Create lstm_check_model_parameter_count.ipynb

lstm_check_model_parameter_count.ipynb

@@ -0,0 +1,63 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from keras import layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Nt = 4096\n",
+    "Nx = 64\n",
+    "Nh = 130\n",
+    "\n",
+    "model = keras.Sequential(\n",
+    "    [\n",
+    "    layers.Input(shape=(Nt, Nx)),\n",
+    "    layers.LSTM(Nh, return_sequences=True)\n",
+    "    ])\n",
+    "model.summary()\n",
+    "print(4 * Nh*Nx + 4 * Nh*Nh + 4 * Nh*1, 4 * Nh * (Nx + Nh + 1))\n",
+    "\n",
+    "model = keras.Sequential(\n",
+    "    [\n",
+    "    layers.Input(shape=(Nt, Nx)),\n",
+    "    layers.Bidirectional(layers.LSTM(Nh, return_sequences=True))\n",
+    "    ])\n",
+    "model.summary()\n",
+    "print(2 * (4 * Nh*Nx + 4 * Nh*Nh + 4 * Nh*1), 2 * 4 * Nh * (Nx + Nh + 1))\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "myddasp",
+   "language": "python",
+   "name": "myddasp"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}