458 lines
261 KiB
Plaintext
458 lines
261 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, sample_gaussian\n",
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"from rbm.torch import Optimizer\n",
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"from rbm.image import SubImage, normalize\n",
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"import math\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": 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": "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": 5,
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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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{
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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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"N = 1000\n",
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"n_side = 10\n",
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"N_VIS = 3072\n",
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"n_sub = 8\n",
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"px = 8\n",
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"py = 8\n",
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"\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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"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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"print(image.shape)\n",
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"\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": 6,
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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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"train_images.shape:(1000, 3072)\n",
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"(16000, 3, 8, 8)\n",
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"(16000, 192)\n"
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]
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}
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],
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"source": [
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"image_indices = np.array(random.sample(range(min(N, n_side*n_side)), n_side*n_side))\n",
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"print(f\"train_images.shape:{train_images.shape}\")\n",
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"mean_training_batch = np.reshape(np.repeat(np.mean(train_images, axis=1), N_VIS, axis=0), train_images.shape)\n",
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"var_training_batch = np.reshape(np.repeat(np.std(train_images, axis=1), N_VIS, axis=0), train_images.shape)\n",
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"#print(f\"mean_training_batch:{mean_training_batch}\")\n",
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"#print(f\"var_training_batch:{var_training_batch}\")\n",
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"train_images_norm = (train_images - mean_training_batch) / var_training_batch\n",
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"sub_generator = SubImage(n_sub,n_sub,px,py)\n",
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"train_subimages = sub_generator(np.reshape(train_images, (N, 3, 32, 32)))\n",
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"print(train_subimages.shape)\n",
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"train_subimages = np.reshape(train_subimages, (train_subimages.shape[0], 3*n_sub*n_sub))\n",
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"print(train_subimages.shape)\n",
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"\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": 7,
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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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|
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"text/plain": [
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"<Figure size 3000x3000 with 100 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"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_subimages[image_indices[index]]\n",
|
|
" img = (1+np.reshape(inp, (3, n_sub, n_sub)))/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": 45,
|
|
"id": "08143a53-12a1-4896-a4b2-166dedf0e021",
|
|
"metadata": {
|
|
"editable": true,
|
|
"slideshow": {
|
|
"slide_type": ""
|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"class TestModel(Model):\n",
|
|
" def __init__(self, name: str, work_dir: str = '.'):\n",
|
|
" super().__init__(name, work_dir)\n",
|
|
" #self.unit1 = Entity((3072, 125), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=True), TrainingParams(learning_rate=0.0001, momentum=0.9, num_epochs=1000, mini_batch_size=0, weight_decay=0.0, l2_lambda=0.0))\n",
|
|
" self.unit1 = Entity((3*n_sub*n_sub, 250), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=False), TrainingParams(learning_rate=0.001, momentum=0.9, num_epochs=1000, mini_batch_size=1000, weight_decay=0.0, l2_lambda=0.01))\n",
|
|
" \n",
|
|
" def forward(self, x: Mat):\n",
|
|
" x = self.unit1.forward(x)\n",
|
|
" return x\n",
|
|
" \n",
|
|
" def backward(self, x: Mat):\n",
|
|
" x = self.unit1.reconstruct(x)\n",
|
|
" return x\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"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",
|
|
"Entity-192x250: progress : 0%\n",
|
|
"Entity-192x250: err_rms : 0.2552476571996566\n",
|
|
"Entity-192x250: l2_norm : 0.0016125501038316927\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-192x250: progress : 10%\n",
|
|
"Entity-192x250: err_rms : 0.1516525262493534\n",
|
|
"Entity-192x250: l2_norm : 0.009351603385287548\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-192x250: progress : 20%\n",
|
|
"Entity-192x250: err_rms : 0.10485132357821063\n",
|
|
"Entity-192x250: l2_norm : 0.020957326637095288\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-192x250: progress : 30%\n",
|
|
"Entity-192x250: err_rms : 0.10399743210498165\n",
|
|
"Entity-192x250: l2_norm : 0.0210767858930078\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-192x250: progress : 40%\n",
|
|
"Entity-192x250: err_rms : 0.10186176305452195\n",
|
|
"Entity-192x250: l2_norm : 0.021742916855928853\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-192x250: progress : 50%\n",
|
|
"Entity-192x250: err_rms : 0.09622639640868645\n",
|
|
"Entity-192x250: l2_norm : 0.023988645922319817\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-192x250: progress : 60%\n",
|
|
"Entity-192x250: err_rms : 0.0851732306762205\n",
|
|
"Entity-192x250: l2_norm : 0.029990712755548935\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-192x250: progress : 70%\n",
|
|
"Entity-192x250: err_rms : 0.07683322558262161\n",
|
|
"Entity-192x250: l2_norm : 0.03824479547441683\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-192x250: progress : 80%\n",
|
|
"Entity-192x250: err_rms : 0.0721457869574031\n",
|
|
"Entity-192x250: l2_norm : 0.04355526454789739\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-192x250: progress : 90%\n",
|
|
"Entity-192x250: err_rms : 0.06557113470862642\n",
|
|
"Entity-192x250: l2_norm : 0.04914726294420429\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-192x250: progress : 100%\n",
|
|
"Entity-192x250: err_rms : 0.057860262953185186\n",
|
|
"Entity-192x250: l2_norm : 0.05734459775710474\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-192x250: progress : 100%\n",
|
|
"Entity-192x250: err_rms_total : 0.05751380634297653\n",
|
|
"Entity-192x250: l2_norm : 0.05780679657692539\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(normalize(train_subimages))\n",
|
|
"model.train(train_subimages)\n",
|
|
"\n",
|
|
"# save state\n",
|
|
"model.save()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"id": "d036b106-77a3-4993-bc2a-5fb164b8ec46",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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|
|
"text/plain": [
|
|
"<Figure size 3000x3000 with 100 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Plot reconstructions\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_subimages[image_indices[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, n_sub, n_sub))\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": 44,
|
|
"id": "9adce292-cae6-455c-9409-c57ae71a36d5",
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(3, 8, 8, 1000)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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|
|
"text/plain": [
|
|
"<Figure size 3000x3000 with 100 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Plot weights\n",
|
|
"weights = model.unit1.state.w_hv\n",
|
|
"n, n_hid = weights.shape\n",
|
|
"weight_indices = np.array(random.sample(range(n_hid), n_side*n_side))\n",
|
|
"w = np.reshape(weights, (3, n_sub, n_sub, n_hid))\n",
|
|
"print(w.shape)\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 = np.asnumpy(w[:,:,:, weight_indices[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": null,
|
|
"id": "c976fed5-162c-4a70-8219-fc6c68c1fb66",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "661dfc9d-e58a-4994-a5fa-c377d7f743e5",
|
|
"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
|
|
}
|