Live loss plot for training models in Keras (see: https://github.com/stared/livelossplot/ for a library)

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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4BKgFruwP9RQPunvc3c8C7gLuDud/Aihx9zjwe8CfmtmCDNWeEUtrgs7cRh3iEZE8kM6e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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
}

Here's a version with loss and accuracy:

class PlotLearning(Callback):
    def on_train_begin(self, logs={}):
        self.i = 0
        self.x = []
        self.losses = []
        self.val_losses = []
        self.acc = []
        self.val_acc = []
        self.fig = plt.figure()
        
        self.logs = []

    def on_epoch_end(self, epoch, logs={}):
        
        self.logs.append(logs)
        self.x.append(self.i)
        self.losses.append(logs.get('loss'))
        self.val_losses.append(logs.get('val_loss'))
        self.acc.append(logs.get('acc'))
        self.val_acc.append(logs.get('val_acc'))
        self.i += 1
        f, (ax1, ax2) = plt.subplots(1, 2, sharex=True)
        
        clear_output(wait=True)
        
        ax1.set_yscale('log')
        ax1.plot(self.x, self.losses, label="loss")
        ax1.plot(self.x, self.val_losses, label="val_loss")
        ax1.legend()
        
        ax2.plot(self.x, self.acc, label="accuracy")
        ax2.plot(self.x, self.val_acc, label="validation accuracy")
        ax2.legend()
        
        plt.show();
        
plot = PlotLearning()

I also switched loss to a log plot since it tends to resolve that way

Works great, thanks!

Thanks for this, it's really nice!

Do you have a way to change the figure size? I'd like it to be larger but something like figsize=(20,10) doesn't work.

In the meantime, I wrote it for another project: https://github.com/deepsense-ai/intel-ai-webinar-neural-networks/blob/master/live_loss_plot.py (supporting multiple loss functions).

In action: https://github.com/deepsense-ai/intel-ai-webinar-neural-networks/blob/master/notebook_done.ipynb

UPDATE: I created a dedicated library: https://github.com/stared/livelossplot/ - contributions are welcomed! :)

You can install it with pip:

pip install livelossplot

Hi, I'd like to know if it is possible to plot loss curves with respect to iteration number in Keras? Thanks!

@ckolluru you can create the above using your own custom callback but in terms of granularity, it looks like Keras supports down to at most a batch level.

how do we add our own model to this?

Thanks for the great project! I was wondering why is my figure show blocking the training? it seems I should close the figure every iteration to let it run and show the updated results. The training does not continue unless I close the figure.

Thanks a lot

Thanks!

Thanks!

Thanks!

Thanks for the great project! I was wondering why is my figure show blocking the training? it seems I should close the figure every iteration to let it run and show the updated results. The training does not continue unless I close the figure.

was your problem solved? I have the same problem

This script is no longer being maintained (for the last year!).
Please use https://github.com/stared/livelossplot/ instead.

This script is no longer being maintained (for the last year!).
Please use https://github.com/stared/livelossplot/ instead.

I'm actually using that , but I have the same problem with that. the figure blocks the process and every epoch I have to close it for the build to continue ...

Thanks a lot it saved my day!
I simply made log-log scale to display th loss ... since I hope the loss decreasing by several orders of magnitude !

is it possible to get the live plot per batch instead of per epoch?

Hi, I have the same problem than @socca. Could somebody help please?

This is just fantastic. Thank you.

@ElaheMrz you need to use Jupyter for that.

Awesome work, it works very well!
Thanks for sharing :)