Live loss plot for training models in Keras

live_loss_plot_keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Live loss plots in Jupyter Notebook for Keras\n",
    "\n",
    "by [Piotr Migdał](http://p.migdal.pl/)\n",
    "\n",
    "* inspired by a Reddit discussion [Live loss plots inside Jupyter Notebook for Keras? - r/MachineLearning](https://www.reddit.com/r/MachineLearning/comments/65jelb/d_live_loss_plots_inside_jupyter_notebook_for/)\n",
    "* my other Keras add-on: [Sequential model in Keras -> parameter ASCII diagram](https://github.com/stared/keras-sequential-ascii)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Flatten, Dense, Activation\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# data loading\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# data preprocessing\n",
    "Y_train = to_categorical(y_train)\n",
    "Y_test = to_categorical(y_test)\n",
    "X_train = X_train.reshape(-1, 28, 28, 1).astype('float32') / 255.\n",
    "X_test = X_test.reshape(-1, 28, 28, 1).astype('float32') / 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# updatable plot\n",
    "# a minimal example (sort of)\n",
    "\n",
    "class PlotLosses(keras.callbacks.Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.i = 0\n",
    "        self.x = []\n",
    "        self.losses = []\n",
    "        self.val_losses = []\n",
    "        \n",
    "        self.fig = plt.figure()\n",
    "        \n",
    "        self.logs = []\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        \n",
    "        self.logs.append(logs)\n",
    "        self.x.append(self.i)\n",
    "        self.losses.append(logs.get('loss'))\n",
    "        self.val_losses.append(logs.get('val_loss'))\n",
    "        self.i += 1\n",
    "        \n",
    "        clear_output(wait=True)\n",
    "        plt.plot(self.x, self.losses, label=\"loss\")\n",
    "        plt.plot(self.x, self.val_losses, label=\"val_loss\")\n",
    "        plt.legend()\n",
    "        plt.show();\n",
    "        \n",
    "plot_losses = PlotLosses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# just logistic regression, to keep it simple and fast\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Flatten(input_shape=(28, 28, 1)))\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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smFme1bpE8o47dLcFQd2+B9rfOPq8LSXM29+AjlaODlNKURqFitmD/mZB5Zzg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\nkkcU+iIieUShLyKSRxT6IiJ55P8DN3jd7uz/A58AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126f27320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x10986c710>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in this static viewer it is not obvious,\n",
    "# but this plot grows step by step\n",
    "\n",
    "model.fit(X_train, Y_train,\n",
    "          epochs=10,\n",
    "          validation_data=(X_test, Y_test),\n",
    "          callbacks=[plot_losses],\n",
    "          verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further ideas\n",
    "\n",
    "* loss and accuracy side by side, as two plots\n",
    "* time per epoch (plot title?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}