超大batch_size

model_training_nnn_tpu/Untitled-1.ipynb

@@ -0,0 +1,19 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acb1482e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

model_training_nnn_tpu/rnn_args.yaml

@@ -74,7 +74,7 @@ dataset:
     smooth_kernel_std: 2 # standard deviation of the smoothing kernel applied to the data
 
   neural_dim: 512 # dimensionality of the neural data
-  batch_size: 32 # batch size for training (reduced for TPU memory constraints)
+  batch_size: 1024 # batch size for training (reduced for TPU memory constraints)
   n_classes: 41 # number of classes (phonemes) in the dataset
   max_seq_elements: 500 # maximum number of sequence elements (phonemes) for any trial
   days_per_batch: 4 # number of randomly-selected days to include in each batch