ugh
HeNine
2 years ago
parent
f701bff0a8
commit
4091c0e79e
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import tensorflow as tf
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def my_loss_fn(y_true, y_pred):
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return tf.reduce_mean(squared_difference, axis=-1) # Note the `axis=-1`
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from tensorflow import keras
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input = keras.Input((2,))
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@ -0,0 +1,155 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"import random"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['🟥', '🟦', '🟧', '🟨', '🟩', '🟪', '🟫']\n"
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]
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}
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],
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"source": [
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"squares = [chr(i) for i in range(0x1F7E5, 0x1F7EC)]\n",
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"tfsquares = tf.constant(squares)\n",
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"print(squares)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"metadata": {},
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"outputs": [],
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"source": [
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"def tensor_to_string(tensor):\n",
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" square_select = tf.math.argmax(tensor, 2)\n",
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"\n",
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" tfstring = tf.strings.join(\n",
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" tf.map_fn(\n",
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" lambda v:\n",
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" tf.strings.join(\n",
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" tf.gather(tfsquares, tf.cast(v, tf.int64))), square_select, fn_output_signature=tf.string), \"\\n\")\n",
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"\n",
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" return tfstring.numpy().decode()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 95,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tf.Tensor([0. 0. 0. 1. 0. 0. 0.], shape=(7,), dtype=float32)\n",
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"tf.Tensor([[3]], shape=(1, 1), dtype=int64)\n",
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"🟦🟪🟦🟫🟦🟧🟨🟨\n",
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"🟦🟫🟧🟫🟪🟥🟧🟪\n",
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"🟫🟩🟨🟧🟨🟫🟪🟨\n",
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"🟧🟦🟨🟪🟦🟨🟥🟨\n",
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"🟫🟧🟧🟨🟧🟧🟩🟪\n",
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"🟦🟦🟨🟦🟥🟫🟧🟩\n",
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"🟫🟩🟧🟫🟨🟫🟥🟩\n",
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"🟧🟫🟫🟩🟧🟧🟫🟩\n",
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"--------------\n",
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"tf.Tensor(\n",
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"[[0. 0. 0. 0. 0. 0. 0. 0.]\n",
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" [0. 0. 0. 0. 0. 0. 0. 0.]\n",
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" [0. 0. 0. 0. 0. 0. 0. 0.]\n",
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" [0. 0. 1. 0. 0. 1. 0. 0.]\n",
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" [0. 0. 1. 0. 0. 1. 0. 0.]\n",
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" [0. 0. 0. 0. 0. 0. 0. 0.]\n",
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" [0. 0. 0. 0. 0. 0. 0. 0.]\n",
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" [0. 1. 0. 0. 0. 0. 1. 0.]], shape=(8, 8), dtype=float32)\n"
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]
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}
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],
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"source": [
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"oh = tf.one_hot(3, 7)\n",
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"print(oh)\n",
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"print(tf.where(oh))\n",
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"\n",
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"# 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",
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"\n",
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"# print([[list(tf.one_hot(random.randrange(7), 7)) for i in range(8) ] for i in range(8)])\n",
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"e = [[random.randrange(7) for i in range(8)] for i in range(8)]\n",
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"# print(e)\n",
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"\n",
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"a = tf.one_hot(tf.constant(e, shape=(8, 8)), len(squares))\n",
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"\n",
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"print(tensor_to_string(a))\n",
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"print(\"--------------\")\n",
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"# print(tensor_to_string(tf.transpose(a, [1, 0, 2])))\n",
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"# print(\"--------------\")\n",
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"# print(tensor_to_string(tf.linalg.matmul( a , tf.constant(\n",
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"# [\n",
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"# [0, 0, 0, 0, 0, 0, 0, 1],\n",
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"# [0, 0, 0, 0, 0, 0, 1, 0],\n",
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"# [0, 0, 0, 0, 0, 1, 0, 0],\n",
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"# [0, 0, 0, 0, 1, 0, 0, 0],\n",
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"# [0, 0, 0, 1, 0, 0, 0, 0],\n",
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"# [0, 0, 1, 0, 0, 0, 0, 0],\n",
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"# [0, 1, 0, 0, 0, 0, 0, 0],\n",
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"# [1, 0, 0, 0, 0, 0, 0, 0]\n",
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"# ], dtype=tf.float32, shape=(8,8,1)\n",
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"# ), transpose_a=True)))\n",
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"# print(\"--------------\")\n",
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"# print(tensor_to_string(tf.reverse(a, (0,))))\n",
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"# print(\"--------------\")\n",
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"# print(tensor_to_string(tf.reverse(a, (1,))))\n",
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"# print(tensor_to_string(\n",
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"# tf.linalg.matmul(\n",
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"# tf.transpose(a, [0, 2, 1]),\n",
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"# tf.reverse(a, (1,)))\n",
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"# ))\n",
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"print(\n",
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" tf.einsum(\n",
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" \"ijk,ijk->ij\",\n",
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" a,\n",
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" tf.reverse(a, (1,)))\n",
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")\n",
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"\n",
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"\n",
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"# print(tf.constant(range(6), shape=(2,3)))\n",
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"# tf.map_fn(tf.math.reduce_sum, tf.constant(range(6), shape=(2,3)))\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".patch_gen_venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.0"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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