677 lines
478 KiB
Plaintext
677 lines
478 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d6cb560a-707c-4c1e-b50f-2b9c8037a3c7",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"from PIL import Image\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"import torch.optim as optim\n",
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"import torchvision\n",
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"import torchvision.transforms as transforms\n",
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"import matplotlib.pyplot as plt\n",
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"from rbm.model import Model\n",
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"from rbm.entity import Entity, EntityParams, TrainingParams\n",
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"from rbm.matrix import Mat, np, rms_error_accu\n",
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"from rbm.torch import Optimizer\n",
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"import math"
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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": 2,
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"id": "4065302d-862f-4639-99d7-e396d055434c",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"transform = transforms.Compose([\n",
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" transforms.ToTensor(),\n",
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" transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) \n",
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"]) "
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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": 3,
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"id": "ffe48fc6-0f57-48a0-b5e9-aa68cbc8edf1",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"train_data = torchvision.datasets.CIFAR10(root='./data', train=True, transform=transform, download=True)\n",
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"test_data = torchvision.datasets.CIFAR10(root='./data', train=False, transform=transform, download=True)\n",
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"\n",
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"train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True, num_workers=2)\n",
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"test_loader = torch.utils.data.DataLoader(test_data, batch_size=32, shuffle=True, num_workers=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": 4,
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"id": "51b90664-e470-4c9a-884d-374a2ece61b2",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"image, label = train_data[0]"
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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": 5,
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"id": "e98f3f60-e764-470b-ada1-d562a48e4813",
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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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"6\n"
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]
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}
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],
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"source": [
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"print(label)"
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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": 6,
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"id": "33501c0f-6d78-43e9-b2ce-adbe40b6d95a",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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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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"Dataset CIFAR10\n",
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" Number of datapoints: 50000\n",
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" Root location: ./data\n",
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" Split: Train\n",
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" StandardTransform\n",
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"Transform: Compose(\n",
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" ToTensor()\n",
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" Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n",
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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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"print(train_data)"
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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": 7,
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"id": "08149024-e441-4786-a956-a068431932ca",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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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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"torch.Size([3, 32, 32])\n"
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]
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}
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],
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"source": [
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"dim = image.size()\n",
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"print(dim)"
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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": null,
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"id": "f333fee6-91df-4153-9cfa-66d6550dd11c",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "aedf601a-9084-4047-98e8-481f4d41a2d9",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"N = 16\n",
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"image, label = train_data[0]\n",
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"size = len(torch.flatten(image))\n",
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"train_images = np.zeros(shape=(N, size))\n",
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"train_images_tensor = torch.Tensor(N, size)\n",
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"#train_labels = np.zeros(shape=(N, size))\n",
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"for i in range(N):\n",
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" image, label = train_data[i]\n",
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" flat_array = torch.flatten(image)\n",
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" train_images[i, :] = np.asarray(flat_array.numpy())\n",
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" train_images_tensor[i, :] = flat_array\n",
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"# train_labels[i, :] = torch.flatten(label).numpy()\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": 9,
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"id": "5ee865c3-2af4-487f-a184-1291f99839b1",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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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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"16\n"
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]
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}
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],
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"source": [
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"print(len(train_images))"
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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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"id": "b4310933-d850-4128-a2dd-27d88c004846",
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"metadata": {},
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"outputs": [
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{
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"data": {
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|
|
"text/plain": [
|
|
"<Figure size 3000x3000 with 16 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"n_side = int(math.sqrt(len(train_images)))\n",
|
|
"fig, axes = plt.subplots(n_side, n_side, figsize=(30,30))\n",
|
|
"index = 0 \n",
|
|
"for x in range(n_side):\n",
|
|
" for y in range(n_side):\n",
|
|
" inp = train_images[index]\n",
|
|
" img = (1+np.reshape(inp, (3, 32, 32)))/2\n",
|
|
" img = img.transpose((1,2,0))\n",
|
|
" axes[x,y].imshow(np.asnumpy(img))\n",
|
|
" axes[x,y].axis('off')\n",
|
|
" index += 1\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "08143a53-12a1-4896-a4b2-166dedf0e021",
|
|
"metadata": {
|
|
"editable": true,
|
|
"slideshow": {
|
|
"slide_type": ""
|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"class TestModel(Model):\n",
|
|
"\tdef __init__(self, name: str, work_dir: str = '.'):\n",
|
|
"\t\tsuper().__init__(name, work_dir)\n",
|
|
"#\t\tself.unit1 = Entity((3072, 16), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=True), TrainingParams(learning_rate=0.0001, momentum=0.9, num_epochs=10000, mini_batch_size=100, weight_decay=0.0))\n",
|
|
"\t\tself.unit1 = Entity((3072, 32), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=False), TrainingParams(learning_rate=0.001, momentum=0.9, num_epochs=10000, mini_batch_size=100, weight_decay=0.0))\n",
|
|
"\n",
|
|
"\tdef forward(self, x: Mat):\n",
|
|
"\t\tx = self.unit1.forward(x)\n",
|
|
"\t\treturn x\n",
|
|
"\n",
|
|
"\tdef backward(self, x: Mat):\n",
|
|
"\t\tx = self.unit1.reconstruct(x)\n",
|
|
"\t\treturn x\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "6b3be9f6-38e1-442d-a7bd-1f3c164ec8d4",
|
|
"metadata": {
|
|
"editable": true,
|
|
"slideshow": {
|
|
"slide_type": ""
|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"results/cifar_test-0-state.npz loaded successfully!\n",
|
|
"-------------------------------------------\n",
|
|
"progress : 0%\n",
|
|
"err_rms : 0.00039061838837145204\n",
|
|
"-------------------------------------------\n",
|
|
"progress : 10%\n",
|
|
"err_rms : 0.00037131690770384845\n",
|
|
"-------------------------------------------\n",
|
|
"progress : 20%\n",
|
|
"err_rms : 0.00036395516084105375\n",
|
|
"-------------------------------------------\n",
|
|
"progress : 30%\n",
|
|
"err_rms : 0.00033038886044653936\n",
|
|
"-------------------------------------------\n",
|
|
"progress : 40%\n",
|
|
"err_rms : 0.00031309300752963807\n",
|
|
"-------------------------------------------\n",
|
|
"progress : 50%\n",
|
|
"err_rms : 0.0003218129823166871\n",
|
|
"-------------------------------------------\n",
|
|
"progress : 60%\n",
|
|
"err_rms : 0.00032128073747992106\n",
|
|
"-------------------------------------------\n",
|
|
"progress : 70%\n",
|
|
"err_rms : 0.00033087258918710005\n",
|
|
"-------------------------------------------\n",
|
|
"progress : 80%\n",
|
|
"err_rms : 0.00033042256078105373\n",
|
|
"-------------------------------------------\n",
|
|
"progress : 90%\n",
|
|
"err_rms : 0.0003412663178393814\n",
|
|
"-------------------------------------------\n",
|
|
"progress : 100%\n",
|
|
"err_rms : 0.0003536440911971254\n",
|
|
"-------------------------------------------\n",
|
|
"progress : 100%\n",
|
|
"err_rms_total : 0.0003500126631902263\n",
|
|
"results/cifar_test-0-state.npz saved successfully!\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"work_dir = \"results\"\n",
|
|
"prj_name = \"cifar_test\"\n",
|
|
"prj_root = \"/home/jens/work/repos/Rbm\"\n",
|
|
"\n",
|
|
"# Create model\n",
|
|
"model = TestModel(prj_name, \"results\")\n",
|
|
"\n",
|
|
"# Init state\n",
|
|
"model.init(0.01)\n",
|
|
"\n",
|
|
"# load state\n",
|
|
"model.load()\n",
|
|
"\n",
|
|
"# Train\n",
|
|
"model.train(train_images)\n",
|
|
"# save state\n",
|
|
"model.save()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "d036b106-77a3-4993-bc2a-5fb164b8ec46",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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|
|
"text/plain": [
|
|
"<Figure size 3000x3000 with 16 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Plot reconstructions\n",
|
|
"n_side = int(math.sqrt(len(train_images)))\n",
|
|
"fig, axes = plt.subplots(n_side, n_side, figsize=(30,30))\n",
|
|
"index = 0 \n",
|
|
"for x in range(n_side):\n",
|
|
" for y in range(n_side):\n",
|
|
" inp = train_images[index]\n",
|
|
" recon = model.backward(model.forward(inp))\n",
|
|
" recon -= np.min(recon)\n",
|
|
" recon = recon / np.max(recon)\n",
|
|
" img = np.reshape(recon, (3, 32, 32))\n",
|
|
" img = img.transpose((1,2,0))\n",
|
|
" axes[x,y].imshow(np.asnumpy(img))\n",
|
|
" axes[x,y].axis('off')\n",
|
|
" index += 1\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "9adce292-cae6-455c-9409-c57ae71a36d5",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(3, 32, 32, 32)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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|
|
"text/plain": [
|
|
"<Figure size 3000x3000 with 25 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Plot weights\n",
|
|
"weights = model.unit1.state.w_hv\n",
|
|
"n, n_hid = weights.shape\n",
|
|
"w = np.reshape(weights, (3, 32, 32, n_hid))\n",
|
|
"print(w.shape)\n",
|
|
"n_side = int(math.sqrt(n_hid))\n",
|
|
"fig, axes = plt.subplots(n_side, n_side, figsize=(30,30))\n",
|
|
"\n",
|
|
"index = 0\n",
|
|
"for x in range(n_side):\n",
|
|
" for y in range(n_side):\n",
|
|
" inp = w[:,:,:, index]\n",
|
|
" inp -= np.min(inp)\n",
|
|
" inp = inp / np.max(inp)\n",
|
|
" img = inp.transpose((1,2,0))\n",
|
|
" axes[x,y].imshow(np.asnumpy(img))\n",
|
|
" axes[x,y].axis('off')\n",
|
|
" index += 1\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "00f7f113-9efa-4c7b-8cb4-eeefb5cb797f",
|
|
"metadata": {
|
|
"editable": true,
|
|
"slideshow": {
|
|
"slide_type": ""
|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# torch style"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "da54ec00-8db1-4dd9-ac4f-755f2886d72b",
|
|
"metadata": {
|
|
"editable": true,
|
|
"slideshow": {
|
|
"slide_type": ""
|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"results/cifar_test-0-state.npz loaded successfully!\n",
|
|
"Training epoch 1000...\n",
|
|
"0.0003709077964170074\n",
|
|
"Training epoch 2000...\n",
|
|
"0.00038718622285670236\n",
|
|
"Training epoch 3000...\n",
|
|
"0.0004005671025317081\n",
|
|
"Training epoch 4000...\n",
|
|
"0.0004041792783408087\n",
|
|
"Training epoch 5000...\n",
|
|
"0.00037528612752424226\n",
|
|
"Training epoch 6000...\n",
|
|
"0.00036610476761507926\n",
|
|
"Training epoch 7000...\n",
|
|
"0.0003594007480121864\n",
|
|
"Training epoch 8000...\n",
|
|
"0.00036999088127786785\n",
|
|
"Training epoch 9000...\n",
|
|
"0.00036930055661853477\n",
|
|
"results/cifar_test-0-state.npz saved successfully!\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model.load()\n",
|
|
"optimizer = Optimizer(model.unit1)\n",
|
|
"report_interval = 1000\n",
|
|
"next_ep = report_interval\n",
|
|
"optimizer.zero_grad()\n",
|
|
"for epoch in range(10000):\n",
|
|
" running_loss = 0.0\n",
|
|
"\n",
|
|
" optimizer.step(train_images)\n",
|
|
"\n",
|
|
" if epoch >= next_ep:\n",
|
|
" print(f'Training epoch {epoch}...')\n",
|
|
" next_ep += report_interval\n",
|
|
" # Update final status\n",
|
|
" err_rms = rms_error_accu(train_images - model.unit1.reconstruct(model.unit1.forward(train_images)))\n",
|
|
" print(err_rms)\n",
|
|
"\n",
|
|
"model.save()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "80b0cdcc-3059-4840-98db-3416c585fa81",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c3238db1-179c-4e7a-a09c-5a2371cdac49",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "66bc0b12-dab5-46a8-b932-1c8ce4bd2175",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "fcc5c072-0e21-4cdc-a05d-ebf8c7cf0db1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "6efdc1ef-6fdd-4dc5-8903-88464cf2d25f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "9bdd50bb-875e-49d7-8a1e-8257a2d4ed25",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "9076754a-51ed-45ed-a0a8-767a3a7eeffd",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "ea6fc3b7-c1b7-45dc-9ef2-4c9dfdf82e18",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "a7c82df5-9eb2-4920-b98f-4798d94ef896",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "eedf5bef-6341-4923-be79-ac8efdf424d3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "2c937475-f297-4f2f-86aa-b28203de8485",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e7fc0b9d-b652-4581-81e0-a0c83bdf4a63",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "544e0ea3-0602-4219-a67a-3162152aa730",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "848dad8e-ae01-4520-9978-9552ca8db9fb",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "ab664e72-ac9b-4c9d-b059-8ceacdc5d211",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b9062597-5973-4c45-97ec-e4b8ee078eba",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c976fed5-162c-4a70-8219-fc6c68c1fb66",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.12.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|