{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n", "- Author: Sebastian Raschka\n", "- GitHub Repository: https://github.com/rasbt/deeplearning-models" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sebastian Raschka \n", "\n", "CPython 3.6.7\n", "IPython 7.1.1\n", "\n", "torch 0.4.1\n" ] } ], "source": [ "%load_ext watermark\n", "%watermark -a 'Sebastian Raschka' -v -p torch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Runs on CPU or GPU (if available)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Zoo -- Plotting Live Training Performance in Jupyter Notebooks via Matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are more sophisticated ways to plot the performance of a model during training than using matplotlib. However, sometimes, we just want to keep it simple, and this notebook shows how we can create a simple plotting function with matplotlib that updates every epoch. \n", "\n", "\n", "A simple example of this is shown below (of course, we could extend it to show e.g., a grid of multiple plots):" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
');\n", " var titletext = $(\n", " '
');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 001/010 | Batch 000/469 | Cost: 2.3016\n", "Epoch: 001/010 | Batch 050/469 | Cost: 2.2714\n", "Epoch: 001/010 | Batch 100/469 | Cost: 1.6118\n", "Epoch: 001/010 | Batch 150/469 | Cost: 0.8000\n", "Epoch: 001/010 | Batch 200/469 | Cost: 0.5079\n", "Epoch: 001/010 | Batch 250/469 | Cost: 0.3221\n", "Epoch: 001/010 | Batch 300/469 | Cost: 0.2851\n", "Epoch: 001/010 | Batch 350/469 | Cost: 0.3117\n", "Epoch: 001/010 | Batch 400/469 | Cost: 0.2836\n", "Epoch: 001/010 | Batch 450/469 | Cost: 0.3169\n", "Epoch: 001/010 training accuracy: 92.72%\n", "Epoch: 002/010 | Batch 000/469 | Cost: 0.2469\n", "Epoch: 002/010 | Batch 050/469 | Cost: 0.2342\n", "Epoch: 002/010 | Batch 100/469 | Cost: 0.2883\n", "Epoch: 002/010 | Batch 150/469 | Cost: 0.2920\n", "Epoch: 002/010 | Batch 200/469 | Cost: 0.1797\n", "Epoch: 002/010 | Batch 250/469 | Cost: 0.2277\n", "Epoch: 002/010 | Batch 300/469 | Cost: 0.1746\n", "Epoch: 002/010 | Batch 350/469 | Cost: 0.2430\n", "Epoch: 002/010 | Batch 400/469 | Cost: 0.1579\n", "Epoch: 002/010 | Batch 450/469 | Cost: 0.1279\n", "Epoch: 002/010 training accuracy: 95.07%\n", "Epoch: 003/010 | Batch 000/469 | Cost: 0.1224\n", "Epoch: 003/010 | Batch 050/469 | Cost: 0.1998\n", "Epoch: 003/010 | Batch 100/469 | Cost: 0.2211\n", "Epoch: 003/010 | Batch 150/469 | Cost: 0.0906\n", "Epoch: 003/010 | Batch 200/469 | Cost: 0.1502\n", "Epoch: 003/010 | Batch 250/469 | Cost: 0.2392\n", "Epoch: 003/010 | Batch 300/469 | Cost: 0.1108\n", "Epoch: 003/010 | Batch 350/469 | Cost: 0.1736\n", "Epoch: 003/010 | Batch 400/469 | Cost: 0.1428\n", "Epoch: 003/010 | Batch 450/469 | Cost: 0.1253\n", "Epoch: 003/010 training accuracy: 96.22%\n", "Epoch: 004/010 | Batch 000/469 | Cost: 0.1369\n", "Epoch: 004/010 | Batch 050/469 | Cost: 0.1984\n", "Epoch: 004/010 | Batch 100/469 | Cost: 0.1297\n", "Epoch: 004/010 | Batch 150/469 | Cost: 0.1437\n", "Epoch: 004/010 | Batch 200/469 | Cost: 0.1140\n", "Epoch: 004/010 | Batch 250/469 | Cost: 0.0566\n", "Epoch: 004/010 | Batch 300/469 | Cost: 0.1120\n", "Epoch: 004/010 | Batch 350/469 | Cost: 0.1777\n", "Epoch: 004/010 | Batch 400/469 | Cost: 0.2209\n", "Epoch: 004/010 | Batch 450/469 | Cost: 0.1390\n", "Epoch: 004/010 training accuracy: 96.77%\n", "Epoch: 005/010 | Batch 000/469 | Cost: 0.1306\n", "Epoch: 005/010 | Batch 050/469 | Cost: 0.0445\n", "Epoch: 005/010 | Batch 100/469 | Cost: 0.1327\n", "Epoch: 005/010 | Batch 150/469 | Cost: 0.0846\n", "Epoch: 005/010 | Batch 200/469 | Cost: 0.0759\n", "Epoch: 005/010 | Batch 250/469 | Cost: 0.0796\n", "Epoch: 005/010 | Batch 300/469 | Cost: 0.1364\n", "Epoch: 005/010 | Batch 350/469 | Cost: 0.1421\n", "Epoch: 005/010 | Batch 400/469 | Cost: 0.0904\n", "Epoch: 005/010 | Batch 450/469 | Cost: 0.0598\n", "Epoch: 005/010 training accuracy: 97.15%\n", "Epoch: 006/010 | Batch 000/469 | Cost: 0.0723\n", "Epoch: 006/010 | Batch 050/469 | Cost: 0.0481\n", "Epoch: 006/010 | Batch 100/469 | Cost: 0.0386\n", "Epoch: 006/010 | Batch 150/469 | Cost: 0.0420\n", "Epoch: 006/010 | Batch 200/469 | Cost: 0.1176\n", "Epoch: 006/010 | Batch 250/469 | Cost: 0.0718\n", "Epoch: 006/010 | Batch 300/469 | Cost: 0.0537\n", "Epoch: 006/010 | Batch 350/469 | Cost: 0.0231\n", "Epoch: 006/010 | Batch 400/469 | Cost: 0.0939\n", "Epoch: 006/010 | Batch 450/469 | Cost: 0.0848\n", "Epoch: 006/010 training accuracy: 97.43%\n", "Epoch: 007/010 | Batch 000/469 | Cost: 0.1984\n", "Epoch: 007/010 | Batch 050/469 | Cost: 0.0445\n", "Epoch: 007/010 | Batch 100/469 | Cost: 0.0525\n", "Epoch: 007/010 | Batch 150/469 | Cost: 0.0640\n", "Epoch: 007/010 | Batch 200/469 | Cost: 0.0669\n", "Epoch: 007/010 | Batch 250/469 | Cost: 0.0952\n", "Epoch: 007/010 | Batch 300/469 | Cost: 0.0293\n", "Epoch: 007/010 | Batch 350/469 | Cost: 0.0972\n", "Epoch: 007/010 | Batch 400/469 | Cost: 0.1133\n", "Epoch: 007/010 | Batch 450/469 | Cost: 0.0554\n", "Epoch: 007/010 training accuracy: 97.77%\n", "Epoch: 008/010 | Batch 000/469 | Cost: 0.1194\n", "Epoch: 008/010 | Batch 050/469 | Cost: 0.1556\n", "Epoch: 008/010 | Batch 100/469 | Cost: 0.0913\n", "Epoch: 008/010 | Batch 150/469 | Cost: 0.0400\n", "Epoch: 008/010 | Batch 200/469 | Cost: 0.0833\n", "Epoch: 008/010 | Batch 250/469 | Cost: 0.0418\n", "Epoch: 008/010 | Batch 300/469 | Cost: 0.0885\n", "Epoch: 008/010 | Batch 350/469 | Cost: 0.0844\n", "Epoch: 008/010 | Batch 400/469 | Cost: 0.0675\n", "Epoch: 008/010 | Batch 450/469 | Cost: 0.1387\n", "Epoch: 008/010 training accuracy: 97.56%\n", "Epoch: 009/010 | Batch 000/469 | Cost: 0.0827\n", "Epoch: 009/010 | Batch 050/469 | Cost: 0.1027\n", "Epoch: 009/010 | Batch 100/469 | Cost: 0.1812\n", "Epoch: 009/010 | Batch 150/469 | Cost: 0.0660\n", "Epoch: 009/010 | Batch 200/469 | Cost: 0.0881\n", "Epoch: 009/010 | Batch 250/469 | Cost: 0.1576\n", "Epoch: 009/010 | Batch 300/469 | Cost: 0.0478\n", "Epoch: 009/010 | Batch 350/469 | Cost: 0.0779\n", "Epoch: 009/010 | Batch 400/469 | Cost: 0.0407\n", "Epoch: 009/010 | Batch 450/469 | Cost: 0.0236\n", "Epoch: 009/010 training accuracy: 97.83%\n", "Epoch: 010/010 | Batch 000/469 | Cost: 0.0182\n", "Epoch: 010/010 | Batch 050/469 | Cost: 0.0742\n", "Epoch: 010/010 | Batch 100/469 | Cost: 0.0425\n", "Epoch: 010/010 | Batch 150/469 | Cost: 0.0332\n", "Epoch: 010/010 | Batch 200/469 | Cost: 0.0795\n", "Epoch: 010/010 | Batch 250/469 | Cost: 0.0571\n", "Epoch: 010/010 | Batch 300/469 | Cost: 0.1068\n", "Epoch: 010/010 | Batch 350/469 | Cost: 0.1661\n", "Epoch: 010/010 | Batch 400/469 | Cost: 0.0202\n", "Epoch: 010/010 | Batch 450/469 | Cost: 0.0613\n", "Epoch: 010/010 training accuracy: 97.77%\n" ] } ], "source": [ "def compute_accuracy(model, data_loader):\n", " correct_pred, num_examples = 0, 0\n", " for features, targets in data_loader:\n", " features = features.to(device)\n", " targets = targets.to(device)\n", " logits, probas = model(features)\n", " _, predicted_labels = torch.max(probas, 1)\n", " num_examples += targets.size(0)\n", " correct_pred += (predicted_labels == targets).sum()\n", " return correct_pred.float()/num_examples * 100\n", " \n", "\n", " \n", "minibatch_costs = []\n", "\n", "%matplotlib notebook\n", "plot = LivePerformanceplot(labels=['Minibatch Cost'],\n", " xlabel='Iteration')\n", "\n", "\n", "for epoch in range(num_epochs):\n", "\n", " model = model.train()\n", " for batch_idx, (features, targets) in enumerate(train_loader):\n", " \n", " features = features.to(device)\n", " targets = targets.to(device)\n", "\n", " ### FORWARD AND BACK PROP\n", " logits, probas = model(features)\n", " cost = cost_fn(logits, targets)\n", " minibatch_costs.append(cost.detach().cpu().numpy())\n", " optimizer.zero_grad()\n", " \n", " cost.backward()\n", " \n", " ### UPDATE MODEL PARAMETERS\n", " optimizer.step()\n", " \n", " ### UPDATE PLOT\n", " \n", " data_dict = {'Minibatch Cost': (range(len(minibatch_costs)), minibatch_costs)}\n", " plot.update(data_dict=data_dict)\n", " \n", " ### LOGGING\n", " if not batch_idx % 50:\n", " print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \n", " %(epoch+1, num_epochs, batch_idx, \n", " len(train_loader), cost))\n", " \n", " model = model.eval()\n", " print('Epoch: %03d/%03d training accuracy: %.2f%%' % (\n", " epoch+1, num_epochs, \n", " compute_accuracy(model, train_loader)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluation" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test accuracy: 97.76%\n" ] } ], "source": [ "print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader)))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.1" }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }