diff --git a/Code/LiveAnimatedPlots_matplotlib.ipynb b/Code/LiveAnimatedPlots_matplotlib.ipynb new file mode 100644 index 0000000..8cf0149 --- /dev/null +++ b/Code/LiveAnimatedPlots_matplotlib.ipynb @@ -0,0 +1,2065 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "LiveAnimatedPlots_matplotlib.ipynb", + "provenance": [], + "authorship_tag": "ABX9TyMV6QXEXoefq2MR19NG98jw", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QZEJAE1pQhBe", + "colab_type": "code", + "colab": {} + }, + "source": [ + "%matplotlib inline\n", + "\n", + "import time\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from google.colab import widgets" + ], + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "I9fJNsRoRr08", + "colab_type": "code", + "colab": {} + }, + "source": [ + "iters = 25\n", + "\n", + "x = [i for i in range(iters)]\n", + "y = [i**2 for i in range(iters)]" + ], + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zUJEcg46jekB", + "colab_type": "text" + }, + "source": [ + "## 1. Plot a simple graph" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-PmcUwMRTFmc", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 268 + }, + "outputId": "703802c8-3b7f-4961-91c7-5c06a76167ca" + }, + "source": [ + "plt.plot(x, y)\n", + "plt.show()" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [ + "id4-0-0", + "outputarea_id4", + "user_output" + ], + "needs_background": "light" + } + }, + { + "output_type": "display_data", + "data": { + "application/javascript": [ + "window[\"c6ddae32-cab9-11ea-b8b0-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"c6c59b62-cab9-11ea-b8b0-0242ac1c0002\"]);\n", + "//# sourceURL=js_93852a79f5" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [ + "id4-0-0", + "outputarea_id4" + ] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gr_rlpzRjr0B", + "colab_type": "text" + }, + "source": [ + "## 3. Save multiple graphs as a video" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "s-0ZetIHiVxP", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from matplotlib.animation import ArtistAnimation" + ], + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "HV3xbCz8UVKw", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "outputId": "bb85f97c-d3a7-4f39-ed43-d9cfd3dc1035" + }, + "source": [ + "fig = plt.figure()\n", + "\n", + "plt.xlim(0, iters)\n", + "plt.ylim(0, iters**2)\n", + "\n", + "imgs = []\n", + "for iter in range(iters):\n", + " x = [i for i in range(iter)]\n", + " y = [i**2 for i in range(iter)]\n", + " \n", + " imgs.append(plt.plot(x, y, 'b-'))\n", + "\n", + "anim = ArtistAnimation(fig, imgs, interval=100)\n", + "anim.save('vid.mp4')" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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3wVom6lTINX9IB+dGkgB7gcNV9YmBXd2dF2ebi5WcFxO/I7d9xeiTLD2y4W8mWtCEJPlNFq/uYfHxGJ/raS6SfB54G4uPzT0BfAT4KnAn8BvAk8D1VfWy/w/Os8zF21j8J3wBTwB/OrCu/bKU5C3AvwEPA79o3R9mcS27q/PiRebiBkY8LyYe+pKkc2fSyzuSpHPI0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kd+T8rUxoW58iWxQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + } + ] +} \ No newline at end of file diff --git a/Data/vid.gif b/Data/vid.gif new file mode 100644 index 0000000..1dbdce3 Binary files /dev/null and b/Data/vid.gif differ diff --git a/README.md b/README.md index 91301b6..5c54753 100644 --- a/README.md +++ b/README.md @@ -199,3 +199,8 @@ Sometimes you would need a functionality which is not directly provided by Keras (i.e. a neural network which takes input from multiple data sources, and does a combined training on this data), and you want that the data generator should be able to handle the data preparation on the fly, you can create a wrapper around ImageDataGenerator class to give the required output.[This notebook](./Code/CustomDataGen_Keras.ipynb) explains a simple solution to this usecase. 2. Another use case could be that you want to resize the images from a shape say 150x150 to a shape 224x224, which is generally utilized by the pretrained models, you can customize the ImageDataGenerator without coding your own data generator from ground up [(Example Notebook)](https://github.com/faizankshaikh/AV_Article_Codes/blob/master/Inception_From_Scratch/improvements/Inception_v1_from_Scratch.ipynb). + +- ### [Data Science Hack #45 Create live animated plots using matplotlib](./Code/LiveAnimatedPlots_matplotlib.ipynb) +Helps you plot live animations during a activate cell run. Use case - Visualize the training of a neural network in real time + +![](./Data/vid.gif)