How to check the number of parameters of the model

To determine the total number of parameters in a machine learning model, access its architecture attributes using framework-specific methods. This provides an integer count of all trainable weights.

The approach depends on your model's framework. In PyTorch, use `sum(p.numel() for p in model.parameters())`. For TensorFlow/Keras, employ `model.count_params()`. Note that this count includes all learnable parameters across layers (like weights and biases), but excludes non-trainable elements such as certain statistics in batch normalization layers. Parameter sharing (e.g., in RNNs) counts shared weights only once. Ensure the model is fully initialized before querying.

Concrete steps include: 1) Load your defined model architecture. 2) In PyTorch, iterate through `model.parameters()` and sum `numel()` values. 3) In TensorFlow/Keras, call the built-in `count_params()` method directly on the model object. This count is crucial for understanding model complexity, memory requirements, and computational costs during training and inference.