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b/SWELL-KW/SWELL-KW_GRU.ipynb |
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"cells": [ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"# SWELL-KW GRU" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"Adapted from Microsoft's notebooks, available at https://github.com/microsoft/EdgeML authored by Dennis et al." |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 1, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import pandas as pd\n", |
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"import numpy as np\n", |
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"from tabulate import tabulate\n", |
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"import os\n", |
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"import datetime as datetime\n", |
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"import pickle as pkl\n", |
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"import pathlib" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 2, |
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"metadata": { |
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"ExecuteTime": { |
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"end_time": "2018-12-14T14:17:51.796585Z", |
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"start_time": "2018-12-14T14:17:49.648375Z" |
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} |
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}, |
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"outputs": [ |
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{ |
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"name": "stderr", |
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"output_type": "stream", |
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"text": [ |
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"Using TensorFlow backend.\n" |
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] |
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} |
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], |
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"source": [ |
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"from __future__ import print_function\n", |
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"import os\n", |
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"import sys\n", |
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"import tensorflow as tf\n", |
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"import numpy as np\n", |
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"# Making sure edgeml is part of python path\n", |
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"sys.path.insert(0, '../../')\n", |
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"#For processing on CPU.\n", |
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"os.environ['CUDA_VISIBLE_DEVICES'] ='0'\n", |
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"\n", |
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"np.random.seed(42)\n", |
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"tf.set_random_seed(42)\n", |
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"\n", |
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"# MI-RNN and EMI-RNN imports\n", |
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"from edgeml.graph.rnn import EMI_DataPipeline\n", |
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"from edgeml.graph.rnn import EMI_GRU\n", |
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"from edgeml.trainer.emirnnTrainer import EMI_Trainer, EMI_Driver\n", |
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"import edgeml.utils\n", |
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"\n", |
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"import keras.backend as K\n", |
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"cfg = K.tf.ConfigProto()\n", |
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"cfg.gpu_options.allow_growth = True\n", |
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"K.set_session(K.tf.Session(config=cfg))" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 4, |
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"metadata": { |
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"ExecuteTime": { |
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"end_time": "2018-12-14T14:17:51.803381Z", |
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"start_time": "2018-12-14T14:17:51.798799Z" |
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} |
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}, |
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"outputs": [], |
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"source": [ |
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"# Network parameters for our LSTM + FC Layer\n", |
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"NUM_HIDDEN = 128\n", |
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"NUM_TIMESTEPS = 8\n", |
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"ORIGINAL_NUM_TIMESTEPS = 20\n", |
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"NUM_FEATS = 22\n", |
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"FORGET_BIAS = 1.0\n", |
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"NUM_OUTPUT = 2\n", |
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"USE_DROPOUT = True\n", |
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"KEEP_PROB = 0.75\n", |
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"\n", |
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"# For dataset API\n", |
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"PREFETCH_NUM = 5\n", |
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"BATCH_SIZE = 32\n", |
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"\n", |
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"# Number of epochs in *one iteration*\n", |
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"NUM_EPOCHS = 2\n", |
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"# Number of iterations in *one round*. After each iteration,\n", |
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"# the model is dumped to disk. At the end of the current\n", |
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"# round, the best model among all the dumped models in the\n", |
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"# current round is picked up..\n", |
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"NUM_ITER = 4\n", |
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"# A round consists of multiple training iterations and a belief\n", |
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"# update step using the best model from all of these iterations\n", |
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"NUM_ROUNDS = 30\n", |
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"LEARNING_RATE=0.001\n", |
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"\n", |
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"# A staging direcory to store models\n", |
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"MODEL_PREFIX = '/home/sf/data/SWELL-KW/models/GRU/model-gru'" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": { |
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"heading_collapsed": true |
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}, |
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"source": [ |
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"# Loading Data" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 5, |
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"metadata": { |
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"ExecuteTime": { |
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"end_time": "2018-12-14T14:17:52.040352Z", |
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"start_time": "2018-12-14T14:17:51.805319Z" |
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}, |
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"hidden": true |
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}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"x_train shape is: (3679, 5, 8, 22)\n", |
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"y_train shape is: (3679, 5, 2)\n", |
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"x_test shape is: (409, 5, 8, 22)\n", |
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"y_test shape is: (409, 5, 2)\n" |
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] |
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} |
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], |
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"source": [ |
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"# Loading the data\n", |
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"x_train, y_train = np.load('/home/sf/data/SWELL-KW/8_3/x_train.npy'), np.load('/home/sf/data/SWELL-KW/8_3/y_train.npy')\n", |
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"x_test, y_test = np.load('/home/sf/data/SWELL-KW/8_3/x_test.npy'), np.load('/home/sf/data/SWELL-KW/8_3/y_test.npy')\n", |
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"x_val, y_val = np.load('/home/sf/data/SWELL-KW/8_3/x_val.npy'), np.load('/home/sf/data/SWELL-KW/8_3/y_val.npy')\n", |
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"\n", |
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"# BAG_TEST, BAG_TRAIN, BAG_VAL represent bag_level labels. These are used for the label update\n", |
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"# step of EMI/MI RNN\n", |
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"BAG_TEST = np.argmax(y_test[:, 0, :], axis=1)\n", |
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"BAG_TRAIN = np.argmax(y_train[:, 0, :], axis=1)\n", |
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"BAG_VAL = np.argmax(y_val[:, 0, :], axis=1)\n", |
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"NUM_SUBINSTANCE = x_train.shape[1]\n", |
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"print(\"x_train shape is:\", x_train.shape)\n", |
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"print(\"y_train shape is:\", y_train.shape)\n", |
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"print(\"x_test shape is:\", x_val.shape)\n", |
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"print(\"y_test shape is:\", y_val.shape)" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"# Computation Graph" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 6, |
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"metadata": { |
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"ExecuteTime": { |
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"end_time": "2018-12-14T14:17:52.053161Z", |
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"start_time": "2018-12-14T14:17:52.042928Z" |
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} |
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}, |
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"outputs": [], |
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"source": [ |
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"# Define the linear secondary classifier\n", |
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"def createExtendedGraph(self, baseOutput, *args, **kwargs):\n", |
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" W1 = tf.Variable(np.random.normal(size=[NUM_HIDDEN, NUM_OUTPUT]).astype('float32'), name='W1')\n", |
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" B1 = tf.Variable(np.random.normal(size=[NUM_OUTPUT]).astype('float32'), name='B1')\n", |
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" y_cap = tf.add(tf.tensordot(baseOutput, W1, axes=1), B1, name='y_cap_tata')\n", |
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" self.output = y_cap\n", |
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" self.graphCreated = True\n", |
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"\n", |
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"def restoreExtendedGraph(self, graph, *args, **kwargs):\n", |
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" y_cap = graph.get_tensor_by_name('y_cap_tata:0')\n", |
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" self.output = y_cap\n", |
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" self.graphCreated = True\n", |
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" \n", |
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"def feedDictFunc(self, keep_prob=None, inference=False, **kwargs):\n", |
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" if inference is False:\n", |
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" feedDict = {self._emiGraph.keep_prob: keep_prob}\n", |
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" else:\n", |
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" feedDict = {self._emiGraph.keep_prob: 1.0}\n", |
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" return feedDict\n", |
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" \n", |
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"EMI_GRU._createExtendedGraph = createExtendedGraph\n", |
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"EMI_GRU._restoreExtendedGraph = restoreExtendedGraph\n", |
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"\n", |
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"if USE_DROPOUT is True:\n", |
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" EMI_Driver.feedDictFunc = feedDictFunc" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 7, |
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"metadata": { |
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"ExecuteTime": { |
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"end_time": "2018-12-14T14:17:52.335299Z", |
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"start_time": "2018-12-14T14:17:52.055483Z" |
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} |
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}, |
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"outputs": [], |
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"source": [ |
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"inputPipeline = EMI_DataPipeline(NUM_SUBINSTANCE, NUM_TIMESTEPS, NUM_FEATS, NUM_OUTPUT)\n", |
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"emiGRU = EMI_GRU(NUM_SUBINSTANCE, NUM_HIDDEN, NUM_TIMESTEPS, NUM_FEATS,\n", |
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" useDropout=USE_DROPOUT)\n", |
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"emiTrainer = EMI_Trainer(NUM_TIMESTEPS, NUM_OUTPUT, lossType='xentropy',\n", |
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" stepSize=LEARNING_RATE)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 8, |
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"metadata": { |
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"ExecuteTime": { |
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"end_time": "2018-12-14T14:18:05.031382Z", |
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"start_time": "2018-12-14T14:17:52.338750Z" |
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} |
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}, |
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"outputs": [], |
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"source": [ |
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"tf.reset_default_graph()\n", |
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"g1 = tf.Graph() \n", |
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"with g1.as_default():\n", |
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" # Obtain the iterators to each batch of the data\n", |
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" x_batch, y_batch = inputPipeline()\n", |
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" # Create the forward computation graph based on the iterators\n", |
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" y_cap = emiGRU(x_batch)\n", |
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" # Create loss graphs and training routines\n", |
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" emiTrainer(y_cap, y_batch)" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"# EMI Driver" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 9, |
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"metadata": { |
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"ExecuteTime": { |
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"end_time": "2018-12-14T14:35:15.209910Z", |
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"start_time": "2018-12-14T14:18:05.034359Z" |
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}, |
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"scrolled": true |
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}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"Update policy: top-k\n", |
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"Training with MI-RNN loss for 15 rounds\n", |
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"Round: 0\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.08950 Acc 0.63125 | Val acc 0.58680 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1000\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.08044 Acc 0.61875 | Val acc 0.60636 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1001\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.08588 Acc 0.59375 | Val acc 0.61369 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1002\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06992 Acc 0.70625 | Val acc 0.63570 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1003\n", |
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"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1003\n", |
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"Round: 1\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.07058 Acc 0.65625 | Val acc 0.64792 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1004\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06464 Acc 0.71250 | Val acc 0.66748 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1005\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06516 Acc 0.70000 | Val acc 0.68704 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1006\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06636 Acc 0.72500 | Val acc 0.69927 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1007\n", |
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"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1007\n", |
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"Round: 2\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06905 Acc 0.68750 | Val acc 0.69438 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1008\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06327 Acc 0.75625 | Val acc 0.68949 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1009\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06798 Acc 0.75625 | Val acc 0.70416 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1010\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06369 Acc 0.71875 | Val acc 0.69193 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1011\n", |
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"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1010\n", |
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"Round: 3\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06522 Acc 0.73750 | Val acc 0.70171 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1012\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06332 Acc 0.74375 | Val acc 0.70905 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1013\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06348 Acc 0.75625 | Val acc 0.73105 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1014\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06125 Acc 0.75625 | Val acc 0.72616 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1015\n", |
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"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1014\n", |
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"Round: 4\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06360 Acc 0.73750 | Val acc 0.72616 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1016\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06455 Acc 0.76250 | Val acc 0.74328 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1017\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.06291 Acc 0.76250 | Val acc 0.74328 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1018\n", |
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"Epoch 1 Batch 110 ( 225) Loss 0.05980 Acc 0.78750 | Val acc 0.75061 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1019\n", |
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"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1019\n", |
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"Round: 5\n", |
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|
306 |
"Epoch 1 Batch 110 ( 225) Loss 0.06897 Acc 0.76250 | Val acc 0.75061 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1020\n", |
|
|
307 |
"Epoch 1 Batch 110 ( 225) Loss 0.06064 Acc 0.75625 | Val acc 0.76039 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1021\n", |
|
|
308 |
"Epoch 1 Batch 110 ( 225) Loss 0.05520 Acc 0.78750 | Val acc 0.76773 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1022\n", |
|
|
309 |
"Epoch 1 Batch 110 ( 225) Loss 0.05275 Acc 0.84375 | Val acc 0.75795 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1023\n", |
|
|
310 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1022\n", |
|
|
311 |
"Round: 6\n", |
|
|
312 |
"Epoch 1 Batch 110 ( 225) Loss 0.05810 Acc 0.78750 | Val acc 0.77017 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1024\n", |
|
|
313 |
"Epoch 1 Batch 110 ( 225) Loss 0.05748 Acc 0.76875 | Val acc 0.76528 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1025\n", |
|
|
314 |
"Epoch 1 Batch 110 ( 225) Loss 0.06343 Acc 0.81250 | Val acc 0.77751 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1026\n", |
|
|
315 |
"Epoch 1 Batch 110 ( 225) Loss 0.05767 Acc 0.78125 | Val acc 0.76039 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1027\n", |
|
|
316 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1026\n", |
|
|
317 |
"Round: 7\n", |
|
|
318 |
"Epoch 1 Batch 110 ( 225) Loss 0.05564 Acc 0.80000 | Val acc 0.76528 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1028\n", |
|
|
319 |
"Epoch 1 Batch 110 ( 225) Loss 0.06886 Acc 0.77500 | Val acc 0.76039 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1029\n", |
|
|
320 |
"Epoch 1 Batch 110 ( 225) Loss 0.05440 Acc 0.78125 | Val acc 0.77751 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1030\n", |
|
|
321 |
"Epoch 1 Batch 110 ( 225) Loss 0.05853 Acc 0.76875 | Val acc 0.78484 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1031\n", |
|
|
322 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1031\n", |
|
|
323 |
"Round: 8\n", |
|
|
324 |
"Epoch 1 Batch 110 ( 225) Loss 0.05785 Acc 0.75000 | Val acc 0.77751 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1032\n", |
|
|
325 |
"Epoch 1 Batch 110 ( 225) Loss 0.05716 Acc 0.78750 | Val acc 0.80196 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1033\n", |
|
|
326 |
"Epoch 1 Batch 110 ( 225) Loss 0.05379 Acc 0.81875 | Val acc 0.79707 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1034\n", |
|
|
327 |
"Epoch 1 Batch 110 ( 225) Loss 0.05206 Acc 0.81875 | Val acc 0.82152 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1035\n", |
|
|
328 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1035\n", |
|
|
329 |
"Round: 9\n", |
|
|
330 |
"Epoch 1 Batch 110 ( 225) Loss 0.05389 Acc 0.80625 | Val acc 0.81418 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1036\n", |
|
|
331 |
"Epoch 1 Batch 110 ( 225) Loss 0.05023 Acc 0.79375 | Val acc 0.82152 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1037\n", |
|
|
332 |
"Epoch 1 Batch 110 ( 225) Loss 0.04696 Acc 0.80625 | Val acc 0.81907 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1038\n", |
|
|
333 |
"Epoch 1 Batch 110 ( 225) Loss 0.04655 Acc 0.82500 | Val acc 0.82641 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1039\n", |
|
|
334 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1039\n", |
|
|
335 |
"Round: 10\n", |
|
|
336 |
"Epoch 1 Batch 110 ( 225) Loss 0.04700 Acc 0.83125 | Val acc 0.81663 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1040\n", |
|
|
337 |
"Epoch 1 Batch 110 ( 225) Loss 0.04426 Acc 0.83125 | Val acc 0.82641 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1041\n", |
|
|
338 |
"Epoch 1 Batch 110 ( 225) Loss 0.05274 Acc 0.81875 | Val acc 0.82885 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1042\n", |
|
|
339 |
"Epoch 1 Batch 110 ( 225) Loss 0.04178 Acc 0.82500 | Val acc 0.83619 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1043\n", |
|
|
340 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1043\n", |
|
|
341 |
"Round: 11\n", |
|
|
342 |
"Epoch 1 Batch 110 ( 225) Loss 0.05123 Acc 0.78125 | Val acc 0.84597 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1044\n", |
|
|
343 |
"Epoch 1 Batch 110 ( 225) Loss 0.05073 Acc 0.82500 | Val acc 0.84352 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1045\n", |
|
|
344 |
"Epoch 1 Batch 110 ( 225) Loss 0.04487 Acc 0.82500 | Val acc 0.83374 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1046\n", |
|
|
345 |
"Epoch 1 Batch 110 ( 225) Loss 0.04353 Acc 0.84375 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1047\n", |
|
|
346 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1044\n", |
|
|
347 |
"Round: 12\n", |
|
|
348 |
"Epoch 1 Batch 110 ( 225) Loss 0.04450 Acc 0.85625 | Val acc 0.85086 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1048\n", |
|
|
349 |
"Epoch 1 Batch 110 ( 225) Loss 0.04483 Acc 0.84375 | Val acc 0.83619 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1049\n", |
|
|
350 |
"Epoch 1 Batch 110 ( 225) Loss 0.03620 Acc 0.86250 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1050\n", |
|
|
351 |
"Epoch 1 Batch 110 ( 225) Loss 0.03725 Acc 0.87500 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1051\n", |
|
|
352 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1048\n", |
|
|
353 |
"Round: 13\n", |
|
|
354 |
"Epoch 1 Batch 110 ( 225) Loss 0.04427 Acc 0.81250 | Val acc 0.83619 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1052\n", |
|
|
355 |
"Epoch 1 Batch 110 ( 225) Loss 0.03978 Acc 0.87500 | Val acc 0.84841 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1053\n", |
|
|
356 |
"Epoch 1 Batch 110 ( 225) Loss 0.04293 Acc 0.83125 | Val acc 0.85086 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1054\n", |
|
|
357 |
"Epoch 1 Batch 110 ( 225) Loss 0.03750 Acc 0.85625 | Val acc 0.84352 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1055\n", |
|
|
358 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1054\n", |
|
|
359 |
"Round: 14\n", |
|
|
360 |
"Epoch 1 Batch 110 ( 225) Loss 0.04008 Acc 0.83125 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1056\n", |
|
|
361 |
"Epoch 1 Batch 110 ( 225) Loss 0.03921 Acc 0.85000 | Val acc 0.84597 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1057\n", |
|
|
362 |
"Epoch 1 Batch 110 ( 225) Loss 0.03854 Acc 0.85625 | Val acc 0.84352 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1058\n", |
|
|
363 |
"Epoch 1 Batch 110 ( 225) Loss 0.03663 Acc 0.88125 | Val acc 0.84352 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1059\n", |
|
|
364 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1057\n", |
|
|
365 |
"Round: 15\n", |
|
|
366 |
"Switching to EMI-Loss function\n", |
|
|
367 |
"Epoch 1 Batch 110 ( 225) Loss 0.45096 Acc 0.84375 | Val acc 0.81663 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1060\n", |
|
|
368 |
"Epoch 1 Batch 110 ( 225) Loss 0.47173 Acc 0.79375 | Val acc 0.82396 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1061\n", |
|
|
369 |
"Epoch 1 Batch 110 ( 225) Loss 0.43876 Acc 0.83750 | Val acc 0.82396 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1062\n", |
|
|
370 |
"Epoch 1 Batch 110 ( 225) Loss 0.43396 Acc 0.80625 | Val acc 0.85330 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1063\n", |
|
|
371 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1063\n", |
|
|
372 |
"Round: 16\n", |
|
|
373 |
"Epoch 1 Batch 110 ( 225) Loss 0.43497 Acc 0.84375 | Val acc 0.84352 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1064\n", |
|
|
374 |
"Epoch 1 Batch 110 ( 225) Loss 0.42715 Acc 0.83750 | Val acc 0.83619 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1065\n", |
|
|
375 |
"Epoch 1 Batch 110 ( 225) Loss 0.42176 Acc 0.82500 | Val acc 0.82885 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1066\n", |
|
|
376 |
"Epoch 1 Batch 110 ( 225) Loss 0.39219 Acc 0.84375 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1067\n", |
|
|
377 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1064\n", |
|
|
378 |
"Round: 17\n", |
|
|
379 |
"Epoch 1 Batch 110 ( 225) Loss 0.39438 Acc 0.85625 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1068\n", |
|
|
380 |
"Epoch 1 Batch 110 ( 225) Loss 0.43058 Acc 0.82500 | Val acc 0.85086 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1069\n", |
|
|
381 |
"Epoch 1 Batch 110 ( 225) Loss 0.40885 Acc 0.87500 | Val acc 0.84597 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1070\n", |
|
|
382 |
"Epoch 1 Batch 110 ( 225) Loss 0.39980 Acc 0.83125 | Val acc 0.84352 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1071\n", |
|
|
383 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1069\n", |
|
|
384 |
"Round: 18\n", |
|
|
385 |
"Epoch 1 Batch 110 ( 225) Loss 0.40417 Acc 0.84375 | Val acc 0.86064 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1072\n", |
|
|
386 |
"Epoch 1 Batch 110 ( 225) Loss 0.43128 Acc 0.83125 | Val acc 0.84597 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1073\n", |
|
|
387 |
"Epoch 1 Batch 110 ( 225) Loss 0.40664 Acc 0.86250 | Val acc 0.85330 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1074\n", |
|
|
388 |
"Epoch 1 Batch 110 ( 225) Loss 0.41514 Acc 0.86250 | Val acc 0.85086 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1075\n", |
|
|
389 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1072\n", |
|
|
390 |
"Round: 19\n", |
|
|
391 |
"Epoch 1 Batch 110 ( 225) Loss 0.38532 Acc 0.85000 | Val acc 0.84597 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1076\n", |
|
|
392 |
"Epoch 1 Batch 110 ( 225) Loss 0.40468 Acc 0.85625 | Val acc 0.84841 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1077\n", |
|
|
393 |
"Epoch 1 Batch 110 ( 225) Loss 0.44360 Acc 0.82500 | Val acc 0.85086 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1078\n", |
|
|
394 |
"Epoch 1 Batch 110 ( 225) Loss 0.39450 Acc 0.85625 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1079\n", |
|
|
395 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1078\n", |
|
|
396 |
"Round: 20\n", |
|
|
397 |
"Epoch 1 Batch 110 ( 225) Loss 0.37850 Acc 0.83750 | Val acc 0.85330 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1080\n", |
|
|
398 |
"Epoch 1 Batch 110 ( 225) Loss 0.37629 Acc 0.86250 | Val acc 0.85086 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1081\n", |
|
|
399 |
"Epoch 1 Batch 110 ( 225) Loss 0.38814 Acc 0.85625 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1082\n", |
|
|
400 |
"Epoch 1 Batch 110 ( 225) Loss 0.37590 Acc 0.90000 | Val acc 0.84841 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1083\n", |
|
|
401 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1080\n", |
|
|
402 |
"Round: 21\n", |
|
|
403 |
"Epoch 1 Batch 110 ( 225) Loss 0.39236 Acc 0.87500 | Val acc 0.85330 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1084\n", |
|
|
404 |
"Epoch 1 Batch 110 ( 225) Loss 0.38403 Acc 0.86875 | Val acc 0.84597 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1085\n", |
|
|
405 |
"Epoch 1 Batch 110 ( 225) Loss 0.39141 Acc 0.86875 | Val acc 0.84841 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1086\n", |
|
|
406 |
"Epoch 1 Batch 110 ( 225) Loss 0.37983 Acc 0.85625 | Val acc 0.84841 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1087\n", |
|
|
407 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1084\n", |
|
|
408 |
"Round: 22\n", |
|
|
409 |
"Epoch 1 Batch 110 ( 225) Loss 0.40008 Acc 0.87500 | Val acc 0.84597 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1088\n", |
|
|
410 |
"Epoch 1 Batch 110 ( 225) Loss 0.34944 Acc 0.89375 | Val acc 0.85086 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1089\n", |
|
|
411 |
"Epoch 1 Batch 110 ( 225) Loss 0.39081 Acc 0.89375 | Val acc 0.83130 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1090\n", |
|
|
412 |
"Epoch 1 Batch 110 ( 225) Loss 0.38600 Acc 0.84375 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1091\n", |
|
|
413 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1089\n" |
|
|
414 |
] |
|
|
415 |
}, |
|
|
416 |
{ |
|
|
417 |
"name": "stdout", |
|
|
418 |
"output_type": "stream", |
|
|
419 |
"text": [ |
|
|
420 |
"Round: 23\n", |
|
|
421 |
"Epoch 1 Batch 110 ( 225) Loss 0.39712 Acc 0.86250 | Val acc 0.85330 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1092\n", |
|
|
422 |
"Epoch 1 Batch 110 ( 225) Loss 0.35570 Acc 0.89375 | Val acc 0.84841 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1093\n", |
|
|
423 |
"Epoch 1 Batch 110 ( 225) Loss 0.35381 Acc 0.88750 | Val acc 0.84841 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1094\n", |
|
|
424 |
"Epoch 1 Batch 110 ( 225) Loss 0.37666 Acc 0.85625 | Val acc 0.84352 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1095\n", |
|
|
425 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1092\n", |
|
|
426 |
"Round: 24\n", |
|
|
427 |
"Epoch 1 Batch 110 ( 225) Loss 0.38746 Acc 0.88750 | Val acc 0.85575 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1096\n", |
|
|
428 |
"Epoch 1 Batch 110 ( 225) Loss 0.36476 Acc 0.91250 | Val acc 0.84597 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1097\n", |
|
|
429 |
"Epoch 1 Batch 110 ( 225) Loss 0.37721 Acc 0.88750 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1098\n", |
|
|
430 |
"Epoch 1 Batch 110 ( 225) Loss 0.35381 Acc 0.90625 | Val acc 0.83130 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1099\n", |
|
|
431 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1096\n", |
|
|
432 |
"Round: 25\n", |
|
|
433 |
"Epoch 1 Batch 110 ( 225) Loss 0.39612 Acc 0.85625 | Val acc 0.83863 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1100\n", |
|
|
434 |
"Epoch 1 Batch 110 ( 225) Loss 0.38607 Acc 0.83750 | Val acc 0.85086 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1101\n", |
|
|
435 |
"Epoch 1 Batch 110 ( 225) Loss 0.37138 Acc 0.90000 | Val acc 0.84841 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1102\n", |
|
|
436 |
"Epoch 1 Batch 110 ( 225) Loss 0.36966 Acc 0.87500 | Val acc 0.85086 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1103\n", |
|
|
437 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1101\n", |
|
|
438 |
"Round: 26\n", |
|
|
439 |
"Epoch 1 Batch 110 ( 225) Loss 0.33496 Acc 0.94375 | Val acc 0.84597 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1104\n", |
|
|
440 |
"Epoch 1 Batch 110 ( 225) Loss 0.35933 Acc 0.87500 | Val acc 0.84841 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1105\n", |
|
|
441 |
"Epoch 1 Batch 110 ( 225) Loss 0.35526 Acc 0.88750 | Val acc 0.84108 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1106\n", |
|
|
442 |
"Epoch 1 Batch 110 ( 225) Loss 0.36529 Acc 0.87500 | Val acc 0.85330 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1107\n", |
|
|
443 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1107\n", |
|
|
444 |
"Round: 27\n", |
|
|
445 |
"Epoch 1 Batch 110 ( 225) Loss 0.35815 Acc 0.90625 | Val acc 0.85330 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1108\n", |
|
|
446 |
"Epoch 1 Batch 110 ( 225) Loss 0.35819 Acc 0.88125 | Val acc 0.85330 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1109\n", |
|
|
447 |
"Epoch 1 Batch 110 ( 225) Loss 0.36880 Acc 0.90000 | Val acc 0.85086 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1110\n", |
|
|
448 |
"Epoch 1 Batch 110 ( 225) Loss 0.35225 Acc 0.90625 | Val acc 0.84841 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1111\n", |
|
|
449 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1108\n", |
|
|
450 |
"Round: 28\n", |
|
|
451 |
"Epoch 1 Batch 110 ( 225) Loss 0.36809 Acc 0.88750 | Val acc 0.85330 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1112\n", |
|
|
452 |
"Epoch 1 Batch 110 ( 225) Loss 0.36660 Acc 0.88125 | Val acc 0.84841 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1113\n", |
|
|
453 |
"Epoch 1 Batch 110 ( 225) Loss 0.33627 Acc 0.88125 | Val acc 0.85575 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1114\n", |
|
|
454 |
"Epoch 1 Batch 110 ( 225) Loss 0.35610 Acc 0.88750 | Val acc 0.85330 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1115\n", |
|
|
455 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1114\n", |
|
|
456 |
"Round: 29\n", |
|
|
457 |
"Epoch 1 Batch 110 ( 225) Loss 0.35923 Acc 0.87500 | Val acc 0.85086 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1116\n", |
|
|
458 |
"Epoch 1 Batch 110 ( 225) Loss 0.34627 Acc 0.89375 | Val acc 0.84841 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1117\n", |
|
|
459 |
"Epoch 1 Batch 110 ( 225) Loss 0.36224 Acc 0.88750 | Val acc 0.84352 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1118\n", |
|
|
460 |
"Epoch 1 Batch 110 ( 225) Loss 0.36262 Acc 0.89375 | Val acc 0.84597 | Model saved to /home/sf/data/SWELL-KW/models/GRU/model-gru, global_step 1119\n", |
|
|
461 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1116\n" |
|
|
462 |
] |
|
|
463 |
} |
|
|
464 |
], |
|
|
465 |
"source": [ |
|
|
466 |
"with g1.as_default():\n", |
|
|
467 |
" emiDriver = EMI_Driver(inputPipeline, emiGRU, emiTrainer)\n", |
|
|
468 |
"\n", |
|
|
469 |
"emiDriver.initializeSession(g1)\n", |
|
|
470 |
"y_updated, modelStats = emiDriver.run(numClasses=NUM_OUTPUT, x_train=x_train,\n", |
|
|
471 |
" y_train=y_train, bag_train=BAG_TRAIN,\n", |
|
|
472 |
" x_val=x_val, y_val=y_val, bag_val=BAG_VAL,\n", |
|
|
473 |
" numIter=NUM_ITER, keep_prob=KEEP_PROB,\n", |
|
|
474 |
" numRounds=NUM_ROUNDS, batchSize=BATCH_SIZE,\n", |
|
|
475 |
" numEpochs=NUM_EPOCHS, modelPrefix=MODEL_PREFIX,\n", |
|
|
476 |
" fracEMI=0.5, updatePolicy='top-k', k=1)" |
|
|
477 |
] |
|
|
478 |
}, |
|
|
479 |
{ |
|
|
480 |
"cell_type": "markdown", |
|
|
481 |
"metadata": {}, |
|
|
482 |
"source": [ |
|
|
483 |
"# Evaluating the trained model" |
|
|
484 |
] |
|
|
485 |
}, |
|
|
486 |
{ |
|
|
487 |
"cell_type": "code", |
|
|
488 |
"execution_count": 10, |
|
|
489 |
"metadata": { |
|
|
490 |
"ExecuteTime": { |
|
|
491 |
"end_time": "2018-12-14T14:35:15.218040Z", |
|
|
492 |
"start_time": "2018-12-14T14:35:15.211771Z" |
|
|
493 |
} |
|
|
494 |
}, |
|
|
495 |
"outputs": [], |
|
|
496 |
"source": [ |
|
|
497 |
"# Early Prediction Policy: We make an early prediction based on the predicted classes\n", |
|
|
498 |
"# probability. If the predicted class probability > minProb at some step, we make\n", |
|
|
499 |
"# a prediction at that step.\n", |
|
|
500 |
"def earlyPolicy_minProb(instanceOut, minProb, **kwargs):\n", |
|
|
501 |
" assert instanceOut.ndim == 2\n", |
|
|
502 |
" classes = np.argmax(instanceOut, axis=1)\n", |
|
|
503 |
" prob = np.max(instanceOut, axis=1)\n", |
|
|
504 |
" index = np.where(prob >= minProb)[0]\n", |
|
|
505 |
" if len(index) == 0:\n", |
|
|
506 |
" assert (len(instanceOut) - 1) == (len(classes) - 1)\n", |
|
|
507 |
" return classes[-1], len(instanceOut) - 1\n", |
|
|
508 |
" index = index[0]\n", |
|
|
509 |
" return classes[index], index\n", |
|
|
510 |
"\n", |
|
|
511 |
"def getEarlySaving(predictionStep, numTimeSteps, returnTotal=False):\n", |
|
|
512 |
" predictionStep = predictionStep + 1\n", |
|
|
513 |
" predictionStep = np.reshape(predictionStep, -1)\n", |
|
|
514 |
" totalSteps = np.sum(predictionStep)\n", |
|
|
515 |
" maxSteps = len(predictionStep) * numTimeSteps\n", |
|
|
516 |
" savings = 1.0 - (totalSteps / maxSteps)\n", |
|
|
517 |
" if returnTotal:\n", |
|
|
518 |
" return savings, totalSteps\n", |
|
|
519 |
" return savings" |
|
|
520 |
] |
|
|
521 |
}, |
|
|
522 |
{ |
|
|
523 |
"cell_type": "code", |
|
|
524 |
"execution_count": 11, |
|
|
525 |
"metadata": { |
|
|
526 |
"ExecuteTime": { |
|
|
527 |
"end_time": "2018-12-14T14:35:16.257489Z", |
|
|
528 |
"start_time": "2018-12-14T14:35:15.221029Z" |
|
|
529 |
} |
|
|
530 |
}, |
|
|
531 |
"outputs": [ |
|
|
532 |
{ |
|
|
533 |
"name": "stdout", |
|
|
534 |
"output_type": "stream", |
|
|
535 |
"text": [ |
|
|
536 |
"Accuracy at k = 2: 0.842466\n", |
|
|
537 |
"Savings due to MI-RNN : 0.600000\n", |
|
|
538 |
"Savings due to Early prediction: 0.162573\n", |
|
|
539 |
"Total Savings: 0.665029\n" |
|
|
540 |
] |
|
|
541 |
} |
|
|
542 |
], |
|
|
543 |
"source": [ |
|
|
544 |
"k = 2\n", |
|
|
545 |
"predictions, predictionStep = emiDriver.getInstancePredictions(x_test, y_test, earlyPolicy_minProb,\n", |
|
|
546 |
" minProb=0.99, keep_prob=1.0)\n", |
|
|
547 |
"bagPredictions = emiDriver.getBagPredictions(predictions, minSubsequenceLen=k, numClass=NUM_OUTPUT)\n", |
|
|
548 |
"print('Accuracy at k = %d: %f' % (k, np.mean((bagPredictions == BAG_TEST).astype(int))))\n", |
|
|
549 |
"mi_savings = (1 - NUM_TIMESTEPS / ORIGINAL_NUM_TIMESTEPS)\n", |
|
|
550 |
"emi_savings = getEarlySaving(predictionStep, NUM_TIMESTEPS)\n", |
|
|
551 |
"total_savings = mi_savings + (1 - mi_savings) * emi_savings\n", |
|
|
552 |
"print('Savings due to MI-RNN : %f' % mi_savings)\n", |
|
|
553 |
"print('Savings due to Early prediction: %f' % emi_savings)\n", |
|
|
554 |
"print('Total Savings: %f' % (total_savings))" |
|
|
555 |
] |
|
|
556 |
}, |
|
|
557 |
{ |
|
|
558 |
"cell_type": "code", |
|
|
559 |
"execution_count": 12, |
|
|
560 |
"metadata": { |
|
|
561 |
"ExecuteTime": { |
|
|
562 |
"end_time": "2018-12-14T14:35:17.044115Z", |
|
|
563 |
"start_time": "2018-12-14T14:35:16.259280Z" |
|
|
564 |
}, |
|
|
565 |
"scrolled": true |
|
|
566 |
}, |
|
|
567 |
"outputs": [ |
|
|
568 |
{ |
|
|
569 |
"name": "stdout", |
|
|
570 |
"output_type": "stream", |
|
|
571 |
"text": [ |
|
|
572 |
" len acc macro-fsc macro-pre macro-rec micro-fsc micro-pre \\\n", |
|
|
573 |
"0 1 0.831703 0.831557 0.835146 0.832988 0.831703 0.831703 \n", |
|
|
574 |
"1 2 0.842466 0.842440 0.842466 0.842688 0.842466 0.842466 \n", |
|
|
575 |
"2 3 0.846380 0.846156 0.846630 0.845956 0.846380 0.846380 \n", |
|
|
576 |
"3 4 0.847358 0.846679 0.850044 0.846213 0.847358 0.847358 \n", |
|
|
577 |
"4 5 0.839530 0.838276 0.845458 0.837832 0.839530 0.839530 \n", |
|
|
578 |
"\n", |
|
|
579 |
" micro-rec fscore_01 \n", |
|
|
580 |
"0 0.831703 0.836502 \n", |
|
|
581 |
"1 0.842466 0.840436 \n", |
|
|
582 |
"2 0.846380 0.840285 \n", |
|
|
583 |
"3 0.847358 0.836478 \n", |
|
|
584 |
"4 0.839530 0.824034 \n", |
|
|
585 |
"Max accuracy 0.847358 at subsequencelength 4\n", |
|
|
586 |
"Max micro-f 0.847358 at subsequencelength 4\n", |
|
|
587 |
"Micro-precision 0.847358 at subsequencelength 4\n", |
|
|
588 |
"Micro-recall 0.847358 at subsequencelength 4\n", |
|
|
589 |
"Max macro-f 0.846679 at subsequencelength 4\n", |
|
|
590 |
"macro-precision 0.850044 at subsequencelength 4\n", |
|
|
591 |
"macro-recall 0.846213 at subsequencelength 4\n" |
|
|
592 |
] |
|
|
593 |
} |
|
|
594 |
], |
|
|
595 |
"source": [ |
|
|
596 |
"# A slightly more detailed analysis method is provided. \n", |
|
|
597 |
"df = emiDriver.analyseModel(predictions, BAG_TEST, NUM_SUBINSTANCE, NUM_OUTPUT)" |
|
|
598 |
] |
|
|
599 |
}, |
|
|
600 |
{ |
|
|
601 |
"cell_type": "markdown", |
|
|
602 |
"metadata": {}, |
|
|
603 |
"source": [ |
|
|
604 |
"## Picking the best model" |
|
|
605 |
] |
|
|
606 |
}, |
|
|
607 |
{ |
|
|
608 |
"cell_type": "code", |
|
|
609 |
"execution_count": 18, |
|
|
610 |
"metadata": { |
|
|
611 |
"ExecuteTime": { |
|
|
612 |
"end_time": "2018-12-14T14:35:54.899340Z", |
|
|
613 |
"start_time": "2018-12-14T14:35:17.047464Z" |
|
|
614 |
}, |
|
|
615 |
"scrolled": true |
|
|
616 |
}, |
|
|
617 |
"outputs": [ |
|
|
618 |
{ |
|
|
619 |
"name": "stdout", |
|
|
620 |
"output_type": "stream", |
|
|
621 |
"text": [ |
|
|
622 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1003\n", |
|
|
623 |
"Round: 0, Validation accuracy: 0.6357, Test Accuracy (k = 4): 0.667319, Total Savings: 0.602926\n", |
|
|
624 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1007\n", |
|
|
625 |
"Round: 1, Validation accuracy: 0.6993, Test Accuracy (k = 4): 0.693738, Total Savings: 0.603063\n", |
|
|
626 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1010\n", |
|
|
627 |
"Round: 2, Validation accuracy: 0.7042, Test Accuracy (k = 4): 0.685910, Total Savings: 0.603875\n", |
|
|
628 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1014\n", |
|
|
629 |
"Round: 3, Validation accuracy: 0.7311, Test Accuracy (k = 4): 0.693738, Total Savings: 0.603806\n", |
|
|
630 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1019\n", |
|
|
631 |
"Round: 4, Validation accuracy: 0.7506, Test Accuracy (k = 4): 0.709393, Total Savings: 0.604521\n", |
|
|
632 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1022\n", |
|
|
633 |
"Round: 5, Validation accuracy: 0.7677, Test Accuracy (k = 4): 0.726027, Total Savings: 0.605832\n", |
|
|
634 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1026\n", |
|
|
635 |
"Round: 6, Validation accuracy: 0.7775, Test Accuracy (k = 4): 0.745597, Total Savings: 0.607730\n", |
|
|
636 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1031\n", |
|
|
637 |
"Round: 7, Validation accuracy: 0.7848, Test Accuracy (k = 4): 0.772016, Total Savings: 0.609295\n", |
|
|
638 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1035\n", |
|
|
639 |
"Round: 8, Validation accuracy: 0.8215, Test Accuracy (k = 4): 0.777886, Total Savings: 0.611252\n", |
|
|
640 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1039\n", |
|
|
641 |
"Round: 9, Validation accuracy: 0.8264, Test Accuracy (k = 4): 0.798434, Total Savings: 0.614902\n", |
|
|
642 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1043\n", |
|
|
643 |
"Round: 10, Validation accuracy: 0.8362, Test Accuracy (k = 4): 0.799413, Total Savings: 0.617515\n", |
|
|
644 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1044\n", |
|
|
645 |
"Round: 11, Validation accuracy: 0.8460, Test Accuracy (k = 4): 0.814090, Total Savings: 0.618190\n", |
|
|
646 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1048\n", |
|
|
647 |
"Round: 12, Validation accuracy: 0.8509, Test Accuracy (k = 4): 0.810176, Total Savings: 0.619305\n", |
|
|
648 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1054\n", |
|
|
649 |
"Round: 13, Validation accuracy: 0.8509, Test Accuracy (k = 4): 0.825832, Total Savings: 0.620881\n", |
|
|
650 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1057\n", |
|
|
651 |
"Round: 14, Validation accuracy: 0.8460, Test Accuracy (k = 4): 0.828767, Total Savings: 0.623914\n", |
|
|
652 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1063\n", |
|
|
653 |
"Round: 15, Validation accuracy: 0.8533, Test Accuracy (k = 4): 0.823875, Total Savings: 0.634061\n", |
|
|
654 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1064\n", |
|
|
655 |
"Round: 16, Validation accuracy: 0.8435, Test Accuracy (k = 4): 0.836595, Total Savings: 0.637857\n", |
|
|
656 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1069\n", |
|
|
657 |
"Round: 17, Validation accuracy: 0.8509, Test Accuracy (k = 4): 0.828767, Total Savings: 0.641301\n", |
|
|
658 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1072\n", |
|
|
659 |
"Round: 18, Validation accuracy: 0.8606, Test Accuracy (k = 4): 0.831703, Total Savings: 0.640753\n", |
|
|
660 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1078\n", |
|
|
661 |
"Round: 19, Validation accuracy: 0.8509, Test Accuracy (k = 4): 0.832681, Total Savings: 0.646027\n", |
|
|
662 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1080\n", |
|
|
663 |
"Round: 20, Validation accuracy: 0.8533, Test Accuracy (k = 4): 0.836595, Total Savings: 0.648141\n", |
|
|
664 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1084\n", |
|
|
665 |
"Round: 21, Validation accuracy: 0.8533, Test Accuracy (k = 4): 0.846380, Total Savings: 0.648679\n", |
|
|
666 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1089\n", |
|
|
667 |
"Round: 22, Validation accuracy: 0.8509, Test Accuracy (k = 4): 0.839530, Total Savings: 0.651624\n", |
|
|
668 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1092\n", |
|
|
669 |
"Round: 23, Validation accuracy: 0.8533, Test Accuracy (k = 4): 0.835616, Total Savings: 0.653933\n", |
|
|
670 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1096\n", |
|
|
671 |
"Round: 24, Validation accuracy: 0.8557, Test Accuracy (k = 4): 0.839530, Total Savings: 0.655998\n", |
|
|
672 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1101\n", |
|
|
673 |
"Round: 25, Validation accuracy: 0.8509, Test Accuracy (k = 4): 0.833659, Total Savings: 0.657808\n", |
|
|
674 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1107\n", |
|
|
675 |
"Round: 26, Validation accuracy: 0.8533, Test Accuracy (k = 4): 0.840509, Total Savings: 0.661654\n", |
|
|
676 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1108\n", |
|
|
677 |
"Round: 27, Validation accuracy: 0.8533, Test Accuracy (k = 4): 0.841487, Total Savings: 0.663053\n", |
|
|
678 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1114\n", |
|
|
679 |
"Round: 28, Validation accuracy: 0.8557, Test Accuracy (k = 4): 0.839530, Total Savings: 0.663738\n", |
|
|
680 |
"INFO:tensorflow:Restoring parameters from /home/sf/data/SWELL-KW/models/GRU/model-gru-1116\n", |
|
|
681 |
"Round: 29, Validation accuracy: 0.8509, Test Accuracy (k = 4): 0.842466, Total Savings: 0.665029\n" |
|
|
682 |
] |
|
|
683 |
} |
|
|
684 |
], |
|
|
685 |
"source": [ |
|
|
686 |
"devnull = open(os.devnull, 'r')\n", |
|
|
687 |
"for val in modelStats:\n", |
|
|
688 |
" round_, acc, modelPrefix, globalStep = val\n", |
|
|
689 |
" emiDriver.loadSavedGraphToNewSession(modelPrefix, globalStep, redirFile=devnull)\n", |
|
|
690 |
" predictions, predictionStep = emiDriver.getInstancePredictions(x_test, y_test, earlyPolicy_minProb,\n", |
|
|
691 |
" minProb=0.99, keep_prob=1.0)\n", |
|
|
692 |
"\n", |
|
|
693 |
" bagPredictions = emiDriver.getBagPredictions(predictions, minSubsequenceLen=k, numClass=NUM_OUTPUT)\n", |
|
|
694 |
" print(\"Round: %2d, Validation accuracy: %.4f\" % (round_, acc), end='')\n", |
|
|
695 |
" print(', Test Accuracy (k = %d): %f, ' % (4, np.mean((bagPredictions == BAG_TEST).astype(int))), end='')\n", |
|
|
696 |
" mi_savings = (1 - NUM_TIMESTEPS / ORIGINAL_NUM_TIMESTEPS)\n", |
|
|
697 |
" emi_savings = getEarlySaving(predictionStep, NUM_TIMESTEPS)\n", |
|
|
698 |
" total_savings = mi_savings + (1 - mi_savings) * emi_savings\n", |
|
|
699 |
" print(\"Total Savings: %f\" % total_savings)" |
|
|
700 |
] |
|
|
701 |
}, |
|
|
702 |
{ |
|
|
703 |
"cell_type": "code", |
|
|
704 |
"execution_count": 14, |
|
|
705 |
"metadata": {}, |
|
|
706 |
"outputs": [], |
|
|
707 |
"source": [ |
|
|
708 |
"params = {\n", |
|
|
709 |
" \"NUM_HIDDEN\" : 128,\n", |
|
|
710 |
" \"NUM_TIMESTEPS\" : 64, #subinstance length.\n", |
|
|
711 |
" \"ORIGINAL_NUM_TIMESTEPS\" : 128,\n", |
|
|
712 |
" \"NUM_FEATS\" : 16,\n", |
|
|
713 |
" \"FORGET_BIAS\" : 1.0,\n", |
|
|
714 |
" \"NUM_OUTPUT\" : 5,\n", |
|
|
715 |
" \"USE_DROPOUT\" : 1, # '1' -> True. '0' -> False\n", |
|
|
716 |
" \"KEEP_PROB\" : 0.75,\n", |
|
|
717 |
" \"PREFETCH_NUM\" : 5,\n", |
|
|
718 |
" \"BATCH_SIZE\" : 32,\n", |
|
|
719 |
" \"NUM_EPOCHS\" : 2,\n", |
|
|
720 |
" \"NUM_ITER\" : 4,\n", |
|
|
721 |
" \"NUM_ROUNDS\" : 10,\n", |
|
|
722 |
" \"LEARNING_RATE\" : 0.001,\n", |
|
|
723 |
" \"MODEL_PREFIX\" : '/home/sf/data/DREAMER/Dominance/model-gru'\n", |
|
|
724 |
"}" |
|
|
725 |
] |
|
|
726 |
}, |
|
|
727 |
{ |
|
|
728 |
"cell_type": "code", |
|
|
729 |
"execution_count": 15, |
|
|
730 |
"metadata": {}, |
|
|
731 |
"outputs": [ |
|
|
732 |
{ |
|
|
733 |
"name": "stdout", |
|
|
734 |
"output_type": "stream", |
|
|
735 |
"text": [ |
|
|
736 |
" len acc macro-fsc macro-pre macro-rec micro-fsc micro-pre \\\n", |
|
|
737 |
"0 1 0.831703 0.831557 0.835146 0.832988 0.831703 0.831703 \n", |
|
|
738 |
"1 2 0.842466 0.842440 0.842466 0.842688 0.842466 0.842466 \n", |
|
|
739 |
"2 3 0.846380 0.846156 0.846630 0.845956 0.846380 0.846380 \n", |
|
|
740 |
"3 4 0.847358 0.846679 0.850044 0.846213 0.847358 0.847358 \n", |
|
|
741 |
"4 5 0.839530 0.838276 0.845458 0.837832 0.839530 0.839530 \n", |
|
|
742 |
"\n", |
|
|
743 |
" micro-rec fscore_01 \n", |
|
|
744 |
"0 0.831703 0.836502 \n", |
|
|
745 |
"1 0.842466 0.840436 \n", |
|
|
746 |
"2 0.846380 0.840285 \n", |
|
|
747 |
"3 0.847358 0.836478 \n", |
|
|
748 |
"4 0.839530 0.824034 \n", |
|
|
749 |
"Max accuracy 0.847358 at subsequencelength 4\n", |
|
|
750 |
"Max micro-f 0.847358 at subsequencelength 4\n", |
|
|
751 |
"Micro-precision 0.847358 at subsequencelength 4\n", |
|
|
752 |
"Micro-recall 0.847358 at subsequencelength 4\n", |
|
|
753 |
"Max macro-f 0.846679 at subsequencelength 4\n", |
|
|
754 |
"macro-precision 0.850044 at subsequencelength 4\n", |
|
|
755 |
"macro-recall 0.846213 at subsequencelength 4\n", |
|
|
756 |
"+----+-------+----------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+\n", |
|
|
757 |
"| | len | acc | macro-fsc | macro-pre | macro-rec | micro-fsc | micro-pre | micro-rec | fscore_01 |\n", |
|
|
758 |
"+====+=======+==========+=============+=============+=============+=============+=============+=============+=============+\n", |
|
|
759 |
"| 0 | 1 | 0.831703 | 0.831557 | 0.835146 | 0.832988 | 0.831703 | 0.831703 | 0.831703 | 0.836502 |\n", |
|
|
760 |
"+----+-------+----------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+\n", |
|
|
761 |
"| 1 | 2 | 0.842466 | 0.84244 | 0.842466 | 0.842688 | 0.842466 | 0.842466 | 0.842466 | 0.840436 |\n", |
|
|
762 |
"+----+-------+----------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+\n", |
|
|
763 |
"| 2 | 3 | 0.84638 | 0.846156 | 0.84663 | 0.845956 | 0.84638 | 0.84638 | 0.84638 | 0.840285 |\n", |
|
|
764 |
"+----+-------+----------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+\n", |
|
|
765 |
"| 3 | 4 | 0.847358 | 0.846679 | 0.850044 | 0.846213 | 0.847358 | 0.847358 | 0.847358 | 0.836478 |\n", |
|
|
766 |
"+----+-------+----------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+\n", |
|
|
767 |
"| 4 | 5 | 0.83953 | 0.838276 | 0.845458 | 0.837832 | 0.83953 | 0.83953 | 0.83953 | 0.824034 |\n", |
|
|
768 |
"+----+-------+----------+-------------+-------------+-------------+-------------+-------------+-------------+-------------+\n" |
|
|
769 |
] |
|
|
770 |
} |
|
|
771 |
], |
|
|
772 |
"source": [ |
|
|
773 |
"gru_dict = {**params}\n", |
|
|
774 |
"gru_dict[\"k\"] = k\n", |
|
|
775 |
"gru_dict[\"accuracy\"] = np.mean((bagPredictions == BAG_TEST).astype(int))\n", |
|
|
776 |
"gru_dict[\"total_savings\"] = total_savings\n", |
|
|
777 |
"gru_dict[\"y_test\"] = BAG_TEST\n", |
|
|
778 |
"gru_dict[\"y_pred\"] = bagPredictions\n", |
|
|
779 |
"\n", |
|
|
780 |
"# A slightly more detailed analysis method is provided. \n", |
|
|
781 |
"df = emiDriver.analyseModel(predictions, BAG_TEST, NUM_SUBINSTANCE, NUM_OUTPUT)\n", |
|
|
782 |
"print (tabulate(df, headers=list(df.columns), tablefmt='grid'))" |
|
|
783 |
] |
|
|
784 |
}, |
|
|
785 |
{ |
|
|
786 |
"cell_type": "code", |
|
|
787 |
"execution_count": 16, |
|
|
788 |
"metadata": {}, |
|
|
789 |
"outputs": [ |
|
|
790 |
{ |
|
|
791 |
"name": "stdout", |
|
|
792 |
"output_type": "stream", |
|
|
793 |
"text": [ |
|
|
794 |
"Results for this run have been saved at /home/sf/data/SWELL/GRU/ .\n" |
|
|
795 |
] |
|
|
796 |
} |
|
|
797 |
], |
|
|
798 |
"source": [ |
|
|
799 |
"dirname = \"/home/sf/data/SWELL/GRU/\"\n", |
|
|
800 |
"pathlib.Path(dirname).mkdir(parents=True, exist_ok=True)\n", |
|
|
801 |
"print (\"Results for this run have been saved at\" , dirname, \".\")\n", |
|
|
802 |
"\n", |
|
|
803 |
"now = datetime.datetime.now()\n", |
|
|
804 |
"filename = list((str(now.year),\"-\",str(now.month),\"-\",str(now.day),\"|\",str(now.hour),\"-\",str(now.minute)))\n", |
|
|
805 |
"filename = ''.join(filename)\n", |
|
|
806 |
"\n", |
|
|
807 |
"#Save the dictionary containing the params and the results.\n", |
|
|
808 |
"pkl.dump(gru_dict,open(dirname + filename + \".pkl\",mode='wb'))" |
|
|
809 |
] |
|
|
810 |
}, |
|
|
811 |
{ |
|
|
812 |
"cell_type": "code", |
|
|
813 |
"execution_count": 17, |
|
|
814 |
"metadata": {}, |
|
|
815 |
"outputs": [ |
|
|
816 |
{ |
|
|
817 |
"data": { |
|
|
818 |
"text/plain": [ |
|
|
819 |
"'/home/sf/data/SWELL/GRU/2019-9-3|15-1.pkl'" |
|
|
820 |
] |
|
|
821 |
}, |
|
|
822 |
"execution_count": 17, |
|
|
823 |
"metadata": {}, |
|
|
824 |
"output_type": "execute_result" |
|
|
825 |
} |
|
|
826 |
], |
|
|
827 |
"source": [ |
|
|
828 |
"dirname+filename+'.pkl'" |
|
|
829 |
] |
|
|
830 |
} |
|
|
831 |
], |
|
|
832 |
"metadata": { |
|
|
833 |
"kernelspec": { |
|
|
834 |
"display_name": "Python 3", |
|
|
835 |
"language": "python", |
|
|
836 |
"name": "python3" |
|
|
837 |
}, |
|
|
838 |
"language_info": { |
|
|
839 |
"codemirror_mode": { |
|
|
840 |
"name": "ipython", |
|
|
841 |
"version": 3 |
|
|
842 |
}, |
|
|
843 |
"file_extension": ".py", |
|
|
844 |
"mimetype": "text/x-python", |
|
|
845 |
"name": "python", |
|
|
846 |
"nbconvert_exporter": "python", |
|
|
847 |
"pygments_lexer": "ipython3", |
|
|
848 |
"version": "3.7.3" |
|
|
849 |
}, |
|
|
850 |
"latex_envs": { |
|
|
851 |
"LaTeX_envs_menu_present": true, |
|
|
852 |
"autoclose": false, |
|
|
853 |
"autocomplete": true, |
|
|
854 |
"bibliofile": "biblio.bib", |
|
|
855 |
"cite_by": "apalike", |
|
|
856 |
"current_citInitial": 1, |
|
|
857 |
"eqLabelWithNumbers": true, |
|
|
858 |
"eqNumInitial": 1, |
|
|
859 |
"hotkeys": { |
|
|
860 |
"equation": "Ctrl-E", |
|
|
861 |
"itemize": "Ctrl-I" |
|
|
862 |
}, |
|
|
863 |
"labels_anchors": false, |
|
|
864 |
"latex_user_defs": false, |
|
|
865 |
"report_style_numbering": false, |
|
|
866 |
"user_envs_cfg": false |
|
|
867 |
} |
|
|
868 |
}, |
|
|
869 |
"nbformat": 4, |
|
|
870 |
"nbformat_minor": 2 |
|
|
871 |
} |