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| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/minesh1291/6ec9220e533210875eadf4c28f886c59/vae_pytorch_lightning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ivhtHC97xXog" | |
| }, | |
| "source": [ | |
| "## \"Auto-Encoding Variational Bayes\" https://arxiv.org/pdf/1312.6114.pdf" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "1IQLPNTeQ-YD", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "892acd1a-5dfe-4961-89a0-969d8b0b5493" | |
| }, | |
| "source": [ | |
| "! pip install pytorch-lightning --quiet\n", | |
| "import pytorch_lightning as pl\n", | |
| "import torch\n", | |
| "from torch import nn\n", | |
| "from torch.nn import functional as F\n", | |
| "import torchvision.transforms as transforms\n", | |
| "from torchvision import datasets\n", | |
| "from torch.utils.data import DataLoader" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
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| "\u001b[K |████████████████████████████████| 396 kB 54.9 MB/s \n", | |
| "\u001b[K |████████████████████████████████| 596 kB 52.0 MB/s \n", | |
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| "\u001b[K |████████████████████████████████| 160 kB 52.2 MB/s \n", | |
| "\u001b[?25h Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Orjvsz6wQCo7" | |
| }, | |
| "source": [ | |
| "class VAE(pl.LightningModule):\n", | |
| " def __init__(self,enc_out_dim=256, latent_dim=16, input_height=784):\n", | |
| " super(VAE, self).__init__()\n", | |
| " self.save_hyperparameters()\n", | |
| " self.encoder = nn.Sequential(nn.Linear(input_height, enc_out_dim), nn.ReLU())\n", | |
| " self.decoder = nn.Sequential(nn.Linear(latent_dim, enc_out_dim), nn.ReLU(), nn.Linear(enc_out_dim, input_height))\n", | |
| " self.fc_mu=nn.Linear(enc_out_dim, latent_dim)\n", | |
| " self.fc_log_var=nn.Linear(enc_out_dim, latent_dim)\n", | |
| "\n", | |
| " def encode(self,x):\n", | |
| " z = self.encoder(x)\n", | |
| " mu = self.fc_mu(z)\n", | |
| " log_var = self.fc_log_var(z)\n", | |
| " return mu,log_var\n", | |
| "\n", | |
| " def sampling(self,mu, log_var):\n", | |
| " std = torch.exp(log_var / 2)\n", | |
| " q = torch.distributions.Normal(mu, std)\n", | |
| " z = q.rsample()\n", | |
| " return z\n", | |
| "\n", | |
| " def decode(self,z):\n", | |
| " reconst=self.decoder(z)\n", | |
| " return torch.sigmoid(reconst)\n", | |
| "\n", | |
| " def loss(self,recons,x, mu, logvar):\n", | |
| " bce = F.binary_cross_entropy(recons, x)\n", | |
| " kld = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n", | |
| " return bce + kld\n", | |
| "\n", | |
| " def training_step(self, batch, batch_idx):\n", | |
| " x, _ = batch\n", | |
| " x= x.view(-1,self.hparams.input_height)\n", | |
| " mu,log_var = self.encode(x)\n", | |
| " z = self.sampling(mu, log_var)\n", | |
| "\n", | |
| " recons = self.decode(z)\n", | |
| "\n", | |
| " vae_loss=self.loss(recons,x,mu,log_var)\n", | |
| " self.log_dict({'vae_loss': vae_loss.mean()})\n", | |
| " return vae_loss\n", | |
| "\n", | |
| " def configure_optimizers(self):\n", | |
| " return torch.optim.Adam(self.parameters(), lr=1e-4)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "jdgNBDBmVD4H", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 439, | |
| "referenced_widgets": [ | |
| "3a869e786a044d87b3b36dbb6de6fa3a", | |
| "07be3131e53d4d1bbdd0979b4aed0668", | |
| "95ea9a25490a441baf656ed93c3e487f", | |
| "967093369a8a4437bc886d63c00a3629", | |
| "3645a6e6b9df4a34827174c923f8ef25", | |
| "be294bf266e04ad0b30a4ec187685b4f", | |
| "0e706c9f844147518da2708a0477fbfe", | |
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| "878937450c3641d888dcd832592fe81b", | |
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| "2e33e9e3298d4e2485027a47c0abf878", | |
| "76057466dadc421e86cfd5b515b06683", | |
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| "d895f6e440024420aea1ee3f82ba2d04", | |
| "766e87e35d7c41b9aea8bc07990e5efa", | |
| "134afa401a8340649a5832b99a6ff660", | |
| "4b70cb0caf7b418586e644fef192c3cd", | |
| "9796f7de3c1449dc9c872b99cf1627d8", | |
| "75657d518d7d4cadaf1d31720341d03f", | |
| "357bc89aa9924d5f841c9694ea110818", | |
| "f922a08e96944525b5c82196a4827bfc", | |
| "63cb9bfc03ae4d09aeae6d88efb0892b", | |
| "b6793d4c1d8f4461bcafb632106ae7d4", | |
| "7b7753a780c6495da8bcdffe5853998b", | |
| "e437fad5e40940c9928c52683786297d", | |
| "8d1070ad01ec440fbb8f7fb51920785d" | |
| ] | |
| }, | |
| "outputId": "f6b27501-3430-40fe-f7a5-c3a0a67a109b" | |
| }, | |
| "source": [ | |
| "# train data\n", | |
| "train_data = datasets.FashionMNIST(root='../input/data',train=True,download=True,transform=transforms.ToTensor())\n", | |
| "\n", | |
| "# training and validation data loaders\n", | |
| "train_loader = DataLoader(train_data,batch_size=64,shuffle=True)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n", | |
| "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ../input/data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "3a869e786a044d87b3b36dbb6de6fa3a", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| " 0%| | 0/26421880 [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Extracting ../input/data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ../input/data/FashionMNIST/raw\n", | |
| "\n", | |
| "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n", | |
| "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ../input/data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "0be97dc9dca646c08f3fddf4edf34088", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
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| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Extracting ../input/data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ../input/data/FashionMNIST/raw\n", | |
| "\n", | |
| "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n", | |
| "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ../input/data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "320fe6172e09496e9999d57825720d73", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
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| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Extracting ../input/data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ../input/data/FashionMNIST/raw\n", | |
| "\n", | |
| "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n", | |
| "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ../input/data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "134afa401a8340649a5832b99a6ff660", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
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| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Extracting ../input/data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ../input/data/FashionMNIST/raw\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "O6ZKHm0vUvYM", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 399, | |
| "referenced_widgets": [ | |
| "95477025b64742b88e58a718ca82dfe4", | |
| "2785a8dc03064947be961552f474adb9", | |
| "c849673b610142e8a59d72e4d6d6d7ae", | |
| "e1472fff59a04d20b2618247014c6f2f", | |
| "03ee862493e0461bb410001cfca620bd", | |
| "12d857003bd044eaa1cfec6778e5bc40", | |
| "6b6229713e114f6895792b2e28d1a4de", | |
| "6a30744037784040b892d14e7ebac107", | |
| "148d934d8a1d4aca9fb41f902dc8e95c", | |
| "ea81698552844ca7a579d290ac8bb47b", | |
| "9362eff94d93488cad70eab9006866f7" | |
| ] | |
| }, | |
| "outputId": "e8c8197f-576f-43eb-89bc-c79544fd0f3b" | |
| }, | |
| "source": [ | |
| "vae = VAE()\n", | |
| "trainer = pl.Trainer(gpus=1, max_epochs=30, progress_bar_refresh_rate=25)\n", | |
| "trainer.fit(vae, train_loader)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/connectors/callback_connector.py:91: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=25)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\n", | |
| " f\"Setting `Trainer(progress_bar_refresh_rate={progress_bar_refresh_rate})` is deprecated in v1.5 and\"\n", | |
| "GPU available: True, used: True\n", | |
| "TPU available: False, using: 0 TPU cores\n", | |
| "IPU available: False, using: 0 IPUs\n", | |
| "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", | |
| "\n", | |
| " | Name | Type | Params\n", | |
| "------------------------------------------\n", | |
| "0 | encoder | Sequential | 200 K \n", | |
| "1 | decoder | Sequential | 205 K \n", | |
| "2 | fc_mu | Linear | 4.1 K \n", | |
| "3 | fc_log_var | Linear | 4.1 K \n", | |
| "------------------------------------------\n", | |
| "415 K Trainable params\n", | |
| "0 Non-trainable params\n", | |
| "415 K Total params\n", | |
| "1.660 Total estimated model params size (MB)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "95477025b64742b88e58a718ca82dfe4", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Training: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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