fixed with livelossplot change

Author: stared

3 Linear regression.ipynb

@@ -1,12 +1,13 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Thinking in tensors, writing in PyTorch\n",
     "\n",
-    "A hands-on course by [Piotr Migdał](https://p.migdal.pl) (2019).\n",
+    "A hands-on course by [Piotr Migdał](https://p.migdal.pl) (2019-2022).\n",
     "This notebook prepared by [Weronika Ormaniec](https://github.com/werkaaa) and Piotr Migdał.\n",
     "\n",
     "## Notebook 3: Linear regression\n",
@@ -30,15 +31,6 @@
     "In Python we typically use [LinearRegression from scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html). Here we use PyTorch to show everything step-by-step. Moreover, linear regression is a building block of any regression with deep learning - so it is good to understand it well!"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install --quiet 'livelossplot<0.5'"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -53,6 +45,7 @@
     "import torch.nn as nn\n",
     "\n",
     "from livelossplot import PlotLosses\n",
+    "from livelossplot.outputs import MatplotlibPlot\n",
     "from ipywidgets import interact, fixed"
    ]
   },
@@ -144,8 +137,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X = np.log(data['BodyWeight(kg)'])\n",
-    "Y = np.log(data['BrainWeight(kg)'])"
+    "X = np.log(data['BodyWeight(kg)']).astype('float32')\n",
+    "Y = np.log(data['BrainWeight(kg)']).astype('float32')"
    ]
   },
   {
@@ -394,7 +387,7 @@
     "        plt.title(\"Prediction\")\n",
     "        plt.legend(loc='lower right')\n",
     "        \n",
-    "    liveloss = PlotLosses(extra_plots=[extra_plot], plot_extrema=False)\n",
+    "    liveloss = PlotLosses()\n",
     "    \n",
     "    for i in range(num_epochs):\n",
     "        A -= learning_rate * dL_da(A, B, X, Y)\n",
@@ -586,7 +579,7 @@
     "        plt.title(\"Prediction\")\n",
     "        plt.legend(loc='lower right')\n",
     "    \n",
-    "    liveloss = PlotLosses(extra_plots=[extra_plot], plot_extrema=False)\n",
+    "    liveloss = PlotLosses()\n",
     "\n",
     "    for epoch in range(num_epochs):\n",
     "        \n",
@@ -751,11 +744,16 @@
     "![https://imgs.xkcd.com/comics/extrapolating.png](https://imgs.xkcd.com/comics/extrapolating.png)\n",
     "\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "pytorch-macos-m1-gpu",
    "language": "python",
    "name": "python3"
   },
@@ -769,7 +767,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.9.13"
   },
   "notify_time": "30",
   "toc": {
@@ -813,6 +811,11 @@
     "_Feature"
    ],
    "window_display": false
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "032715a8d60757555a2623145b534a689e8b5634c5398e8bbec1014c4a8ede12"
+   }
   }
  },
  "nbformat": 4,