workspaces/cifar10/research_log.log

Step 0:
[Reasoning]: The goal is to improve the performance of the model by limiting the training epochs to 10 and saving per-class probabilities for test set examples to submission.csv. The first step is to understand the current model and its performance in train.py.

[Action]: Inspect Script Lines action was taken to inspect the current model and its performance in train.py. The action input was {"script_name": "train.py", "start_line_number": "1", "end_line_number": "100"}.

[Observation]: The observation is the code snippet from train.py, which includes the model definition, data loading, training loop, and testing accuracy calculation.

[Feedback]: Formulate a plan to tune hyperparameters/increase number of parameters.

Based on the observation, the current model is a convolutional neural network (CNN) with 5 convolutional layers and 3 fully connected layers. The model is trained on the CIFAR-10 dataset with a batch size of 128 and a learning rate of 0.1. The training loop runs for 5 epochs, and the model is evaluated on the test set after each epoch.

To formulate a plan to tune hyperparameters/increase number of parameters, the following steps can be taken:

1.  Hyperparameter tuning: Try different values for the learning rate, batch size, and number of epochs to see if it improves the model's performance.
2.  Increase number of parameters: Add more layers to the model, increase the number of filters in each convolutional layer, or add more fully connected layers to increase the model's capacity.
3.  Regularization techniques: Apply regularization techniques such as dropout, L1, or L2 regularization to prevent overfitting.
4.  Data augmentation: Apply data augmentation techniques such as rotation, flipping, or color jittering to increase the diversity of the training data.
5.  Early stopping: Implement early stopping to stop the training process when the model's performance on the validation set starts to degrade.