{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Image classification of simulated AT-TPC events\n", "\n", "Welcome to this project in applied machine learning. In this project we will tackle a simple classification problem of two different classes. The classes are simulated reaction types for the Ar(p, p') experiment conducted at MSU, in this task we'll focus on the classification task and simply treat the experiment as a black box. \n", "\n", "### This is a completed notebook with solution examples, for your implementation we suggest you implement your own solution in the `project.ipynb` notebook\n", "\n", "This project has three tasks with a recommendation for the time to spend on each task: \n", "\n", "- Preparation, Data exploration and standardization: 0.5hr\n", "- Model construction: 1hr\n", "- Hyperparameter tuning and performance validation: 1hr\n", "\n", "There is a notebook `project_solution.ipynb` included with suggestions to solutions for each task included, for reference or to easily move on to a part of the project more appealing to your interests. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparation: \n", "\n", "This project uses python and the machine learning library `keras`. As well as some functionality from `numpy` and `scikit-learn`. We recommend a `Python` verson of `>3.4`. These libraries should be installed to your specific system by using the command `pip3 install --user LIBRARY_NAME`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Task 1: Data exploration and standardization \n", "\n", "In machine learning, as in many other fields, the task of preparing data for analysis is as vital as it can be troublesome and tedious. In data-analysis the researcher can expect to spend the majority of their time merely processing data to prepare for analysis. In this projcet we will focus more on the entire pipeline of analysis, and so the data at hand has already been shaped to an image format suitable for our analysis. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Task 1a: Loading the data\n", "\n", "The data is stored in the `.npy` format using vecotrized code to speed up the read process. The files pointed to in this task are downsampled images with dimensions $64 x 64$ (if the images are to big for your laptop to handle, the script included in `../scripts/downsample_images.py` can further reduce the dimension). " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data shape: (8000, 64, 64, 1)\n" ] } ], "source": [ "import numpy as np # we'll be using this shorthand for the NumPy library throughout\n", "\n", "dataset = np.load(\"../data/images/project_data.npy\")\n", "n_samples = dataset.shape[0]\n", "\n", "print(\"Data shape: \", dataset.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Task 1b: Inspecting the data\n", "\n", "The data is stored as xy projections of the real events, who take place in a 3d volume. This allows a simple exploratiuon of the data as images. In this task you should plot a few different events in a grid using `matplotlib.pyplot` " ] }, { "cell_type": "code", "execution_count": 27, "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" }, { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# copied from https://keras.io/visualization/\n", "# Plot training & validation accuracy values\n", "fig, axs= plt.subplots(figsize=(10, 8), nrows=2)\n", "fig.suptitle('Model performance')\n", "axs[0].plot(history.history['acc'], \"x-\",alpha=0.8)\n", "axs[0].plot(history.history['val_acc'], \"x-\", alpha=0.8)\n", "\n", "axs[0].set_ylabel('Accuracy')\n", "axs[0].set_xlabel('Epoch')\n", "axs[0].legend(['Train', 'Test'], loc='upper left')\n", "\n", "\n", "# Plot training & validation loss values\n", "\n", "axs[1].plot(history.history['loss'], \"o-\",alpha=0.8)\n", "axs[1].plot(history.history['val_loss'], \"o-\", alpha=0.8)\n", "\n", "axs[1].set_ylabel('Loss')\n", "axs[1].legend(['Train', 'Test'], loc='upper left')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3a: Performance validation\n", "\n", "As mentioned in the lectures machine learning models suffer from the problem of overfitting as scaling to an almost arbitrary complexity is simple with todays hardware. The challenge then is often that of finding the correct architecture and type of model suitable for your problem. In this task you will be familiarized with some tools to monitor and estimate the degree of overfitting.\n", "\n", "In task 1c you separated your data in training and test sets. The test set is used to estimate generalization performance. Typically we use this to fine-tune the hyperparameters. Another trick is to use a validation set during training, which we implemented in the last task for the training. \n", "\n", "In this task we will start with attaching callbacks to the fitting process. They are listed in the documentation for `Keras`here: https://keras.io/callbacks/\n", "Pick ones you think are suitable for our problem and re-run the training from above." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 5780 samples, validate on 1020 samples\n", "Epoch 1/150\n", " - 2s - loss: 0.4109 - acc: 0.9566 - val_loss: 0.4189 - val_acc: 0.9510\n", "Epoch 2/150\n", " - 2s - loss: 0.4241 - acc: 0.9502 - val_loss: 0.4276 - val_acc: 0.9569\n", "Epoch 3/150\n", " - 2s - loss: 0.4174 - acc: 0.9559 - val_loss: 0.4488 - val_acc: 0.9343\n", "Epoch 4/150\n", " - 2s - loss: 0.4388 - acc: 0.9514 - val_loss: 0.4379 - val_acc: 0.9451\n", "Epoch 5/150\n", " - 2s - loss: 0.4200 - acc: 0.9543 - val_loss: 0.4451 - val_acc: 0.9392\n" ] } ], "source": [ "from keras.callbacks import EarlyStopping, ModelCheckpoint\n", "\n", "callbacks = [EarlyStopping(min_delta=0.0001, patience=4), ModelCheckpoint(\"../checkpoints/ckpt\")\n", "\n", "history = model_o.fit(\n", " x=train_X,\n", " y=onehot_train_y,\n", " batch_size=50,\n", " epochs=150,\n", " validation_split=0.15,\n", " verbose=2,\n", " callbacks=callbacks\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3b: Hyperparameter tuning\n", "\n", "Hyperparameters, like the number of layers, learning rate or others can have a very big impact on the model quality. The model performance should also be statistically quantified using cross validation, bootstrapped confidence intervals for your performance metrics or other tools depending on model. \n", "In this task you should then implement a function or for loop doing either random search or a grid search over parameters and finally you should plot those results in a suitable way." ] } ], "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }