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{
"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'))"
]
}
],
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
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"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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