Epoch = 1000 input_size = 4 hidden_size = 12 output_size = 3 LR = 0.005 batchsize = 30

These are the hyperparameters for a neural network model with the following specifications:

- Epoch = 1000: The model will be trained for 1000 iterations over the entire dataset.
- Input size = 4: The input layer of the model will have 4 input nodes, which means the model will expect input data with 4 features.
- Hidden size = 12: The hidden layer of the model will have 12 nodes, which means the model will have 12 neurons to learn and extract features from the input data.
- Output size = 3: The output layer of the model will have 3 nodes, which means the model will have to predict 3 classes or values.
- Learning rate (LR) = 0.005: This is the step size at which the model will update its weights during each iteration of training. A higher learning rate can lead to faster convergence, but it may also cause the model to overshoot the optimal weights and perform poorly.
- Batch size = 30: During each iteration of training, the model will process 30 samples at a time. This is a common practice to speed up the training process and reduce the memory requirements of the model.