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--- a +++ b/SWELL-KW/SWELL-KW_FastGRNN.ipynb @@ -0,0 +1,978 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SWELL-KW FastGRNN" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adapted from Microsoft's notebooks, available at https://github.com/microsoft/EdgeML authored by Dennis et al." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from tabulate import tabulate\n", + "import os\n", + "import datetime as datetime\n", + "import pickle as pkl\n", + "from sklearn.model_selection import train_test_split\n", + "import pathlib\n", + "from os import mkdir" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def loadData(dirname):\n", + " x_train = np.load(dirname + '/' + 'x_train.npy')\n", + " y_train = np.load(dirname + '/' + 'y_train.npy')\n", + " x_test = np.load(dirname + '/' + 'x_test.npy')\n", + " y_test = np.load(dirname + '/' + 'y_test.npy')\n", + " x_val = np.load(dirname + '/' + 'x_val.npy')\n", + " y_val = np.load(dirname + '/' + 'y_val.npy')\n", + " return x_train, y_train, x_test, y_test, x_val, y_val\n", + "def makeEMIData(subinstanceLen, subinstanceStride, sourceDir, outDir):\n", + " x_train, y_train, x_test, y_test, x_val, y_val = loadData(sourceDir)\n", + " x, y = bagData(x_train, y_train, subinstanceLen, subinstanceStride)\n", + " np.save(outDir + '/x_train.npy', x)\n", + " np.save(outDir + '/y_train.npy', y)\n", + " print('Num train %d' % len(x))\n", + " x, y = bagData(x_test, y_test, subinstanceLen, subinstanceStride)\n", + " np.save(outDir + '/x_test.npy', x)\n", + " np.save(outDir + '/y_test.npy', y)\n", + " print('Num test %d' % len(x))\n", + " x, y = bagData(x_val, y_val, subinstanceLen, subinstanceStride)\n", + " np.save(outDir + '/x_val.npy', x)\n", + " np.save(outDir + '/y_val.npy', y)\n", + " print('Num val %d' % len(x))\n", + "def bagData(X, Y, subinstanceLen, subinstanceStride):\n", + " numClass = 2\n", + " numSteps = 20\n", + " numFeats = 22\n", + " assert X.ndim == 3\n", + " print(X.shape)\n", + " assert X.shape[1] == numSteps\n", + " assert X.shape[2] == numFeats\n", + " assert subinstanceLen <= numSteps\n", + " assert subinstanceLen > 0\n", + " assert subinstanceStride <= numSteps\n", + " assert subinstanceStride >= 0\n", + " assert len(X) == len(Y)\n", + " assert Y.ndim == 2\n", + " assert Y.shape[1] == numClass\n", + " x_bagged = []\n", + " y_bagged = []\n", + " for i, point in enumerate(X[:, :, :]):\n", + " instanceList = []\n", + " start = 0\n", + " end = subinstanceLen\n", + " while True:\n", + " x = point[start:end, :]\n", + " if len(x) < subinstanceLen:\n", + " x_ = np.zeros([subinstanceLen, x.shape[1]])\n", + " x_[:len(x), :] = x[:, :]\n", + " x = x_\n", + " instanceList.append(x)\n", + " if end >= numSteps:\n", + " break\n", + " start += subinstanceStride\n", + " end += subinstanceStride\n", + " bag = np.array(instanceList)\n", + " numSubinstance = bag.shape[0]\n", + " label = Y[i]\n", + " label = np.argmax(label)\n", + " labelBag = np.zeros([numSubinstance, numClass])\n", + " labelBag[:, label] = 1\n", + " x_bagged.append(bag)\n", + " label = np.array(labelBag)\n", + " y_bagged.append(label)\n", + " return np.array(x_bagged), np.array(y_bagged)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3679, 20, 22)\n", + "Num train 3679\n", + "(1022, 20, 22)\n", + "Num test 1022\n", + "(409, 20, 22)\n", + "Num val 409\n" + ] + } + ], + "source": [ + "subinstanceLen=8\n", + "subinstanceStride=3\n", + "extractedDir = '/home/sf/data/SWELL-KW/'\n", + "#mkdir('/home/sf/data/SWELL-KW/FG_8_3')\n", + "rawDir = extractedDir + '/RAW'\n", + "sourceDir = rawDir\n", + "outDir = extractedDir + '/FG_%d_%d/' % (subinstanceLen, subinstanceStride)\n", + "makeEMIData(subinstanceLen, subinstanceStride, sourceDir, outDir)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-19T12:39:06.272261Z", + "start_time": "2018-08-19T12:39:05.330668Z" + } + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "import os\n", + "import sys\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "os.environ['CUDA_VISIBLE_DEVICES'] ='0'\n", + "\n", + "# FastGRNN and FastRNN imports\n", + "from edgeml.graph.rnn import EMI_DataPipeline\n", + "from edgeml.graph.rnn import EMI_FastGRNN\n", + "from edgeml.graph.rnn import EMI_FastRNN\n", + "from edgeml.trainer.emirnnTrainer import EMI_Trainer, EMI_Driver\n", + "import edgeml.utils" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-19T12:39:06.292205Z", + "start_time": "2018-08-19T12:39:06.274254Z" + } + }, + "outputs": [], + "source": [ + "# Network parameters for our FastGRNN + FC Layer\n", + "NUM_HIDDEN = 128\n", + "NUM_TIMESTEPS = 8\n", + "NUM_FEATS = 22\n", + "FORGET_BIAS = 1.0\n", + "NUM_OUTPUT = 2\n", + "USE_DROPOUT = False\n", + "KEEP_PROB = 0.9\n", + "\n", + "# Non-linearities can be chosen among \"tanh, sigmoid, relu, quantTanh, quantSigm\"\n", + "UPDATE_NL = \"quantTanh\"\n", + "GATE_NL = \"quantSigm\"\n", + "\n", + "# Ranks of Parameter matrices for low-rank parameterisation to compress models.\n", + "WRANK = 5\n", + "URANK = 6\n", + "\n", + "# For dataset API\n", + "PREFETCH_NUM = 5\n", + "BATCH_SIZE = 32\n", + "\n", + "# Number of epochs in *one iteration*\n", + "NUM_EPOCHS = 3\n", + "# Number of iterations in *one round*. After each iteration,\n", + "# the model is dumped to disk. At the end of the current\n", + "# round, the best model among all the dumped models in the\n", + "# current round is picked up..\n", + "NUM_ITER = 4\n", + "# A round consists of multiple training iterations and a belief\n", + "# update step using the best model from all of these iterations\n", + "NUM_ROUNDS = 30\n", + "\n", + "# A staging direcory to store models\n", + "MODEL_PREFIX = '/home/sf/data/SWELL-KW/FG_8_13/model-fgrnn'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading Data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-19T12:39:06.410372Z", + "start_time": "2018-08-19T12:39:06.294014Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x_train shape is: (3679, 5, 8, 22)\n", + "y_train shape is: (3679, 5, 2)\n", + "x_test shape is: (409, 5, 8, 22)\n", + "y_test shape is: (409, 5, 2)\n" + ] + } + ], + "source": [ + "# Loading the data\n", + "path='/home/sf/data/SWELL-KW/FG_8_3/'\n", + "x_train, y_train = np.load(path + 'x_train.npy'), np.load(path + 'y_train.npy')\n", + "x_test, y_test = np.load(path + 'x_test.npy'), np.load(path + 'y_test.npy')\n", + "x_val, y_val = np.load(path + 'x_val.npy'), np.load(path + 'y_val.npy')\n", + "\n", + "# BAG_TEST, BAG_TRAIN, BAG_VAL represent bag_level labels. These are used for the label update\n", + "# step of EMI/MI RNN\n", + "BAG_TEST = np.argmax(y_test[:, 0, :], axis=1)\n", + "BAG_TRAIN = np.argmax(y_train[:, 0, :], axis=1)\n", + "BAG_VAL = np.argmax(y_val[:, 0, :], axis=1)\n", + "NUM_SUBINSTANCE = x_train.shape[1]\n", + "print(\"x_train shape is:\", x_train.shape)\n", + "print(\"y_train shape is:\", y_train.shape)\n", + "print(\"x_test shape is:\", x_val.shape)\n", + "print(\"y_test shape is:\", y_val.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Computation Graph" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-19T12:39:06.653612Z", + "start_time": "2018-08-19T12:39:06.412290Z" + } + }, + "outputs": [], + "source": [ + "# Define the linear secondary classifier\n", + "def createExtendedGraph(self, baseOutput, *args, **kwargs):\n", + " W1 = tf.Variable(np.random.normal(size=[NUM_HIDDEN, NUM_OUTPUT]).astype('float32'), name='W1')\n", + " B1 = tf.Variable(np.random.normal(size=[NUM_OUTPUT]).astype('float32'), name='B1')\n", + " y_cap = tf.add(tf.tensordot(baseOutput, W1, axes=1), B1, name='y_cap_tata')\n", + " self.output = y_cap\n", + " self.graphCreated = True\n", + "\n", + "def restoreExtendedGraph(self, graph, *args, **kwargs):\n", + " y_cap = graph.get_tensor_by_name('y_cap_tata:0')\n", + " self.output = y_cap\n", + " self.graphCreated = True\n", + " \n", + "def feedDictFunc(self, keep_prob=None, inference=False, **kwargs):\n", + " if inference is False:\n", + " feedDict = {self._emiGraph.keep_prob: keep_prob}\n", + " else:\n", + " feedDict = {self._emiGraph.keep_prob: 1.0}\n", + " return feedDict\n", + "\n", + " \n", + "EMI_FastGRNN._createExtendedGraph = createExtendedGraph\n", + "EMI_FastGRNN._restoreExtendedGraph = restoreExtendedGraph\n", + "if USE_DROPOUT is True:\n", + " EMI_FastGRNN.feedDictFunc = feedDictFunc" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-19T12:39:06.701740Z", + "start_time": "2018-08-19T12:39:06.655328Z" + } + }, + "outputs": [], + "source": [ + "inputPipeline = EMI_DataPipeline(NUM_SUBINSTANCE, NUM_TIMESTEPS, NUM_FEATS, NUM_OUTPUT)\n", + "emiFastGRNN = EMI_FastGRNN(NUM_SUBINSTANCE, NUM_HIDDEN, NUM_TIMESTEPS, NUM_FEATS, wRank=WRANK, uRank=URANK, \n", + " gate_non_linearity=GATE_NL, update_non_linearity=UPDATE_NL, useDropout=USE_DROPOUT)\n", + "emiTrainer = EMI_Trainer(NUM_TIMESTEPS, NUM_OUTPUT, lossType='xentropy')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x_train shape is: (3679, 5, 8, 22)\n", + "y_train shape is: (3679, 5, 2)\n", + "x_test shape is: (409, 5, 8, 22)\n", + "y_test shape is: (409, 5, 2)\n" + ] + } + ], + "source": [ + "print(\"x_train shape is:\", x_train.shape)\n", + "print(\"y_train shape is:\", y_train.shape)\n", + "print(\"x_test shape is:\", x_val.shape)\n", + "print(\"y_test shape is:\", y_val.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-19T12:39:14.187456Z", + "start_time": "2018-08-19T12:39:06.703481Z" + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "tf.reset_default_graph()\n", + "g1 = tf.Graph() \n", + "with g1.as_default():\n", + " # Obtain the iterators to each batch of the data\n", + " x_batch, y_batch = inputPipeline()\n", + " # Create the forward computation graph based on the iterators\n", + " y_cap = emiFastGRNN(x_batch)\n", + " # Create loss graphs and training routines\n", + " emiTrainer(y_cap, y_batch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# EMI Driver " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-19T12:51:45.803360Z", + "start_time": "2018-08-19T12:39:14.189648Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Update policy: top-k\n", + "Training with MI-RNN loss for 15 rounds\n", + "Round: 0\n", + "Epoch 2 Batch 100 ( 330) Loss 0.09010 Acc 0.49375 | Val acc 0.65037 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1000\n", + "Epoch 2 Batch 100 ( 330) Loss 0.08737 Acc 0.53750 | Val acc 0.66748 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1001\n", + "Epoch 2 Batch 100 ( 330) Loss 0.08466 Acc 0.50625 | Val acc 0.68215 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1002\n", + "Epoch 2 Batch 100 ( 330) Loss 0.08016 Acc 0.57500 | Val acc 0.69193 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1003\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1003\n", + "Round: 1\n", + "Epoch 2 Batch 100 ( 330) Loss 0.07575 Acc 0.64375 | Val acc 0.70416 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1004\n", + "Epoch 2 Batch 100 ( 330) Loss 0.07176 Acc 0.71875 | Val acc 0.71883 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1005\n", + "Epoch 2 Batch 100 ( 330) Loss 0.06881 Acc 0.72500 | Val acc 0.71883 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1006\n", + "Epoch 2 Batch 100 ( 330) Loss 0.06622 Acc 0.72500 | Val acc 0.72372 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1007\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1007\n", + "Round: 2\n", + "Epoch 2 Batch 100 ( 330) Loss 0.06392 Acc 0.72500 | Val acc 0.72616 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1008\n", + "Epoch 2 Batch 100 ( 330) Loss 0.06182 Acc 0.73125 | Val acc 0.74083 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1009\n", + "Epoch 2 Batch 100 ( 330) Loss 0.06007 Acc 0.74375 | Val acc 0.75061 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1010\n", + "Epoch 2 Batch 100 ( 330) Loss 0.05848 Acc 0.76250 | Val acc 0.76528 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1011\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1011\n", + "Round: 3\n", + "Epoch 2 Batch 100 ( 330) Loss 0.05694 Acc 0.75000 | Val acc 0.76039 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1012\n", + "Epoch 2 Batch 100 ( 330) Loss 0.05462 Acc 0.73125 | Val acc 0.76528 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1013\n", + "Epoch 2 Batch 100 ( 330) Loss 0.05265 Acc 0.78750 | Val acc 0.77751 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1014\n", + "Epoch 2 Batch 100 ( 330) Loss 0.05050 Acc 0.78750 | Val acc 0.77262 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1015\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1014\n", + "Round: 4\n", + "Epoch 2 Batch 100 ( 330) Loss 0.05050 Acc 0.78750 | Val acc 0.77262 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1016\n", + "Epoch 2 Batch 100 ( 330) Loss 0.04971 Acc 0.77500 | Val acc 0.78240 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1017\n", + "Epoch 2 Batch 100 ( 330) Loss 0.04890 Acc 0.76250 | Val acc 0.77751 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1018\n", + "Epoch 2 Batch 100 ( 330) Loss 0.04847 Acc 0.76250 | Val acc 0.78484 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1019\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1019\n", + "Round: 5\n", + "Epoch 2 Batch 100 ( 330) Loss 0.04872 Acc 0.79375 | Val acc 0.78973 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1020\n", + "Epoch 2 Batch 100 ( 330) Loss 0.04798 Acc 0.79375 | Val acc 0.78973 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1021\n", + "Epoch 2 Batch 100 ( 330) Loss 0.04730 Acc 0.79375 | Val acc 0.79462 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1022\n", + "Epoch 2 Batch 100 ( 330) Loss 0.04622 Acc 0.80000 | Val acc 0.79707 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1023\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1023\n", + "Round: 6\n", + "Epoch 2 Batch 100 ( 330) Loss 0.04515 Acc 0.80000 | Val acc 0.80685 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1024\n", + "Epoch 2 Batch 100 ( 330) Loss 0.04360 Acc 0.80625 | Val acc 0.80929 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1025\n", + "Epoch 2 Batch 100 ( 330) Loss 0.04234 Acc 0.81875 | Val acc 0.80440 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1026\n", + "Epoch 2 Batch 100 ( 330) Loss 0.04157 Acc 0.83750 | Val acc 0.81907 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1027\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1027\n", + "Round: 7\n", + "Epoch 2 Batch 100 ( 330) Loss 0.04077 Acc 0.85000 | Val acc 0.81174 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1028\n", + "Epoch 2 Batch 100 ( 330) Loss 0.04000 Acc 0.85000 | Val acc 0.81418 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1029\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03823 Acc 0.85000 | Val acc 0.81418 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1030\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03676 Acc 0.85625 | Val acc 0.83619 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1031\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1031\n", + "Round: 8\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03610 Acc 0.85625 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1032\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03498 Acc 0.86250 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1033\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03424 Acc 0.87500 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1034\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03375 Acc 0.88125 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1035\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1033\n", + "Round: 9\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03424 Acc 0.87500 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1036\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03375 Acc 0.88125 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1037\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03238 Acc 0.87500 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1038\n", + "Epoch 2 Batch 100 ( 330) Loss 0.02963 Acc 0.90000 | Val acc 0.83619 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1039\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1036\n", + "Round: 10\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03375 Acc 0.88125 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1040\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03238 Acc 0.87500 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1041\n", + "Epoch 2 Batch 100 ( 330) Loss 0.02963 Acc 0.90000 | Val acc 0.83619 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1042\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03045 Acc 0.89375 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1043\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1041\n", + "Round: 11\n", + "Epoch 2 Batch 100 ( 330) Loss 0.02963 Acc 0.90000 | Val acc 0.83619 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1044\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03045 Acc 0.89375 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1045\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03176 Acc 0.88750 | Val acc 0.84352 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1046\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2 Batch 100 ( 330) Loss 0.03206 Acc 0.88750 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1047\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1046\n", + "Round: 12\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03206 Acc 0.88750 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1048\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03282 Acc 0.89375 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1049\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03429 Acc 0.89375 | Val acc 0.83619 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1050\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03082 Acc 0.92500 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1051\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1051\n", + "Round: 13\n", + "Epoch 2 Batch 100 ( 330) Loss 0.02883 Acc 0.94375 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1052\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03036 Acc 0.93125 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1053\n", + "Epoch 2 Batch 100 ( 330) Loss 0.02961 Acc 0.91875 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1054\n", + "Epoch 2 Batch 100 ( 330) Loss 0.02976 Acc 0.93125 | Val acc 0.83619 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1055\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1052\n", + "Round: 14\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03036 Acc 0.93125 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1056\n", + "Epoch 2 Batch 100 ( 330) Loss 0.02961 Acc 0.91875 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1057\n", + "Epoch 2 Batch 100 ( 330) Loss 0.02976 Acc 0.93125 | Val acc 0.83619 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1058\n", + "Epoch 2 Batch 100 ( 330) Loss 0.03076 Acc 0.91250 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1059\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1057\n", + "Round: 15\n", + "Switching to EMI-Loss function\n", + "Epoch 2 Batch 100 ( 330) Loss 0.35416 Acc 0.88125 | Val acc 0.80685 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1060\n", + "Epoch 2 Batch 100 ( 330) Loss 0.33687 Acc 0.86875 | Val acc 0.81174 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1061\n", + "Epoch 2 Batch 100 ( 330) Loss 0.33655 Acc 0.87500 | Val acc 0.81418 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1062\n", + "Epoch 2 Batch 100 ( 330) Loss 0.33672 Acc 0.88750 | Val acc 0.81907 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1063\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1063\n", + "Round: 16\n", + "Epoch 2 Batch 100 ( 330) Loss 0.32793 Acc 0.88750 | Val acc 0.81418 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1064\n", + "Epoch 2 Batch 100 ( 330) Loss 0.32143 Acc 0.86250 | Val acc 0.82152 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1065\n", + "Epoch 2 Batch 100 ( 330) Loss 0.31822 Acc 0.86250 | Val acc 0.81663 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1066\n", + "Epoch 2 Batch 100 ( 330) Loss 0.31108 Acc 0.87500 | Val acc 0.82396 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1067\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1067\n", + "Round: 17\n", + "Epoch 2 Batch 100 ( 330) Loss 0.30779 Acc 0.86875 | Val acc 0.81907 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1068\n", + "Epoch 2 Batch 100 ( 330) Loss 0.30074 Acc 0.88125 | Val acc 0.82152 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1069\n", + "Epoch 2 Batch 100 ( 330) Loss 0.29439 Acc 0.88125 | Val acc 0.82641 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1070\n", + "Epoch 2 Batch 100 ( 330) Loss 0.28360 Acc 0.89375 | Val acc 0.83374 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1071\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1071\n", + "Round: 18\n", + "Epoch 2 Batch 100 ( 330) Loss 0.28049 Acc 0.88750 | Val acc 0.83374 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1072\n", + "Epoch 2 Batch 100 ( 330) Loss 0.27575 Acc 0.90000 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1073\n", + "Epoch 2 Batch 100 ( 330) Loss 0.27683 Acc 0.88750 | Val acc 0.84352 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1074\n", + "Epoch 2 Batch 100 ( 330) Loss 0.27200 Acc 0.90000 | Val acc 0.84597 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1075\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1075\n", + "Round: 19\n", + "Epoch 2 Batch 100 ( 330) Loss 0.26647 Acc 0.93750 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1076\n", + "Epoch 2 Batch 100 ( 330) Loss 0.26391 Acc 0.93750 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1077\n", + "Epoch 2 Batch 100 ( 330) Loss 0.26801 Acc 0.92500 | Val acc 0.83619 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1078\n", + "Epoch 2 Batch 100 ( 330) Loss 0.26092 Acc 0.92500 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1079\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1076\n", + "Round: 20\n", + "Epoch 2 Batch 100 ( 330) Loss 0.26391 Acc 0.93750 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1080\n", + "Epoch 2 Batch 100 ( 330) Loss 0.26801 Acc 0.92500 | Val acc 0.83619 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1081\n", + "Epoch 2 Batch 100 ( 330) Loss 0.26092 Acc 0.92500 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1082\n", + "Epoch 2 Batch 100 ( 330) Loss 0.26757 Acc 0.90625 | Val acc 0.85330 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1083\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1083\n", + "Round: 21\n", + "Epoch 2 Batch 100 ( 330) Loss 0.26281 Acc 0.91875 | Val acc 0.85575 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1084\n", + "Epoch 2 Batch 100 ( 330) Loss 0.25737 Acc 0.93125 | Val acc 0.85575 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1085\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24958 Acc 0.93750 | Val acc 0.87042 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1086\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24881 Acc 0.93750 | Val acc 0.87042 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1087\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1086\n", + "Round: 22\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24881 Acc 0.93750 | Val acc 0.87042 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1088\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24322 Acc 0.94375 | Val acc 0.85819 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1089\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24998 Acc 0.91875 | Val acc 0.86797 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1090\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24372 Acc 0.91875 | Val acc 0.86553 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1091\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1088\n", + "Round: 23\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24322 Acc 0.94375 | Val acc 0.85819 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1092\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24998 Acc 0.91875 | Val acc 0.86797 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1093\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24372 Acc 0.91875 | Val acc 0.86553 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1094\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24040 Acc 0.93750 | Val acc 0.86308 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1095\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1093\n", + "Round: 24\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24372 Acc 0.91875 | Val acc 0.86553 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1096\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24040 Acc 0.93750 | Val acc 0.86308 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1097\n", + "Epoch 2 Batch 100 ( 330) Loss 0.22721 Acc 0.95000 | Val acc 0.86553 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1098\n", + "Epoch 2 Batch 100 ( 330) Loss 0.22792 Acc 0.94375 | Val acc 0.86308 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1099\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1096\n", + "Round: 25\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24040 Acc 0.93750 | Val acc 0.86308 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1100\n", + "Epoch 2 Batch 100 ( 330) Loss 0.22721 Acc 0.95000 | Val acc 0.86553 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1101\n", + "Epoch 2 Batch 100 ( 330) Loss 0.22792 Acc 0.94375 | Val acc 0.86308 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1102\n", + "Epoch 2 Batch 100 ( 330) Loss 0.23063 Acc 0.94375 | Val acc 0.86553 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1103\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1101\n", + "Round: 26\n", + "Epoch 2 Batch 100 ( 330) Loss 0.22792 Acc 0.94375 | Val acc 0.86308 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1104\n", + "Epoch 2 Batch 100 ( 330) Loss 0.23063 Acc 0.94375 | Val acc 0.86553 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1105\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24060 Acc 0.93125 | Val acc 0.87042 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1106\n", + "Epoch 2 Batch 100 ( 330) Loss 0.25265 Acc 0.90625 | Val acc 0.87775 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1107\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1107\n", + "Round: 27\n", + "Epoch 2 Batch 100 ( 330) Loss 0.23588 Acc 0.91875 | Val acc 0.87531 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1108\n", + "Epoch 2 Batch 100 ( 330) Loss 0.22337 Acc 0.93750 | Val acc 0.86797 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1109\n", + "Epoch 2 Batch 100 ( 330) Loss 0.22149 Acc 0.96250 | Val acc 0.88020 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1110\n", + "Epoch 2 Batch 100 ( 330) Loss 0.22539 Acc 0.92500 | Val acc 0.87775 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1111\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1110\n", + "Round: 28\n", + "Epoch 2 Batch 100 ( 330) Loss 0.22539 Acc 0.92500 | Val acc 0.87775 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1112\n", + "Epoch 2 Batch 100 ( 330) Loss 0.21537 Acc 0.92500 | Val acc 0.87042 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1113\n", + "Epoch 2 Batch 100 ( 330) Loss 0.20826 Acc 0.94375 | Val acc 0.88020 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1114\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24497 Acc 0.92500 | Val acc 0.87531 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1115\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1114\n", + "Round: 29\n", + "Epoch 2 Batch 100 ( 330) Loss 0.24497 Acc 0.92500 | Val acc 0.87531 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1116\n", + "Epoch 2 Batch 100 ( 330) Loss 0.20842 Acc 0.95625 | Val acc 0.87775 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1117\n", + "Epoch 2 Batch 100 ( 330) Loss 0.20437 Acc 0.97500 | Val acc 0.87531 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1118\n", + "Epoch 2 Batch 100 ( 330) Loss 0.20019 Acc 0.96250 | Val acc 0.87042 | Model saved to /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn, global_step 1119\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1117\n" + ] + } + ], + "source": [ + "with g1.as_default():\n", + " emiDriver = EMI_Driver(inputPipeline, emiFastGRNN, emiTrainer)\n", + "\n", + "emiDriver.initializeSession(g1)\n", + "y_updated, modelStats = emiDriver.run(numClasses=NUM_OUTPUT, x_train=x_train,\n", + " y_train=y_train, bag_train=BAG_TRAIN,\n", + " x_val=x_val, y_val=y_val, bag_val=BAG_VAL,\n", + " numIter=NUM_ITER, keep_prob=KEEP_PROB,\n", + " numRounds=NUM_ROUNDS, batchSize=BATCH_SIZE,\n", + " numEpochs=NUM_EPOCHS, modelPrefix=MODEL_PREFIX,\n", + " fracEMI=0.5, updatePolicy='top-k', k=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluating the trained model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-19T12:51:45.832728Z", + "start_time": "2018-08-19T12:51:45.805984Z" + } + }, + "outputs": [], + "source": [ + "# Early Prediction Policy: We make an early prediction based on the predicted classes\n", + "# probability. If the predicted class probability > minProb at some step, we make\n", + "# a prediction at that step.\n", + "def earlyPolicy_minProb(instanceOut, minProb, **kwargs):\n", + " assert instanceOut.ndim == 2\n", + " classes = np.argmax(instanceOut, axis=1)\n", + " prob = np.max(instanceOut, axis=1)\n", + " index = np.where(prob >= minProb)[0]\n", + " if len(index) == 0:\n", + " assert (len(instanceOut) - 1) == (len(classes) - 1)\n", + " return classes[-1], len(instanceOut) - 1\n", + " index = index[0]\n", + " return classes[index], index\n", + "\n", + "def getEarlySaving(predictionStep, numTimeSteps, returnTotal=False):\n", + " predictionStep = predictionStep + 1\n", + " predictionStep = np.reshape(predictionStep, -1)\n", + " totalSteps = np.sum(predictionStep)\n", + " maxSteps = len(predictionStep) * numTimeSteps\n", + " savings = 1.0 - (totalSteps / maxSteps)\n", + " if returnTotal:\n", + " return savings, totalSteps\n", + " return savings" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-19T12:51:46.210240Z", + "start_time": "2018-08-19T12:51:45.834534Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy at k = 2: 0.868885\n", + "Additional savings: 0.295475\n" + ] + } + ], + "source": [ + "k = 2\n", + "predictions, predictionStep = emiDriver.getInstancePredictions(x_test, y_test, earlyPolicy_minProb, minProb=0.99)\n", + "bagPredictions = emiDriver.getBagPredictions(predictions, minSubsequenceLen=k, numClass=NUM_OUTPUT)\n", + "print('Accuracy at k = %d: %f' % (k, np.mean((bagPredictions == BAG_TEST).astype(int))))\n", + "print('Additional savings: %f' % getEarlySaving(predictionStep, NUM_TIMESTEPS))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-19T12:51:46.677691Z", + "start_time": "2018-08-19T12:51:46.212285Z" + }, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " len acc macro-fsc macro-pre macro-rec micro-fsc micro-pre \\\n", + "0 1 0.860078 0.859642 0.868740 0.862055 0.860078 0.860078 \n", + "1 2 0.868885 0.868860 0.870591 0.869796 0.868885 0.868885 \n", + "2 3 0.875734 0.875619 0.875752 0.875529 0.875734 0.875734 \n", + "3 4 0.866928 0.866436 0.869102 0.865945 0.866928 0.866928 \n", + "4 5 0.863992 0.862807 0.871518 0.862185 0.863992 0.863992 \n", + "\n", + " micro-rec fscore_01 \n", + "0 0.860078 0.867470 \n", + "1 0.868885 0.870656 \n", + "2 0.875734 0.871847 \n", + "3 0.866928 0.858333 \n", + "4 0.863992 0.850054 \n", + "Max accuracy 0.875734 at subsequencelength 3\n", + "Max micro-f 0.875734 at subsequencelength 3\n", + "Micro-precision 0.875734 at subsequencelength 3\n", + "Micro-recall 0.875734 at subsequencelength 3\n", + "Max macro-f 0.875619 at subsequencelength 3\n", + "macro-precision 0.875752 at subsequencelength 3\n", + "macro-recall 0.875529 at subsequencelength 3\n" + ] + } + ], + "source": [ + "# A slightly more detailed analysis method is provided. \n", + "df = emiDriver.analyseModel(predictions, BAG_TEST, NUM_SUBINSTANCE, NUM_OUTPUT)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Picking the best model" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-19T13:06:04.024660Z", + "start_time": "2018-08-19T13:04:47.045787Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1032\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1003\n", + "Round: 0, Validation accuracy: 0.6919, Test Accuracy (k = 3): 0.689824, Additional savings: 0.000024\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1007\n", + "Round: 1, Validation accuracy: 0.7237, Test Accuracy (k = 3): 0.708415, Additional savings: 0.000171\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1011\n", + "Round: 2, Validation accuracy: 0.7653, Test Accuracy (k = 3): 0.727006, Additional savings: 0.001492\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1014\n", + "Round: 3, Validation accuracy: 0.7775, Test Accuracy (k = 3): 0.732877, Additional savings: 0.002275\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1019\n", + "Round: 4, Validation accuracy: 0.7848, Test Accuracy (k = 3): 0.739726, Additional savings: 0.009247\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1023\n", + "Round: 5, Validation accuracy: 0.7971, Test Accuracy (k = 3): 0.766145, Additional savings: 0.018493\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1027\n", + "Round: 6, Validation accuracy: 0.8191, Test Accuracy (k = 3): 0.775930, Additional savings: 0.026272\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1031\n", + "Round: 7, Validation accuracy: 0.8362, Test Accuracy (k = 3): 0.793542, Additional savings: 0.035152\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1033\n", + "Round: 8, Validation accuracy: 0.8411, Test Accuracy (k = 3): 0.801370, Additional savings: 0.038454\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1036\n", + "Round: 9, Validation accuracy: 0.8411, Test Accuracy (k = 3): 0.802348, Additional savings: 0.041120\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1041\n", + "Round: 10, Validation accuracy: 0.8411, Test Accuracy (k = 3): 0.808219, Additional savings: 0.048483\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1046\n", + "Round: 11, Validation accuracy: 0.8435, Test Accuracy (k = 3): 0.818982, Additional savings: 0.058953\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1051\n", + "Round: 12, Validation accuracy: 0.8411, Test Accuracy (k = 3): 0.825832, Additional savings: 0.073679\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1052\n", + "Round: 13, Validation accuracy: 0.8411, Test Accuracy (k = 3): 0.818004, Additional savings: 0.074046\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1057\n", + "Round: 14, Validation accuracy: 0.8411, Test Accuracy (k = 3): 0.835616, Additional savings: 0.082021\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1063\n", + "Round: 15, Validation accuracy: 0.8191, Test Accuracy (k = 3): 0.825832, Additional savings: 0.126981\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1067\n", + "Round: 16, Validation accuracy: 0.8240, Test Accuracy (k = 3): 0.828767, Additional savings: 0.154892\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1071\n", + "Round: 17, Validation accuracy: 0.8337, Test Accuracy (k = 3): 0.831703, Additional savings: 0.175636\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1075\n", + "Round: 18, Validation accuracy: 0.8460, Test Accuracy (k = 3): 0.842466, Additional savings: 0.199902\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1076\n", + "Round: 19, Validation accuracy: 0.8411, Test Accuracy (k = 3): 0.844423, Additional savings: 0.203082\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1083\n", + "Round: 20, Validation accuracy: 0.8533, Test Accuracy (k = 3): 0.857143, Additional savings: 0.218567\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1086\n", + "Round: 21, Validation accuracy: 0.8704, Test Accuracy (k = 3): 0.863014, Additional savings: 0.233439\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1088\n", + "Round: 22, Validation accuracy: 0.8704, Test Accuracy (k = 3): 0.859100, Additional savings: 0.237696\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1093\n", + "Round: 23, Validation accuracy: 0.8680, Test Accuracy (k = 3): 0.865949, Additional savings: 0.242343\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1096\n", + "Round: 24, Validation accuracy: 0.8655, Test Accuracy (k = 3): 0.870841, Additional savings: 0.248679\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1101\n", + "Round: 25, Validation accuracy: 0.8655, Test Accuracy (k = 3): 0.872798, Additional savings: 0.258708\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1107\n", + "Round: 26, Validation accuracy: 0.8778, Test Accuracy (k = 3): 0.878669, Additional savings: 0.264873\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1110\n", + "Round: 27, Validation accuracy: 0.8802, Test Accuracy (k = 3): 0.876712, Additional savings: 0.281238\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1114\n", + "Round: 28, Validation accuracy: 0.8802, Test Accuracy (k = 3): 0.881605, Additional savings: 0.291389\n", + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1117\n", + "Round: 29, Validation accuracy: 0.8778, Test Accuracy (k = 3): 0.875734, Additional savings: 0.295475\n" + ] + } + ], + "source": [ + "k=3\n", + "emiDriver.loadSavedGraphToNewSession(MODEL_PREFIX , 1032)\n", + "devnull = open(os.devnull, 'r')\n", + "for val in modelStats:\n", + " round_, acc, modelPrefix, globalStep = val\n", + " emiDriver.loadSavedGraphToNewSession(modelPrefix, globalStep, redirFile=devnull)\n", + " predictions, predictionStep = emiDriver.getInstancePredictions(x_test, y_test, earlyPolicy_minProb,\n", + " minProb=0.99, keep_prob=1.0)\n", + " \n", + " bagPredictions = emiDriver.getBagPredictions(predictions, minSubsequenceLen=k, numClass=NUM_OUTPUT)\n", + " print(\"Round: %2d, Validation accuracy: %.4f\" % (round_, acc), end='')\n", + " print(', Test Accuracy (k = %d): %f, ' % (k, np.mean((bagPredictions == BAG_TEST).astype(int))), end='')\n", + " print('Additional savings: %f' % getEarlySaving(predictionStep, NUM_TIMESTEPS)) " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "MODEL_PREFIX = '/home/sf/data/SWELL-KW/FG_8_13/model-fgrnn'" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/FG_8_13/model-fgrnn-1118\n", + "-1.243401288986206\n", + "Accuracy at k = 2: 0.870841\n" + ] + } + ], + "source": [ + "import time\n", + "k=2\n", + "start = time.time()\n", + "emiDriver.loadSavedGraphToNewSession(MODEL_PREFIX , 1118)\n", + "predictions, predictionStep = emiDriver.getInstancePredictions(x_test, y_test, earlyPolicy_minProb,\n", + " minProb=0.99, keep_prob=1.0)###\n", + "bagPredictions = emiDriver.getBagPredictions(predictions, minSubsequenceLen=k, numClass=NUM_OUTPUT)###\n", + "end = time.time()\n", + "print(start-end)\n", + "print('Accuracy at k = %d: %f' % (k, np.mean((bagPredictions == BAG_TEST).astype(int))))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "params = {\n", + " \"NUM_HIDDEN\" : 128,\n", + " \"NUM_TIMESTEPS\" : 8, #subinstance length.\n", + " \"NUM_FEATS\" : 22,\n", + " \"FORGET_BIAS\" : 1.0,\n", + " \"UPDATE_NL\" : \"quantTanh\",\n", + " \"GATE_NL\" : \"quantSigm\",\n", + " \"NUM_OUTPUT\" : 3,\n", + " \"WRANK\" : 5,\n", + " \"URANK\" : 6,\n", + " \"USE_DROPOUT\" : False,\n", + " \"KEEP_PROB\" : 0.9,\n", + " \"PREFETCH_NUM\" : 5,\n", + " \"BATCH_SIZE\" : 32,\n", + " \"NUM_EPOCHS\" : 2,\n", + " \"NUM_ITER\" : 4,\n", + " \"NUM_ROUNDS\" : 10,\n", + " \"MODEL_PREFIX\" : '/home/sf/data/DREAMER/Dominance/48_16/models/Fast-GRNN/model-fgrnn'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " len acc macro-fsc macro-pre macro-rec micro-fsc micro-pre \\\n", + "0 1 0.853229 0.852651 0.863397 0.855376 0.853229 0.853229 \n", + "1 2 0.870841 0.870792 0.873219 0.871904 0.870841 0.870841 \n", + "2 3 0.878669 0.878602 0.878571 0.878641 0.878669 0.878669 \n", + "3 4 0.868885 0.868431 0.870852 0.867953 0.868885 0.868885 \n", + "4 5 0.869863 0.868957 0.875526 0.868309 0.869863 0.869863 \n", + "\n", + " micro-rec fscore_01 \n", + "0 0.853229 0.861878 \n", + "1 0.870841 0.873321 \n", + "2 0.878669 0.875752 \n", + "3 0.868885 0.860707 \n", + "4 0.869863 0.858058 \n", + "Max accuracy 0.878669 at subsequencelength 3\n", + "Max micro-f 0.878669 at subsequencelength 3\n", + "Micro-precision 0.878669 at subsequencelength 3\n", + "Micro-recall 0.878669 at subsequencelength 3\n", + "Max macro-f 0.878602 at subsequencelength 3\n", + "macro-precision 0.878571 at subsequencelength 3\n", + "macro-recall 0.878641 at subsequencelength 3\n", + "+----+-------+----------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+\n", + "| | len | acc | macro-fsc | macro-pre | macro-rec | micro-fsc | micro-pre | micro-rec | fscore_01 |\n", + "+====+=======+==========+=============+=============+=============+=============+=============+=============+=============+\n", + "| 0 | 1 | 0.853229 | 0.852651 | 0.863397 | 0.855376 | 0.853229 | 0.853229 | 0.853229 | 0.861878 |\n", + "+----+-------+----------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+\n", + "| 1 | 2 | 0.870841 | 0.870792 | 0.873219 | 0.871904 | 0.870841 | 0.870841 | 0.870841 | 0.873321 |\n", + "+----+-------+----------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+\n", + "| 2 | 3 | 0.878669 | 0.878602 | 0.878571 | 0.878641 | 0.878669 | 0.878669 | 0.878669 | 0.875752 |\n", + "+----+-------+----------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+\n", + "| 3 | 4 | 0.868885 | 0.868431 | 0.870852 | 0.867953 | 0.868885 | 0.868885 | 0.868885 | 0.860707 |\n", + "+----+-------+----------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+\n", + "| 4 | 5 | 0.869863 | 0.868957 | 0.875526 | 0.868309 | 0.869863 | 0.869863 | 0.869863 | 0.858058 |\n", + "+----+-------+----------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+\n" + ] + } + ], + "source": [ + "fgrnn_dict = {**params}\n", + "fgrnn_dict[\"k\"] = k\n", + "fgrnn_dict[\"accuracy\"] = np.mean((bagPredictions == BAG_TEST).astype(int))\n", + "fgrnn_dict[\"total_savings\"] = getEarlySaving(predictionStep, NUM_TIMESTEPS)\n", + "fgrnn_dict[\"y_test\"] = BAG_TEST\n", + "fgrnn_dict[\"y_pred\"] = bagPredictions\n", + "\n", + "# A slightly more detailed analysis method is provided. \n", + "df = emiDriver.analyseModel(predictions, BAG_TEST, NUM_SUBINSTANCE, NUM_OUTPUT)\n", + "print (tabulate(df, headers=list(df.columns), tablefmt='grid'))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for this run have been saved at /home/sf/data/SWELL-KW/ .\n" + ] + } + ], + "source": [ + "dirname = \"/home/sf/data/SWELL-KW/\"\n", + "pathlib.Path(dirname).mkdir(parents=True, exist_ok=True)\n", + "print (\"Results for this run have been saved at\" , dirname, \".\")\n", + "\n", + "now = datetime.datetime.now()\n", + "filename = list((str(now.year),\"-\",str(now.month),\"-\",str(now.day),\"|\",str(now.hour),\"-\",str(now.minute)))\n", + "filename = ''.join(filename)\n", + "\n", + "#Save the dictionary containing the params and the results.\n", + "pkl.dump(fgrnn_dict,open(dirname + filename + \".pkl\",mode='wb'))" + ] + } + ], + "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 +}