TensorFlow与PyTorch中RMSProp的alpha与rho参数对比：二者是否为同名异参？

Great question! Let's clarify this directly: these two parameters are fundamentally the same concept—they just have different names, and both represent the decay factor used to compute the exponential moving average (EMA) of squared gradients in the RMSProp algorithm.

Here's the detailed breakdown: #

• At the core of RMSProp, we maintain an EMA of the squared gradients, which follows this formula:

  moving_average_sq_grad = decay_factor * moving_average_sq_grad + (1 - decay_factor) * current_grad²
  

  In PyTorch, this decay_factor is exactly the alpha parameter (default value: 0.99). In TensorFlow, this same decay_factor is called rho (it used to be named decay in older versions, default value: 0.9).
• The only meaningful difference between them is their default values: PyTorch's alpha=0.99 gives more weight to historical gradient data, resulting in a smoother moving average. TensorFlow's rho=0.9 gives relatively more weight to recent gradients.
• To replicate identical RMSProp behavior across the two frameworks, you just need to set alpha in PyTorch to the same value as rho in TensorFlow (and vice versa).

Example of matching behavior: #

• PyTorch setup: torch.optim.RMSprop(model.parameters(), alpha=0.9)
• TensorFlow setup: tf.keras.optimizers.RMSprop(rho=0.9)

These two configurations will compute the squared gradient moving average in exactly the same way.

内容的提问来源于stack exchange，提问作者Tamir