1059 lines
773 KiB
Plaintext
1059 lines
773 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Preparations"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'\\n*Uncomment if running on colab* \\nSet Runtime -> Change runtime type -> Under Hardware Accelerator select GPU in Google Colab \\n'"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"\"\"\n",
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"*Uncomment if running on colab* \n",
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"Set Runtime -> Change runtime type -> Under Hardware Accelerator select GPU in Google Colab \n",
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"\"\"\"\n",
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"# !git clone https://codes.paul-corbalan.com/Paul-Corbalan/variational_autoencoder.git\n",
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"# !mv Code-VAE/* ./"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Import libraries"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torchvision\n",
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"from torch import nn\n",
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"from torch.utils.data import DataLoader\n",
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"from torchvision import transforms\n",
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"from torchvision.datasets import MNIST\n",
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"\n",
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"import numpy as np\n",
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"from sklearn.manifold import TSNE\n",
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"from matplotlib import pyplot as plt\n",
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"\n",
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"from utils import *"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Set random seeds"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"torch.manual_seed(1)\n",
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"torch.cuda.manual_seed(1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Define data loading step"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"batch_size = 256\n",
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"\n",
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"kwargs = {'num_workers': 1, 'pin_memory': True}\n",
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"train_loader = torch.utils.data.DataLoader(\n",
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" MNIST('./data', train=True, download=True,\n",
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" transform=transforms.ToTensor()),\n",
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" batch_size=batch_size, shuffle=True, **kwargs)\n",
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"test_loader = torch.utils.data.DataLoader(\n",
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" MNIST('./data', train=False, transform=transforms.ToTensor()),\n",
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" batch_size=batch_size, shuffle=True, **kwargs)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Defining the device"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"device, list_ids_devices = prepare_device(1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Variational Autoencoder"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from models import VAE as VAEm\n",
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"from trainer import VAE as VAEt"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Defining the model\n",
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"\n",
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"d = 20\n",
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"\n",
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"model = VAEm.Model(d=d).to(device)"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Model(\n",
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" (encoder): Sequential(\n",
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" (0): Linear(in_features=784, out_features=400, bias=True)\n",
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" (1): ReLU()\n",
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" (2): Linear(in_features=400, out_features=40, bias=True)\n",
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" )\n",
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" (decoder): Sequential(\n",
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" (0): Linear(in_features=20, out_features=400, bias=True)\n",
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" (1): ReLU()\n",
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" (2): Linear(in_features=400, out_features=784, bias=True)\n",
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" (3): Sigmoid()\n",
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" )\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(model)"
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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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"# Setting the optimiser\n",
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"\n",
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"learning_rate = 1e-3\n",
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"\n",
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"optimizer = VAEm.optimizer(model, learning_rate=learning_rate)"
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Reconstruction + β * KL divergence losses summed over all elements and batch\n",
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"\n",
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"loss_function = VAEm.loss_function(f=nn.functional.binary_cross_entropy, β=1)"
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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": 12,
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"metadata": {
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"scrolled": false,
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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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"====> Test set loss: 544.4362\n",
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"---\n",
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"====> Epoch: 1 Average loss: 190.7399\n",
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"====> Test set loss: 138.4847\n",
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"---\n",
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"====> Epoch: 2 Average loss: 133.5370\n",
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"====> Test set loss: 116.0096\n",
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"---\n",
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"====> Epoch: 3 Average loss: 120.8788\n",
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"====> Test set loss: 108.4733\n",
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"---\n",
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"====> Epoch: 4 Average loss: 115.8060\n",
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"====> Test set loss: 105.1933\n",
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"---\n",
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"====> Epoch: 5 Average loss: 113.1306\n",
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"====> Test set loss: 103.5372\n",
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"---\n",
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"====> Epoch: 6 Average loss: 111.4213\n",
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"====> Test set loss: 101.1358\n",
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"---\n",
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"====> Epoch: 7 Average loss: 110.1533\n",
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"====> Test set loss: 100.2345\n",
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"---\n",
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"====> Epoch: 8 Average loss: 109.2291\n",
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"====> Test set loss: 99.6173\n",
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"---\n",
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"====> Epoch: 9 Average loss: 108.4496\n",
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"====> Test set loss: 98.4703\n",
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"---\n",
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"====> Epoch: 10 Average loss: 107.8486\n",
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"====> Test set loss: 99.1964\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"c:\\Users\\Main-user\\Desktop\\Code\\utils\\plot_lib.py:18: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
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" plt.figure(figsize=(18, 4))\n"
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]
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},
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{
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"data": {
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"text/plain": [
|
||
|
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"<Figure size 1296x288 with 4 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
|
]
|
||
|
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},
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"metadata": {
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||
|
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"needs_background": "light"
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},
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||
|
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"output_type": "display_data"
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},
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{
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|
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"data": {
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"text/plain": [
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"<Figure size 1296x288 with 4 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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"output_type": "display_data"
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{
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"data": {
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||
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"text/plain": [
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||
|
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"<Figure size 1296x432 with 4 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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||
|
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"text/plain": [
|
||
|
|
"<Figure size 1296x288 with 4 Axes>"
|
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|
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]
|
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},
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|
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"metadata": {
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||
|
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"needs_background": "light"
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},
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|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
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"metadata": {
|
||
|
|
"needs_background": "light"
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||
|
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},
|
||
|
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"output_type": "display_data"
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||
|
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},
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||
|
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{
|
||
|
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"data": {
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"text/plain": [
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||
|
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"<Figure size 1296x288 with 4 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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||
|
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"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
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||
|
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]
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||
|
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},
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||
|
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"metadata": {
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||
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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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 1296x288 with 4 Axes>"
|
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]
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},
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"metadata": {
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||
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
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]
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||
|
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},
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|
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"metadata": {
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||
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"needs_background": "light"
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},
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||
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"output_type": "display_data"
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},
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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 1296x288 with 4 Axes>"
|
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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{
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"data": {
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||
|
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"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
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]
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||
|
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},
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||
|
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"metadata": {
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||
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"needs_background": "light"
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},
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||
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"output_type": "display_data"
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},
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{
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"data": {
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|
||
|
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"text/plain": [
|
||
|
|
"<Figure size 1296x288 with 4 Axes>"
|
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|
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]
|
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},
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|
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"metadata": {
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||
|
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
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||
|
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},
|
||
|
|
"output_type": "display_data"
|
||
|
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},
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||
|
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{
|
||
|
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"data": {
|
||
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"text/plain": [
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||
|
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"<Figure size 1296x288 with 4 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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{
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"data": {
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||
|
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"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
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]
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||
|
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},
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"metadata": {
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"needs_background": "light"
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},
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||
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1296x288 with 4 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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{
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"data": {
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||
|
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"text/plain": [
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||
|
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"<Figure size 1296x432 with 4 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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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 1296x288 with 4 Axes>"
|
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
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"needs_background": "light"
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|
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},
|
||
|
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"output_type": "display_data"
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||
|
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},
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||
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{
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|
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"data": {
|
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|
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x288 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Training and testing the VAE\n",
|
||
|
|
"\n",
|
||
|
|
"epochs = 10\n",
|
||
|
|
"Trainer = VAEt.Trainer(model, optimizer, loss_function, device, train_loader, test_loader, epochs = epochs)\n",
|
||
|
|
"_, codes = Trainer.train()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"for x, y in test_loader:\n",
|
||
|
|
" x = x.to(device)\n",
|
||
|
|
" x_hat, mu, logvar = model(x)\n",
|
||
|
|
" pass"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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"text/plain": [
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"<Figure size 1296x432 with 4 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1296x432 with 4 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Generating a few samples\n",
|
||
|
|
"\n",
|
||
|
|
"N = 16\n",
|
||
|
|
"z = torch.randn((N, d)).to(device)\n",
|
||
|
|
"sample = model.decoder(z)\n",
|
||
|
|
"display_images(None, sample, N // 4, count=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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"text/plain": [
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"text/plain": [
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"<Figure size 1296x432 with 4 Axes>"
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{
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|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Display last test batch\n",
|
||
|
|
"\n",
|
||
|
|
"display_images(None, x, 4, count=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Choose starting and ending point for the interpolation -> shows original and reconstructed\n",
|
||
|
|
"\n",
|
||
|
|
"A, B = 1, 14\n",
|
||
|
|
"sample = model.decoder(torch.stack((mu[A].data, mu[B].data), 0))\n",
|
||
|
|
"display_images(None, torch.stack(((\n",
|
||
|
|
" x[A].data.view(-1),\n",
|
||
|
|
" x[B].data.view(-1),\n",
|
||
|
|
" sample.data[0],\n",
|
||
|
|
" sample.data[1]\n",
|
||
|
|
")), 0))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA/oAAAD4CAYAAABcxp03AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAhE0lEQVR4nO3de7SdZ10n8Gfvc83Jpbk09zRt0qRpuRVoRYFyG1phEMTbiDI6LgGdhYI4uHRcMjqMDOOMziDjjIozCogFpNQOohQckHIRBEovoTQtTdMmaZs095zk3M/Z+50/4sxyWD6/BzlJ98mTz2et/pNvn/0+Z5/9/t73d97k/FpN0yQAAACgDu1ebwAAAAA4ezT6AAAAUBGNPgAAAFREow8AAAAV0egDAABARTT6AAAAUBGNPgAAAFREo1+PlSml/5VSGk8p7Uspvbq32wEWgDeklL6aUppOKb23t1sBFoChlNIfpTP3CadTSnellP5pT3cELAQ3ppQOppROpZQeSCm9rrfb4Wzo7/UGOGt+N6U0k1Jam1J6ekrpYymlnSmle3u4J6C3DqSU/n1K6SUppUU93gvQe/0ppUdSSi9IKe1PKb0spXRTSumpKaW9vdsW0GO/kVJ6bTrzYODKlNJn0pkfBN7Rwz0xT57o12FxSukHU0q/mlIaSyn9TUrpoymlH+/lpoCeuyWl9JGU0rEe7wNYGMZTSm9NZ5r6bkrpL1NKD6eUrundloAF4N50pslPKaXm7/67vHfb4WzQ6NfhipRSJ535qzb/186U0pN7sx0A4DywNp25h/C3/4DfSylNpJTuT2f+Gv+tvd0O86XRr8OSlNLoN/3ZaEppaQ/2AgAsfAMppfenlP44nbmxBy5sP5PO9A7PS2f+RuB0/L+z0Gn06zCWUlr2TX+2LJ35RTsAAH9fO6X0J+nM7/Z5Q4/3AiwcnXTmnwBvSim9vsd7YZ40+nV4IJ35BTvb/96fXZ38VTwA4P/XSmd+8/7adOb3+8z2djvAAtSf/Bv9855Gvw7j6cxfsfn1dOYX8z03pfTKdOan9cCFqz+lNJxS6vu7/4aTaStwofv9lNJVKaVXpJQme7wXoPfWpJR+JJ35p8B96cyknh9NKX26l5ti/lpN0/R6D5wdK1NK704p3ZDO/IbtX04pfaCnOwJ67a0ppX/7TX/27/7uz4ELz6XpzG/cn04pzf29P/+X6cy/1wcuPKtTSjenM38buJ1S2pdS+p2U0v/s5aaYP40+AAAAVMRf3QcAAICKaPQBAACgIhp9AAAAqIhGHwAAACoSjlm6of3P/KY+eAJ8svvhVq/38K1SF+CJoS4A30xdAL5Zri54og8AAAAV0egDAABARTT6AAAAUBGNPgAAAFREow8AAAAV0egDAABARTT6AAAAUBGNPgAAAFREow8AAAAV0egDAABARTT6AAAAUBGNPgAAAFREow8AAAAV0egDAABARTT6AAAAUBGNPgAAAFREow8AAAAV0egDAABARTT6AAAAUBGNPgAAAFREow8AAAAV0egDAABARfp7vQEAAAAItVr5rGmeuH2cJzzRBwAAgIpo9AEAAKAiGn0AAACoiEYfAAAAKqLRBwAAgIpo9AEAAKAiGn0AAACoSH+vN8C3od2XjVp9+SyllNqLhsO8teKiMG+GBuP145Nh3jl8NP/ac7PhWvMxIRDMli3VhdbQUJi3lxfqwrLFYZ7mOvH6x49ks+74RPza3fi14YIWzZxuxc96WgPxLWJ72bL42CvivBkeiI9/6Hh+7eipcG13pnA/oW7APyyqGSmlVn983rYXL4rXF+4nuhfF9xPt0fFs1oyejl97LL82pTr7EE/0AQAAoCIafQAAAKiIRh8AAAAqotEHAACAimj0AQAAoCIafQAAAKiIRh8AAAAqEg9J5Zxo9cdve9+6tWE+tWNdNjv6tHge9tR3jYX5ymXxjMljo/F8y7nDF4f5lo+sz2aDX7ovXNudnAzz83G+Jfw/7XjWfd+Kwiz7S/J14eST4nnWj1/XDfPF6+K60OnEPzOenozn7q76TL7mrf7YnnBt91h+1nZKKTVzc2EOC1phpnV7yZJ4+dr8NXnqspXh2gPPi+8nZrfG1+SRJdNh3m7F1+ypr1+ezbbePBq/9p5Hwrw7Ft8LuZ9gQSvVhaH43G2vWJ7NOhtWhWsPPTu+Fxnd0QnzoXUTYb5yaXy/cfDx1dls40fzPUZKKS27/bEw7xw+EubNzEyYL8S64Yk+AAAAVESjDwAAABXR6AMAAEBFNPoAAABQEY0+AAAAVESjDwAAABXR6AMAAEBF4oHu/INa/fHb1rd2TZgfe9HmMF/+mnj+65s3vz+bbR84Fq4dLsytfbwTz968fzqeUfnVsS1h/onVT8pmlx+N35fWrgfD3LxseqrdF8Z9K+LZs9NPj8+dh14bz6b9uatvy2bPGdkdrl3ang3zI51FYb5nJq55909uCPPPrdmWzcYPXxauHbktnrnbdOL3bSHOvaUipXnXIyPx+m3xdfHBVy8P85f8kzuz2feu+Mtw7cb+U2F+rBvXhQOzK8L88bm4Jt63IX+/8cXRZ4RrNx8dDfNmcjLO3U9wLhXqQmtwMMz71sXX3AMvvyTMl37vwWz2mks/Ga69cjC/NqWUxpt470fmloX5TBPfSx2/ZEk2+6/Ni8O1VxxYGebtY8fDvGkVno83hfuNHvBEHwAAACqi0QcAAICKaPQBAACgIhp9AAAAqIhGHwAAACqi0QcAAICKaPQBAACgIvFA+JoVZlj2rczPfx198RXh2rVv2BPmv3PJfwvz1e3pMF/azu99tjATeufMqjD/0NFnhfmRqfz8ypRS2rrkaJh/97b7s9nnX3RNuHbjY/HszU5h/iUUlerC0qXZbOaa/Cz4lFI6+KZ4bvNvPuXGMN86EH++VwY/tp1vXXjfoeeE+YHxeB725qXx3q9dvT+bffy71oZrt+1aHebdvY+E+UKce8sCU6gL7UXBPPntl4Zrv/FT8XXtLS/+8zC/ZnhfmG/qz8+D74YrU7p7enmYv/fQdWH+6Fi8fvPSE2F++eIj2Wzsyplw7ezmi8O8fSS+V0mdQl0o1FRoDQ1ls7718XVt/w9vCvOX/sjfhvmvLI/rxo6B+H4ksnMmrlnvOfS8MN9/Ot9fpZTS5RfF5+bVS/PX9LVrR8O1p7bEdWHF/QNhnqbi/qx0rehF3fBEHwAAACqi0QcAAICKaPQBAACgIhp9AAAAqIhGHwAAACqi0QcAAICKaPQBAACgIv293sA5U5qHvSaevXzfWy/LZr9/w3vCtdcOzW+e+6FOX5h/fvySbPbRI08P19738SvCfPVds2E+uzTe21/cEM/+fMaOvdns1NXxXNxNNw2G+UKcX8kCU6oLy5eH+UM/f1U2+8UfviVc+7LFD4b5QGFvo9348/vFqTXZ7C+OPyNc+ze3Xh3ma78S14VUOPW+9Pz1Yb7xmgPZrLl8IlzbDMVzb1vteHNNaZg49SvVhaVLw/zgjz0lm13/unje9R9c/PkwHynsbapwXds1uySb3XLi2nDtxz/+HWG+/otzYT4wHZ9cdz0rrgt7nn8omy1bNR6ubfqGw7zVF9/LNO4XKGiPjIT52Eufms1GfvaxcO2fbfutMF9duK51Uvz5fXQu3/7dPBrXhZs+cV2Yr/9CJ8wHT8d1486r47rwtevz+fBA/NpprnBeF+pC0QKsG57oAwAAQEU0+gAAAFARjT4AAABURKMPAAAAFdHoAwAAQEU0+gAAAFCR83e8XmkczsUXh/l9/2FzmH/q+ndks039Q+HaqSb++cm7TjwtzN/74RvCfMMXprPZ0IFT4dpLH/t6mDcz8Yi7kcXxOJG5oR1hPrUtPwrr0k1Hw7XdNSvCPD2eH8XDBWKe4/N2/+srw/xPX/XObPaUwfjYs01cbm8ei2vS2z7xA2G+/vP5sS5L9o6Fa7c8dF+YN5OTYd4ajmvihiYe63nsynxdWbcyrmmza5aHed/uMOZCMM/xeY++Lj8+L6WUfuNn3p3Nrl90OlzbaeKxsbdNLQvzN37h1WG+6jP5c3PFN+LRlZfvjke
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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"text/plain": [
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"<Figure size 1296x432 with 4 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1296x432 with 4 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x432 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Perform an interpolation between input A and B, in N steps\n",
|
||
|
|
"\n",
|
||
|
|
"N = 16\n",
|
||
|
|
"code = torch.Tensor(N, 20).to(device)\n",
|
||
|
|
"sample = torch.Tensor(N, 28, 28).to(device)\n",
|
||
|
|
"for i in range(N):\n",
|
||
|
|
" code[i] = i / (N - 1) * mu[B].data + (1 - i / (N - 1) ) * mu[A].data\n",
|
||
|
|
" # sample[i] = i / (N - 1) * x[B].data + (1 - i / (N - 1) ) * x[A].data\n",
|
||
|
|
"sample = model.decoder(code)\n",
|
||
|
|
"display_images(None, sample, N // 4, count=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"set_default(figsize=(15, 4))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"X, Y, E, losses = list(), list(), list(), list() # input, classes, embeddings\n",
|
||
|
|
"N = 1000 # samples per epoch\n",
|
||
|
|
"epochs = (0, 5, 10)\n",
|
||
|
|
"for epoch in epochs:\n",
|
||
|
|
" X.append(codes['μ'][epoch][:N])\n",
|
||
|
|
" E.append(TSNE(n_components=2).fit_transform(X[-1]))\n",
|
||
|
|
" Y.append(codes['y'][epoch][:N])\n",
|
||
|
|
" losses.append(codes['loss'][epoch][:N])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 20,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1500x400 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"for i, epoch in enumerate(epochs):\n",
|
||
|
|
"\tplt.plot(losses[i], label=f\"{epoch}th epoch\")\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"plt.title(\"Loss per epoch\")\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.colorbar.Colorbar at 0x1d2b8d23808>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABC8AAAFsCAYAAADsRZNYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd5gV1fnA8e+02+v2BksHAZFml2LDllijGIxiT4wajSYWNFHyCzFqEksCMYnRqLFFY0nsnVixggpKr9vYenfv3j5zfn8sLHvZvVtgl6Wcz/PMo8ydO3Pu3d0zM++85z0KIJAkSZIkSZIkSZIkSdpNqf3dAEmSJEmSJEmSJEmSpM7I4IUkSZIkSZIkSZIkSbs1GbyQJEmSJEmSJEmSJGm3JoMXkiRJkiRJkiRJkiTt1mTwQpIkSZIkSZIkSZKk3ZoMXkiSJEmSJEmSJEmStFuTwQtJkiRJkiRJkiRJknZrMnghSZIkSZIkSZIkSdJuTQYvJEmSJEmSJEmSJEnarcnghbTPuuWWWxBCMG3atP5uiiRJ0m5F9o+SJO3rZD8oSbsfGbyQdpgQosvlgAMO6O9m7lKHHnooL774IrW1tTQ3N7NkyRKuuuoqVFX+qUnSvkT2j9uUlpZ2+j08/vjj/d1ESZL6gOwHt9F1nZ/85Cc88MADfPHFF8TjcYQQXHTRRV2+97zzzmPRokU0NTXR0NDA22+/zUknnbQLWi1Jux+9vxsg7fluvfXWjK9VVlbuuob0s5NPPpl///vfxGIxnnzySerq6vjud7/L3XffzeGHH85ZZ53V302UJGkXk/3jNosXL+a5555rt/7rr7/e9Y2RJGmXkf0guN1u7rnnHqDlM1dWVjJw4MAu33fnnXfys5/9jI0bN/K3v/0Nm83G2WefzQsvvMAVV1zB/Pnz+7rpkrTbEXKRy44sW/V3O3Z0ueWWW4QQQkybNm2n9+X1esXmzZtFLBYTkyZNal1vt9vF+++/L4QQYubMmf3+meUiF7nsmkX2j9uW0tJSIYQQDz74YL9/LrnIRS67bpH94LbFMAxx/PHHi4KCgrR9X3TRRRnfc+ihhwohhFi5cqUIBAKt60tLS0VNTY2IRqOitLS0378nuchlVy4yl13aZdqOHTzvvPP4/PPPiUQiVFVV8fe//538/PwO3zd8+HAefvhhysrKiMfjlJWV8fDDDzN8+PAOt1dVlR/+8Ie89957NDQ0EIlEWLlyJX/7298YNmxYh+8544wzWLRoEc3NzdTW1vLEE09QXFzc7c925plnkpuby+OPP85nn33Wuj4ej3PzzTcD8OMf/7jb+5Mkad+yN/ePkiRJ3bE394PJZJJXXnmlR5kml112GQC//vWvaWhoaF2/fv165s+fj8Ph4MILL+z2/iRpb9HvERS57JlLTyPqW6PMzz33nIhEIuLBBx8Uv/nNb8T//vc/IYQQq1evFjk5OWnvOeigg0QoFBKmaYpnnnlGzJs3TzzzzDPCNE0RCoXEQQcdlLa9YRjitddeE0IIsX79ejF//nzx29/+VjzxxBOipqZGzJ49u117nnzySRGNRsWTTz4p7rjjDrFw4UIhhBDffvutsNls3fpsjzzySMbsCk3TRDgcFolEotv7k4tc5LJnL7J/3LZszbx49dVXxaWXXipuvPFGcemll4r999+/339OcpGLXPpukf1g15+1s8yLjRs3CiGEyM/Pb/faIYccIoQQ4n//+1+//5zlIpddvPR7A+Syhy5b3XLLLR0u119/fdr2WzvqeDwuxo8fn/baH/7wByGEEPfff3/rOkVRxDfffCOEEOKss85K2/7ss89uPXEoitK6ft68eUIIIZ5//vl2JxSbzZZ20tvanlAoJMaOHZu27aOPPtrhcTMtH3/8sRBCiIkTJ3b4+ldffSWEEGLUqFH9/nOTi1zk0veL7B+3LVuDFx156623xIABA/r95yUXucil9xfZD2ZeugpeuFwuIYQQjY2NHb6enZ0thBCisrKy33/OcpHLLl76vQFy2UOXrtTX16dtv7Wjbnvi2br4fD5RX18vIpFI68nk8MMPF0II8e6773Z4/Pfee08IIcSUKVMEIFRVFfX19aK5uVkUFhZ22f6t7fnVr37V7rXp06cLIYS48847u/VdLF++XAghxNChQztt6yGHHNLvPze5yEUufb/I/nHbkpubK+bOnSsmTJgg/H6/8Pv9YsqUKeLNN98UQgixYsUK4XK5+v1nJhe5yKV3F9kPdr3vTMGLwsJCIYQQGzdu7PB1XdeFEELEYrF+/znLRS67cpE1L6SdpihKh0swGOxw+4ULF7Zb19jYyOLFi3E6ney3334ATJgwAYC33367w/288cYbAEycOBGAUaNGEQgE+PLLL6moqOh2+z/99NN26zZu3AiQ8TP0lKIoAAghemV/kiTtGWT/CNXV1dxyyy188cUXhEIhQqEQ7777LjNmzOCjjz5i+PDhXHzxxd1ukyRJexbZD/YdeV0p7Wtk8ELa5aqqqjpcv7WIkd/vT/tvpuJGW088W7cLBAIAlJWV9ag9oVCo3bpUKgWApmk92sfWtmzP5/NlPJYkSdJWe2P/mIlpmtx///0ATJ06daf2JUnS3mNf6ge7Omam68qt6+V1pbSvkcELaZfLVC26oKAA2NYRb/3v1vXbKywsTNtuayXm/qiCv3z5cgBGjBjR7jVN0xg8eDDJZJI1a9bs6qZJkrQH2Rv7x85s3rwZALfb3c8tkSRpd7Gv9YMdiUQibNq0Ca/X2+Hn2zqTyooVK3Z10ySpX8nghbTLTZs2rd06n8/H+PHjiUajfPPNNwB88cUXAEyfPr3D/Wxd//nnnwPw7bffUl9fz7hx4zKeyPrKW2+9BcDxxx/f7rWpU6fidrv54IMPSCQSu7RdkiTtWfbG/rEzBx98MIAM7EqS1Gpf6wcz6eza8oQTTkjbRpL2FTJ4Ie1y5557LuPHj09bd+uttxIIBHj88cdbb/Dff/99vv32W6ZMmcIZZ5yRtv0ZZ5zBtGnTWL58Oe+99x4AlmWxYMECXC4XCxYswDCMtPcYhkFOTk6ffKann36a6upqzj77bCZNmtS63m638+tf/xqAP//5z31ybEmS9h57Y/940EEHtTsetAR2r7nmGgD++c9/9smxJUna8+yN/eCOuO+++wC46aabWoe8AJSWlnL55ZcTi8V48MEH+6l1ktQ/9P5ugLTnu+WWWzK+9txzz7FkyZK0dS+99BLvv/8+//rXv6ioqOCII45gypQprF27lhtuuCFt29mzZ/P666/z5JNP8vzzz/Ptt98ycuRITj31VBobGznvvPPSihXNnTuXgw8+mNNOO40VK1bw4osv0tTUxIABA5gxYwY///nPeeihh3r3CwCampq45JJLePrpp3nnnXd44oknqKur4+STT2bUqFE89dRTPPnkk71+XEmSdm+yf4Tbb7+dMWPG8M4777Bp0yYA9t9/f4455hgAbr75Zj788MNeP64kSbsH2Q+2uP766xk1ahRAa3Dmggsu4IgjjgDgvffe4+9//3vr9h9++CG///3vufbaa/nyyy95+umnsdlszJw5k+zsbK644grWr1/fJ22VpN1Zv095Ipc9c+mO2bNnt26/dVqoadOmifPOO0988cUXIhKJiM2bN4sHHnhAFBQUdHicESNGiIcffliUl5eLRCIhysvLxSOPPCJGjBjR4faaponLL79cLFq0SDQ1NYlwOCxWrFgh/vKXv6RNZdq2Pdvvo7S0VAghxIMPPtij7+Swww4TL774oqirqxORSER8+eWX4uqrrxaqqvb7z0sucpHLrltk/7htufDCC8V///tfsXbtWtHU1CRisZhYv369eOKJJ8QRRxzR7z8ruchFLn2zyH4wfXn77bc7/S4y7eu8884TH3/8sQiHw6KxsVG888474qSTTur3n69c5NJPS783QC77yNLZSUAucpGLXPblRfaPcpGLXPb1RfaDcpGLXLpaZM0LSZIkSZIkSZIkSZJ2azJ4IUmSJEmSJEmSJEnSbk0GLyRJkiRJkiRJkiRJ2q0ptIwfkSRJkiRJkiRJkiRJ2i3
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1500x400 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"f, a = plt.subplots(ncols=3)\n",
|
||
|
|
"for i, e in enumerate(epochs):\n",
|
||
|
|
" s = a[i].scatter(E[i][:,0], E[i][:,1], c=Y[i], cmap='tab10')\n",
|
||
|
|
" a[i].grid(False)\n",
|
||
|
|
" a[i].set_title(f'Epoch {e}')\n",
|
||
|
|
" a[i].axis('equal')\n",
|
||
|
|
"f.colorbar(s, ax=a[:], ticks=np.arange(10), boundaries=np.arange(11) - .5)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Variational Autoencoder with dense decoder"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from models import VAE_with_dense_decoder as VAE_ddm\n",
|
||
|
|
"from trainer import VAE_with_dense_decoder as VAE_ddt"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"d = 20\n",
|
||
|
|
"model_with_dense_decoder = VAE_ddm.Model(d=d, model=model).to(device)\n",
|
||
|
|
"\n",
|
||
|
|
"loss_function = VAE_ddm.loss_function(f=nn.functional.binary_cross_entropy, β=0)\n",
|
||
|
|
"\n",
|
||
|
|
"learning_rate = 1e-3\n",
|
||
|
|
"optimizer = VAE_ddm.optimizer(model_with_dense_decoder, learning_rate=learning_rate)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"====> Test set loss: 3.2647\t Accuracy rate: 2%\n",
|
||
|
|
"---\n",
|
||
|
|
"====> Epoch: 1 Average loss: 0.6177\t Accuracy rate: 89%\n",
|
||
|
|
"====> Test set loss: 0.2420\t Accuracy rate: 16%\n",
|
||
|
|
"---\n",
|
||
|
|
"====> Epoch: 2 Average loss: 0.2880\t Accuracy rate: 95%\n",
|
||
|
|
"====> Test set loss: 0.1803\t Accuracy rate: 16%\n",
|
||
|
|
"---\n",
|
||
|
|
"====> Epoch: 3 Average loss: 0.2422\t Accuracy rate: 96%\n",
|
||
|
|
"====> Test set loss: 0.1497\t Accuracy rate: 16%\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"epochs = 3\n",
|
||
|
|
"Trainer_with_dense_decoder = VAE_ddt.Trainer(model_with_dense_decoder, optimizer, loss_function, device, train_loader, test_loader, epochs = epochs)\n",
|
||
|
|
"codes_with_dense_decoder = Trainer_with_dense_decoder.train()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"tensor([5, 2, 0, 0, 9, 3, 8, 0, 6, 5, 5, 6, 5, 0, 5, 7])\n",
|
||
|
|
"tensor([5, 2, 0, 0, 9, 3, 8, 0, 6, 5, 5, 6, 5, 0, 5, 7], device='cuda:0')\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x600 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
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|
|
"text/plain": [
|
||
|
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"<Figure size 1800x600 with 4 Axes>"
|
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|
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]
|
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},
|
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|
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"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAABYcAAAFaCAYAAABFZ+INAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAgK0lEQVR4nO3debjVdb0v8O/eewEKMgkoOUQQIeYQiKWoORy1B4dwHm6GHserGVGiZR3NW9e0wSyHRMXSsqMGpnkdwuGGmYLzFKKIgCIEMgoCm80efuePe/TJq0c/i7PXXrC/r9fzfJ9H13rz/X0Ww2+v/d5f2DUppSIBAAAAAJCV2moPAAAAAABA21MOAwAAAABkSDkMAAAAAJAh5TAAAAAAQIaUwwAAAAAAGVIOAwAAAABkSDkMAAAAAJAh5TAAAAAAQIaUwwAAAAAAGVIOAwAAAABkSDlM1Xz5y19ODz74YHrzzTfTmjVr0qxZs9KECRPS7rvvXu3RAPhPJ598cpo6dWpauXJlWr16dXr22WfT6NGjU22ttxAAbe2oo45KV155ZXrkkUfSihUrUlEU6eabb/7IHzN8+PB07733pqVLl6bVq1enF154IY0ZM8Z9HKCCyrlfl0ql9I1vfCP95je/Sc8991xqaGhIRVGkU089tY2nJmeFZbX1+tnPflYURVEsXry4GD9+fHHppZcWEydOLBoaGorm5uZi1KhRVZ/Rsiwr9/W73/2uKIqiWLhwYTF+/Pjil7/8ZTFt2rSiKIpi4sSJVZ/Psiwrt/Xcc88VRVEUK1euLKZPn14URVHcfPPN/2V+5MiRRWNjY/HOO+8UN9xwQ/HTn/60ePnll4uiKIoJEyZU/fVYlmW111XO/bp79+7FuxYsWFC88cYbRVEUxamnnlr112Fls6o+gJXZ2nLLLYumpqZiwYIFRZ8+fd733L777lsURVHMnj276nNalmXlvA477LCiKIpi1qxZRa9evd57vFQqFXfccUdRFEVx0kknVX1Oy7KsnNa+++5bDBw4sEgpFfvss89Hlg1du3YtFi1aVKxdu7YYNmzYe4936tSpeOyxx4qiKIrjjjuu6q/JsiyrPa5y7tcdOnQoRowYUfTt27dIKRUXXXSRcthq0+XvEtHm+vXrl+rq6tITTzyRFi9e/L7nHn744bRy5crUu3fvKk0HQEopHXnkkSmllH7+85+npUuXvvd4U1NTuvDCC1NKKY0ePboqswHk6uGHH06vvfZaKHvMMcekPn36pFtvvTU988wz7z3e0NCQLrjggpRSSl/72tcqMidA7sq5Xzc2NqZJkyalhQsXVngq+HDKYdrczJkz09q1a9Nuu+2W+vTp877n9t1339StW7f04IMPVmk6AFJKqW/fvimllGbPnv2B5959bNiwYalHjx5tORYAQfvtt19KKaVJkyZ94LlHHnkkrV69Og0fPjx17NixrUcDADYgymHa3PLly9N5552X+vTpk6ZPn56uv/76dMkll6QJEyakSZMmpfvvvz+deeaZ1R4TIGtLlixJKaXUv3//Dzw3YMCA9/57u+22a7OZAIh79/48c+bMDzzX3Nyc5syZkzp06PC+ezoAkB/lMFVx9dVXpyOOOCLV1tam008/PX33u99NxxxzTJo7d2767W9/+4F/bgKAtnXPPfeklFI655xzUs+ePd97vK6uLv3gBz947///+TkANhzdu3dPKaW0YsWKD33+3cf9DRAAyJtymKo4//zz05133pluuummNGDAgNS5c+e0yy67pNmzZ6dbbrkl/eQnP6n2iABZu+2229J9992XBg4cmKZPn56uu+669Itf/CI9//zz6eCDD06vvvpqSun/nT4DYONTU1OTUkqpKIoqTwIAVJNymDa33377pUsvvTTdddddaezYsWnOnDmpvr4+Pffcc+mII45I8+bNS2PHjvVX3ACqqCiKNHLkyDR27Ni0cOHCNGrUqHTKKaekefPmpb322uu9b1K3aNGiKk8KwId592TwuyeI/3/dunV7Xw4AyJNymDZ3yCGHpJRSmjx58geeq6+vT08++WSqq6tLQ4cObevRAPgnzc3N6fLLL09Dhw5NnTt3Tt27d08HHXRQmj59ehoyZEhas2ZNeumll6o9JgAfYsaMGSmllAYNGvSB5+rq6lL//v1TY2Pjh37jUQAgH8ph2ty73xG5T58+H/r8u483NDS02UwAxI0aNSptuummacKECampqana4wDwIf7yl7+klFIaMWLEB57be++9U5cuXdKUKVPSunXr2no0AGADohymzf3tb39LKaV0xhlnpK222up9z40YMSLtueeeqb6+Pk2ZMqUa4wHwn7p27fqBx3bdddf04x//OL3zzjvphz/8YRWmAiDi9ttvT4sXL07HH398GjZs2HuPd+rUKV188cUppZTGjRtXrfEAgA1ETUrJdyCgTdXU1KT7778/HXjggWnlypXpzjvvTAsXLkzbb799OvTQQ1NtbW0aM2ZMuvLKK6s9KkDWHn/88bRmzZo0bdq0tGrVqrTDDjukgw8+ODU0NKQjjzwyPfDAA9UeESArhx12WDr88MNTSin17ds3jRgxIs2aNeu9wxdLlixJ55133vvyt99+e1q7dm267bbb0rJly9LIkSPT4MGD08SJE9Oxxx5bjZcB0O6Ve7/+zne+kwYPHpxSSmnIkCFpyJAh6bHHHkszZ85MKaX06KOPpl//+tdt+yLISmFZbb1KpVIxZsyYYurUqcWKFSuKxsbG4q233iruvvvu4sADD6z6fJZlWVYqzj333OLpp58uli9fXqxdu7aYPXt2MW7cuKJfv35Vn82yLCvHddFFFxUfZc6cOR/4MXvssUdx7733FsuWLSvWrFlTvPjii8U3v/nNora2tuqvx7Isq72ucu/XkydP/sj8jTfeWPXXZLXf5eQwAAAAAECG/JvDAAAAAAAZUg4DAAAAAGRIOQwAAAAAkCHlMAAAAABAhpTDAAAAAAAZUg4DAAAAAGRIOQwAAAAAkKFSNHhAzdGVnAOgYh4qbq/2CG3K/RrYWLlfA2wc3K8BNg6R+7WTwwAAAAAAGVIOAwAAAABkSDkMAAAAAJAh5TAAAAAAQIaUwwAAAAAAGVIOAwAAAABkSDkMAAAAAJAh5TAAAAAAQIaUwwAAAAAAGSpVewAAAAAAyNH8O3YIZy/feUI4+8vDjgxnm1+aEc7S/jg5DAAAAACQIeUwAAAAAECGlMMAAAAAABlSDgMAAAAAZEg5DAAAAACQIeUwAAAAAECGlMMAAAAAABlSDgMAAAAAZEg5DAAAAACQIeUwAAAAAECGStUegMqpKcV+eVt22zG852k33hnOzlvXK5z9v4fGZ2h6fW44CwAAANCW6nbYLpw997MPhrP7b9oQzl7yqW7hbKeXwlHaISeHAQAAAAAypBwGAAAAAMiQchgAAAAAIEPKYQAAAACADCmHAQAAAAAypBwGAAAAAMiQchgAAAAAIEPKYQAAAACADCmHAQAAAAAypBwGAAAAAMhQqdoDUDktu+0Yyt09YXxlBuiyJBy96vwDwtlBZ85dn2kAAAAA1ktNp07h7LF/nBzOjuq6MJy9dkW/cLbL8/PD2aZwkvbIyWEAAAAAgAwphwEAAAAAMqQcBgAAAADIkHIYAAAAACBDymEAAAAAgAwphwEAAAAAMqQcBgAAAADIkHIYAAAAACBDymEAAAAAgAyVqj0AlbPy31ZV9fqT6zcLZwfe2ljBSQAAAAD+G3YeFI6O6jq1IiNcO+OL4exW86dXZAbaHyeHAQAAAAAypBwGAAAAAMiQchgAAAAAIEPKYQAAAACADCmHAQAAAAAypBwGAAAAAMiQchgAAAAAIEPKYQAAAACADCmHAQAAAAAypBwGAAAAAMhQqdoDUJ6F39wjnH36c1eFci1lXH9y/Wbh7GWnnxDO1v312TKmAID2p277z4SzRacO4WzL89PXZxwAAP7Ja8d3qci+s5rqw9ktr9ykIjOQNyeHAQAAAAAypBwGAAAAAMiQchgAAAAAIEPKYQAAAACADCmHAQAAAAAypBwGAAAAAMiQchgAAAAAIEP
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x600 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
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},
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{
|
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"data": {
|
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|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x600 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"display_images(None, x, 4, count=True)\n",
|
||
|
|
"print(y)\n",
|
||
|
|
"y_hat = model_with_dense_decoder(x)[0]\n",
|
||
|
|
"print(torch.argmax(y_hat, dim=1))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"codes = codes_with_dense_decoder[1]\n",
|
||
|
|
"X, Y, E, losses, AR = list(), list(), list(), list(), list() # input, classes, embeddings\n",
|
||
|
|
"N = 1000 # samples per epoch\n",
|
||
|
|
"epochs = range(epochs)\n",
|
||
|
|
"for epoch in epochs:\n",
|
||
|
|
" X.append(codes['μ'][epoch][:N])\n",
|
||
|
|
" E.append(TSNE(n_components=2).fit_transform(X[-1]))\n",
|
||
|
|
" Y.append(codes['y'][epoch][:N])\n",
|
||
|
|
" losses.append(codes['loss'][epoch][:N])\n",
|
||
|
|
" AR.append(codes['AR'][epoch][:N])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1500x400 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"for i, epoch in enumerate(epochs):\n",
|
||
|
|
"\tplt.plot(losses[i], label=f\"{epoch}th epoch\")\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"plt.title(\"Loss per epoch\")\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1500x400 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"for i, epoch in enumerate(epochs):\n",
|
||
|
|
"\tplt.plot(AR[i], label=f\"{epoch}th epoch\")\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"plt.title(\"Accuracy per epoch\")\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "905fbff07f04ba78e3b93ef9539561fd1c6ae76c32e6111cf52f7c174132ed83"
|
||
|
|
},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3.7.9 64-bit ('3ma-stage': conda)",
|
||
|
|
"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": "3.7.9"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|