizz_buzz_encode(2)))
print(fizz_buzz_decode(5, fizz_buzz_encode(5)))
print(fizz_buzz_decode(12, fizz_buzz_encode(12)))
print(fizz_buzz_decode(15, fizz_buzz_encode(15)))

我们首先定义模型的输入与输出(训练数据)

import numpy as np
import torch

NUM_DIGITS = 10

# Represent each input by an array of its binary digits.将输入转化为2进制表示
def binary_encode(i, num_digits):
    return np.array([i >> d & 1 for d in range(num_digits)])

trX = torch.Tensor([binary_encode(i, NUM_DIGITS) for i in range(201, 2 ** NUM_DIGITS)])
trY = torch.LongTensor([fizz_buzz_encode(i) for i in range(201, 2 ** NUM_DIGITS)])

然后我们用PyTorch定义模型

# Define the model
NUM_HIDDEN = 100
model = torch.nn.Sequential(
    torch.nn.Linear(NUM_DIGITS, NUM_HIDDEN),
    torch.nn.ReLU(),
    torch.nn.Linear(NUM_HIDDEN, 4)
)

• 为了让我们的模型学会FizzBuzz这个游戏，我们需要定义一个损失函数，和一个优化算法。
• 这个优化算法会不断优化（降低）损失函数，使得模型的在该任务上取得尽可能低的损失值。
• 损失值低往往表示我们的模型表现好，损失值高表示我们的模型表现差。
• 由于FizzBuzz游戏本质上是一个分类问题，我们选用Cross Entropyy Loss函数。
• 优化函数我们选用Stochastic Gradient Descent。

  loss_fn = torch.nn.CrossEntropyLoss()
  optimizer = torch.optim.SGD(model.parameters(), lr = 0.05)

  以下是模型的训练代码

  # Start training it
  BATCH_SIZE = 128
  for epoch in range(10000):
      for start in range(0, len(trX), BATCH_SIZE):
          end = start + BATCH_SIZE
          batchX = trX[start:end]
          batchY = trY[start:end]
  
          y_pred = model(batchX)
          loss = loss_fn(y_pred, batchY)
  
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
      # Find loss on training data
      loss = loss_fn(model(trX), trY).item()
      print('Epoch:', epoch, 'Loss:', loss)

  最后我们用训练好的模型尝试在1到100这些数字上玩FizzBuzz游戏

  # Output now
  testX = torch.Tensor([binary_encode(i, NUM_DIGITS) for i in range(1, 101)])
  with torch.no_grad():
      testY = model(testX)
  predictions = zip(range(1, 101), list(testY.max(1)[1].data.tolist()))
  
  print([fizz_buzz_decode(i, x) for (i, x) in predictions])

  结果：

  ['1', '2', 'fizz', '4', 'buzz', 'fizz', '7', '8', 'fizz', 'buzz', '11', '12', '13', '14', 'fizzbuzz', '16', '17', 'fizz', '19', 'buzz', 'fizz', '22', '23', 'fizz', 'buzz', '26', 'fizz', '28', '29', 'fizzbuzz', '31', '32', 'fizz', '34', 'buzz', 'fizz', '37', '38', 'fizz', 'buzz', '41', '42', '43', '44', 'fizzbuzz', '46', '47', 'fizz', '49', 'buzz', 'fizz', '52', '53', 'fizz', 'buzz', '56', 'fizz', '58', '59', 'fizzbuzz', '61', '62', 'fizz', '64', 'buzz', 'fizz', '67', 'buzz', 'fizz', '70', '71', 'fizz', '73', '74', 'fizzbuzz', '76', '77', 'fizz', '79', 'buzz', 'fizz', '82', '83', 'fizz', 'buzz', '86', 'fizz', '88', '89', 'fizzbuzz', '91', '92', 'fizz', '94', 'buzz', 'fizz', '97', '98', 'fizz', 'buzz']