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399 lines
13 KiB
Plaintext
399 lines
13 KiB
Plaintext
1 year ago
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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": 8,
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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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"The autoreload extension is already loaded. To reload it, use:\n",
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" %reload_ext autoreload\n"
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]
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}
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],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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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": 9,
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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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"from tensorflow.keras.layers import Dense, Conv2D, Reshape\n",
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"from polymap import Polymap\n",
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"from aesthetic_loss import aesthetic_loss"
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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": 46,
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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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"from math import pi\n",
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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)\n",
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"colors = tf.constant([10, 219, 38, 48, 80, 282, 20] * tf.constant(pi/180.0), dtype=tf.float32)\n",
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"color_vectors = tf.transpose(\n",
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" tf.stack([\n",
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" tf.math.cos(colors),\n",
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" tf.math.sin(colors)]\n",
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" )\n",
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")\n",
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"\n",
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"def vector_to_index(tensor):\n",
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" return tf.argmax((tf.einsum(\"ijk,lk->ijl\", tensor, color_vectors)), axis=-1)\n",
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"\n",
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"def index_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))), tensor, fn_output_signature=tf.string), \"\\n\")\n",
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"\n",
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" return tfstring.numpy().decode()"
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"batch_size = 16"
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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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"# class PatchGen(tf.keras.Model):\n",
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"\n",
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"# def __init__(self, batch_size):\n",
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"# super(PatchGen, self).__init__()\n",
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"# self.pm = Polymap(8, 3)(input)\n",
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"# self.d1 = Dense(64)\n",
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"# self.rs = Reshape([8,8,1])\n",
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"# self.dc1 = Conv2D(3,(1,1))\n",
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"\n",
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"# def call(self, inputs):\n",
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"# x = self.block_1(inputs)\n",
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"# x = self.block_2(x)\n",
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"# x = self.global_pool(x)\n",
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"# return self.classifier(x)\n",
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"\n",
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"input = tf.random.normal(shape=[batch_size,2])\n",
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"pm = Polymap(8, 3)\n",
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"d1 = Dense(64, activation=\"relu\")\n",
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"d2 = Dense(64, activation=\"relu\")\n",
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"rs = Reshape([8,8,1])\n",
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"dc1 = Conv2D(2,(1,1))\n",
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"\n",
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"def gen():\n",
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" input = tf.random.normal(shape=[batch_size,2])\n",
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" x = pm(input)\n",
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" x = d1(x)\n",
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" x = d2(x)\n",
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" x = rs(x)\n",
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" return dc1(x)\n",
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"# gen()"
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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": 52,
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"metadata": {},
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"outputs": [],
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"source": [
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"optimizer = tf.keras.optimizers.Adam()\n",
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"train_loss = tf.keras.metrics.Mean(name='train_loss')"
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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": 53,
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"metadata": {},
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"outputs": [],
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"source": [
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"@tf.function\n",
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"def train_step():\n",
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" with tf.GradientTape() as tape:\n",
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" pics = gen()\n",
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" loss = aesthetic_loss(pics)\n",
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" tv = sum([pm.trainable_weights, d1.trainable_weights,\n",
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" dc1.trainable_weights], [])\n",
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" print(tv)\n",
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" gradients = tape.gradient(loss, tv)\n",
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" optimizer.apply_gradients(zip(gradients, tv))\n",
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"\n",
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" train_loss(loss)\n",
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" # train_accuracy(labels, predictions)"
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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": 57,
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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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"Epoch 1, Loss: 0.0004605448921211064, \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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"Epoch 2, Loss: 9.45829197007697e-06, \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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"Epoch 3, Loss: 8.17671389086172e-05, \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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"Epoch 4, Loss: 0.0001505890249973163, \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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|
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
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"Epoch 5, Loss: 0.00018983485642820597, \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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"Epoch 6, Loss: 0.00012902110756840557, \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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||
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
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"Epoch 7, Loss: 1.342521863989532e-05, \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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"Epoch 8, Loss: 0.00034237385261803865, \n",
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"🟩🟩🟩🟩🟩🟩🟩🟩\n",
|
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"🟩🟩🟩🟩🟩🟩🟩🟩\n",
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"🟩🟩🟩🟩🟩🟩🟩🟩\n",
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||
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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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||
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"Epoch 9, Loss: 8.251221879618242e-05, \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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||
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
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||
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
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"Epoch 10, Loss: 1.0330303013006414e-07, \n",
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||
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"🟧🟧🟧🟧🟧🟧🟧🟧\n",
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||
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"🟧🟧🟧🟧🟧🟧🟧🟧\n",
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||
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"🟧🟧🟧🟧🟧🟧🟧🟧\n",
|
||
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"🟧🟧🟧🟧🟧🟧🟧🟧\n",
|
||
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"🟧🟧🟧🟧🟧🟧🟧🟧\n",
|
||
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"🟧🟧🟧🟧🟧🟧🟧🟧\n",
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||
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"🟧🟧🟧🟧🟧🟧🟧🟧\n",
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||
|
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"🟧🟧🟧🟧🟧🟧🟧🟧\n",
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||
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"Epoch 11, Loss: 3.0386236176127568e-05, \n",
|
||
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"🟥🟥🟥🟥🟥🟥🟥🟥\n",
|
||
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"🟥🟥🟥🟥🟥🟥🟥🟥\n",
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"🟥🟥🟥🟥🟥🟥🟥🟥\n",
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||
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"🟥🟥🟥🟥🟥🟥🟥🟥\n",
|
||
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"🟥🟥🟥🟥🟥🟥🟥🟥\n",
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||
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"🟥🟥🟥🟥🟥🟥🟥🟥\n",
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||
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"🟥🟥🟥🟥🟥🟥🟥🟥\n",
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||
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"🟥🟥🟥🟥🟥🟥🟥🟥\n",
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"Epoch 12, Loss: 4.6750748879276216e-05, \n",
|
||
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
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||
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
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||
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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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"Epoch 13, Loss: 7.047886185773677e-08, \n",
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"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"Epoch 14, Loss: 9.711433317672463e-14, \n",
|
||
|
|
"🟥🟥🟥🟥🟥🟥🟥🟥\n",
|
||
|
|
"🟥🟥🟥🟥🟥🟥🟥🟥\n",
|
||
|
|
"🟥🟥🟥🟥🟥🟥🟥🟥\n",
|
||
|
|
"🟥🟥🟥🟥🟥🟥🟥🟥\n",
|
||
|
|
"🟥🟥🟥🟥🟥🟥🟥🟥\n",
|
||
|
|
"🟥🟥🟥🟥🟥🟥🟥🟥\n",
|
||
|
|
"🟥🟥🟥🟥🟥🟥🟥🟥\n",
|
||
|
|
"🟥🟥🟥🟥🟥🟥🟥🟥\n",
|
||
|
|
"Epoch 15, Loss: 7.075177338602006e-23, \n",
|
||
|
|
"🟪🟪🟪🟪🟪🟪🟪🟪\n",
|
||
|
|
"🟪🟪🟪🟪🟪🟪🟪🟪\n",
|
||
|
|
"🟪🟪🟪🟪🟪🟪🟪🟪\n",
|
||
|
|
"🟪🟪🟪🟪🟪🟪🟪🟪\n",
|
||
|
|
"🟪🟪🟪🟪🟪🟪🟪🟪\n",
|
||
|
|
"🟪🟪🟪🟪🟪🟪🟪🟪\n",
|
||
|
|
"🟪🟪🟪🟪🟪🟪🟪🟪\n",
|
||
|
|
"🟪🟪🟪🟪🟪🟪🟪🟪\n",
|
||
|
|
"Epoch 16, Loss: 0.11943158507347107, \n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"Epoch 17, Loss: 0.002167836995795369, \n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"🟨🟨🟨🟨🟨🟨🟨🟨\n",
|
||
|
|
"Epoch 18, Loss: 6.590542034246027e-05, \n",
|
||
|
|
"🟧🟧🟧🟧🟧🟧🟧🟧\n",
|
||
|
|
"🟧🟧🟧🟧🟧🟧🟧🟧\n",
|
||
|
|
"🟧🟧🟧🟧🟧🟧🟧🟧\n",
|
||
|
|
"🟧🟧🟧🟧🟧🟧🟧🟧\n",
|
||
|
|
"🟧🟧🟧🟧🟧🟧🟧🟧\n",
|
||
|
|
"🟧🟧🟧🟧🟧🟧🟧🟧\n",
|
||
|
|
"🟧🟧🟧🟧🟧🟧🟧🟧\n",
|
||
|
|
"🟧🟧🟧🟧🟧🟧🟧🟧\n",
|
||
|
|
"Epoch 19, Loss: 0.0017910723108798265, \n",
|
||
|
|
"🟩🟩🟩🟩🟩🟩🟩🟩\n",
|
||
|
|
"🟩🟩🟩🟩🟩🟩🟩🟩\n",
|
||
|
|
"🟩🟩🟩🟩🟩🟩🟩🟩\n",
|
||
|
|
"🟩🟩🟩🟩🟩🟩🟩🟩\n",
|
||
|
|
"🟩🟩🟩🟩🟩🟩🟩🟩\n",
|
||
|
|
"🟩🟩🟩🟩🟩🟩🟩🟩\n",
|
||
|
|
"🟩🟩🟩🟩🟩🟩🟩🟩\n",
|
||
|
|
"🟩🟩🟩🟩🟩🟩🟩🟩\n",
|
||
|
|
"Epoch 20, Loss: 0.004703103564679623, \n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n",
|
||
|
|
"🟦🟦🟦🟦🟦🟦🟦🟦\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"EPOCHS = 50\n",
|
||
|
|
"\n",
|
||
|
|
"for epoch in range(EPOCHS):\n",
|
||
|
|
" # Reset the metrics at the start of the next epoch\n",
|
||
|
|
" train_loss.reset_states()\n",
|
||
|
|
"# train_accuracy.reset_states()\n",
|
||
|
|
"# test_loss.reset_states()\n",
|
||
|
|
"# test_accuracy.reset_states()\n",
|
||
|
|
"\n",
|
||
|
|
"# for images, labels in train_ds:\n",
|
||
|
|
" for i in range(50):\n",
|
||
|
|
" train_step()\n",
|
||
|
|
"\n",
|
||
|
|
"# for test_images, test_labels in test_ds:\n",
|
||
|
|
"# test_step(test_images, test_labels)\n",
|
||
|
|
"\n",
|
||
|
|
" print(\n",
|
||
|
|
" f'Epoch {epoch + 1}, '\n",
|
||
|
|
" f'Loss: {train_loss.result()}, '\n",
|
||
|
|
" # f'Accuracy: {train_accuracy.result() * 100}, '\n",
|
||
|
|
" # f'Test Loss: {test_loss.result()}, '\n",
|
||
|
|
" # f'Test Accuracy: {test_accuracy.result() * 100}'\n",
|
||
|
|
" )\n",
|
||
|
|
"\n",
|
||
|
|
" ps = gen()\n",
|
||
|
|
" # print(ps[0])\n",
|
||
|
|
" print(index_to_string(vector_to_index(ps[0])))"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": ".venv_patch_gen",
|
||
|
|
"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": "3.10.9"
|
||
|
|
},
|
||
|
|
"orig_nbformat": 4
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|