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Merge pull request #6 from jennalandy/master
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Can now pass empty specs to display props only
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@jennalandy
jennalandy authored Aug 6, 2018
2 parents afff8f8 + e7e7b3d commit 2fceb10
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44 changes: 20 additions & 24 deletions IntroductionToPlyto.ipynb
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Expand Up @@ -6,7 +6,7 @@
"source": [
"## Introduction to Plyto with Keras\n",
"#### Python Machine Learning Visualization Toolkit\n",
"This notebook will demonstrate how to use our Training Information Callback function with the PlytoAPI to visualize model accuracy and loss throughout the training process of a machine learning algorithm, as well as a tutorial on how to create your own callback function"
"This notebook will demonstrate how to use our Keras Accuracy Loss function with the PlytoAPI to visualize model accuracy and loss throughout the training process of a machine learning algorithm, as well as a tutorial on how to create your own callback function"
]
},
{
Expand All @@ -18,11 +18,19 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 1,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from plyto import PlytoAPI, TrainingInfoCallback"
"from plyto import PlytoAPI, KerasAccuracyLossCallback"
]
},
{
Expand All @@ -41,21 +49,9 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'PlytoAPI' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-ab255c47190c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;31m# pass the spec into PlytoAPI\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mplytoInstance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPlytoAPI\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;31m# pass the plyto instance into the callback function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'PlytoAPI' is not defined"
]
}
],
"outputs": [],
"source": [
"# an array of altair specs with one plot of samples versus accuracy\n",
"spec = [\n",
Expand Down Expand Up @@ -98,7 +94,7 @@
"plytoInstance = PlytoAPI(spec)\n",
"\n",
"# pass the plyto instance into the callback function\n",
"callback = TrainingInfoCallback(plytoInstance)"
"callback = KerasAccuracyLossCallback(plytoInstance)"
]
},
{
Expand All @@ -111,7 +107,7 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -221,7 +217,7 @@
},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -291,13 +287,13 @@
" self.plyto.send_data()\n",
"\n",
" \n",
"basicPlatoInstance = PlytoAPI({})\n",
"basicPlatoInstance = PlytoAPI([])\n",
"basicCallback = Basic(basicPlatoInstance)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"execution_count": 5,
"metadata": {},
"outputs": [
{
Expand All @@ -307,7 +303,7 @@
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-37-db1f4865e5f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbasicCallback\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-5-db1f4865e5f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbasicCallback\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m 1035\u001b[0m \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1036\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1037\u001b[0;31m validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m 1038\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1039\u001b[0m def evaluate(self, x=None, y=None,\n",
"\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 200\u001b[0m \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 2670\u001b[0m \u001b[0;34m'In order to feed symbolic tensors to a Keras model '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2671\u001b[0m 'in TensorFlow, you need tensorflow 1.8 or higher.')\n\u001b[0;32m-> 2672\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2673\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
Expand Down
18 changes: 18 additions & 0 deletions src/StatusItemWidget.tsx
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Expand Up @@ -82,6 +82,24 @@ class StatusItem extends React.Component<IStatusItemProps, IStatusItemState> {
);
}
});

this.props.tracker.currentWidget.session.kernelChanged.connect(() => {
let widget: NotebookPanel | null = this.props.tracker.currentWidget;
if (widget) {
console.log('new widget. re-registering comm targets');

this.setState(
{
kernel: widget.session.kernel as Kernel.IKernel
},
() => {
this.state.kernel.iopubMessage.connect(this.onMessage, this);

this.state.kernel.registerCommTarget('plyto', (comm, msg) => {});
}
);
}
})
}

onMessage(sender: Kernel.IKernel, msg: KernelMessage.IIOPubMessage) {
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2 changes: 1 addition & 1 deletion src/components/ModelViewer.tsx
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Expand Up @@ -50,7 +50,7 @@ export class ModelViewer extends React.Component<IModelViewerProps, {}> {
)}
</div>
<div className={graphsStyle}>
{this.props.spec.map(spec => {
{this.props.spec !== [] && this.props.spec.map(spec => {
return (
<div
key={spec['name']}
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