[ot][spam]gpt-4 completions

Undescribed Horrific Abuse, One Victim & Survivor of Many gmkarl at gmail.com
Sat Mar 18 15:21:03 PDT 2023


{"time": "2023-03-18T18:18:38.310968", "lm": "spellbook", "url":
"https://dashboard.scale.com/spellbook/api/v2/deploy/", "input":
"Write a training loop for pytorch models that starts with only 1 item
in the training set, trains on it until validation loss stops
reducing, and then moves 1 item to the training set from the
validation set, in a loop until half of all the items are trained
on.", "output": "Here's a training loop that follows the described
procedure:\n\n```python\nimport torch\nimport torch.nn as nn\nimport
torch.optim as optim\nfrom torch.utils.data import DataLoader,
random_split\n\n# Assuming you have a dataset, model, criterion, and
optimizer defined\n# dataset = ...\n# model = ...\n# criterion =
...\n# optimizer = ...\n\n# Split the dataset into training and
validation sets\ntrain_size = 1\nval_size = len(dataset) -
train_size\ntrain_dataset, val_dataset = random_split(dataset,
[train_size, val_size])\n\n# Create data loaders for training and
validation sets\ntrain_loader = DataLoader(train_dataset,
batch_size=1, shuffle=True)\nval_loader = DataLoader(val_dataset,
batch_size=1, shuffle=False)\n\n# Function to train the model on the
current training set\ndef train_model():\n    model.train()\n
running_loss = 0.0\n    for inputs, targets in train_loader:\n
optimizer.zero_grad()\n        outputs = model(inputs)\n        loss =
criterion(outputs, targets)\n        loss.backward()\n
optimizer.step()\n        running_loss += loss.item()\n    return
running_loss / len(train_loader)\n\n# Function to evaluate the model
on the validation set\ndef evaluate_model():\n    model.eval()\n
running_loss = 0.0\n    with torch.no_grad():\n        for inputs,
targets in val_loader:\n            outputs = model(inputs)\n
  loss = criterion(outputs, targets)\n            running_loss +=
loss.item()\n    return running_loss / len(val_loader)\n\n# Training
loop\nwhile len(train_dataset) <= len(dataset) // 2:\n
print(f\"Training on {len(train_dataset)} samples\")\n    \n    #
Train until validation loss stops reducing\n    best_val_loss =
float('inf')\n    patience = 3\n    counter = 0\n    while True:\n
   train_loss = train_model()\n        val_loss = evaluate_model()\n
     print(f\"Train Loss: {train_loss:.4f}, Val Loss:
{val_loss:.4f}\")\n\n        if val_loss < best_val_loss:\n
best_val_loss = val_loss\n            counter = 0\n        else:\n
       counter += 1\n\n        if counter >= patience:\n
break\n\n    # Move one item from the validation set to the training
set\n    if len(val_dataset) > 0:\n
train_dataset.indices.append(val_dataset.indices.pop(0))\n
train_loader = DataLoader(train_dataset, batch_size=1,
shuffle=True)\n```\n\nThis training loop starts with only 1 item in
the training set and trains the model until the validation loss stops
reducing. Then, it moves 1 item from the validation set to the
training set and continues the process until half of the items are
trained on. Note that this approach may not be the most efficient way
to train a model, but it follows the requested procedure."}


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