4235 lines
688 KiB
Plaintext
4235 lines
688 KiB
Plaintext
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{
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"cells": [
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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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"ExecuteTime": {
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"end_time": "2021-04-07T02:25:33.515104Z",
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"start_time": "2021-04-07T02:25:32.233774Z"
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},
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"tags": [
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"remove_cell"
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]
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"import torchvision \n",
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"import math\n",
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"from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n",
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"from torchvision import transforms\n",
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"\n",
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"from torch.utils.data import Dataset, DataLoader\n",
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"\n",
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"from tqdm.autonotebook import tqdm\n",
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"\n",
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"from idlmam import set_seed\n",
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"\n",
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"import numpy as np\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from matplotlib.pyplot import imshow\n",
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"\n",
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"import pandas as pd\n",
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"\n",
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"from sklearn.metrics import accuracy_score\n",
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"\n",
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"import time\n",
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"\n",
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"from idlmam import LastTimeStep, train_network, Flatten, weight_reset, View, LambdaLayer\n",
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"from idlmam import AttentionAvg, GeneralScore, DotScore, AdditiveAttentionScore #For attention mechanism use"
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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": "2021-04-07T02:25:33.520944Z",
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"start_time": "2021-04-07T02:25:33.516913Z"
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},
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"tags": [
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"remove_cell"
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]
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"from IPython.display import set_matplotlib_formats\n",
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"set_matplotlib_formats('png', 'pdf')\n",
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"\n",
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"from IPython.display import display_pdf\n",
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"from IPython.display import Latex"
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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": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-04-07T02:25:33.531384Z",
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"start_time": "2021-04-07T02:25:33.522006Z"
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},
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"tags": [
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"remove_cell"
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]
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},
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"outputs": [],
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"source": [
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"torch.backends.cudnn.deterministic=True\n",
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"set_seed(42)"
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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": "2021-04-07T02:25:33.543972Z",
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"start_time": "2021-04-07T02:25:33.540893Z"
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}
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},
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"outputs": [],
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"source": [
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"# !conda install -c pytorch torchtext \n",
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"# !conda install -c powerai sentencepiece \n",
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"# !pip install torchtext \n",
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"# !pip install sentencepiece "
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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": "2021-04-07T02:25:33.549155Z",
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"start_time": "2021-04-07T02:25:33.546202Z"
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},
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"tags": [
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"remove_cell"
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]
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},
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"outputs": [],
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"source": [
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"set_seed(42)"
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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": "2021-04-07T02:25:33.582335Z",
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"start_time": "2021-04-07T02:25:33.551009Z"
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},
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"tags": [
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"remove_cell"
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]
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},
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"outputs": [],
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"source": [
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"device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")"
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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": "2021-04-07T02:25:34.052578Z",
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"start_time": "2021-04-07T02:25:33.584019Z"
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}
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},
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"outputs": [],
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"source": [
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"import torchtext\n",
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"from torchtext.datasets import AG_NEWS\n",
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"\n",
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"train_iter, test_iter = AG_NEWS(root='./data', split=('train', 'test'))\n",
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"train_dataset = list(train_iter)\n",
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"test_dataset = list(test_iter)"
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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": "2021-04-07T02:25:34.057959Z",
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"start_time": "2021-04-07T02:25:34.053958Z"
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}
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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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"(3, \"Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\\\band of ultra-cynics, are seeing green again.\")\n"
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]
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}
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],
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"source": [
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"print(train_dataset[0])"
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]
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},
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{
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"cell_type": "code",
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|
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"execution_count": 10,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-04-07T02:25:36.970338Z",
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"start_time": "2021-04-07T02:25:34.059228Z"
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}
|
||
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},
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"outputs": [],
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"source": [
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"from torchtext.data.utils import get_tokenizer#tokenizers break strings like \"this is a string\" into lists of tokens like ['this', 'is', 'a', 'string']\n",
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"tokenizer = get_tokenizer('basic_english') #we will be fine with the default english style tokenizer\n",
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"\n",
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"from collections import Counter #how many lines in this dataset\n",
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"from torchtext.vocab import Vocab #we need to create a vocabulary of all the words in the training set\n",
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"\n",
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"counter = Counter() \n",
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"for (label, line) in train_dataset: #loop through the training data \n",
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" counter.update(tokenizer(line)) #count the number of unique tokens we see and how often we see them (e.g., we will see \"the\" a lot, but \"sasquatch\" maybe once or not at all.)\n",
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"vocab = Vocab(counter, min_freq=10, specials=('<unk>', '<BOS>', '<EOS>', '<PAD>')) #create a vocab object, removing any word that didn't occur at least 10 times, and add special vocab items for unkown, begining of sentance, end of sentance, and \"padding\"\n"
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]
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},
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{
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||
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"cell_type": "code",
|
||
|
|
"execution_count": 11,
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||
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"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
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"end_time": "2021-04-07T02:25:36.976414Z",
|
||
|
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"start_time": "2021-04-07T02:25:36.971729Z"
|
||
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}
|
||
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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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"[1, 434, 428, 4, 1608, 14841, 116, 69, 5, 851, 16, 30, 17, 30, 18, 0, 6, 434, 377, 19, 12, 0, 9, 0, 6, 45, 4012, 786, 328, 4, 2]\n"
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||
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]
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||
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}
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||
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],
|
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"source": [
|
||
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"def text_transform(x): #string -> list of integers\n",
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" return [vocab['<BOS>']] + [vocab[token] for token in tokenizer(x)] + [vocab['<EOS>']] #vocab acts like a dictionary, handls unkown tokens for us, and we can make it pre and post-pend with the start and end markers respectively.\n",
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"\n",
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"def label_transform(x): \n",
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" return x-1 #labes are originally [1, 2, 3, 4] but we need them as [0, 1, 2, 3] \n",
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"\n",
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"#Transform the first data point's text into a list of tokens\n",
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"print(text_transform(train_dataset[0][1])) "
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]
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},
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{
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||
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"cell_type": "code",
|
||
|
|
"execution_count": 12,
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||
|
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"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-07T02:25:37.000752Z",
|
||
|
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"start_time": "2021-04-07T02:25:36.977667Z"
|
||
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}
|
||
|
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},
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"outputs": [
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||
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{
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||
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"name": "stdout",
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||
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"output_type": "stream",
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"text": [
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"Vocab: 20647\n",
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||
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"Num Classes: 4\n"
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||
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]
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||
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}
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||
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],
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||
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"source": [
|
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"VOCAB_SIZE = len(vocab)\n",
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"NUM_CLASS = len(np.unique([z[0] for z in train_dataset])) \n",
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"print(\"Vocab: \", VOCAB_SIZE)\n",
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"print(\"Num Classes: \", NUM_CLASS)\n",
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"\n",
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"padding_idx = vocab[\"<PAD>\"]\n",
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"\n",
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"embed_dim = 128\n",
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"B = 64\n",
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"epochs = 15"
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]
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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": 13,
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"metadata": {
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||
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"ExecuteTime": {
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||
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"end_time": "2021-04-07T02:25:37.007547Z",
|
||
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"start_time": "2021-04-07T02:25:37.002022Z"
|
||
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}
|
||
|
|
},
|
||
|
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"outputs": [],
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||
|
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"source": [
|
||
|
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"def pad_batch(batch):\n",
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" \"\"\"\n",
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" Pad items in the batch to the length of the longest item in the batch. \n",
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" Also, re-order so that the values are returned (input, label)\n",
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" \"\"\"\n",
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" labels = [label_transform(z[0]) for z in batch] #get and transform every label in the batch\n",
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||
|
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" texts = [torch.tensor(text_transform(z[1]), dtype=torch.int64) for z in batch] #get, tokenizer, and put into a tensor every text\n",
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" #what is the longest sequence in this batch? \n",
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||
|
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" max_len = max([text.size(0) for text in texts])\n",
|
||
|
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" #pad each text tensor by whatever amount gets it to the max_len\n",
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||
|
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" texts = [F.pad(text, (0,max_len-text.size(0)), value=padding_idx) for text in texts]\n",
|
||
|
|
" #make x and y a single tensor\n",
|
||
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" x, y = torch.stack(texts), torch.tensor(labels, dtype=torch.int64)\n",
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||
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" \n",
|
||
|
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" return x, y"
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||
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]
|
||
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},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-07T02:25:37.013459Z",
|
||
|
|
"start_time": "2021-04-07T02:25:37.008785Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"train_loader = DataLoader(train_dataset, batch_size=B, shuffle=True, collate_fn=pad_batch)\n",
|
||
|
|
"test_loader = DataLoader(test_dataset, batch_size=B, collate_fn=pad_batch)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-07T02:38:08.704939Z",
|
||
|
|
"start_time": "2021-04-07T02:25:37.014665Z"
|
||
|
|
},
|
||
|
|
"tags": [
|
||
|
|
"remove_output"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
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|
|
"data": {
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|
|
"application/vnd.jupyter.widget-view+json": {
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"model_id": "3d83ca88d6404842ab0a6921e3bd8e97",
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"version_major": 2,
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|
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"version_minor": 0
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|
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},
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|
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"text/plain": [
|
||
|
|
"HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=15.0), HTML(value='')))"
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|
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]
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},
|
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|
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"metadata": {},
|
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|
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"output_type": "display_data"
|
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|
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},
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{
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"data": {
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=1875.0), HTML(value='')))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"HBox(children=(HTML(value='Validating'), FloatProgress(value=0.0, max=119.0), HTML(value='')))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"gru = nn.Sequential(\n",
|
||
|
|
" nn.Embedding(VOCAB_SIZE, embed_dim, padding_idx=padding_idx), #(B, T) -> (B, T, D)\n",
|
||
|
|
" nn.GRU(embed_dim, embed_dim, num_layers=3, batch_first=True, bidirectional=True), #(B, T, D) -> ( (B,T,D) , (S, B, D) )\n",
|
||
|
|
" LastTimeStep(rnn_layers=3, bidirectional=True), #We need to take the RNN output and reduce it to one item, (B, 2*D)\n",
|
||
|
|
" nn.Linear(embed_dim*2, NUM_CLASS), #(B, D) -> (B, classes)\n",
|
||
|
|
")\n",
|
||
|
|
"\n",
|
||
|
|
"loss_func = nn.CrossEntropyLoss()\n",
|
||
|
|
"gru_results = train_network(gru, loss_func, train_loader, val_loader=test_loader, score_funcs={'Accuracy': accuracy_score}, device=device, epochs=epochs)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-07T02:38:08.925765Z",
|
||
|
|
"start_time": "2021-04-07T02:38:08.706187Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='epoch', ylabel='val Accuracy'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/pdf": "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
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.lineplot(x='epoch', y='val Accuracy', data=gru_results, label='GRU')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-07T02:42:20.089138Z",
|
||
|
|
"start_time": "2021-04-07T02:38:08.927045Z"
|
||
|
|
},
|
||
|
|
"tags": [
|
||
|
|
"remove_output"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "77cc4fc789dd42fbbb6a6edecec9fc8f",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=15.0), HTML(value='')))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
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|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
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{
|
||
|
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"data": {
|
||
|
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"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=1875.0), HTML(value='')))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
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},
|
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{
|
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|
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"data": {
|
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"application/vnd.jupyter.widget-view+json": {
|
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|
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"model_id": "",
|
||
|
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"version_major": 2,
|
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|
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"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"HBox(children=(HTML(value='Validating'), FloatProgress(value=0.0, max=119.0), HTML(value='')))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"simpleEmbdAvg = nn.Sequential(\n",
|
||
|
|
" nn.Embedding(VOCAB_SIZE, embed_dim, padding_idx=padding_idx), #(B, T) -> (B, T, D) \n",
|
||
|
|
" nn.Linear(embed_dim, embed_dim),\n",
|
||
|
|
" nn.LeakyReLU(),\n",
|
||
|
|
" nn.Linear(embed_dim, embed_dim),\n",
|
||
|
|
" nn.LeakyReLU(),\n",
|
||
|
|
" nn.Linear(embed_dim, embed_dim),\n",
|
||
|
|
" nn.LeakyReLU(),\n",
|
||
|
|
" nn.AdaptiveAvgPool2d((1,embed_dim)), #(B, T, D) -> (B, 1, D)\n",
|
||
|
|
" nn.Flatten(), #(B, 1, D) -> (B, D)\n",
|
||
|
|
" nn.Linear(embed_dim, embed_dim),\n",
|
||
|
|
" nn.LeakyReLU(),\n",
|
||
|
|
" nn.BatchNorm1d(embed_dim),\n",
|
||
|
|
" nn.Linear(embed_dim, NUM_CLASS)\n",
|
||
|
|
")\n",
|
||
|
|
"simpleEmbdAvg_results = train_network(simpleEmbdAvg, loss_func, train_loader, val_loader=test_loader, score_funcs={'Accuracy': accuracy_score}, device=device, epochs=epochs)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-07T02:42:20.350331Z",
|
||
|
|
"start_time": "2021-04-07T02:42:20.090598Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='epoch', ylabel='val Accuracy'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
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|
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"data": {
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.lineplot(x='epoch', y='val Accuracy', data=gru_results, label='GRU')\n",
|
||
|
|
"sns.lineplot(x='epoch', y='val Accuracy', data=simpleEmbdAvg_results, label='Average Embedding')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-07T02:42:20.595775Z",
|
||
|
|
"start_time": "2021-04-07T02:42:20.351558Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='total time', ylabel='val Accuracy'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/pdf": "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
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.lineplot(x='total time', y='val Accuracy', data=gru_results, label='GRU')\n",
|
||
|
|
"sns.lineplot(x='total time', y='val Accuracy', data=simpleEmbdAvg_results, label='Average Embedding')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 20,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-07T02:42:20.603927Z",
|
||
|
|
"start_time": "2021-04-07T02:42:20.598556Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"class EmbeddingAttentionBag(nn.Module):\n",
|
||
|
|
"\n",
|
||
|
|
" def __init__(self, vocab_size, D, embd_layers=3, padding_idx=None):\n",
|
||
|
|
" super(EmbeddingAttentionBag, self).__init__()\n",
|
||
|
|
" self.padding_idx = padding_idx\n",
|
||
|
|
" self.embd = nn.Embedding(vocab_size, D, padding_idx=padding_idx)\n",
|
||
|
|
" if isinstance(embd_layers, int):\n",
|
||
|
|
" self.embd_layers = nn.Sequential( #(B, T, D) -> (B, T, D) \n",
|
||
|
|
" *[nn.Sequential(nn.Linear(embed_dim, embed_dim),\n",
|
||
|
|
" nn.LeakyReLU()) for _ in range(embd_layers)]\n",
|
||
|
|
" )\n",
|
||
|
|
" else:\n",
|
||
|
|
" self.embd_layers = embd_layers\n",
|
||
|
|
" self.attn = AttentionAvg(AdditiveAttentionScore(D))# functions defined back in Chapter 10\n",
|
||
|
|
" \n",
|
||
|
|
" def forward(self, input):\n",
|
||
|
|
" \"\"\"\n",
|
||
|
|
" input: (B, T) shape, dtype=int64\n",
|
||
|
|
" output: (B, D) shape, dtype=float32\n",
|
||
|
|
" \"\"\"\n",
|
||
|
|
" if self.padding_idx is not None:\n",
|
||
|
|
" mask = input != self.padding_idx\n",
|
||
|
|
" else:\n",
|
||
|
|
" mask = input == input #All entries are `True`\n",
|
||
|
|
" #mask is shape (B, T)\n",
|
||
|
|
" x = self.embd(input) #(B, T, D)\n",
|
||
|
|
" x = self.embd_layers(x)#(B, T, D) \n",
|
||
|
|
" #average over time\n",
|
||
|
|
" context = x.sum(dim=1)/(mask.sum(dim=1).unsqueeze(1)+1e-5) #(B, T, D) -> (B, D)\n",
|
||
|
|
" #If we wanted to just do normal averaging, we could return the context variable right now!\n",
|
||
|
|
" return self.attn(x, context, mask=mask) # ((B, T, D), (B, D)) -> (B, D)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
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"output_type": "display_data"
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"data": {
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},
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"text/plain": [
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]
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},
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"metadata": {},
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"output_type": "display_data"
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"data": {
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"text/plain": [
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]
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},
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"metadata": {},
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"output_type": "display_data"
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"data": {
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"text/plain": [
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]
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},
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"metadata": {},
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"output_type": "display_data"
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{
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"data": {
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"model_id": "",
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"version_major": 2,
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},
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"text/plain": [
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"HBox(children=(HTML(value='Validating'), FloatProgress(value=0.0, max=119.0), HTML(value='')))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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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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"\n"
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]
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}
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],
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"source": [
|
||
|
|
"#Now we can define a simple model!\n",
|
||
|
|
"attnEmbd = nn.Sequential(\n",
|
||
|
|
" EmbeddingAttentionBag(VOCAB_SIZE, embed_dim, padding_idx=padding_idx), #(B, T) -> (B, D) \n",
|
||
|
|
" nn.Linear(embed_dim, embed_dim),\n",
|
||
|
|
" nn.LeakyReLU(),\n",
|
||
|
|
" nn.BatchNorm1d(embed_dim),\n",
|
||
|
|
" nn.Linear(embed_dim, NUM_CLASS)\n",
|
||
|
|
")\n",
|
||
|
|
"attnEmbd_results = train_network(attnEmbd, loss_func, train_loader, val_loader=test_loader, score_funcs={'Accuracy': accuracy_score}, device=device, epochs=epochs)"
|
||
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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": 22,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-04-07T02:48:48.780398Z",
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|
"start_time": "2021-04-07T02:48:48.484395Z"
|
||
|
|
}
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||
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},
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"outputs": [
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||
|
|
{
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|
"data": {
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"text/plain": [
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"<AxesSubplot:xlabel='total time', ylabel='val Accuracy'>"
|
||
|
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]
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||
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|
},
|
||
|
|
"execution_count": 22,
|
||
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"metadata": {},
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"output_type": "execute_result"
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},
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"data": {
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"text": [
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}
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],
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"source": [
|
||
|
|
"def cnnLayer(in_size, out_size): #I'm being lazy, we should make k_size an argument too\n",
|
||
|
|
" return nn.Sequential(\n",
|
||
|
|
" nn.Conv1d(in_size, out_size, kernel_size=k_size, padding=k_size//2),\n",
|
||
|
|
" nn.LeakyReLU(),\n",
|
||
|
|
" nn.BatchNorm1d(out_size))\n",
|
||
|
|
"\n",
|
||
|
|
"k_size = 3\n",
|
||
|
|
"cnnOverTime = nn.Sequential(\n",
|
||
|
|
" nn.Embedding(VOCAB_SIZE, embed_dim, padding_idx=padding_idx), #(B, T) -> (B, T, D) \n",
|
||
|
|
" LambdaLayer(lambda x : x.permute(0,2,1)), #(B, T, D) -> (B, D, T)\n",
|
||
|
|
" #now we will pretend that D is the number of channels in this new interpreation of thed ata!\n",
|
||
|
|
" cnnLayer(embed_dim, embed_dim),\n",
|
||
|
|
" cnnLayer(embed_dim, embed_dim),\n",
|
||
|
|
" nn.AvgPool1d(2), #(B, D, T) -> (B, D, T/2) \n",
|
||
|
|
" cnnLayer(embed_dim, embed_dim*2),\n",
|
||
|
|
" cnnLayer(embed_dim*2, embed_dim*2),\n",
|
||
|
|
" nn.AvgPool1d(2), #(B, 2*D, T/2) -> (B, 2*D, T/4) \n",
|
||
|
|
" cnnLayer(embed_dim*2, embed_dim*4),\n",
|
||
|
|
" cnnLayer(embed_dim*4, embed_dim*4),\n",
|
||
|
|
" #Now that we have done some rounds of pooling and convolution, reduce to a fixed length!\n",
|
||
|
|
" nn.AdaptiveMaxPool1d(1), #(B, 4*D, T/4) -> (B, 4*D, 1)\n",
|
||
|
|
" nn.Flatten(), #(B, 4*D, 1) -> (B, 4*D)\n",
|
||
|
|
" nn.Linear(4*embed_dim, embed_dim),\n",
|
||
|
|
" nn.LeakyReLU(),\n",
|
||
|
|
" nn.BatchNorm1d(embed_dim),\n",
|
||
|
|
" nn.Linear(embed_dim, NUM_CLASS)\n",
|
||
|
|
")\n",
|
||
|
|
"cnn_results = train_network(cnnOverTime, loss_func, train_loader, val_loader=test_loader, score_funcs={'Accuracy': accuracy_score}, device=device, epochs=epochs)"
|
||
|
|
]
|
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},
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{
|
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"cell_type": "code",
|
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"execution_count": 24,
|
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"metadata": {
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"ExecuteTime": {
|
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"end_time": "2021-04-07T02:57:52.384621Z",
|
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"start_time": "2021-04-07T02:57:52.082894Z"
|
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}
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},
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"outputs": [
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"data": {
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"text/plain": [
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"<AxesSubplot:xlabel='total time', ylabel='val Accuracy'>"
|
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]
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},
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|
"execution_count": 24,
|
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"metadata": {},
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"output_type": "execute_result"
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.lineplot(x='total time', y='val Accuracy', data=gru_results, label='GRU')\n",
|
||
|
|
"sns.lineplot(x='total time', y='val Accuracy', data=simpleEmbdAvg_results, label='Average Embedding')\n",
|
||
|
|
"sns.lineplot(x='total time', y='val Accuracy', data=attnEmbd_results, label='Attention Embedding')\n",
|
||
|
|
"sns.lineplot(x='total time', y='val Accuracy', data=cnn_results, label='CNN Adaptive Pooling')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-07T02:57:52.608599Z",
|
||
|
|
"start_time": "2021-04-07T02:57:52.385990Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/home/edraff/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
|
||
|
|
" FutureWarning\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"position = np.arange(0, 100)\n",
|
||
|
|
"sns.lineplot(position, np.sin(position), label=\"sin(position)\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-07T02:57:52.867523Z",
|
||
|
|
"start_time": "2021-04-07T02:57:52.609849Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"position = np.arange(0, 100)\n",
|
||
|
|
"sns.lineplot(x=position, y=np.sin(position), label=\"sin(position)\")\n",
|
||
|
|
"sns.lineplot(x=position, y=np.sin(position/10), label=\"sin(position/10)\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-07T02:57:53.248815Z",
|
||
|
|
"start_time": "2021-04-07T02:57:52.868732Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/pdf": "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
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"dimensions = 6 \n",
|
||
|
|
"position = np.expand_dims(np.arange(0, 100), 1)\n",
|
||
|
|
"#this computes the frequency f in a numerically stable way. \n",
|
||
|
|
"div = np.exp(np.arange(0, dimensions*2, 2) * (-math.log(10000.0) / (dimensions*2)))\n",
|
||
|
|
"for i in range(dimensions):\n",
|
||
|
|
" sns.lineplot(x=position[:,0], y=np.sin(position*div)[:,i], label=\"Dim-\"+str(i))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-07T02:57:53.256441Z",
|
||
|
|
"start_time": "2021-04-07T02:57:53.250045Z"
|
||
|
|
},
|
||
|
|
"tags": [
|
||
|
|
"remove_cell"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#Adapted from from https://github.com/pytorch/examples/blob/0c1654d6913f77f09c0505fb284d977d89c17c1a/word_language_model/model.py#L63\n",
|
||
|
|
"class PositionalEncoding(nn.Module):\n",
|
||
|
|
" r\"\"\"Inject some information about the relative or absolute position of the tokens\n",
|
||
|
|
" in the sequence. The positional encodings have the same dimension as\n",
|
||
|
|
" the embeddings, so that the two can be summed. Here, we use sine and cosine\n",
|
||
|
|
" functions of different frequencies.\n",
|
||
|
|
" .. math::\n",
|
||
|
|
" \\text{PosEncoder}(pos, 2i) = sin(pos/10000^(2i/d_model))\n",
|
||
|
|
" \\text{PosEncoder}(pos, 2i+1) = cos(pos/10000^(2i/d_model))\n",
|
||
|
|
" \\text{where pos is the word position and i is the embed idx)\n",
|
||
|
|
" Args:\n",
|
||
|
|
" d_model: the embed dim (required).\n",
|
||
|
|
" dropout: the dropout value (default=0.1).\n",
|
||
|
|
" max_len: the max. length of the incoming sequence (default=5000).\n",
|
||
|
|
" Examples:\n",
|
||
|
|
" >>> pos_encoder = PositionalEncoding(d_model)\n",
|
||
|
|
" \"\"\"\n",
|
||
|
|
"\n",
|
||
|
|
" def __init__(self, d_model, dropout=0.1, max_len=5000, batch_first=False):\n",
|
||
|
|
" super(PositionalEncoding, self).__init__()\n",
|
||
|
|
" self.dropout = nn.Dropout(p=dropout)\n",
|
||
|
|
" self.d_model = d_model\n",
|
||
|
|
"\n",
|
||
|
|
" pe = torch.zeros(max_len, d_model)\n",
|
||
|
|
" position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n",
|
||
|
|
" div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\n",
|
||
|
|
" pe[:, 0::2] = torch.sin(position * div_term)\n",
|
||
|
|
" pe[:, 1::2] = torch.cos(position * div_term)\n",
|
||
|
|
" pe = pe.unsqueeze(0).transpose(0, 1)\n",
|
||
|
|
" self.register_buffer('pe', pe)\n",
|
||
|
|
" \n",
|
||
|
|
" self.batch_first = batch_first\n",
|
||
|
|
"\n",
|
||
|
|
" def forward(self, x):\n",
|
||
|
|
" r\"\"\"Inputs of forward function\n",
|
||
|
|
" Args:\n",
|
||
|
|
" x: the sequence fed to the positional encoder model (required).\n",
|
||
|
|
" Shape:\n",
|
||
|
|
" x: [sequence length, batch size, embed dim]\n",
|
||
|
|
" output: [sequence length, batch size, embed dim]\n",
|
||
|
|
" Examples:\n",
|
||
|
|
" >>> output = pos_encoder(x)\n",
|
||
|
|
" \"\"\"\n",
|
||
|
|
" if self.batch_first: #go from (B, T, D) input shape to (T, B, D)\n",
|
||
|
|
" x = x.permute(1, 0, 2)\n",
|
||
|
|
"\n",
|
||
|
|
" x = x *np.sqrt(self.d_model) + self.pe[:x.size(0), :]\n",
|
||
|
|
" x = self.dropout(x)\n",
|
||
|
|
" \n",
|
||
|
|
" if self.batch_first: #now go back to (B, T, D) shape\n",
|
||
|
|
" x = x.permute(1, 0, 2)\n",
|
||
|
|
" \n",
|
||
|
|
" return x"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-07T02:57:53.292611Z",
|
||
|
|
"start_time": "2021-04-07T02:57:53.257648Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"simplePosEmbdAvg = nn.Sequential(\n",
|
||
|
|
" nn.Embedding(VOCAB_SIZE, embed_dim, padding_idx=padding_idx), #(B, T) -> (B, T, D) \n",
|
||
|
|
" PositionalEncoding(embed_dim, batch_first=True),\n",
|
||
|
|
" nn.Linear(embed_dim, embed_dim),\n",
|
||
|
|
" nn.LeakyReLU(),\n",
|
||
|
|
" nn.Linear(embed_dim, embed_dim),\n",
|
||
|
|
" nn.LeakyReLU(),\n",
|
||
|
|
" nn.Linear(embed_dim, embed_dim),\n",
|
||
|
|
" nn.LeakyReLU(),\n",
|
||
|
|
" nn.AdaptiveAvgPool2d((1,None)), #(B, T, D) -> (B, 1, D)\n",
|
||
|
|
" nn.Flatten(), #(B, 1, D) -> (B, D)\n",
|
||
|
|
" nn.Linear(embed_dim, embed_dim),\n",
|
||
|
|
" nn.LeakyReLU(),\n",
|
||
|
|
" nn.BatchNorm1d(embed_dim),\n",
|
||
|
|
" nn.Linear(embed_dim, NUM_CLASS)\n",
|
||
|
|
")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
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|
|
"cell_type": "code",
|
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|
|
"execution_count": 30,
|
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|
|
"metadata": {
|
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|
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"ExecuteTime": {
|
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|
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"end_time": "2021-04-07T03:08:48.075532Z",
|
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|
|
"start_time": "2021-04-07T02:57:53.293890Z"
|
||
|
|
},
|
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|
|
"tags": [
|
||
|
|
"remove_output"
|
||
|
|
]
|
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|
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},
|
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|
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},
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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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"\n"
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]
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}
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],
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"source": [
|
||
|
|
"embd_layers = nn.Sequential( #(B, T, D) -> (B, T, D) \n",
|
||
|
|
" *([PositionalEncoding(embed_dim, batch_first=True)]+\n",
|
||
|
|
" [nn.Sequential(nn.Linear(embed_dim, embed_dim), nn.LeakyReLU()) for _ in range(3)])\n",
|
||
|
|
")\n",
|
||
|
|
"\n",
|
||
|
|
"attnPosEmbd = nn.Sequential(\n",
|
||
|
|
" EmbeddingAttentionBag(VOCAB_SIZE, embed_dim, padding_idx=padding_idx, embd_layers=embd_layers), #(B, T) -> (B, D) \n",
|
||
|
|
" nn.Linear(embed_dim, embed_dim),\n",
|
||
|
|
" nn.LeakyReLU(),\n",
|
||
|
|
" nn.BatchNorm1d(embed_dim),\n",
|
||
|
|
" nn.Linear(embed_dim, NUM_CLASS)\n",
|
||
|
|
")\n",
|
||
|
|
"\n",
|
||
|
|
"posEmbdAvg_results = train_network(simplePosEmbdAvg, loss_func, train_loader, val_loader=test_loader, score_funcs={'Accuracy': accuracy_score}, device=device, epochs=epochs)\n",
|
||
|
|
"attnPosEmbd_results = train_network(attnPosEmbd, loss_func, train_loader, val_loader=test_loader, score_funcs={'Accuracy': accuracy_score}, device=device, epochs=epochs)"
|
||
|
|
]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {
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"ExecuteTime": {
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|
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"end_time": "2021-04-07T03:08:48.416412Z",
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"start_time": "2021-04-07T03:08:48.076998Z"
|
||
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<AxesSubplot:xlabel='total time', ylabel='val Accuracy'>"
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]
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},
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|
|
"execution_count": 31,
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"metadata": {},
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"output_type": "execute_result"
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.lineplot(x='total time', y='val Accuracy', data=simpleEmbdAvg_results, label='Average Embedding')\n",
|
||
|
|
"sns.lineplot(x='total time', y='val Accuracy', data=posEmbdAvg_results, label='Average Positional Embedding')\n",
|
||
|
|
"sns.lineplot(x='total time', y='val Accuracy', data=attnEmbd_results, label='Attention Embedding')\n",
|
||
|
|
"sns.lineplot(x='total time', y='val Accuracy', data=attnPosEmbd_results, label='Attention Positional Embedding')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-07T03:08:48.689923Z",
|
||
|
|
"start_time": "2021-04-07T03:08:48.417649Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='total time', ylabel='val Accuracy'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/pdf": "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
|
||
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],
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"source": [
|
||
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"class SimpleTransformerClassifier(nn.Module):\n",
|
||
|
|
"\n",
|
||
|
|
" def __init__(self, vocab_size, D, padding_idx=None):\n",
|
||
|
|
" super(SimpleTransformerClassifier, self).__init__()\n",
|
||
|
|
" self.padding_idx = padding_idx\n",
|
||
|
|
" self.embd = nn.Embedding(vocab_size, D, padding_idx=padding_idx)\n",
|
||
|
|
" self.position = PositionalEncoding(D, batch_first=True)\n",
|
||
|
|
" #This below line is the main work for our transformer implementation!\n",
|
||
|
|
" self.transformer = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=D, nhead=8),num_layers=3)\n",
|
||
|
|
" self.attn = AttentionAvg(AdditiveAttentionScore(D))\n",
|
||
|
|
" self.pred = nn.Sequential(\n",
|
||
|
|
" nn.Flatten(), #(B, 1, D) -> (B, D)\n",
|
||
|
|
" nn.Linear(D, D),\n",
|
||
|
|
" nn.LeakyReLU(),\n",
|
||
|
|
" nn.BatchNorm1d(D),\n",
|
||
|
|
" nn.Linear(D, NUM_CLASS)\n",
|
||
|
|
" )\n",
|
||
|
|
" \n",
|
||
|
|
" def forward(self, input):\n",
|
||
|
|
" if self.padding_idx is not None:\n",
|
||
|
|
" mask = input != self.padding_idx\n",
|
||
|
|
" else:\n",
|
||
|
|
" mask = input == input #All entries are `True`\n",
|
||
|
|
" x = self.embd(input) #(B, T, D)\n",
|
||
|
|
" x = self.position(x) #(B, T, D)\n",
|
||
|
|
" #Because the resut of our code is (B, T, D), but transformers \n",
|
||
|
|
" #take input as (T, B, D), we will have to permute the order \n",
|
||
|
|
" #of the dimensions before and after \n",
|
||
|
|
" x = self.transformer(x.permute(1,0,2)) #(T, B, D)\n",
|
||
|
|
" x = x.permute(1,0,2) #(B, T, D)\n",
|
||
|
|
" #average over time\n",
|
||
|
|
" context = x.sum(dim=1)/mask.sum(dim=1).unsqueeze(1)\n",
|
||
|
|
" return self.pred(self.attn(x, context, mask=mask))\n",
|
||
|
|
"#Build and train this model! \n",
|
||
|
|
"simpleTransformer = SimpleTransformerClassifier(VOCAB_SIZE, embed_dim, padding_idx=padding_idx)\n",
|
||
|
|
"transformer_results = train_network(simpleTransformer, loss_func, train_loader, val_loader=test_loader, score_funcs={'Accuracy': accuracy_score}, device=device, epochs=epochs)"
|
||
|
|
]
|
||
|
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},
|
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|
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{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 34,
|
||
|
|
"metadata": {
|
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|
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"ExecuteTime": {
|
||
|
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"end_time": "2021-04-07T03:27:59.831168Z",
|
||
|
|
"start_time": "2021-04-07T03:27:59.513994Z"
|
||
|
|
}
|
||
|
|
},
|
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"outputs": [
|
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|
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{
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"data": {
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"text/plain": [
|
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|
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"<AxesSubplot:xlabel='total time', ylabel='val Accuracy'>"
|
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|
|
]
|
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|
|
},
|
||
|
|
"execution_count": 34,
|
||
|
|
"metadata": {},
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"output_type": "execute_result"
|
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},
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}
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],
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"source": [
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"sns.lineplot(x='total time', y='val Accuracy', data=gru_results, label='GRU')\n",
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"sns.lineplot(x='total time', y='val Accuracy', data=attnPosEmbd_results, label='Attention Positional Embedding')\n",
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]
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}
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"author": "Multi-head Attention Standard Equations",
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"bibliofile": "biblio.bib",
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"cite_by": "apalike",
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"title": "Network Design Alternatives to RNNs"
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