435 lines
226 KiB
Plaintext
435 lines
226 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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"from rbm.image import SubImage\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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"]) "
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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.MNIST(root='./data', train=True, transform=transform, download=True)\n",
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"test_data = torchvision.datasets.MNIST(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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"60000\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_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": "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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"[28, 28]\n"
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]
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}
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],
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"source": [
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"print(list(train_data[1][0].size()[1:3]))"
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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": "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 = 600\n",
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"size = 784\n",
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"n_side = 10\n",
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"train_images = np.zeros(shape=(N,size))\n",
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"for i in range(N):\n",
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" image = train_data[i][0]\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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]
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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": "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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"(600, 784)\n",
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"(264600, 8, 8)\n",
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"(264600, 64)\n"
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]
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}
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],
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"source": [
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"print(train_images.shape)\n",
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"image_indices = np.random.randint(0, N-1, n_side*n_side)\n",
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"sub_generator = SubImage(8,8,1,1)\n",
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"train_subimages = sub_generator(np.reshape(train_images, (N, 28,28)))\n",
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"print(train_subimages.shape)\n",
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"train_subimages = np.reshape(train_subimages, (train_subimages.shape[0], 8*8))\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": 8,
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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 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 = np.reshape(inp, (8, 8))\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": 41,
|
|
"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((8*8, 100), EntityParams(do_gaussian_visible=False, do_gaussian_hidden=False), TrainingParams(learning_rate=0.1, momentum=0.9, num_epochs=100, mini_batch_size=1000, l2_lambda=0.04, do_rao_blackwell=True), True)\n",
|
|
"#\t\tself.unit2 = Entity((500, 64), EntityParams(do_gaussian_visible=False, do_gaussian_hidden=False), TrainingParams(learning_rate=0.04, momentum=0.9, num_epochs=1000, mini_batch_size=100, l2_lambda=0.01, do_rao_blackwell=True), True)\n",
|
|
"\n",
|
|
"\tdef forward(self, x: Mat):\n",
|
|
"\t\tx = self.unit1.forward(x)\n",
|
|
"#\t\tx = self.unit2.forward(x)\n",
|
|
"\t\treturn x\n",
|
|
"\n",
|
|
"\tdef backward(self, x: Mat):\n",
|
|
"#\t\tx = self.unit2.reconstruct(x)\n",
|
|
"\t\tx = self.unit1.reconstruct(x)\n",
|
|
"\t\treturn x\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 42,
|
|
"id": "6b3be9f6-38e1-442d-a7bd-1f3c164ec8d4",
|
|
"metadata": {
|
|
"editable": true,
|
|
"slideshow": {
|
|
"slide_type": ""
|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"results/mnist_test-0-state.npz loaded successfully!\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-64x100: progress : 0%\n",
|
|
"Entity-64x100: err_rms : 0.010188036334750098\n",
|
|
"Entity-64x100: l2_norm : 7.826830734264725\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-64x100: progress : 10%\n",
|
|
"Entity-64x100: err_rms : 0.0108727259125326\n",
|
|
"Entity-64x100: l2_norm : 6.646768137653653\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-64x100: progress : 20%\n",
|
|
"Entity-64x100: err_rms : 0.01148280269633328\n",
|
|
"Entity-64x100: l2_norm : 6.048016201107305\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-64x100: progress : 30%\n",
|
|
"Entity-64x100: err_rms : 0.01188136081400481\n",
|
|
"Entity-64x100: l2_norm : 5.734695106279423\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-64x100: progress : 40%\n",
|
|
"Entity-64x100: err_rms : 0.012118078633578716\n",
|
|
"Entity-64x100: l2_norm : 5.554934735685889\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-64x100: progress : 50%\n",
|
|
"Entity-64x100: err_rms : 0.012250536945822052\n",
|
|
"Entity-64x100: l2_norm : 5.443740082022105\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-64x100: progress : 60%\n",
|
|
"Entity-64x100: err_rms : 0.012318625433640864\n",
|
|
"Entity-64x100: l2_norm : 5.370527604197259\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-64x100: progress : 70%\n",
|
|
"Entity-64x100: err_rms : 0.012347321067533317\n",
|
|
"Entity-64x100: l2_norm : 5.319703362621786\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-64x100: progress : 80%\n",
|
|
"Entity-64x100: err_rms : 0.012351893635492953\n",
|
|
"Entity-64x100: l2_norm : 5.282789901756718\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-64x100: progress : 90%\n",
|
|
"Entity-64x100: err_rms : 0.01234167199630445\n",
|
|
"Entity-64x100: l2_norm : 5.254932522549195\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-64x100: progress : 100%\n",
|
|
"Entity-64x100: err_rms : 0.012322399010058828\n",
|
|
"Entity-64x100: l2_norm : 5.233226324578263\n",
|
|
"-------------------------------------------\n",
|
|
"Entity-64x100: progress : 100%\n",
|
|
"Entity-64x100: err_rms_total : 0.012334024223296906\n",
|
|
"Entity-64x100: l2_norm : 5.231595937286716\n",
|
|
"results/mnist_test-0-state.npz saved successfully!\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"work_dir = \"results\"\n",
|
|
"prj_name = \"mnist_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_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, (8, 8))\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": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(8, 8, 100)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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"<Figure size 3000x3000 with 100 Axes>"
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]
|
|
},
|
|
"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.random.randint(0, N-1, n_hid)\n",
|
|
"w = np.reshape(weights, (8, 8, 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",
|
|
" axes[x,y].imshow(inp)\n",
|
|
" axes[x,y].axis('off')\n",
|
|
" index += 1\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "2a44660c-6ce7-4363-b345-5745f9b6f2d0",
|
|
"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
|
|
}
|