ugh

aesthetic_loss.py

@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+def my_loss_fn(y_true, y_pred):
+    
+    return tf.reduce_mean(squared_difference, axis=-1)  # Note the `axis=-1`

patch_gen.py

@@ -0,0 +1,4 @@
+from tensorflow import keras
+
+input = keras.Input((2,))
+

playgrund.ipynb

@@ -0,0 +1,155 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['🟥', '🟦', '🟧', '🟨', '🟩', '🟪', '🟫']\n"
+     ]
+    }
+   ],
+   "source": [
+    "squares = [chr(i) for i in range(0x1F7E5, 0x1F7EC)]\n",
+    "tfsquares = tf.constant(squares)\n",
+    "print(squares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tensor_to_string(tensor):\n",
+    "    square_select = tf.math.argmax(tensor, 2)\n",
+    "\n",
+    "    tfstring = tf.strings.join(\n",
+    "        tf.map_fn(\n",
+    "            lambda v:\n",
+    "            tf.strings.join(\n",
+    "                tf.gather(tfsquares, tf.cast(v, tf.int64))), square_select, fn_output_signature=tf.string), \"\\n\")\n",
+    "\n",
+    "    return tfstring.numpy().decode()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tf.Tensor([0. 0. 0. 1. 0. 0. 0.], shape=(7,), dtype=float32)\n",
+      "tf.Tensor([[3]], shape=(1, 1), dtype=int64)\n",
+      "🟦🟪🟦🟫🟦🟧🟨🟨\n",
+      "🟦🟫🟧🟫🟪🟥🟧🟪\n",
+      "🟫🟩🟨🟧🟨🟫🟪🟨\n",
+      "🟧🟦🟨🟪🟦🟨🟥🟨\n",
+      "🟫🟧🟧🟨🟧🟧🟩🟪\n",
+      "🟦🟦🟨🟦🟥🟫🟧🟩\n",
+      "🟫🟩🟧🟫🟨🟫🟥🟩\n",
+      "🟧🟫🟫🟩🟧🟧🟫🟩\n",
+      "--------------\n",
+      "tf.Tensor(\n",
+      "[[0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 1. 0. 0. 1. 0. 0.]\n",
+      " [0. 0. 1. 0. 0. 1. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 1. 0. 0. 0. 0. 1. 0.]], shape=(8, 8), dtype=float32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "oh = tf.one_hot(3, 7)\n",
+    "print(oh)\n",
+    "print(tf.where(oh))\n",
+    "\n",
+    "# a = tf.constant([[list(tf.one_hot(random.randrange(7), 7)) for i in range(8) ] for i in range(8)], shape=(8,8,len(squares)))\n",
+    "\n",
+    "# print([[list(tf.one_hot(random.randrange(7), 7)) for i in range(8) ] for i in range(8)])\n",
+    "e = [[random.randrange(7) for i in range(8)] for i in range(8)]\n",
+    "# print(e)\n",
+    "\n",
+    "a = tf.one_hot(tf.constant(e, shape=(8, 8)), len(squares))\n",
+    "\n",
+    "print(tensor_to_string(a))\n",
+    "print(\"--------------\")\n",
+    "# print(tensor_to_string(tf.transpose(a, [1, 0, 2])))\n",
+    "# print(\"--------------\")\n",
+    "# print(tensor_to_string(tf.linalg.matmul( a , tf.constant(\n",
+    "#     [\n",
+    "#         [0, 0, 0, 0, 0, 0, 0, 1],\n",
+    "#         [0, 0, 0, 0, 0, 0, 1, 0],\n",
+    "#         [0, 0, 0, 0, 0, 1, 0, 0],\n",
+    "#         [0, 0, 0, 0, 1, 0, 0, 0],\n",
+    "#         [0, 0, 0, 1, 0, 0, 0, 0],\n",
+    "#         [0, 0, 1, 0, 0, 0, 0, 0],\n",
+    "#         [0, 1, 0, 0, 0, 0, 0, 0],\n",
+    "#         [1, 0, 0, 0, 0, 0, 0, 0]\n",
+    "#     ], dtype=tf.float32, shape=(8,8,1)\n",
+    "# ), transpose_a=True)))\n",
+    "# print(\"--------------\")\n",
+    "# print(tensor_to_string(tf.reverse(a, (0,))))\n",
+    "# print(\"--------------\")\n",
+    "# print(tensor_to_string(tf.reverse(a, (1,))))\n",
+    "# print(tensor_to_string(\n",
+    "#     tf.linalg.matmul(\n",
+    "#         tf.transpose(a, [0, 2, 1]),\n",
+    "#         tf.reverse(a, (1,)))\n",
+    "# ))\n",
+    "print(\n",
+    "    tf.einsum(\n",
+    "        \"ijk,ijk->ij\",\n",
+    "        a,\n",
+    "        tf.reverse(a, (1,)))\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# print(tf.constant(range(6), shape=(2,3)))\n",
+    "# tf.map_fn(tf.math.reduce_sum, tf.constant(range(6), shape=(2,3)))\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".patch_gen_venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}