Delete train.py

train.py

@@ -1,84 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import matplotlib.pyplot as plt
-from model import Transformer
-
-with open('/Users/deepaksharma/Documents/Python/Kaggle/GenerateKanyeLyrics/Kanye West Lyrics.txt','r',encoding='utf-8') as f:
-    text = f.read()
-
-chars = sorted(list(set(text)))
-
-stoi = {ch:i for i,ch in enumerate(chars)}
-itos = {i:ch for i,ch in enumerate(chars)}
-
-encode = lambda s: [stoi[c] for c in s]
-decode = lambda l: ''.join([itos[c] for c in l])
-
-data = torch.tensor(encode(text), dtype=torch.long)
-
-n = int(0.9*len(text))
-train_data = data[:n]
-val_data = data[n:]
-
-def get_batch(split):
-    if split == 'train':
-        data = train_data
-    elif split == 'val':
-        data = val_data
-    else:
-        raise ValueError("Invalid split")
-  
-    ix = torch.randint(len(data)-block_size,(batch_size,))
-    x = torch.stack([data[i:i+block_size] for i in ix])
-    y = torch.stack([data[i+1:i+block_size+1] for i in ix])
-    return x, y
-
-# hyperparameters
-batch_size = 16 # how many independent sequences will we process in parallel?
-block_size = 64 # what is the maximum context length for predictions?
-max_iters = 5000
-eval_interval = 100
-learning_rate = 1e-3
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-eval_iters = 200
-n_embd = 128
-n_head = 8
-n_layer = 4
-dropout = 0.0
-vocab = len(chars)
-# ------------
-
-
-model = Transformer(n_embd,n_layer)
-
-print("Total params: ", sum(p.numel() for p in model.parameters()))
-
-optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3)
-
-for steps in range(20000):
-    x,y = get_batch('train')
-    logits, loss = model(x, y)
-    optimizer.zero_grad()
-    loss.backward()
-    optimizer.step()
-    if steps % 100 == 0:
-        print("Step: ", steps, " Loss: ", loss.item())
-
-# Print model's state_dict
-print("Model's state_dict:")
-for param_tensor in model.state_dict():
-    print(param_tensor, "\t", model.state_dict()[param_tensor].size())
-
-# Print optimizer's state_dict
-print("Optimizer's state_dict:")
-for var_name in optimizer.state_dict():
-    print(var_name, "\t", optimizer.state_dict()[var_name])
-
-torch.save(model.state_dict(), 'kanye_weights.pth')
-
-lyrics = encode("Bitch I am back on my comma , sipping on my CocaCola, driving on a hangover ")
-lyrics = torch.tensor(lyrics, dtype=torch.long)
-lyrics = torch.stack([lyrics for _ in range(1)], dim=0)
-
-print(decode(model.generate(lyrics, max_tokens=1000)[0].tolist()))