From 698862e144d2d9598329c0477765de83fcead7f1 Mon Sep 17 00:00:00 2001 From: Tim Sainburg Date: Tue, 14 May 2019 00:53:00 -0700 Subject: [PATCH] updated several notebooks for json errors (#5) --- 3.0-WGAN-GP-fashion-mnist.ipynb | 341 ++++------ 5.0-GAIA-fashion-mnist.ipynb | 250 +++---- 6.0-VAE-GAN-fashion-mnist.ipynb | 177 +++-- ...ensorflow-spectrograms-and-inversion.ipynb | 636 ++++++++++++------ 9.0-seq2seq-NSYNTH.ipynb | 421 ++++++------ readme.md | 5 +- 6 files changed, 1064 insertions(+), 766 deletions(-) diff --git a/3.0-WGAN-GP-fashion-mnist.ipynb b/3.0-WGAN-GP-fashion-mnist.ipynb index 193c149..77f2711 100644 --- a/3.0-WGAN-GP-fashion-mnist.ipynb +++ b/3.0-WGAN-GP-fashion-mnist.ipynb @@ -2,7 +2,10 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "25P38JgWSYbZ" + }, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timsainb/tensorflow2-generative-models/blob/master/3.0-WGAN-GP-fashion-mnist.ipynb)\n", "\n", @@ -10,62 +13,30 @@ "\n", "WGAN-GP is a GAN that improves over the original loss function to improve training stability. \n", "\n", - "![wgan gp](imgs/gan.png)" + "![wgan gp](https://github.com/timsainb/tensorflow2-generative-models/blob/f3360a819b5773692e943dfe181972a76b9d91bb/imgs/gan.png?raw=1)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "DoEPSlfmSYbc" + }, "source": [ "### Install packages if in colab" ] }, { -<<<<<<< HEAD -======= "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "### install necessary packages if in colab\n", - "def run_subprocess_command(cmd):\n", - " process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)\n", - " for line in process.stdout:\n", - " print(line.decode().strip())\n", - " \n", - "import sys, subprocess\n", - "IN_COLAB = 'google.colab' in sys.modules\n", - "colab_requirements = ['pip install tf-nightly-gpu-2.0-preview==2.0.0.dev20190513']\n", - "if IN_COLAB:\n", - " for i in colab_requirements:\n", - " run_subprocess_command(i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### load packages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { ->>>>>>> master - "cell_type": "code", - "execution_count": 1, + "execution_count": 0, "metadata": { "ExecuteTime": { -<<<<<<< HEAD "end_time": "2019-05-14T06:31:29.973887Z", "start_time": "2019-05-14T06:31:29.969185Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "WbqrTgB_SYbf" }, "outputs": [], "source": [ @@ -85,30 +56,39 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "3eKFKF5HSYbi" + }, "source": [ "### load packages" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "UjL-sOZzSYbj" + }, "outputs": [], - "source": [] + "source": [ + "" + ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T06:31:30.061880Z", "start_time": "2019-05-14T06:31:29.975587Z" -======= - "end_time": "2019-05-14T05:33:58.021847Z", - "start_time": "2019-05-14T05:33:58.017694Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "at1xYevFSYbl", + "outputId": "d70e29a0-b0d0-416b-b163-28974fab61fa" }, "outputs": [ { @@ -121,26 +101,21 @@ ], "source": [ "# make visible the only one GPU\n", -<<<<<<< HEAD "%env CUDA_VISIBLE_DEVICES=3" -======= - "#%env CUDA_VISIBLE_DEVICES=3" ->>>>>>> master ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 0, "metadata": { "ExecuteTime": { -<<<<<<< HEAD "end_time": "2019-05-14T06:31:33.702580Z", "start_time": "2019-05-14T06:31:30.063437Z" -======= - "end_time": "2019-05-14T05:34:01.656804Z", - "start_time": "2019-05-14T05:33:58.023739Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "759gzUFlSYbq", + "outputId": "d2ec559a-bbb8-4785-8e42-f355c270fbce" }, "outputs": [ { @@ -164,17 +139,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 0, "metadata": { "ExecuteTime": { -<<<<<<< HEAD "end_time": "2019-05-14T06:31:33.711214Z", "start_time": "2019-05-14T06:31:33.706313Z" -======= - "end_time": "2019-05-14T05:34:01.662618Z", - "start_time": "2019-05-14T05:34:01.659017Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "AxY3I4SfSYbt", + "outputId": "64769acf-bcf3-4ab4-d753-a69ec24b2ee5" }, "outputs": [ { @@ -191,24 +165,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "LdCkp6ybSYbw" + }, "source": [ "### Create a fashion-MNIST dataset" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 0, "metadata": { "ExecuteTime": { -<<<<<<< HEAD "end_time": "2019-05-14T06:31:33.803523Z", "start_time": "2019-05-14T06:31:33.714599Z" -======= - "end_time": "2019-05-14T05:34:01.752889Z", - "start_time": "2019-05-14T05:34:01.665368Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "Ypym6ZAESYbx" }, "outputs": [], "source": [ @@ -222,17 +197,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 0, "metadata": { "ExecuteTime": { -<<<<<<< HEAD "end_time": "2019-05-14T06:31:38.044471Z", "start_time": "2019-05-14T06:31:33.805821Z" -======= - "end_time": "2019-05-14T05:34:06.207124Z", - "start_time": "2019-05-14T05:34:01.754973Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "xhqU6sqiSYbz" }, "outputs": [], "source": [ @@ -260,24 +233,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "HLxPlL7QSYb1" + }, "source": [ "### Define the network as tf.keras.model object" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 0, "metadata": { "ExecuteTime": { -<<<<<<< HEAD "end_time": "2019-05-14T06:31:38.068468Z", "start_time": "2019-05-14T06:31:38.046751Z" -======= - "end_time": "2019-05-14T05:34:06.238083Z", - "start_time": "2019-05-14T05:34:06.209524Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "Wyipg-4oSYb1" }, "outputs": [], "source": [ @@ -372,24 +346,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "qEVl58nDSYb4" + }, "source": [ "### Define the network architecture" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 0, "metadata": { "ExecuteTime": { -<<<<<<< HEAD "end_time": "2019-05-14T06:31:38.219862Z", "start_time": "2019-05-14T06:31:38.070570Z" -======= - "end_time": "2019-05-14T05:34:06.383083Z", - "start_time": "2019-05-14T05:34:06.240901Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "dyU21SGbSYb4" }, "outputs": [], "source": [ @@ -428,7 +403,9 @@ "ExecuteTime": { "end_time": "2019-05-10T18:40:40.306731Z", "start_time": "2019-05-10T18:40:40.292930Z" - } + }, + "colab_type": "text", + "id": "wi_ZuWBdSYb6" }, "source": [ "### Create Model" @@ -436,20 +413,15 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 9, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T06:31:39.047233Z", "start_time": "2019-05-14T06:31:38.222179Z" -======= - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2019-05-14T05:34:07.369682Z", - "start_time": "2019-05-14T05:34:06.385531Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "dSYjNRAwSYb7" }, "outputs": [], "source": [ @@ -469,27 +441,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "qwBg8NwrSYb9" + }, "source": [ "### Train the model" ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 10, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T06:31:39.056490Z", "start_time": "2019-05-14T06:31:39.049635Z" -======= - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2019-05-14T05:34:07.380217Z", - "start_time": "2019-05-14T05:34:07.372927Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "47sz8RMeSYb-" }, "outputs": [], "source": [ @@ -507,20 +477,15 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 11, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T06:31:39.152670Z", "start_time": "2019-05-14T06:31:39.058505Z" -======= - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2019-05-14T05:34:07.497997Z", - "start_time": "2019-05-14T05:34:07.382473Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "pKkEX9yBSYcB" }, "outputs": [], "source": [ @@ -530,61 +495,37 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 19, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T07:04:26.791634Z", "start_time": "2019-05-14T07:04:17.126436Z" -======= - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2019-05-14T05:46:58.409Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "00dI2M4iSYcE", + "outputId": "8312d004-9e5d-43a1-f28f-6d182bd9add2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ -<<<<<<< HEAD "Epoch: 0 | disc_loss: -0.050283897668123245 | gen_loss: 0.5204998254776001\n" -======= - "Epoch: 49 | disc_loss: 0.10031372308731079 | gen_loss: -1.267067790031433\n" ->>>>>>> master ] }, { "data": { -<<<<<<< HEAD "image/png": "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\n", -======= - "image/png": "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\n", ->>>>>>> master "text/plain": [ "
" ] }, "metadata": { - "needs_background": "light" + "needs_background": "light", + "tags": [] }, "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2d1cbf55b81a496393677ebf33e8ee89", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(IntProgress(value=0, max=117), HTML(value='')))" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -614,52 +555,40 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 15, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T06:46:24.425722Z", "start_time": "2019-05-14T06:46:24.188266Z" -======= - "execution_count": 14, - "metadata": { - "ExecuteTime": { - "end_time": "2019-05-14T05:46:51.961766Z", - "start_time": "2019-05-14T05:46:51.760166Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "XZbwB70ESYcH", + "outputId": "40913f5e-a991-4d25-c56a-086acb4c0cd6" }, "outputs": [ { "data": { "text/plain": [ -<<<<<<< HEAD "[]" ] }, "execution_count": 15, -======= - "[]" - ] + "metadata": { + "tags": [] }, - "execution_count": 14, ->>>>>>> master - "metadata": {}, "output_type": "execute_result" }, { "data": { -<<<<<<< HEAD "image/png": "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\n", -======= - "image/png": "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\n", ->>>>>>> master "text/plain": [ "
" ] }, "metadata": { - "needs_background": "light" + "needs_background": "light", + "tags": [] }, "output_type": "display_data" } @@ -670,13 +599,16 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 17, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T06:46:38.649136Z", "start_time": "2019-05-14T06:46:38.440378Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "FydTnKDxSYcL", + "outputId": "503e0a49-2ed1-4c2d-9072-adc57dae5ae8" }, "outputs": [ { @@ -686,7 +618,9 @@ ] }, "execution_count": 17, - "metadata": {}, + "metadata": { + "tags": [] + }, "output_type": "execute_result" }, { @@ -697,7 +631,8 @@ ] }, "metadata": { - "needs_background": "light" + "needs_background": "light", + "tags": [] }, "output_type": "display_data" } @@ -708,20 +643,26 @@ }, { "cell_type": "code", -======= ->>>>>>> master - "execution_count": null, - "metadata": {}, + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "R4EjPZD1SYcO" + }, "outputs": [], - "source": [] + "source": [ + "" + ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], + "name": "3.0-WGAN-GP-fashion-mnist.ipynb", "provenance": [], - "toc_visible": true + "toc_visible": true, + "version": "0.3.2" }, "kernelspec": { "display_name": "Python 3", @@ -742,5 +683,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 0 } diff --git a/5.0-GAIA-fashion-mnist.ipynb b/5.0-GAIA-fashion-mnist.ipynb index c507676..867c1b5 100644 --- a/5.0-GAIA-fashion-mnist.ipynb +++ b/5.0-GAIA-fashion-mnist.ipynb @@ -2,48 +2,37 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "xRTcUSEcTw_2" + }, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timsainb/tensorflow2-generative-models/blob/master/5.0-GAIA-fashion-mnist.ipynb)\n", "\n", "## Generative adversarial interpolative autoencoder (GAIA) ([article](https://arxiv.org/abs/1807.06650)) \n", "GAIA is an autoencoder trained to learn convex latent representations by adversarially training on interpolations in latent space projections of real data. \n", "\n", - "![generative adversarial interpolative autoencoding network](imgs/gaia.png)" + "![generative adversarial interpolative autoencoding network](https://github.com/timsainb/tensorflow2-generative-models/blob/64fc8d699fc093cd6e8e2da689bd77db0c52f577/imgs/gaia.png?raw=1)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "EqJby4CNTw_-" + }, "source": [ "### Install packages if in colab" ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 1, + "execution_count": 0, "metadata": { - "ExecuteTime": { - "end_time": "2019-05-14T06:22:25.372435Z", - "start_time": "2019-05-14T06:22:25.367698Z" - } + "colab": {}, + "colab_type": "code", + "id": "5OS1jEoNTxAA" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "env: CUDA_VISIBLE_DEVICES=0\n" - ] - } - ], - "source": [ - "# make visible the only one GPU\n", - "%env CUDA_VISIBLE_DEVICES=0" -======= - "execution_count": null, - "metadata": {}, "outputs": [], "source": [ "### install necessary packages if in colab\n", @@ -64,70 +53,36 @@ }, { "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Note: This is a modified version of GAIA from the [original manuscript](https://arxiv.org/abs/1807.06650). It's been simplified significantly and no longer requires any hyperparameters (other than learning rate)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### load packages" ->>>>>>> master - ] - }, - { - "cell_type": "code", - "execution_count": 2, "metadata": { - "ExecuteTime": { - "end_time": "2019-05-14T06:22:26.015155Z", - "start_time": "2019-05-14T06:22:26.009098Z" - } + "colab_type": "text", + "id": "8vi5CAnfTxAx" }, - "outputs": [], - "source": [ - "### install necessary packages if in colab\n", - "def run_subprocess_command(cmd):\n", - " process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)\n", - " for line in process.stdout:\n", - " print(line.decode().strip())\n", - "\n", - "\n", - "import sys, subprocess\n", - "\n", - "IN_COLAB = \"google.colab\" in sys.modules\n", - "colab_requirements = [\"pip install tf-nightly-gpu-2.0-preview==2.0.0.dev20190513\",\n", - " \"pip install tfp-nightly==0.7.0.dev20190508\",\n", - "]\n", - "if IN_COLAB:\n", - " for i in colab_requirements:\n", - " run_subprocess_command(i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, "source": [ "#### Note: This is a modified version of GAIA from the [original manuscript](https://arxiv.org/abs/1807.06650). It's been simplified significantly and no longer requires any hyperparameters (other than learning rate)." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "xBfoZFlGTxA0" + }, "source": [ "### load packages" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 0, "metadata": { "ExecuteTime": { - "end_time": "2019-05-14T06:22:32.004226Z", - "start_time": "2019-05-14T06:22:27.061218Z" - } + "end_time": "2019-05-10T23:02:53.080422Z", + "start_time": "2019-05-10T23:02:48.695964Z" + }, + "colab": {}, + "colab_type": "code", + "id": "9YdL0K5xTxA2", + "outputId": "c869609a-7d77-41c9-b4c3-0880ddf23bb3" }, "outputs": [ { @@ -152,55 +107,51 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2019-05-14T06:22:48.388355Z", - "start_time": "2019-05-14T06:22:48.383957Z" - } - }, - "outputs": [], - "source": [ - "import tensorflow_probability as tfp" - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "execution_count": 0, "metadata": { "ExecuteTime": { - "end_time": "2019-05-14T06:23:21.132453Z", - "start_time": "2019-05-14T06:23:21.125584Z" - } + "end_time": "2019-05-10T23:02:53.085836Z", + "start_time": "2019-05-10T23:02:53.082662Z" + }, + "colab": {}, + "colab_type": "code", + "id": "Vo19TAeVTxBI", + "outputId": "57385c69-319e-4c14-8baf-b8fcb1d00603" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2.0.0-dev20190513 0.7.0-dev20190510\n" + "2.0.0-dev20190510\n" ] } ], "source": [ - "print(tf.__version__, tfp.__version__)" + "print(tf.__version__)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "LM1xalhkTxBR" + }, "source": [ "### Create a fashion-MNIST dataset" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-10T23:02:53.176505Z", "start_time": "2019-05-10T23:02:53.087656Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "GC0gNAvHTxBU" }, "outputs": [], "source": [ @@ -214,12 +165,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-10T23:02:58.612141Z", "start_time": "2019-05-10T23:02:53.179752Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "aOfDZYTxTxBb" }, "outputs": [], "source": [ @@ -247,19 +201,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "KimCFUI_TxBh" + }, "source": [ "### Define the network as tf.keras.model object" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-10T23:02:58.638319Z", "start_time": "2019-05-10T23:02:58.614106Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "nSqSteVYTxBj" }, "outputs": [], "source": [ @@ -368,7 +328,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "NfpitTlFTxBv" + }, "source": [ "### Define the network architecture\n", "- GAIA has an autoencoder as its generator, and a UNET as its descriminator" @@ -376,12 +339,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-10T23:02:58.746308Z", "start_time": "2019-05-10T23:02:58.639967Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "S_4k4XoCTxBw" }, "outputs": [], "source": [ @@ -390,12 +356,15 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-10T23:02:58.833907Z", "start_time": "2019-05-10T23:02:58.747971Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "rBpSToGHTxCC" }, "outputs": [], "source": [ @@ -509,7 +478,9 @@ "ExecuteTime": { "end_time": "2019-05-10T18:40:40.306731Z", "start_time": "2019-05-10T18:40:40.292930Z" - } + }, + "colab_type": "text", + "id": "NPYtYgxSTxCH" }, "source": [ "### Create Model" @@ -517,12 +488,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-10T23:02:59.710355Z", "start_time": "2019-05-10T23:02:58.835576Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "IDg5by1OTxCK" }, "outputs": [], "source": [ @@ -543,19 +517,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "-HJ6qrw5TxCS" + }, "source": [ "### Train the model" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-10T23:03:00.338845Z", "start_time": "2019-05-10T23:02:59.712093Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "42tCXUHMTxCU" }, "outputs": [], "source": [ @@ -565,12 +545,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-10T23:03:07.331536Z", "start_time": "2019-05-10T23:03:00.340829Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "MCklCQJzTxCb" }, "outputs": [], "source": [ @@ -579,12 +562,15 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-10T23:03:07.346895Z", "start_time": "2019-05-10T23:03:07.333797Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "4xwG1HCqTxCk" }, "outputs": [], "source": [ @@ -607,12 +593,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-10T23:03:07.465158Z", "start_time": "2019-05-10T23:03:07.350193Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "7xco2ckaTxCn" }, "outputs": [], "source": [ @@ -622,12 +611,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-11T00:56:46.240031Z", "start_time": "2019-05-10T23:07:32.155916Z" }, + "colab": {}, + "colab_type": "code", + "id": "0Trrpp4kTxCu", + "outputId": "7cbd8af2-3559-48a8-b1bf-1c953a4256c8", "scrolled": false }, "outputs": [ @@ -646,7 +639,8 @@ ] }, "metadata": { - "needs_background": "light" + "needs_background": "light", + "tags": [] }, "output_type": "display_data" } @@ -676,18 +670,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "vyFWbSlgTxCy" + }, "outputs": [], - "source": [] + "source": [ + "" + ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], + "name": "5.0-GAIA-fashion-mnist.ipynb", "provenance": [], - "toc_visible": true + "toc_visible": true, + "version": "0.3.2" }, "kernelspec": { "display_name": "Python 3", @@ -708,5 +710,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 0 } diff --git a/6.0-VAE-GAN-fashion-mnist.ipynb b/6.0-VAE-GAN-fashion-mnist.ipynb index b8b55fc..ae348bc 100644 --- a/6.0-VAE-GAN-fashion-mnist.ipynb +++ b/6.0-VAE-GAN-fashion-mnist.ipynb @@ -2,31 +2,37 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "6S9hJ4VbTSb-" + }, "source": [ -<<<<<<< HEAD "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timsainb/tensorflow2-generative-models/blob/master/6.0-VAE-GAN-fashion-mnist.ipynb)\n", -======= - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timsainb/tensorflow2-generative-models/blob/master/4.0-seq2seq-fashion-mnist.ipynb)\n", ->>>>>>> master "\n", "## VAE-GAN ([article](https://arxiv.org/abs/1512.09300)) \n", "VAE-GAN combines the VAE and GAN to autoencode over a latent representation of data in the generator to improve over the pixelwise error function used in autoencoders. \n", "\n", - "![vae gan](imgs/vaegan.png)" + "![vae gan](https://github.com/timsainb/tensorflow2-generative-models/blob/f3360a819b5773692e943dfe181972a76b9d91bb/imgs/vaegan.png?raw=1)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "9cjmaU9fTScD" + }, "source": [ "### Install packages if in colab" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "PNgGaUToTScE" + }, "outputs": [], "source": [ "### install necessary packages if in colab\n", @@ -50,7 +56,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "DE4MtOOLTScI" + }, "source": [ "#### Note: to get this working on fashion MNIST without using any sort of batch normalization I added two parameters: `latent_loss_div` and `recon_loss_div`. Their purpose is just to scale the loss of the KL Divergence and the reconstruction error in order to balance the three losses in the generator (reconstruction, KL divergence, GAN). Similar to $\\beta$-VAE. In addition I balance the generator and discriminator loss by squashing the discriminator loss with a sigmoid based on how much it is beating the generator by.\n", "\n", @@ -59,19 +68,26 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "tI6r7NYqTScJ" + }, "source": [ "### load packages" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-12T22:52:58.541506Z", "start_time": "2019-05-12T22:52:54.469984Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "wTGSakMtTScL", + "outputId": "fb952c24-2ffd-4e7e-b77e-43ddf7cdd01a" }, "outputs": [ { @@ -97,12 +113,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-12T22:52:58.547455Z", "start_time": "2019-05-12T22:52:58.543973Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "afLlwrbqTScQ", + "outputId": "2e911024-7b0f-4528-f941-3f71009b21c5" }, "outputs": [ { @@ -119,19 +139,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "AYpFTIb_TScU" + }, "source": [ "### Create a fashion-MNIST dataset" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-12T22:52:58.638637Z", "start_time": "2019-05-12T22:52:58.549332Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "Mu4ZvNCTTScV" }, "outputs": [], "source": [ @@ -145,12 +171,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-12T22:53:03.378562Z", "start_time": "2019-05-12T22:52:58.640651Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "SmK4qK_QTScX" }, "outputs": [], "source": [ @@ -178,19 +207,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "swaswhSJTScZ" + }, "source": [ "### Define the network as tf.keras.model object" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-12T22:53:03.443093Z", "start_time": "2019-05-12T22:53:03.381042Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "yh33F1KQTSca" }, "outputs": [], "source": [ @@ -348,7 +383,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "EtWizbufTScc" + }, "source": [ "### Define the network architecture\n", "- GAIA has an autoencoder as its generator, and a UNET as its descriminator" @@ -356,12 +394,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-12T22:53:03.581122Z", "start_time": "2019-05-12T22:53:03.445100Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "2pMgFfFETSce" }, "outputs": [], "source": [ @@ -370,12 +411,15 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-12T22:53:03.704579Z", "start_time": "2019-05-12T22:53:03.585028Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "k9SohQvnTSch" }, "outputs": [], "source": [ @@ -425,7 +469,9 @@ "ExecuteTime": { "end_time": "2019-05-10T18:40:40.306731Z", "start_time": "2019-05-10T18:40:40.292930Z" - } + }, + "colab_type": "text", + "id": "BCp-NE8HTSck" }, "source": [ "### Create Model" @@ -433,12 +479,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-12T22:53:04.748386Z", "start_time": "2019-05-12T22:53:03.707925Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "HQAi3QxwTScl" }, "outputs": [], "source": [ @@ -461,19 +510,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "Kaj9-dcUTSco" + }, "source": [ "### Train the model" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-12T22:53:05.434725Z", "start_time": "2019-05-12T22:53:04.752033Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "SsVWJ-PXTScp" }, "outputs": [], "source": [ @@ -483,12 +538,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-12T22:53:07.958473Z", "start_time": "2019-05-12T22:53:05.437136Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "FbSd4aD2TScr" }, "outputs": [], "source": [ @@ -497,12 +555,15 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-12T22:53:07.991485Z", "start_time": "2019-05-12T22:53:07.962885Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "ixWnqbggTSct" }, "outputs": [], "source": [ @@ -543,12 +604,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-12T22:53:08.132405Z", "start_time": "2019-05-12T22:53:07.995313Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "VwHuGzHcTScw" }, "outputs": [], "source": [ @@ -565,12 +629,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-13T06:18:45.251515Z", "start_time": "2019-05-12T22:53:08.139350Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "o4jBsKGBTScy", + "outputId": "ff5895fa-32ce-4050-a61b-9a733259f071" }, "outputs": [ { @@ -587,7 +655,9 @@ "
" ] }, - "metadata": {}, + "metadata": { + "tags": [] + }, "output_type": "display_data" }, { @@ -598,7 +668,8 @@ ] }, "metadata": { - "needs_background": "light" + "needs_background": "light", + "tags": [] }, "output_type": "display_data" } @@ -629,18 +700,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "AOfetB3bTSc2" + }, "outputs": [], - "source": [] + "source": [ + "" + ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], + "name": "6.0-VAE-GAN-fashion-mnist.ipynb", "provenance": [], - "toc_visible": true + "toc_visible": true, + "version": "0.3.2" }, "kernelspec": { "display_name": "Python 3", @@ -661,5 +740,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 0 } diff --git a/7.0-Tensorflow-spectrograms-and-inversion.ipynb b/7.0-Tensorflow-spectrograms-and-inversion.ipynb index 17ccaab..a180a3a 100644 --- a/7.0-Tensorflow-spectrograms-and-inversion.ipynb +++ b/7.0-Tensorflow-spectrograms-and-inversion.ipynb @@ -1,9 +1,11 @@ -<<<<<<< HEAD { "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "lrmWDrhyQ1eJ" + }, "source": [ " [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timsainb/tensorflow2-generative-models/blob/master/7.0-Tensorflow-spectrograms-and-inversion.ipynb)\n", " \n", @@ -11,20 +13,35 @@ "\n", "Tensorflow as a signal processing package that allows us to generate spectrograms from waveforms in numpy. This notebook can serve as background for the Iterator for Nsynth notebook, or for any other spectrogram inversion in Tensorflow project. Spectrogram inversion is done using the Griffin-Lim algorithm. \n", "\n", - "![spectrogram inversion in tensorflow 2.0](imgs/spectrogram-inversion.png)" + "![spectrogram inversion in tensorflow 2.0](https://github.com/timsainb/tensorflow2-generative-models/blob/183d63ec51e508c5f568bacb84f0ae27fe6e3047/imgs/spectrogram-inversion.png?raw=1)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "53XOuLqoQ1eL" + }, "source": [ "### Install packages if in colab" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-14T07:38:01.415402Z", + "start_time": "2019-05-14T07:38:01.406182Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 703 + }, + "colab_type": "code", + "id": "Lr4jj7OiQ1eM", + "outputId": "20e07702-f7d5-4408-c139-50cad4636d72" + }, "outputs": [], "source": [ "### install necessary packages if in colab\n", @@ -48,19 +65,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "jZO7PgpiQ1eQ" + }, "source": [ "### load packages" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:08.535675Z", - "start_time": "2019-05-13T19:30:07.942810Z" - } + "end_time": "2019-05-14T07:38:02.375526Z", + "start_time": "2019-05-14T07:38:01.419400Z" + }, + "colab": {}, + "colab_type": "code", + "id": "eGuC9rsYQ1eR" }, "outputs": [], "source": [ @@ -75,12 +98,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:12.356035Z", - "start_time": "2019-05-13T19:30:08.537906Z" - } + "end_time": "2019-05-14T07:38:07.507792Z", + "start_time": "2019-05-14T07:38:02.379833Z" + }, + "colab": {}, + "colab_type": "code", + "id": "OS21amUOQ1eV" }, "outputs": [], "source": [ @@ -89,19 +115,26 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:12.362097Z", - "start_time": "2019-05-13T19:30:12.358547Z" - } + "end_time": "2019-05-14T07:38:07.516626Z", + "start_time": "2019-05-14T07:38:07.510582Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 33 + }, + "colab_type": "code", + "id": "U20QiT5aQ1eY", + "outputId": "ec194304-dc7b-45cb-9343-1090a4186304" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2.0.0-dev20190510\n" + "2.0.0-dev20190513\n" ] } ], @@ -111,19 +144,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "O5oNkzQZQ1ef" + }, "source": [ "### download and load the waveform" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:12.452064Z", - "start_time": "2019-05-13T19:30:12.363954Z" - } + "end_time": "2019-05-14T07:38:07.665238Z", + "start_time": "2019-05-14T07:38:07.521232Z" + }, + "colab": {}, + "colab_type": "code", + "id": "Lm_q5GMTQ1ef" }, "outputs": [], "source": [ @@ -132,12 +171,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:12.967991Z", - "start_time": "2019-05-13T19:30:12.454407Z" - } + "end_time": "2019-05-14T07:38:08.157416Z", + "start_time": "2019-05-14T07:38:07.669079Z" + }, + "colab": {}, + "colab_type": "code", + "id": "_W7FpDSyQ1ei" }, "outputs": [], "source": [ @@ -147,12 +189,19 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:13.118698Z", - "start_time": "2019-05-13T19:30:12.971799Z" - } + "end_time": "2019-05-14T07:38:08.309015Z", + "start_time": "2019-05-14T07:38:08.161431Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 75 + }, + "colab_type": "code", + "id": "nEdv4mhyQ1el", + "outputId": "271ba5c5-9952-4ee4-cd07-c4561854f7b2" }, "outputs": [ { @@ -169,7 +218,7 @@ "" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -180,12 +229,19 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:13.411431Z", - "start_time": "2019-05-13T19:30:13.121269Z" - } + "end_time": "2019-05-14T07:38:08.657333Z", + "start_time": "2019-05-14T07:38:08.311886Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 247 + }, + "colab_type": "code", + "id": "2nFmEE6zQ1ep", + "outputId": "de18f276-570c-4b4c-c8b6-00bdf5a5093a" }, "outputs": [ { @@ -194,7 +250,7 @@ "(-10027.050000000001, 210568.05, -0.7884475708007812, 0.8979751586914062)" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -223,7 +279,9 @@ "ExecuteTime": { "end_time": "2019-05-12T23:08:08.939470Z", "start_time": "2019-05-12T23:08:08.916653Z" - } + }, + "colab_type": "text", + "id": "unOag-KkQ1et" }, "source": [ "### Create the spectrogram\n", @@ -232,13 +290,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:13.422465Z", - "start_time": "2019-05-13T19:30:13.413460Z" + "end_time": "2019-05-14T07:38:08.668769Z", + "start_time": "2019-05-14T07:38:08.659906Z" }, - "code_folding": [] + "code_folding": [], + "colab": {}, + "colab_type": "code", + "id": "2B0Xxo1sQ1eu" }, "outputs": [], "source": [ @@ -274,12 +335,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:13.525697Z", - "start_time": "2019-05-13T19:30:13.424260Z" - } + "end_time": "2019-05-14T07:38:08.788792Z", + "start_time": "2019-05-14T07:38:08.670584Z" + }, + "colab": {}, + "colab_type": "code", + "id": "ioWhvuO8Q1ex" }, "outputs": [], "source": [ @@ -292,12 +356,15 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:13.637095Z", - "start_time": "2019-05-13T19:30:13.530474Z" - } + "end_time": "2019-05-14T07:38:08.876408Z", + "start_time": "2019-05-14T07:38:08.790556Z" + }, + "colab": {}, + "colab_type": "code", + "id": "ukYJtH-mQ1e0" }, "outputs": [], "source": [ @@ -322,12 +389,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:15.273766Z", - "start_time": "2019-05-13T19:30:13.641179Z" - } + "end_time": "2019-05-14T07:38:13.508567Z", + "start_time": "2019-05-14T07:38:08.878184Z" + }, + "colab": {}, + "colab_type": "code", + "id": "R_FuCehvQ1e2" }, "outputs": [], "source": [ @@ -336,21 +406,28 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:15.760100Z", - "start_time": "2019-05-13T19:30:15.280136Z" - } + "end_time": "2019-05-14T07:38:14.045321Z", + "start_time": "2019-05-14T07:38:13.510774Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 307 + }, + "colab_type": "code", + "id": "nsGhjaoDQ1e6", + "outputId": "c79cb9d9-fdb8-48d3-e5fa-bef4ab02904e" }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, @@ -375,7 +452,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "eZCGtUlkQ1fA" + }, "source": [ "### Inverting the spectrogram\n", "**Note: at the time of writing, tf.signal.istft is broken (2.0 alpha), so I'm implementing inversion partially in numpy. This should matter much in our case because inversion is being done offline.**" @@ -383,12 +463,15 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:16.740091Z", - "start_time": "2019-05-13T19:30:15.762922Z" - } + "end_time": "2019-05-14T07:38:15.720092Z", + "start_time": "2019-05-14T07:38:14.051892Z" + }, + "colab": {}, + "colab_type": "code", + "id": "gHd11qwUQ1fB" }, "outputs": [], "source": [ @@ -462,12 +545,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:34.780203Z", - "start_time": "2019-05-13T19:30:16.742243Z" - } + "end_time": "2019-05-14T07:38:33.367824Z", + "start_time": "2019-05-14T07:38:15.726661Z" + }, + "colab": {}, + "colab_type": "code", + "id": "uWTHfxHKQ1fE" }, "outputs": [], "source": [ @@ -476,12 +562,19 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:35.377630Z", - "start_time": "2019-05-13T19:30:34.784452Z" - } + "end_time": "2019-05-14T07:38:33.957273Z", + "start_time": "2019-05-14T07:38:33.370538Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 292 + }, + "colab_type": "code", + "id": "QiB9mVlaQ1fI", + "outputId": "8e40ad05-afa4-4afe-fcc0-a7c27386c79a" }, "outputs": [ { @@ -489,7 +582,7 @@ "text/html": [ "\n", " \n", " " @@ -498,13 +591,13 @@ "" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -524,19 +617,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "cdIlFcIXQ1fO" + }, "source": [ "### Create the mel filter" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:35.398468Z", - "start_time": "2019-05-13T19:30:35.379523Z" - } + "end_time": "2019-05-14T07:38:33.975779Z", + "start_time": "2019-05-14T07:38:33.959331Z" + }, + "colab": {}, + "colab_type": "code", + "id": "CevQYhHIQ1fQ" }, "outputs": [], "source": [ @@ -554,21 +653,28 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:35.753064Z", - "start_time": "2019-05-13T19:30:35.400340Z" - } + "end_time": "2019-05-14T07:38:34.345864Z", + "start_time": "2019-05-14T07:38:33.977560Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "colab_type": "code", + "id": "3Whrpvk1Q1fU", + "outputId": "1c72c93e-5583-4d6b-a74d-4042f3b39483" }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, @@ -593,19 +699,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "W4oIBQrHQ1fX" + }, "source": [ "#### This is what the mel spectrogram would look like if we didn't normalize the mel filter" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:35.926284Z", - "start_time": "2019-05-13T19:30:35.754996Z" - } + "end_time": "2019-05-14T07:38:34.550910Z", + "start_time": "2019-05-14T07:38:34.347629Z" + }, + "colab": {}, + "colab_type": "code", + "id": "5YFjdY6SQ1fY" }, "outputs": [], "source": [ @@ -614,21 +726,28 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:36.288740Z", - "start_time": "2019-05-13T19:30:35.928190Z" - } + "end_time": "2019-05-14T07:38:34.917330Z", + "start_time": "2019-05-14T07:38:34.552861Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "colab_type": "code", + "id": "xWuhE7eBQ1fa", + "outputId": "82ba2aa3-d329-4e27-b705-1b040afc9aed" }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, @@ -653,19 +772,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "Wontr4QQQ1fe" + }, "source": [ "### Normalize the mel filter" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:36.294073Z", - "start_time": "2019-05-13T19:30:36.290772Z" - } + "end_time": "2019-05-14T07:38:34.923521Z", + "start_time": "2019-05-14T07:38:34.919842Z" + }, + "colab": {}, + "colab_type": "code", + "id": "FBz9rM3qQ1ff" }, "outputs": [], "source": [ @@ -674,12 +799,15 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:36.413956Z", - "start_time": "2019-05-13T19:30:36.295799Z" - } + "end_time": "2019-05-14T07:38:35.008556Z", + "start_time": "2019-05-14T07:38:34.926033Z" + }, + "colab": {}, + "colab_type": "code", + "id": "VMLUI-AxQ1fh" }, "outputs": [], "source": [ @@ -693,12 +821,15 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:36.498742Z", - "start_time": "2019-05-13T19:30:36.415680Z" - } + "end_time": "2019-05-14T07:38:35.092451Z", + "start_time": "2019-05-14T07:38:35.010641Z" + }, + "colab": {}, + "colab_type": "code", + "id": "f_YS29U1Q1fj" }, "outputs": [], "source": [ @@ -711,21 +842,28 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:36.774460Z", - "start_time": "2019-05-13T19:30:36.500523Z" - } + "end_time": "2019-05-14T07:38:35.400618Z", + "start_time": "2019-05-14T07:38:35.095152Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 196 + }, + "colab_type": "code", + "id": "Uc8V6uZkQ1fm", + "outputId": "157b29f7-d844-446a-f891-9e8fcc6f8a20" }, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, @@ -749,12 +887,15 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:36.778814Z", - "start_time": "2019-05-13T19:30:36.776190Z" - } + "end_time": "2019-05-14T07:38:35.406556Z", + "start_time": "2019-05-14T07:38:35.402980Z" + }, + "colab": {}, + "colab_type": "code", + "id": "q33TvMoAQ1fs" }, "outputs": [], "source": [ @@ -763,12 +904,15 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:36.869040Z", - "start_time": "2019-05-13T19:30:36.780514Z" - } + "end_time": "2019-05-14T07:38:35.492460Z", + "start_time": "2019-05-14T07:38:35.408390Z" + }, + "colab": {}, + "colab_type": "code", + "id": "NYsDmVTFQ1fu" }, "outputs": [], "source": [ @@ -777,21 +921,28 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:37.243289Z", - "start_time": "2019-05-13T19:30:36.872915Z" - } + "end_time": "2019-05-14T07:38:35.861282Z", + "start_time": "2019-05-14T07:38:35.495156Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "colab_type": "code", + "id": "qmDzjjG5Q1fy", + "outputId": "70813d34-bce8-4684-edcf-11f6843b878f" }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 28, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, @@ -816,19 +967,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "NqEWcPe_Q1f1" + }, "source": [ "### Create the Mel spectrogram" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:37.248973Z", - "start_time": "2019-05-13T19:30:37.245115Z" - } + "end_time": "2019-05-14T07:38:35.868474Z", + "start_time": "2019-05-14T07:38:35.863631Z" + }, + "colab": {}, + "colab_type": "code", + "id": "aGt1g04HQ1f2" }, "outputs": [], "source": [ @@ -837,21 +994,28 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:37.681917Z", - "start_time": "2019-05-13T19:30:37.250707Z" - } + "end_time": "2019-05-14T07:38:36.332388Z", + "start_time": "2019-05-14T07:38:35.870746Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "colab_type": "code", + "id": "1NbY9b57Q1f4", + "outputId": "421661f6-f962-4a9c-bbd2-6a3193ad9633" }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 30, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, @@ -876,19 +1040,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "LEa8G2J_Q1f6" + }, "source": [ "### Get MFCCs" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:37.691992Z", - "start_time": "2019-05-13T19:30:37.683850Z" - } + "end_time": "2019-05-14T07:38:36.347299Z", + "start_time": "2019-05-14T07:38:36.335205Z" + }, + "colab": {}, + "colab_type": "code", + "id": "GqsrEOchQ1f8" }, "outputs": [], "source": [ @@ -897,21 +1067,28 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:38.098121Z", - "start_time": "2019-05-13T19:30:37.693916Z" - } + "end_time": "2019-05-14T07:38:36.820495Z", + "start_time": "2019-05-14T07:38:36.349526Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 286 + }, + "colab_type": "code", + "id": "1mzfM_MwQ1f_", + "outputId": "134c5884-34b3-42c6-9df6-d6e9c5d72d1d" }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 32, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, @@ -936,19 +1113,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "uQTKL-DmQ1gE" + }, "source": [ "### Create the mel inversion filter" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:38.120025Z", - "start_time": "2019-05-13T19:30:38.100137Z" - } + "end_time": "2019-05-14T07:38:36.842470Z", + "start_time": "2019-05-14T07:38:36.822487Z" + }, + "colab": {}, + "colab_type": "code", + "id": "OVR6_HMNQ1gF" }, "outputs": [], "source": [ @@ -962,21 +1145,28 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:38.449639Z", - "start_time": "2019-05-13T19:30:38.121895Z" - } + "end_time": "2019-05-14T07:38:37.176046Z", + "start_time": "2019-05-14T07:38:36.844099Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "colab_type": "code", + "id": "v8wo00qfQ1gG", + "outputId": "6daebdb3-c6ec-4869-ee16-f80cb5e903d8" }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, @@ -1001,19 +1191,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "r7QkFOrOQ1gJ" + }, "source": [ "### Invert from mel to linear" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:38.456292Z", - "start_time": "2019-05-13T19:30:38.451634Z" - } + "end_time": "2019-05-14T07:38:37.184020Z", + "start_time": "2019-05-14T07:38:37.178602Z" + }, + "colab": {}, + "colab_type": "code", + "id": "d48kHUweQ1gK" }, "outputs": [], "source": [ @@ -1022,21 +1218,28 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:38.972690Z", - "start_time": "2019-05-13T19:30:38.458105Z" - } + "end_time": "2019-05-14T07:38:37.646079Z", + "start_time": "2019-05-14T07:38:37.186223Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 307 + }, + "colab_type": "code", + "id": "N_wcLhw1Q1gM", + "outputId": "2b28a00c-a070-4b20-e4f6-3af9a0f3edbf" }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 36, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, @@ -1062,12 +1265,19 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:39.854882Z", - "start_time": "2019-05-13T19:30:38.975400Z" - } + "end_time": "2019-05-14T07:38:38.481612Z", + "start_time": "2019-05-14T07:38:37.647978Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 318 + }, + "colab_type": "code", + "id": "uUzvTpn1Q1gP", + "outputId": "9b21b40b-476c-427f-ebfd-d210b6c827b1" }, "outputs": [ { @@ -1097,12 +1307,19 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:40.839258Z", - "start_time": "2019-05-13T19:30:39.857423Z" - } + "end_time": "2019-05-14T07:38:39.441469Z", + "start_time": "2019-05-14T07:38:38.483664Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 318 + }, + "colab_type": "code", + "id": "Im5QTYOgQ1gR", + "outputId": "38acfc54-8b74-4454-91b9-a7e216627015" }, "outputs": [ { @@ -1132,19 +1349,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "5xm2rpkzQ1gW" + }, "source": [ "### Invert the mel spectrogram " ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:58.596013Z", - "start_time": "2019-05-13T19:30:40.841620Z" - } + "end_time": "2019-05-14T07:38:56.096441Z", + "start_time": "2019-05-14T07:38:39.443351Z" + }, + "colab": {}, + "colab_type": "code", + "id": "u6g3DUj9Q1gY" }, "outputs": [], "source": [ @@ -1153,12 +1376,19 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": { "ExecuteTime": { - "end_time": "2019-05-13T19:30:59.188270Z", - "start_time": "2019-05-13T19:30:58.598663Z" + "end_time": "2019-05-14T07:38:56.668894Z", + "start_time": "2019-05-14T07:38:56.098922Z" }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 292 + }, + "colab_type": "code", + "id": "dB6qvU78Q1gf", + "outputId": "d3005df1-9883-4c9f-8ed7-1505fb9d383d", "scrolled": true }, "outputs": [ @@ -1167,7 +1397,7 @@ "text/html": [ "\n", " \n", " " @@ -1176,13 +1406,13 @@ "" ] }, - "execution_count": 40, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1203,7 +1433,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "0Cq8DXxcQ1gl" + }, "outputs": [], "source": [] } @@ -1212,8 +1446,10 @@ "accelerator": "GPU", "colab": { "collapsed_sections": [], + "name": "7.0-Tensorflow-spectrograms-and-inversion.ipynb", "provenance": [], - "toc_visible": true + "toc_visible": true, + "version": "0.3.2" }, "kernelspec": { "display_name": "Python 3", @@ -1234,7 +1470,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 1 } -======= ->>>>>>> master diff --git a/9.0-seq2seq-NSYNTH.ipynb b/9.0-seq2seq-NSYNTH.ipynb index f0fbd76..cc9e604 100644 --- a/9.0-seq2seq-NSYNTH.ipynb +++ b/9.0-seq2seq-NSYNTH.ipynb @@ -2,27 +2,37 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "mbkdsxm4UjlZ" + }, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timsainb/tensorflow2-generative-models/blob/master/9.0-seq2seq-NSYNTH.ipynb)\n", "\n", "### Seq2Seq Autoencoder (without attention) \n", "Seq2Seq models use recurrent neural network cells (like LSTMs) to better capture sequential organization in data. This implementation uses Convolutional Layers as input to the LSTM cells, and a single Bidirectional LSTM layer. \n", "\n", - "![a seq2seq bidirectional lstm in tensorflow 2.0](imgs/seq2seq.png)" + "![a seq2seq bidirectional lstm in tensorflow 2.0](https://github.com/timsainb/tensorflow2-generative-models/blob/f3360a819b5773692e943dfe181972a76b9d91bb/imgs/seq2seq.png?raw=1)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "7ZpchPpwUjlb" + }, "source": [ "### Install packages if in colab" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "EvXRBCw7Ujlc" + }, "outputs": [], "source": [ "### install necessary packages if in colab\n", @@ -44,26 +54,39 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "XCVo73YzUjlg" + }, "source": [ "### load packages" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "1mDpGonuUjlg" + }, "outputs": [], - "source": [] + "source": [ + "" + ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:25.609133Z", "start_time": "2019-05-14T05:33:25.605104Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "7JW9zJj6Ujlj", + "outputId": "344667f0-a44f-421f-8557-145656ea04ff" }, "outputs": [ { @@ -81,12 +104,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:31.950743Z", "start_time": "2019-05-14T05:33:25.610868Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "KZB9pwb-Ujlp" }, "outputs": [], "source": [ @@ -105,12 +131,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:31.963252Z", "start_time": "2019-05-14T05:33:31.952815Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "ZFqhdD89Ujls", + "outputId": "34876516-b1ee-435e-8c9a-492ac226c059" }, "outputs": [ { @@ -127,7 +157,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "pAXySDnqUjlv" + }, "source": [ "### Download or load dataset\n", "Tensorflow datasets will automatically download or load the dataset for you at this location" @@ -135,12 +168,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:32.048595Z", "start_time": "2019-05-14T05:33:31.965075Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "IploxhlMUjlw", + "outputId": "237c204f-de34-41e3-83c4-f087412902e2" }, "outputs": [ { @@ -150,7 +187,9 @@ ] }, "execution_count": 4, - "metadata": {}, + "metadata": { + "tags": [] + }, "output_type": "execute_result" } ], @@ -161,12 +200,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:36.728321Z", "start_time": "2019-05-14T05:33:32.050453Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "1d0S_llSUjl0" }, "outputs": [], "source": [ @@ -177,12 +219,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:36.736428Z", "start_time": "2019-05-14T05:33:36.730184Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "k8E1fJStUjl3", + "outputId": "99275da1-5b31-4ade-9110-441ec244049a" }, "outputs": [ { @@ -192,7 +238,9 @@ ] }, "execution_count": 6, - "metadata": {}, + "metadata": { + "tags": [] + }, "output_type": "execute_result" } ], @@ -202,20 +250,26 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "x7X5QJCbUjl6" + }, "source": [ "### Prepare spectrogramming and parameters" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:36.820836Z", "start_time": "2019-05-14T05:33:36.738100Z" }, - "code_folding": [] + "code_folding": [], + "colab": {}, + "colab_type": "code", + "id": "G23vz1NEUjl7" }, "outputs": [], "source": [ @@ -251,12 +305,15 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:36.946645Z", "start_time": "2019-05-14T05:33:36.822589Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "Cu0jsEdIUjl9" }, "outputs": [], "source": [ @@ -269,12 +326,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:37.028015Z", "start_time": "2019-05-14T05:33:36.948405Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "Wmnn230sUjmA" }, "outputs": [], "source": [ @@ -307,7 +367,9 @@ "ExecuteTime": { "end_time": "2019-05-13T19:31:28.258830Z", "start_time": "2019-05-13T19:31:28.254192Z" - } + }, + "colab_type": "text", + "id": "sTSg-xkRUjmC" }, "source": [ "### Create the dataset class" @@ -315,12 +377,15 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:37.153107Z", "start_time": "2019-05-14T05:33:37.029665Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "4SbKOmDOUjmD" }, "outputs": [], "source": [ @@ -439,19 +504,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "gaswmAhaUjmF" + }, "source": [ "### Produce the dataset from tfrecords" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:37.372727Z", "start_time": "2019-05-14T05:33:37.154927Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "K0HHLRyuUjmG" }, "outputs": [], "source": [ @@ -460,12 +531,15 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:38.357385Z", "start_time": "2019-05-14T05:33:37.375071Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "tMtMjFEfUjmI" }, "outputs": [], "source": [ @@ -474,12 +548,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:40.697363Z", "start_time": "2019-05-14T05:33:38.359394Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "oLSS_iIqUjmN" }, "outputs": [], "source": [ @@ -488,12 +565,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:41.056935Z", "start_time": "2019-05-14T05:33:40.702553Z" - } + }, + "colab": {}, + "colab_type": "code", + "id": "csUDjCsVUjmQ", + "outputId": "74a2a55f-3075-479d-9abd-85f9d45fa626" }, "outputs": [ { @@ -503,7 +584,9 @@ ] }, "execution_count": 14, - "metadata": {}, + "metadata": { + "tags": [] + }, "output_type": "execute_result" }, { @@ -514,7 +597,8 @@ ] }, "metadata": { - "needs_background": "light" + "needs_background": "light", + "tags": [] }, "output_type": "display_data" } @@ -527,19 +611,16 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 15, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:41.063844Z", "start_time": "2019-05-14T05:33:41.059196Z" -======= - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2019-05-14T05:33:25.305Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "zuwGKocLUjmW", + "outputId": "b3d761fe-644a-4284-e83f-a7efbbe9206a" }, "outputs": [ { @@ -549,7 +630,9 @@ ] }, "execution_count": 15, - "metadata": {}, + "metadata": { + "tags": [] + }, "output_type": "execute_result" } ], @@ -560,26 +643,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "JSIPUvyhUjma" + }, "source": [ "### Define the network as tf.keras.model object" ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 16, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:41.146482Z", "start_time": "2019-05-14T05:33:41.065669Z" -======= - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2019-05-14T05:33:25.313Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "sriWcAe4Ujmb" }, "outputs": [], "source": [ @@ -629,7 +711,9 @@ "ExecuteTime": { "end_time": "2019-05-13T20:04:30.551321Z", "start_time": "2019-05-13T20:04:30.464323Z" - } + }, + "colab_type": "text", + "id": "Z7oirx76Ujmh" }, "source": [ "### Define the network architecture" @@ -637,19 +721,15 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 17, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:41.274897Z", "start_time": "2019-05-14T05:33:41.148797Z" -======= - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2019-05-14T05:33:25.322Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "TNTlOEDrUjmi" }, "outputs": [], "source": [ @@ -726,7 +806,9 @@ "ExecuteTime": { "end_time": "2019-05-10T18:40:40.306731Z", "start_time": "2019-05-10T18:40:40.292930Z" - } + }, + "colab_type": "text", + "id": "iv4pD_ERUjmk" }, "source": [ "### Create Model" @@ -734,19 +816,15 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 18, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:41.348434Z", "start_time": "2019-05-14T05:33:41.276659Z" -======= - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2019-05-14T05:33:25.326Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "lIRszaLyUjmk" }, "outputs": [], "source": [ @@ -763,19 +841,16 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 19, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:43.741275Z", "start_time": "2019-05-14T05:33:41.350078Z" -======= - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2019-05-14T05:33:25.331Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "IDewWhjJUjmm", + "outputId": "a1773dbf-b94f-4507-b6d9-535b45b1338e" }, "outputs": [ { @@ -785,7 +860,9 @@ ] }, "execution_count": 19, - "metadata": {}, + "metadata": { + "tags": [] + }, "output_type": "execute_result" } ], @@ -797,19 +874,16 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 20, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:45.879263Z", "start_time": "2019-05-14T05:33:43.743480Z" -======= - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2019-05-14T05:33:25.338Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "k4lzePZ7Ujmp", + "outputId": "534e248f-1f6a-42fa-c848-fe0e1a3ea32b" }, "outputs": [ { @@ -827,19 +901,16 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 21, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:47.873647Z", "start_time": "2019-05-14T05:33:45.883038Z" -======= - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2019-05-14T05:33:25.345Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "vApYbrYZUjmr", + "outputId": "755f82e3-395c-4e19-9163-bd1c36091929" }, "outputs": [ { @@ -857,26 +928,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "2nLurN91Ujmt" + }, "source": [ "### Train model" ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 22, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:47.893342Z", "start_time": "2019-05-14T05:33:47.878152Z" -======= - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2019-05-14T05:33:25.350Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "28vJxJEfUjmu" }, "outputs": [], "source": [ @@ -903,19 +973,15 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 23, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:48.013562Z", "start_time": "2019-05-14T05:33:47.896060Z" -======= - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2019-05-14T05:33:25.357Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "0i4wcme_Ujmw" }, "outputs": [], "source": [ @@ -925,19 +991,16 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 24, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:48.100233Z", "start_time": "2019-05-14T05:33:48.017505Z" -======= - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2019-05-14T05:33:25.362Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "3drrskLFUjmz", + "outputId": "3f8aa6e8-a54c-4a44-e32a-9e8bc50618a7" }, "outputs": [ { @@ -947,7 +1010,9 @@ ] }, "execution_count": 24, - "metadata": {}, + "metadata": { + "tags": [] + }, "output_type": "execute_result" } ], @@ -959,19 +1024,15 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 25, + "execution_count": 0, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T05:33:48.178891Z", "start_time": "2019-05-14T05:33:48.104169Z" -======= - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2019-05-14T05:33:25.366Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "hFdQTAsZUjm1" }, "outputs": [], "source": [ @@ -981,59 +1042,35 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 0, "metadata": { "ExecuteTime": { -<<<<<<< HEAD "end_time": "2019-05-14T05:57:51.419502Z", "start_time": "2019-05-14T05:33:48.182759Z" -======= - "start_time": "2019-05-14T05:33:25.373Z" ->>>>>>> master - } + }, + "colab": {}, + "colab_type": "code", + "id": "zAopv_8YUjm3", + "outputId": "2c940a43-ec8d-4ecd-82a7-75bae18a89ff" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ -<<<<<<< HEAD "Epoch: 49 | MSE: 0.0038840165361762047\n" ] }, { "data": { "image/png": "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\n", -======= - "Epoch: 42 | MSE: 0.0039087627083063126\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ef051162c32e4a2f8b39199b2be33ceb", - "version_major": 2, - "version_minor": 0 - }, ->>>>>>> master "text/plain": [ "
" ] }, "metadata": { - "needs_background": "light" + "needs_background": "light", + "tags": [] }, "output_type": "display_data" } @@ -1065,18 +1102,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "oXUzLBnwUjm6" + }, "outputs": [], - "source": [] + "source": [ + "" + ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], + "name": "9.0-seq2seq-NSYNTH.ipynb", "provenance": [], - "toc_visible": true + "toc_visible": true, + "version": "0.3.2" }, "kernelspec": { "display_name": "Python 3", @@ -1097,5 +1142,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 0 } diff --git a/readme.md b/readme.md index 432cb84..09fa3fb 100644 --- a/readme.md +++ b/readme.md @@ -30,11 +30,8 @@ WGAN-GP is a GAN that improves over the original loss function to improve traini ![wgan gp](imgs/gan.png) -<<<<<<< HEAD + ### VAE-GAN ([article](https://arxiv.org/abs/1512.09300)) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timsainb/tensorflow2-generative-models/blob/master/6.0-VAE-GAN-fashion-mnist.ipynb) -======= -### VAE-GAN ([article](https://arxiv.org/abs/1512.09300)) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timsainb/tensorflow2-generative-models/blob/master/4.0-seq2seq-fashion-mnist.ipynb) ->>>>>>> master VAE-GAN combines the VAE and GAN to autoencode over a latent representation of data in the generator to improve over the pixelwise error function used in autoencoders.