{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#
Visualizing Deep Neural Networks and Optimization Paths
\n", "##
Spencer Jenkins
\n", "###
CS 6804: Optimization in Machine Learning - Final Project
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This project is a preliminary implementation of some of the techniques found in the recent paper by Li, et al. [(Visualizing the Loss Landscape of Neural Nets)](https://arxiv.org/abs/1712.09913). We seek to be able to visualize high-dimensional, non-convex objective functions that appear when using a deep neural network. We would also like to be able to visualize the paths that various optimization algorithms take through this loss surface. \n", "\n", "We do this by picking two direction vectors of the same dimension as the parameter vector of the network. These two vectors become the axes for our plots. These are taken as standard normal vectors in this implementation. To capture the path of the optimization algorithm, we can not use random vectors. Instead, we take our direction vectors to be the first two PCA components of the matrix of differences between the final network parameters and the network parameters at each step of the optimization process. We let the user determine which plotting procedure to use by changing the showPath variable.\n", "\n", "The user can change the size and shape of the network layers as well as the optimization algorithm and its associated hyperparameters. This allows the user to visually experiment with changing the approach to their problem. Currently, the dataset being used is the MNIST dataset contained in the sklearn.datasets library. \n", "\n", "### Future Work\n", "As stated, this project is preliminary. The original paper did not provide a code repository, so this is an original implementation of the ideas presented in the paper. Thus, we are using a (relatively) small feed-forward network.\n", "\n", "The largest difference between this implementation and the paper lies in the scaling of the direction vectors used for plotting. In the paper, a filter-wise normalization was proposed. Because we are not using a convolutional neural network, we do not perform a filter-wise scaling. We chose to scale the direction vectors to the magnitude of the vector containing all the parameters of the network. When plotting the path on the objective function, we do not perform scaling. Eventually, I would like to perform filter-wise scaling for both plotting approachs (with and without the optimization path).\n", "\n", "Secondly, the resolution of the plots obtained in this implementation are less than those in the paper. This is due to computing constraints. On 4 GPUs, some of the authors' low resolution plots took around an hour to produce! Some consideration into the computational efficiency of the algorithms used might be a helful future step.\n", "\n", "Lastly, we are considering a MLPClassifier from scikit-learn as our network. This made things easier to set up, as well as kept the number of parameters manageable to allow succesful plotting of the objective function. Eventually, it would be nice to allow the user to interface with a larger, more powerful model (e.g., a network created in TensorFlow). Though this would be more computationally taxing, it would probably provide more practical benefit to the user. \n", "\n", "Overall, however, the ability to visualize the objective function and the path of your optimization algorithm through it is of great value to understanding and successfully applying these deep neural networks." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# USER SETTINGS \n", "# verbose: print statements for debugging\n", "# iterations: number of training iterations\n", "# showPath: display the optimization path on the objective function plot\n", "verbose = True\n", "iterations = 25 #50\n", "showPath = True #True" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.neural_network import MLPClassifier\n", "from sklearn.datasets import fetch_mldata\n", "from sklearn.metrics import log_loss\n", "from sklearn.utils import shuffle\n", "import copy\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "# Import MNIST data\n", "mnist = fetch_mldata('MNIST original')\n", "# Split data into training and test sets\n", "mnist.data, mnist.target = shuffle(mnist.data, mnist.target, random_state = 0)\n", "trainData = mnist.data[0:60000,:]/255.\n", "trainLabels = mnist.target[0:60000]\n", "testData = mnist.data[60000:,:]/255.\n", "testLabels = mnist.target[60000:]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 1, loss = 2.27458428\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/bert/anaconda/lib/python2.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1) reached and the optimization hasn't converged yet.\n", " % self.max_iter, ConvergenceWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration 2, loss = 1.97659630\n", "Iteration 3, loss = 1.56892341\n", "Iteration 4, loss = 1.23871473\n", "Iteration 5, loss = 1.01745649\n", "Iteration 6, loss = 0.84916384\n", "Iteration 7, loss = 0.72800790\n", "Iteration 8, loss = 0.64138160\n", "Iteration 9, loss = 0.57607383\n", "Iteration 10, loss = 0.52464105\n", "Iteration 11, loss = 0.48533431\n", "Iteration 12, loss = 0.45660109\n", "Iteration 13, loss = 0.43532459\n", "Iteration 14, loss = 0.41924716\n", "Iteration 15, loss = 0.40661407\n", "Iteration 16, loss = 0.39579202\n", "Iteration 17, loss = 0.38646310\n", "Iteration 18, loss = 0.37826508\n", "Iteration 19, loss = 0.37126972\n", "Iteration 20, loss = 0.36493746\n", "Iteration 21, loss = 0.35861693\n", "Iteration 22, loss = 0.35323892\n", "Iteration 23, loss = 0.34764832\n", "Iteration 24, loss = 0.34293090\n", "Iteration 25, loss = 0.33812171\n" ] } ], "source": [ "# Train network one iteration at a time, saving weights for trajectory visualization\n", "\n", "# NOTE: change optimization algorithm by adjusting the 'solver' parameter\n", "# NOTE: the number and shapes of the network's hidden layers can be \n", "# modified through the 'hidden_layer_sizes' parameter\n", "network = MLPClassifier(hidden_layer_sizes = (10,10,10,), max_iter = 1, solver='sgd', \\\n", " learning_rate='constant', warm_start=True, verbose = True)\n", "weights = []\n", "intercepts = []\n", "params = []\n", "for i in range(0,iterations):\n", " network.fit(trainData,trainLabels)\n", " w = network.coefs_[0]\n", " for i in range(1,len(network.coefs_)):\n", " w = np.append(w,network.coefs_[i])\n", " inter = network.intercepts_[0]\n", " for i in range(1,len(network.intercepts_)):\n", " inter = np.append(inter,network.intercepts_[i])\n", " params.append(np.reshape(np.append(w,inter),(-1,1)))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Saves the final weights and intercepts of the network \n", "opt_coeffs = copy.deepcopy(network.coefs_)\n", "opt_intercepts = copy.deepcopy(network.intercepts_)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Accuracy:', 0)\n" ] } ], "source": [ "# Determine the accuracy of the network on the test data\n", "accuracy = np.sum(network.predict(testData) == testLabels)/len(testLabels)\n", "if verbose:\n", " print('Accuracy:',accuracy)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Saves the network attributes at the completion of training into a single parameter\n", "# vector. The shapes and lengths of these attributes are also stored for later use\n", "wVals = []\n", "wShapes = []\n", "wLens = []\n", "bVals = []\n", "bShapes = []\n", "bLens = []\n", "for i in range(0,len(network.coefs_)):\n", " wVals.append(np.reshape(network.coefs_[i],(-1,1)))\n", " wShapes.append(network.coefs_[i].shape)\n", " wLens.append(len(wVals[i]))\n", "for i in range(0,len(network.intercepts_)):\n", " bVals.append(np.reshape(network.intercepts_[i],(-1,1)))\n", " bShapes.append(network.intercepts_[i].shape)\n", " bLens.append(len(bVals[i]))\n", "w = np.reshape(network.coefs_[0],(-1,1))\n", "for i in range(1,len(wVals)):\n", " w = np.reshape(np.append(w,network.coefs_[i]),(-1,1))\n", "for i in range(0,len(bVals)):\n", " w = np.reshape(np.append(w,network.intercepts_[i]),(-1,1))\n", "w_length = len(w)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Determines the differences at each optimization step between the current and\n", "# final network parameters\n", "differences = np.zeros((w_length,len(params)-1))\n", "for i in range(0,len(params)-1):\n", " differences[:,i]=np.reshape((params[-1]-params[i]),w_length)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n", " svd_solver='auto', tol=0.0, whiten=False)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Perform PCA on the matrix of differences computed above. \n", "from sklearn.decomposition import PCA\n", "pca = PCA(n_components = 2)\n", "pca.fit(np.transpose(differences))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Determine the vectors defining the axes of the visualization.\n", "# If showPath, we take the vectors to be the PCA components determined above.\n", "# If not showPath, we take random normal vectors and scale them to the norm of \n", "# the final parameter vector.\n", "if showPath:\n", " d0 = np.reshape(pca.components_[0],(-1,1))\n", " d1 = np.reshape(pca.components_[1],(-1,1))\n", "else:\n", " d0 = np.random.randn(w_length,1)\n", " d0 = d0*np.linalg.norm(params[-1])/np.linalg.norm(d0)\n", " d1 = np.random.randn(w_length,1)\n", " d1 = d1*np.linalg.norm(params[-1])/np.linalg.norm(d1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Transforms the parameter vectors along the optimization path into the PCA space\n", "transformed = []\n", "for i in range(0,len(params)):\n", " transformed.append(pca.transform(np.transpose(params[i])))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Transforms the final parameter vector into the PCA space\n", "w_star_pca = pca.transform(np.transpose(params[-1]))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Determines the distance between each parameter vector along \n", "# the optimization path and the final parameter vector\n", "plotPath = []\n", "for i in range(0,len(transformed)):\n", " plotPath.append(transformed[i] - w_star_pca)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Determines the coordinates of the optimization path for plotting\n", "x_plot = []\n", "y_plot = []\n", "for i in range(0,len(transformed)):\n", " x_plot.append(plotPath[i][0][1])\n", " y_plot.append(plotPath[i][0][0])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, 0)\n", "(0, 1)\n", "(0, 2)\n", "(0, 3)\n", "(0, 4)\n", "(0, 5)\n", "(0, 6)\n", "(0, 7)\n", "(0, 8)\n", "(0, 9)\n", "(1, 0)\n", "(1, 1)\n", "(1, 2)\n", "(1, 3)\n", "(1, 4)\n", "(1, 5)\n", "(1, 6)\n", "(1, 7)\n", "(1, 8)\n", "(1, 9)\n", "(2, 0)\n", "(2, 1)\n", "(2, 2)\n", "(2, 3)\n", "(2, 4)\n", "(2, 5)\n", "(2, 6)\n", "(2, 7)\n", "(2, 8)\n", "(2, 9)\n", "(3, 0)\n", "(3, 1)\n", "(3, 2)\n", "(3, 3)\n", "(3, 4)\n", "(3, 5)\n", "(3, 6)\n", "(3, 7)\n", "(3, 8)\n", "(3, 9)\n", "(4, 0)\n", "(4, 1)\n", "(4, 2)\n", "(4, 3)\n", "(4, 4)\n", "(4, 5)\n", "(4, 6)\n", "(4, 7)\n", "(4, 8)\n", "(4, 9)\n", "(5, 0)\n", "(5, 1)\n", "(5, 2)\n", "(5, 3)\n", "(5, 4)\n", "(5, 5)\n", "(5, 6)\n", "(5, 7)\n", "(5, 8)\n", "(5, 9)\n", "(6, 0)\n", "(6, 1)\n", "(6, 2)\n", "(6, 3)\n", "(6, 4)\n", "(6, 5)\n", "(6, 6)\n", "(6, 7)\n", "(6, 8)\n", "(6, 9)\n", "(7, 0)\n", "(7, 1)\n", "(7, 2)\n", "(7, 3)\n", "(7, 4)\n", "(7, 5)\n", "(7, 6)\n", "(7, 7)\n", "(7, 8)\n", "(7, 9)\n", "(8, 0)\n", "(8, 1)\n", "(8, 2)\n", "(8, 3)\n", "(8, 4)\n", "(8, 5)\n", "(8, 6)\n", "(8, 7)\n", "(8, 8)\n", "(8, 9)\n", "(9, 0)\n", "(9, 1)\n", "(9, 2)\n", "(9, 3)\n", "(9, 4)\n", "(9, 5)\n", "(9, 6)\n", "(9, 7)\n", "(9, 8)\n", "(9, 9)\n" ] } ], "source": [ "# Determine the loss values within a specified range of the final parameters, traveling \n", "# along the previously computed direction vectors\n", "\n", "# Determine the range:\n", "if showPath:\n", " maxPath = np.max(plotPath)\n", " minPath = np.min(plotPath)\n", " boundary = max(abs(maxPath),abs(minPath))+0.2*max(abs(maxPath),abs(minPath))\n", " optimalRate = boundary*2/10\n", "else:\n", " boundary = 10\n", " optimalRate = 2\n", "\n", "# NOTE: optimalRate determines the \"resolution\" of the plot. A smaller number will result in\n", "# a higher resolution, but will take longer to run\n", "\n", "alpha = np.arange(boundary,-boundary,-optimalRate)\n", "beta = np.arange(-boundary,boundary,optimalRate)\n", "losses = np.zeros((len(alpha),len(beta)))\n", "\n", "# Determine the loss values:\n", "for b in range(0, len(beta)):\n", " for a in range(0, len(alpha)):\n", " w_current = params[-1] + alpha[a]*d0 + beta[b]*d1\n", " network.coefs_[0] = np.reshape(w_current[0:wLens[0]],wShapes[0])\n", " for i in range(1,len(network.coefs_)):\n", " network.coefs_[i] = np.reshape(w_current[np.sum(wLens[0:i]):np.sum(wLens[0:i+1])],wShapes[i])\n", " network.intercepts_[0] = np.reshape(w_current[np.sum(wLens):np.sum(wLens)+bLens[0]],bShapes[0])\n", " for i in range(1,len(network.intercepts_)):\n", " network.intercepts_[i] = np.reshape(w_current[np.sum(wLens)+np.sum(bLens[0:i]):np.sum(wLens)+np.sum(bLens[0:i+1])],bShapes[i])\n", " \n", " predicted = network.predict_proba(trainData)\n", " losses[a,b] = log_loss(trainLabels,predicted)\n", " if verbose:\n", " print (b,a)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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1izfqWSLzBm/DzNKY1YETsK2QifHBtXCWjd1dvtv0jRO1QFLE90O3E/Ewjukb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emt424nsnz0FlKevaNln8YMX9ALAxM9SuVlHFh6mn2LTgjNTNtHE9StvqCSeZVB9NmHAkce9Zz0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\ny8tZeGQCV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sRT9PS1aNKiBs3b+FCzTnXEYjGm5gZ07duQrn0bkvIiQ9gUzsawZM4x/lpw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Ia5WufOpcmyXaWw18k9DM677LHgvGERRsbakMZZhPhlu1PwwlduMDyeNKyU+mJTmHe6LcrS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\nWDxKQyFCKQUugvrQVKANypRSynhXv04lhmERk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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the objective function (and optimization path)\n", "\n", "# Note: the third argument to the plt.contour function is the number of levels to display. If\n", "# you find your plot doesn't contain enough information or is obscured by too much of it, try adjusting this number\n", "if showPath:\n", " plt.contour(np.arange(-boundary,boundary,optimalRate),np.arange(-boundary,boundary,optimalRate),losses,250)\n", " plt.plot(x_plot[0:],y_plot[0:],'r-')\n", "else:\n", " plt.contour(np.arange(-boundary,boundary,optimalRate),np.arange(-boundary,boundary,optimalRate),losses,250)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }