{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MNIST-Overfit-Weight-Decay" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# coding: utf-8\n", "import sys, os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import math\n", "\n", "sys.path.append(os.pardir)\n", "from deeplink.mnist import *\n", "from deeplink.networks import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multilayer Neural Network Model (Two Hidden Layers) and Learing/Validation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multi Layer Model Class" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class MultiLayerNetExtended(MultiLayerNet):\n", " def __init__(self, input_size, hidden_size_list, output_size, activation='ReLU', initializer='N2', \n", " optimizer='AdaGrad', learning_rate=0.01, \n", " use_batch_normalization=False, \n", " use_weight_decay=False, weight_decay_lambda=0.0):\n", " self.input_size = input_size\n", " self.output_size = output_size\n", " self.hidden_size_list = hidden_size_list\n", " self.hidden_layer_num = len(hidden_size_list)\n", " \n", " self.use_batch_normalization = use_batch_normalization\n", "\n", " self.use_weight_decay = use_weight_decay\n", " self.weight_decay_lambda = weight_decay_lambda\n", " \n", " # Weight Initialization\n", " self.params = {}\n", " self.weight_initialization(initializer)\n", " \n", " # Layering\n", " self.layers = OrderedDict()\n", " self.last_layer = None\n", " self.layering(activation)\n", "\n", " # Optimization Method\n", " self.optimizer = optimizers[optimizer](lr=learning_rate)\n", " \n", " def weight_initialization(self, initializer):\n", " params_size_list = [self.input_size] + self.hidden_size_list + [self.output_size]\n", " initializer_obj = initializers[initializer](self.params, \n", " params_size_list, \n", " self.use_batch_normalization)\n", " initializer_obj.initialize_params();\n", " \n", " def layering(self, activation):\n", " for idx in range(1, self.hidden_layer_num + 1):\n", " self.layers['Affine' + str(idx)] = Affine(self.params['W' + str(idx)], self.params['b' + str(idx)])\n", " if self.use_batch_normalization:\n", " self.layers['Batch_Normalization' + str(idx)] = BatchNormalization(self.params['gamma' + str(idx)], \n", " self.params['beta' + str(idx)])\n", " self.layers['Activation' + str(idx)] = activation_layers[activation]()\n", "\n", " idx = self.hidden_layer_num + 1\n", " self.layers['Affine' + str(idx)] = Affine(self.params['W' + str(idx)], self.params['b' + str(idx)])\n", "\n", " self.last_layer = SoftmaxWithCrossEntropyLoss() \n", "\n", " def predict(self, x, is_train=False):\n", " for key, layer in self.layers.items():\n", " if \"BatchNorm\" in key:\n", " x = layer.forward(x, is_train)\n", " else:\n", " x = layer.forward(x)\n", " return x\n", "\n", " def loss(self, x, t, is_train=False):\n", " y = self.predict(x, is_train)\n", "\n", " if self.use_weight_decay:\n", " weight_decay = 0.0\n", " for idx in range(1, self.hidden_layer_num + 2):\n", " W = self.params['W' + str(idx)]\n", " weight_decay += 0.5 * self.weight_decay_lambda * np.sum(W**2)\n", " return self.last_layer.forward(y, t) + weight_decay\n", " else:\n", " return self.last_layer.forward(y, t)\n", "\n", " def accuracy(self, x, t):\n", " y = self.predict(x, is_train=False)\n", " y = np.argmax(y, axis=1)\n", " if t.ndim != 1 : t = np.argmax(t, axis=1)\n", "\n", " accuracy = np.sum(y == t) / float(x.shape[0])\n", " return accuracy \n", "\n", " def backpropagation_gradient(self, x, t):\n", " # forward\n", " self.loss(x, t, is_train=True)\n", "\n", " # backward\n", " din = 1\n", " din = self.last_layer.backward(din)\n", "\n", " layers = list(self.layers.values())\n", " layers.reverse()\n", " for layer in layers:\n", " din = layer.backward(din)\n", "\n", " grads = {}\n", " for idx in range(1, self.hidden_layer_num + 2):\n", " if self.use_weight_decay:\n", " grads['W' + str(idx)] = self.layers['Affine' + str(idx)].dW + self.weight_decay_lambda * self.params['W' + str(idx)]\n", " else:\n", " grads['W' + str(idx)] = self.layers['Affine' + str(idx)].dW\n", " grads['b' + str(idx)] = self.layers['Affine' + str(idx)].db\n", "\n", " if self.use_batch_normalization and idx <= self.hidden_layer_num:\n", " grads['gamma' + str(idx)] = self.layers['Batch_Normalization' + str(idx)].dgamma\n", " grads['beta' + str(idx)] = self.layers['Batch_Normalization' + str(idx)].dbeta\n", " \n", " return grads\n", "\n", " def learning(self, x_batch, t_batch):\n", " grads = self.backpropagation_gradient(x_batch, t_batch)\n", " self.optimizer.update(self.params, grads)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training and Evaluation" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = mnist_data(\"/Users/yhhan/git/aiclass/0.Professor/data/MNIST_data/.\")\n", "(img_train, label_train), (img_validation, label_validation), (img_test, label_test) = data.load_mnist(flatten=True, normalize=True, one_hot_label=True)\n", "\n", "# 오버피팅을 유도하기 위하여 데이터 수를 대폭 줄임\n", "img_train = img_train[:200]\n", "label_train = label_train[:200]\n", "\n", "# 오버피팅을 유도하기 위하여 레이어를 깊게 가져가고 파라미터를 대폭 늘림\n", "input_size=784\n", "hidden_layer1_size=128\n", "hidden_layer2_size=128\n", "hidden_layer3_size=128\n", "hidden_layer4_size=128\n", "hidden_layer5_size=128\n", "hidden_layer6_size=128\n", "output_size=10\n", "\n", "num_epochs = 200\n", "train_size = img_train.shape[0]\n", "batch_size = 100\n", "learning_rate = 0.1\n", "\n", "markers = {\"Xavier, AdaGrad, No_Batch_Norm, lambda=0.0\": \"x\", \"Xavier, AdaGrad, No_Batch_Norm, lambda=0.1\": \"o\"}\n", "\n", "networks = {}\n", "train_errors = {}\n", "validation_errors = {}\n", "test_accuracy_values = {}\n", "max_test_accuracy_epoch = {}\n", "max_test_accuracy_value = {}\n", "\n", "for key in markers.keys():\n", " if key == \"Xavier, AdaGrad, No_Batch_Norm, lambda=0.0\":\n", " networks[key] = MultiLayerNetExtended(input_size, \n", " [hidden_layer1_size, hidden_layer2_size, hidden_layer3_size, hidden_layer4_size, hidden_layer5_size, hidden_layer6_size], \n", " output_size, \n", " activation='ReLU', \n", " initializer='Xavier',\n", " optimizer='AdaGrad', learning_rate=learning_rate,\n", " use_batch_normalization=False, \n", " use_weight_decay=False, weight_decay_lambda=0.0)\n", " elif key == \"Xavier, AdaGrad, No_Batch_Norm, lambda=0.1\":\n", " networks[key] = MultiLayerNetExtended(input_size, \n", " [hidden_layer1_size, hidden_layer2_size, hidden_layer3_size, hidden_layer4_size, hidden_layer5_size, hidden_layer6_size], \n", " output_size, \n", " activation='ReLU', \n", " initializer='Xavier',\n", " optimizer='AdaGrad', learning_rate=learning_rate,\n", " use_batch_normalization=False, \n", " use_weight_decay=True, weight_decay_lambda=0.1)\n", " \n", " train_errors[key] = [] \n", " validation_errors[key] = []\n", " test_accuracy_values[key] = []\n", " max_test_accuracy_epoch[key] = 0\n", " max_test_accuracy_value[key] = 0.0" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "../common/functions.py:56: RuntimeWarning: divide by zero encountered in log\n", " return -np.sum(np.log(y[np.arange(batch_size), t])) / batch_size\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, " ] } ], "source": [ "epoch_list = []\n", "\n", "num_batch = math.ceil(train_size / batch_size)\n", "\n", "for i in range(num_epochs):\n", " epoch_list.append(i)\n", " for key in markers.keys():\n", " for k in range(num_batch):\n", " x_batch = img_train[k * batch_size : k * batch_size + batch_size]\n", " t_batch = label_train[k * batch_size : k * batch_size + batch_size]\n", " networks[key].learning(x_batch, t_batch)\n", "\n", " train_loss = networks[key].loss(x_batch, t_batch, is_train=True)\n", " train_errors[key].append(train_loss)\n", "\n", " validation_loss = networks[key].loss(img_validation, label_validation, is_train=False)\n", " validation_errors[key].append(validation_loss) \n", "\n", " test_accuracy = networks[key].accuracy(img_test, label_test)\n", " test_accuracy_values[key].append(test_accuracy)\n", " if test_accuracy > max_test_accuracy_value[key]:\n", " max_test_accuracy_epoch[key] = i \n", " max_test_accuracy_value[key] = test_accuracy\n", "# print(\"{0:26s}-Epoch:{1:3d}, Train Err.:{2:7.5f}, Validation Err.:{3:7.5f}, Test Accuracy:{4:7.5f}, Max Test Accuracy:{5:7.5f}\".format(\n", "# key,\n", "# i,\n", "# train_loss,\n", "# validation_loss,\n", "# test_accuracy,\n", "# max_test_accuracy_value[key]\n", "# ))\n", " print(i, end=\", \") " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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rLkpKSo47Lz09nTfffJM+ffrEIcraz8xygUVAezPbYma/8L01lDJT5cysuZn5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G4aM5t3bj6twMTi4ftqwfpdHJeeQq1qVejVoi5mxr0XnsyfP1vBP6Z9T4/mdTkuPYVfjcsgPTWF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\nc/nll9OuXTs6duzI9ddfT79+/QDv7OEtt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fmve+5zJhIJGhoasj/3pCZDqfdp5jHLPUf+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\nMlH0G0D/pHl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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, axarr = plt.subplots(2, 2, figsize=(20, 12))\n", "for key in markers.keys():\n", " axarr[0, 0].plot(epoch_list[1:], train_errors[key][1:], marker=markers[key], markevery=2, label=key)\n", "axarr[0, 0].set_ylabel('Train - Total Error')\n", "axarr[0, 0].set_xlabel('Epochs')\n", "axarr[0, 0].grid(True)\n", "axarr[0, 0].set_title('Train Error')\n", "axarr[0, 0].legend(loc='upper right')\n", "\n", "for key in markers.keys():\n", " axarr[0, 1].plot(epoch_list[1:], validation_errors[key][1:], marker=markers[key], markevery=2, label=key)\n", "axarr[0, 1].set_ylabel('Validation - Total Error')\n", "axarr[0, 1].set_xlabel('Epochs')\n", "axarr[0, 1].grid(True)\n", "axarr[0, 1].set_title('Validation Error')\n", "axarr[0, 1].legend(loc='upper right')\n", "\n", "for key in markers.keys():\n", " axarr[1, 0].plot(epoch_list[1:], train_errors[key][1:], marker=markers[key], markevery=2, label=key)\n", "axarr[1, 0].set_ylabel('Train - Total Error')\n", "axarr[1, 0].set_xlabel('Epochs')\n", "axarr[1, 0].grid(True)\n", "axarr[1, 0].set_ylim(2.25, 2.4)\n", "axarr[1, 0].set_title('Train Error (2.25 ~ 2.4)')\n", "axarr[1, 0].legend(loc='upper right')\n", "\n", "for key in markers.keys():\n", " axarr[1, 1].plot(epoch_list[1:], validation_errors[key][1:], marker=markers[key], markevery=2, label=key)\n", "axarr[1, 1].set_ylabel('Validation - Total Error')\n", "axarr[1, 1].set_xlabel('Epochs')\n", "axarr[1, 1].grid(True)\n", "axarr[1, 1].set_ylim(2.25, 2.4)\n", "axarr[1, 1].set_title('Validation Error (2.25 ~ 2.4)')\n", "axarr[1, 1].legend(loc='upper right')\n", "\n", "f.subplots_adjust(hspace=0.3)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JrU9u59cPr+Spd49xuMXOzYtyONnZB/QPUh+4sog/fHKS49aeYWWKjCQxUcZQ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\nuXPnMm7cOCorK3nyySe58MILW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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, axarr = plt.subplots(2, 1, figsize=(15,10))\n", "for key in markers.keys():\n", " axarr[0].plot(epoch_list[1:], test_accuracy_values[key][1:], marker=markers[key], markevery=1, label=key)\n", "axarr[0].set_ylabel('Test Accuracy')\n", "axarr[0].set_xlabel('Epochs')\n", "axarr[0].grid(True)\n", "axarr[0].set_title('Test Accuracy')\n", "axarr[0].legend(loc='lower right')\n", "\n", "for key in markers.keys():\n", " axarr[1].plot(epoch_list[1:], test_accuracy_values[key][1:], marker=markers[key], markevery=1, label=key)\n", "axarr[1].set_ylabel('Test Accuracy')\n", "axarr[1].set_xlabel('Epochs')\n", "axarr[1].grid(True)\n", "axarr[1].set_ylim(0.94, 0.99)\n", "axarr[1].set_title('Test Accuracy (0.7 ~ 1.0)')\n", "axarr[1].legend(loc='lower right')\n", "\n", "f.subplots_adjust(hspace=0.3)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Xavier, AdaGrad, No_Batch_Norm, lambda=0.0 - Epoch: 50, Max Test Accuracy: 0.61630\n", "Xavier, AdaGrad, No_Batch_Norm, lambda=0.1 - Epoch: 2, Max Test Accuracy: 0.11350\n" ] } ], "source": [ "for key in markers.keys():\n", " print(\"{0:26s} - Epoch:{1:3d}, Max Test Accuracy: {2:7.5f}\".format(key, max_test_accuracy_epoch[key], max_test_accuracy_value[key]))" ] } ], "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 0 }