{ "model": "gpt-4", "messages": [ { "role": "system", "content": "Do dialog like codegolf, with the fewest characters possible." }, { "role": "user", "content": "Construct, train, and save, a torch.nn.Transformer . Generate data so it increments floats." }, { "role": "assistant", "content": "```python\nimport torch\nfrom torch import nn\nimport numpy as np\n\n# Construct\nm=nn.Transformer()\n\n# Generate data\nx=np.arange(0,1,0.01)\ny=x+1\n\n# Train\no=torch.optim.Adam(m.parameters())\nl=nn.MSELoss()\nfor _ in range(100):\n p=m(torch.tensor(x).unsqueeze(0))\n o.zero_grad()\n e=l(p,torch.tensor(y).unsqueeze(0))\n e.backward()\n o.step()\n\n# Save\ntorch.save(m.state_dict(),'m.pt')\n```" }, { "role": "user", "content": "fix this: Transformer.forward() takes two parameters, src and tgt.\nAlso, load a previous version of the model on bootup if it's been saved, version the saved models with their training data, and give progress output to the user." }, { "role": "assistant", "content": "```python\nimport torch\nfrom torch import nn\nimport numpy as np\nimport os\n\n# Construct\nm=nn.Transformer()\n\n# Load previous model if exists\nif os.path.exists('m.pt'):\n m.load_state_dict(torch.load('m.pt'))\n\n# Generate data\nx=np.arange(0,1,0.01)\ny=x+1\n\n# Versioning\nv = str(len(os.listdir())) # number of files in directory\ntorch.save((x,y), f'data_{v}.pt')\n\n# Train\no=torch.optim.Adam(m.parameters())\nl=nn.MSELoss()\nfor i in range(100):\n src = torch.tensor(x).unsqueeze(0)\n tgt = torch.tensor(y).unsqueeze(0)\n p=m(src, tgt)\n o.zero_grad()\n e=l(p,tgt)\n e.backward()\n o.step()\n if i%10==0: print(f'Epoch {i}, Loss {e.item()}')\n\n# Save\ntorch.save(m.state_dict(),f'm_{v}.pt')\n```" } ], "temperature": 0, "max_tokens": 256, "top_p": 1, "frequency_penalty": 0, "presence_penalty": 0 }