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import os
from pathlib import Path
import string
import torch
import torch.nn.functional as F
class NeuralNetwork:
def __init__(self, lr, manual_seed) -> None:
self.lr = lr
chars = list(string.ascii_lowercase)
chars.insert(0, '.')
# string to index mapping we will call it as stoi
self.stoi = {s:i for i,s in enumerate(chars)}
self.itos = {i:s for s,i in self.stoi.items()}
# For character level model, 26 character and +1 for start and end special character
self.num_classes=27
self.g = torch.Generator().manual_seed(manual_seed)
def initialise_weights(self):
# initialise the 'network'
w = torch.randn((27,27), generator=self.g, requires_grad=True)
return w
def forward_pass(self, xs, w):
# forward pass
xenc = F.one_hot(xs, num_classes=self.num_classes).float() # input to the network one hot encodding
logits = xenc @ w
counts = logits.exp()
probs = counts / counts.sum(1, keepdims=True)
return probs
def loss(self, probs, ys, num):
loss = -probs[torch.arange(num), ys].log().mean()
print(f'{loss.item()=}')
return loss
def backword_pass(self, loss, w):
# backward pass
w.grad = None
loss.backward()
def update_weights(self,w):
# update
w.data += -self.lr * w.grad
def example_gen(self, words):
xs, ys = [], []
for w in words:
chs = '.' + w+ '.'
for ch1, ch2 in zip(chs, chs[1:]):
ix1 = self.stoi[ch1]
ix2 = self.stoi[ch2]
xs.append(ix1)
ys.append(ix2)
xs = torch.tensor(xs)
ys = torch.tensor(ys)
num=xs.nelement()
print(f'number of eaxmples: {num}')
return xs, ys, num
if __name__ == '__main__':
get_cwd = os.getcwd()
project_dir = Path(get_cwd).parents[0]
data_path = os.path.join(project_dir, 'data')
words = open(os.path.join(data_path, "names.txt"), 'r').read().splitlines()
nn = NeuralNetwork(1,2147483647)
# Gen examples
xs, ys, num = nn.example_gen(words)
w = nn.initialise_weights()
for k in range(500):
# forward pass
probs = nn.forward_pass(xs,w)
# loss
loss = nn.loss(probs, ys, num)
# backward pass
nn.backword_pass(loss, w)
#update
nn.update_weights(w)
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