Comparing TensorFlow (Keras), PyTorch, & MLX – Day 46

 Comparing Deep Learning on TensorFlow (Keras), PyTorch, and Apple’s MLXDeep learning frameworks such as TensorFlow (Keras), PyTorch, and Apple’s MLX offer powerful tools to build and train machine learning models. Despite solving similar problems, these frameworks have different philosophies, APIs, and optimizations under the hood. In this post, we will examine how the same model is implemented on each platform and why the differences in code arise, especially focusing on why MLX is more similar to PyTorch than TensorFlow.1. Model in PyTorchPyTorch is known for giving developers granular control over model-building and training processes. The framework encourages writing custom training loops, making it highly flexible, especially for research purposes.PyTorch Code:What’s Happening Behind the Scenes in PyTorch?PyTorch gives the developer direct control over every step of the model training process. The training loop is written manually, where:Forward pass: Defined in the forward() method, explicitly computing the output layer by layer.Backward pass: After calculating the loss, the gradients are computed using loss.backward().Gradient updates: The optimizer manually updates the weights after each batch using optimizer.step().This manual training loop allows researchers and developers to experiment with unconventional architectures or optimization methods. The gradient clipping function torch.nn.utils.clip_grad_norm_ prevents exploding gradients during backpropagation.2. Model in TensorFlow…