From 9f69a45afd5ff93c74bb4fab88cb03b3cefed600 Mon Sep 17 00:00:00 2001
From: yinon <inon51@gmail.com>
Date: Fri, 4 Aug 2023 13:46:30 +0000
Subject: [PATCH] pytorch - documentation update

---
 docs/freqai-parameter-table.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/freqai-parameter-table.md b/docs/freqai-parameter-table.md
index 5e60d2a07..de0b666ca 100644
--- a/docs/freqai-parameter-table.md
+++ b/docs/freqai-parameter-table.md
@@ -100,12 +100,12 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 
 #### trainer_kwargs
 
-|  Parameter | Description |
-|------------|-------------|
-|  |  **Model training parameters within the `freqai.model_training_parameters.model_kwargs` sub dictionary**
-| `max_iters` | The number of training iterations to run. iteration here refers to the number of times we call self.optimizer.step(). used to calculate n_epochs. <br> **Datatype:** int. <br> Default: `100`.
-| `batch_size` | The size of the batches to use during training.. <br> **Datatype:** int. <br> Default: `64`.
-| `max_n_eval_batches` | The maximum number batches to use for evaluation.. <br> **Datatype:** int, optional. <br> Default: `None`.
+| Parameter            | Description |
+|----------------------|-------------|
+|                      |  **Model training parameters within the `freqai.model_training_parameters.model_kwargs` sub dictionary**
+| `n_epochs`           | The `n_epochs` parameter is a crucial setting in the PyTorch training loop that determines the number of times the entire training dataset will be used to update the model's parameters. An epoch represents one full pass through the entire training dataset. <br> **Datatype:** int. <br> Default: `10`.
+| `n_steps`          | An alternative way of setting `n_epochs` -  the number of training iterations to run. Iteration here refer to the number of times we call `optimizer.step()`. a simplified version of the function: <br><br> n_epochs = n_steps / (n_obs / batch_size) <br><br> The motivation here is that `n_steps` is easier to optimize and keep stable across different n_obs - the number of data points.  <br> <br> **Datatype:** int. optional. <br> Default: `None`.
+| `batch_size`         | The size of the batches to use during training.. <br> **Datatype:** int. <br> Default: `64`.
 
 
 ### Additional parameters