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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,90 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "metadata": {}, | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "# Computing the log determinant of the Jacobian\n", | ||
| "\n", | ||
| "We show how to compute and retrieve the log determinant of the Jacobian of a bijective transformation. We use Real NVP as an example." | ||
| ], | ||
| "id": "624f99599895f0fd" | ||
| }, | ||
| { | ||
| "metadata": { | ||
| "ExecuteTime": { | ||
| "end_time": "2024-08-13T16:39:40.646799Z", | ||
| "start_time": "2024-08-13T16:39:38.868039Z" | ||
| } | ||
| }, | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "import torch\n", | ||
| "from torchflows import Flow\n", | ||
| "from torchflows.architectures import RealNVP\n", | ||
| "\n", | ||
| "torch.manual_seed(0)\n", | ||
| "\n", | ||
| "batch_shape = (5, 7)\n", | ||
| "event_shape = (2, 3)\n", | ||
| "x = torch.randn(size=(*batch_shape, *event_shape))\n", | ||
| "z = torch.randn(size=(*batch_shape, *event_shape))\n", | ||
| "\n", | ||
| "bijection = RealNVP(event_shape=event_shape)\n", | ||
| "flow = Flow(bijection)\n", | ||
| "\n", | ||
| "_, log_det_forward = flow.bijection.forward(x)\n", | ||
| "_, log_det_inverse = flow.bijection.inverse(z)" | ||
| ], | ||
| "id": "3f74b61a9929dd3b", | ||
| "outputs": [], | ||
| "execution_count": 1 | ||
| }, | ||
| { | ||
| "metadata": { | ||
| "ExecuteTime": { | ||
| "end_time": "2024-08-13T16:39:40.662420Z", | ||
| "start_time": "2024-08-13T16:39:40.653696Z" | ||
| } | ||
| }, | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "print(f'{log_det_forward.shape = }')\n", | ||
| "print(f'{log_det_inverse.shape = }')" | ||
| ], | ||
| "id": "3c49e132d9c041c2", | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "log_det_forward.shape = torch.Size([5, 7])\n", | ||
| "log_det_inverse.shape = torch.Size([5, 7])\n" | ||
| ] | ||
| } | ||
| ], | ||
| "execution_count": 2 | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 2 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython2", | ||
| "version": "2.7.6" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,133 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "metadata": {}, | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "# Image modeling with normalizing flows\n", | ||
| "\n", | ||
| "When working with images, we can use specialized multiscale flow architectures. We can also use standard normalizing flows, which internally work with a flattened image. Note that multiscale architectures expect input images with shape `(channels, height, width)`." | ||
| ], | ||
| "id": "df68afe10da259a1" | ||
| }, | ||
| { | ||
| "metadata": { | ||
| "ExecuteTime": { | ||
| "end_time": "2024-08-13T17:20:05.803231Z", | ||
| "start_time": "2024-08-13T17:20:03.001656Z" | ||
| } | ||
| }, | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "from torchvision.datasets import MNIST\n", | ||
| "import torch\n", | ||
| "\n", | ||
| "torch.manual_seed(0)\n", | ||
| "\n", | ||
| "# pip install torchvision\n", | ||
| "dataset = MNIST(root='./data', download=True, train=True)\n", | ||
| "train_data = dataset.data.float()[:, None]\n", | ||
| "train_data = train_data[torch.randperm(len(train_data))]\n", | ||
| "train_data = (train_data - torch.mean(train_data)) / torch.std(train_data)\n", | ||
| "x_train, x_val = train_data[:1000], train_data[1000:1200]\n", | ||
| "\n", | ||
| "print(f'{x_train.shape = }')\n", | ||
| "print(f'{x_val.shape = }')\n", | ||
| "\n", | ||
| "image_shape = train_data.shape[1:]\n", | ||
| "print(f'{image_shape = }')" | ||
| ], | ||
| "id": "b4d5e1888ff6a0e7", | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "x_train.shape = torch.Size([1000, 1, 28, 28])\n", | ||
| "x_val.shape = torch.Size([200, 1, 28, 28])\n", | ||
| "image_shape = torch.Size([1, 28, 28])\n" | ||
| ] | ||
| } | ||
| ], | ||
| "execution_count": 1 | ||
| }, | ||
| { | ||
| "metadata": { | ||
| "ExecuteTime": { | ||
| "end_time": "2024-08-13T17:20:06.058329Z", | ||
| "start_time": "2024-08-13T17:20:05.891695Z" | ||
| } | ||
| }, | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "from torchflows.flows import Flow\n", | ||
| "from torchflows.architectures import RealNVP, MultiscaleRealNVP\n", | ||
| "\n", | ||
| "real_nvp = Flow(RealNVP(image_shape))\n", | ||
| "multiscale_real_nvp = Flow(MultiscaleRealNVP(image_shape))" | ||
| ], | ||
| "id": "744513899ffa6a46", | ||
| "outputs": [], | ||
| "execution_count": 2 | ||
| }, | ||
| { | ||
| "metadata": { | ||
| "ExecuteTime": { | ||
| "end_time": "2024-08-13T17:26:11.651540Z", | ||
| "start_time": "2024-08-13T17:20:06.378393Z" | ||
| } | ||
| }, | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "real_nvp.fit(x_train, x_val=x_val, early_stopping=True, show_progress=True)\n", | ||
| "multiscale_real_nvp.fit(x_train, x_val=x_val, early_stopping=True, show_progress=True)" | ||
| ], | ||
| "id": "7a439e2565ce5a25", | ||
| "outputs": [ | ||
| { | ||
| "name": "stderr", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Fitting NF: 30%|███ | 151/500 [00:18<00:42, 8.30it/s, Training loss (batch): -0.2608, Validation loss: 1.3448 [best: 0.1847 @ 100]] \n", | ||
| "Fitting NF: 30%|███ | 152/500 [05:47<13:14, 2.28s/it, Training loss (batch): -0.3050, Validation loss: 0.9754 [best: 0.1744 @ 101]] \n" | ||
| ] | ||
| } | ||
| ], | ||
| "execution_count": 3 | ||
| }, | ||
| { | ||
| "metadata": { | ||
| "ExecuteTime": { | ||
| "end_time": "2024-08-13T17:26:11.699539Z", | ||
| "start_time": "2024-08-13T17:26:11.686539Z" | ||
| } | ||
| }, | ||
| "cell_type": "code", | ||
| "source": "", | ||
| "id": "c38fc6cc58bdc0b2", | ||
| "outputs": [], | ||
| "execution_count": null | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 2 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython2", | ||
| "version": "2.7.6" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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