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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import eikon as ek\n",
"import numpy as np\n",
"from IPython.display import HTML\n",
"import os\n",
"from time import sleep\n",
"from tqdm import tqdm\n",
"import sys\n",
"import datetime\n",
"from pytz import timezone # set timezone\n",
"# Ignore harmless warnings\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"pd.set_option('display.max_rows', 1000)\n",
"pd.set_option('display.max_columns', 1000)\n",
"pd.set_option('display.width', 1000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Get the data and news headlines for the symbols of Dow Jones, Nasdaq100, SP500.\n",
"2. Prepare the data\n",
"- PCO to intialize weights help in time computation reduction and global optima finding\n",
"- Denoising input data helps predict small price changes\n",
"3. Build Languauge model based on character embedding\n",
"- Epoch means one pass over the full training set\n",
"- Batch means that you use all your data to compute the gradient during one iteration.\n",
"- Mini-batch means you only take a subset of all your data during one iteration.\n",
"- In the context of SGD, \"Minibatch\" means that the gradient is calculated across the entire batch before updating weights. If you are not using a \"minibatch\", every training example in a \"batch\" updates the learning algorithm's parameters independently.\n",
"\n",
"- Batch Gradient Descent. Batch size is set to the total number of examples in the training dataset. (batch_size = len(train))\n",
"- Stochastic Gradient Descent. Batch size is set to one. (batch_size = 1)\n",
"- Minibatch Gradient Descent. Batch size is set to more than one and less than the total number of examples in the training dataset. (batch_size = 32,64...)\n",
"\n",
"\n",
"##### Benefits of Charac2vec:\n",
"- Having the character embedding, every single word’s vector can be formed even it is out-of-vocabulary words (optional). On the other hand, word embedding can only handle those seen words.\n",
"- Good fits for misspelling words\n",
"- handles infrequent words better than word2vec embedding as later one suffers from lack of enough training opportunity for those rare words\n",
"- Reduces model complexity and improving the performance (in terms of speed)\n",
"\n",
"##### Byte Level:\n",
"- When ASCII encoding is used, there is no difference between reading characters or bytes. The ASCII-way of encoding characters allows for 256 characters to be encoded and (surprise…) these 256 possible characters are stored as bytes.\n",
"4. Train Language Model and save embeddings representation and weights of the model.\n",
"5. Use weights and embeddings representation of language model to intialize new model that predict price direction movement, ultimaetly.\n",
"- Direction prediction correctness(DPC) will be used as final metric to evaluate on test data.\n",
"- DPC is %CorrectPredictions. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get all data "
]
},
{
"cell_type": "raw",
"metadata": {
"jupyter": {
"outputs_hidden": true
}
},
"source": [
"# https://github.com/Refinitiv-API-Samples/Article.EikonAPI.Python.NewsSentimentAnalysis\n",
"#LMITS : https://developers.refinitiv.com/eikon-apis/eikon-data-api/docs?content=49692&type=documentation_item\n",
"# 10,000 reuests per day --- 5 requests per second --- 50 MB per minute ---\n",
"# import logging\n",
"# logger = logging.getLogger('pyeikon')\n",
"# logger.setLevel(5)\n",
"\n",
"ek.set_app_key('#######################')\n",
"ek.get_port_number()\n",
"# Get Index Stock Constitutes, separate them by a space, use the expression to search news\n",
"df, err = ek.get_data('0#.SPX','TR.RIC')\n",
"instr = df['Instrument'].tolist()\n",
"news_search_expr = ' '.join(instr)\n",
"ek.get_news_headlines(news_search_expr)\n",
"\n",
"sp500_symbols = list(ek.get_data(['0#.SPX'], 'TR.RIC')[0]['RIC'])\n",
"dowjones_symboles = list(ek.get_data(['0#.DJI'], 'TR.RIC')[0]['RIC']) \n",
"nasdaq100_symbols = list(ek.get_data(['0#.NDX'], 'TR.RIC')[0]['RIC'])\n",
"unique_index_symbols = list(np.unique(sp500_symbols + dowjones_symboles + nasdaq100_symbols))\n",
"db = pd.HDFStore('news_db.h5')\n",
"\n",
"for t in tqdm(unique_index_symbols):\n",
" for start in pd.date_range(start = '2020-09-01', end = datetime.datetime.now(),normalize=True, tz = 'US/Eastern',freq = 'D'):\n",
" end = start + datetime.timedelta(days=1)\n",
" start = start.strftime('%Y-%m-%dT%H:%M:%S')\n",
" end = end.strftime('%Y-%m-%dT%H:%M:%S') \n",
" news = ek.get_news_headlines('R:%s AND Language:LEN' % t, \n",
" count= 100,\n",
" date_from= start,\n",
" date_to= end)\n",
" if len(news) != 0:\n",
" try:\n",
" news['ticker'] = t.strip('.')[0]\n",
" db.put(t.strip('.')[0], news, format = 'table',append = True, \n",
" data_columns = True, \n",
" min_itemsize={'text': 1000, 'sourceCode': 1000, 'storyId': 100},\n",
" encoding = 'UTF-8')\n",
" print(t+':'+start+':'+end)\n",
" except IOError as err:\n",
" print(\"I/O error: {0}\".format(err))\n",
" except:\n",
" print(\"Unexpected error:\", sys.exc_info()[0])\n",
" raise\n",
" sleep(0.25)\n",
" else:\n",
" print('No News Found: '+t+':'+start+':'+end)\n",
" pass\n",
" sleep(0.25)\n",
" sleep(1)\n",
"db.close()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"625478\n"
]
}
],
"source": [
"length = 0\n",
"with pd.HDFStore('./news_update/news_db.h5', mode = 'r') as store:\n",
" for i in store.keys():\n",
" length += len(store[i])\n",
"print(length)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"unique_index_symbols.reverse()"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2020-08-14 20:03:19+0000', tz='UTC')"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[:, 'versionCreated'].min()"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2020-08-14 20:03:19+0000', tz='UTC')"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = db[unique_index_symbols[524].split('.')[0]]\n",
"df.versionCreated.min()"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'2020-07-27'"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(df.loc[:, 'versionCreated'].min() - datetime.timedelta(days=18)).strftime('%Y-%m-%d')"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"start: 2020-07-27T00:00:00 end: 2020-07-28T00:00:00\n",
"start: 2020-07-28T00:00:00 end: 2020-07-29T00:00:00\n",
"start: 2020-07-29T00:00:00 end: 2020-07-30T00:00:00\n",
"start: 2020-07-30T00:00:00 end: 2020-07-31T00:00:00\n",
"start: 2020-07-31T00:00:00 end: 2020-08-01T00:00:00\n",
"start: 2020-08-01T00:00:00 end: 2020-08-02T00:00:00\n",
"start: 2020-08-02T00:00:00 end: 2020-08-03T00:00:00\n",
"start: 2020-08-03T00:00:00 end: 2020-08-04T00:00:00\n",
"start: 2020-08-04T00:00:00 end: 2020-08-05T00:00:00\n",
"start: 2020-08-05T00:00:00 end: 2020-08-06T00:00:00\n",
"start: 2020-08-06T00:00:00 end: 2020-08-07T00:00:00\n",
"start: 2020-08-07T00:00:00 end: 2020-08-08T00:00:00\n",
"start: 2020-08-08T00:00:00 end: 2020-08-09T00:00:00\n",
"start: 2020-08-09T00:00:00 end: 2020-08-10T00:00:00\n",
"start: 2020-08-10T00:00:00 end: 2020-08-11T00:00:00\n",
"start: 2020-08-11T00:00:00 end: 2020-08-12T00:00:00\n",
"start: 2020-08-12T00:00:00 end: 2020-08-13T00:00:00\n",
"start: 2020-08-13T00:00:00 end: 2020-08-14T00:00:00\n"
]
}
],
"source": [
"end_at = (df.loc[:, 'versionCreated'].min() - datetime.timedelta(days=1)).strftime('%Y-%m-%d')\n",
"begin_from = (df.loc[:, 'versionCreated'].min() - datetime.timedelta(days=18)).strftime('%Y-%m-%d')\n",
"for start in pd.date_range(start = begin_from, end = end_at,normalize=True, tz = 'US/Eastern',freq = 'D'):\n",
" end = start + datetime.timedelta(days=1)\n",
" start = start.strftime('%Y-%m-%dT%H:%M:%S')\n",
" end = end.strftime('%Y-%m-%dT%H:%M:%S') \n",
" print('start: ',start, 'end: ', end)\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"length = 0\n",
"for i in range(ord('A'), ord('Z') + 1):\n",
" length += len(db[chr(i)])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'2020-07-27'"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = db['AAPL.N'.split('.')[0][0]].\n",
"df.loc[:, 'versionCreated'].min().strftime('%Y-%m-%d')"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [],
"source": [
"\n",
"import json\n",
"import os.path\n",
"\n",
"if os.path.isfile('unique_tickers.json') == False:\n",
" with open('unique_tickers.json', 'w', encoding='utf-8') as f:\n",
" json.dump(unique_index_symbols, f, ensure_ascii=False, indent=4)\n",
" \n",
"with open('unique_tickers.json', 'r') as data_file:\n",
" unique_index_symbols = json.load(data_file)\n"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"r'' will treat input string as raw (with \\n)\n",
"\\W for all non-words i.e. all special characters *&^%$ etc excluding underscore _\n",
"+ will match zero to unlimited matches, similar to * (one to more)\n",
"| is logical OR\n",
"_ stands for underscore\n",
"\n",
"#re.sub(r'(?<=\\d)\\s(?=\\d)(?=\\d)', '.',i)) #add \".\" btween numbers\n",
"#(\"\".join(headline)).strip().encode('utf-8')\n",
"# import re\n",
"# re.findall(r'\\s[0-9]\\s[0-9][0-9]',x)\n",
"# re.sub(r'(?<=\\d)\\s(?=\\d)(?=\\d)', '.',x)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|████████████████████████████████████████████████████████████████████████████████| 525/525 [06:58<00:00, 1.26it/s]\n"
]
}
],
"source": [
"import requests\n",
"import datetime\n",
"from time import sleep\n",
"from tqdm import tqdm\n",
"data = pd.DataFrame()\n",
"for t in tqdm(unique_index_symbols):\n",
" r = requests.get(\"https://finnhub.io/api/v1/press-releases?symbol=\"+t+\"&token=bsbhg3nrh5rd8518ll30\")\n",
" df = pd.DataFrame(r.json()['majorDevelopment'])\n",
" data = data.append(df, ignore_index=True)\n",
" sleep(0.5)\n",
"\n",
"data = data.rename(columns={'datetime': 'time'})\n",
"data.time = pd.to_datetime(data.time)\n",
"data.time = data.time.apply(lambda dt: int((dt - datetime.datetime(1970,1,1)).total_seconds()))\n",
"# data['description'] = data['description'].str.replace(r\"\\W+|_\", ' ')\n",
"# data['headline'] = data['headline'].str.replace(r\"\\W+|_\", ' ')\n",
"data.to_csv('news_test.csv', header = True, index = False)\n",
"#df.datetime = pd.to_datetime(df.datetime, unit='s')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"r2 = requests.get('https://finnhub.io/api/v1/company-news?symbol=DUK&from=2006-01-01&to=2006-01-02&token=bsbhg3nrh5rd8518ll30')\n",
"df = pd.DataFrame(r2.json())\n",
"# df = df[['datetime', 'headline', 'id','related', 'source', 'summary']]\n",
"# df['summary'] = df['summary'].str.replace(r\"\\W+|_\", ' ')\n",
"# df['headline'] = df['headline'].str.replace(r\"\\W+|_\", ' ')\n",
"# df = df.rename(columns={'related': 'symbol'})"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(Timestamp('2019-09-02 00:00:00'),\n",
" Timestamp('2020-08-10 23:14:47'),\n",
" 32.88030888030888)"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Summary Statistics\n",
"pd.to_datetime(data.time, unit='s').min(), pd.to_datetime(data.time, unit='s').max(), data.groupby('symbol')['headline'].count().mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare Data for Model"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
"from keras.utils import to_categorical, plot_model\n",
"from keras.preprocessing.sequence import pad_sequences\n",
"from keras.models import Sequential\n",
"from keras.layers import LSTM, Dense, GRU, Embedding, Bidirectional, TimeDistributed\n",
"from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using CPU\n",
"[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:XLA_CPU:0', device_type='XLA_CPU')]\n"
]
}
],
"source": [
"gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
"\n",
"if gpu_devices:\n",
" print('Using GPU')\n",
" tf.config.experimental.set_memory_growth(gpu_devices[0], True)\n",
"else:\n",
" print('Using CPU')\n",
" print(tf.config.list_physical_devices(device_type=None))\n",
" tf.config.optimizer.set_jit(True) #@ "
]
},
{
"cell_type": "code",
"execution_count": 1100,
"metadata": {},
"outputs": [],
"source": [
"#clean the already cleaned headlines but pre/appen token(<s>)\n",
"#8 bits will let you express 2^8 == 256 possible values.Thus restrict characters to 0 < ord() < 256. \n",
"def clean_text(df, column):\n",
" import re \n",
" #(\"\".join(headline)).strip()\n",
" headline = []\n",
" for i in df[column].apply(lambda x: '<s>'+x+'<\\s>'):\n",
" #headline.append(re.sub('[!,*)@=#({|}_‑–?^;:{|}˚~\\t\\n“—’”/_]',r'',i))\n",
" headline.append(i)\n",
" return headline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://realpython.com/python-encodings-guide/"
]
},
{
"cell_type": "code",
"execution_count": 1101,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[' ', '!', '\"', '#', '$', '%', '&', \"'\", '(', ')', '*', '+', ',', '-', '.', '/', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '<', '=', '>', '?', '@', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '\\\\', '_', '`', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '|', '~', '\\xa0', '£', '¥', '®', '´', 'µ', 'Æ', 'É', '×', 'Ø', 'à', 'á', 'ä', 'å', 'é', 'ê', 'ë', 'í', 'ï', 'ñ', 'ó', 'ö', 'ú', 'ü', 'ē', 'Š', '˚', '\\u200b', '\\u200c', '\\u200d', '‐', '‑', '–', '—', '’', '“', '”', '€', '™']\n",
"129\n"
]
}
],
"source": [
"text = clean_text(data, 'headline')\n",
"txt = ''\n",
"# Count Unique Characters\n",
"for doc in text:\n",
" for s in doc:\n",
" txt += s\n",
"chars = sorted(set(txt))\n",
"print(chars)\n",
"print(len(chars)) #52+10+25"
]
},
{
"cell_type": "code",
"execution_count": 1102,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" : 160\n",
"£ : 163\n",
"¥ : 165\n",
"® : 174\n",
"´ : 180\n",
"µ : 181\n",
"Æ : 198\n",
"É : 201\n",
"× : 215\n",
"Ø : 216\n",
"à : 224\n",
"á : 225\n",
"ä : 228\n",
"å : 229\n",
"é : 233\n",
"ê : 234\n",
"ë : 235\n",
"í : 237\n",
"ï : 239\n",
"ñ : 241\n",
"ó : 243\n",
"ö : 246\n",
"ú : 250\n",
"ü : 252\n",
"ē : 275\n",
"Š : 352\n",
"˚ : 730\n",
" : 8203\n",
" : 8204\n",
" : 8205\n",
"‐ : 8208\n",
"‑ : 8209\n",
"– : 8211\n",
"— : 8212\n",
"’ : 8217\n",
"“ : 8220\n",
"” : 8221\n",
"€ : 8364\n",
"™ : 8482\n"
]
}
],
"source": [
"#reserve 0 for padding: check if it is found.\n",
"#NOTE: (len(bytes(i, encoding = 'utf-8')) > 1) == ord(i) > 127\n",
"for i in chars: \n",
" if (ord(i) == 0) | (ord(i) > 127):\n",
" print(i,':',ord(i))\n",
"# {char: i for i, char in zip(map(ord, chars),chars)}"
]
},
{
"cell_type": "code",
"execution_count": 1103,
"metadata": {},
"outputs": [],
"source": [
"def encode2bytes(text):\n",
" #text = tf.strings.unicode_split(text, 'UTF-8').to_list()\n",
" final_list = []\n",
" for sent in text:\n",
" temp_list = []\n",
" for char in sent:\n",
" if ord(char) < 128 :\n",
" temp_list.append(ord(char))\n",
" final_list.append(temp_list)\n",
" return final_list"
]
},
{
"cell_type": "code",
"execution_count": 1104,
"metadata": {},
"outputs": [],
"source": [
"b_text = encode2bytes(text)"
]
},
{
"cell_type": "code",
"execution_count": 1105,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"412"
]
},
"execution_count": 1105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max_sentence_len = max([len(sentence) for sentence in b_text])\n",
"max_sentence_len"
]
},
{
"cell_type": "code",
"execution_count": 1112,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Agilent Technologies Announces Pricing Of $500 Million Of Senior Notes\n",
"<s>Agilent Technologies Announces Pricing Of $500 Million Of Senior Notes<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 32, 65, 110, 110, 111, 117, 110, 99, 101, 115, 32, 80, 114, 105, 99, 105, 110, 103, 32, 79, 102, 32, 36, 53, 48, 48, 32, 77, 105, 108, 108, 105, 111, 110, 32, 79, 102, 32, 83, 101, 110, 105, 111, 114, 32, 78, 111, 116, 101, 115, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent Technologies Files For Potential Senior Notes Offering Size Not Disclosed\n",
"<s>Agilent Technologies Files For Potential Senior Notes Offering Size Not Disclosed<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 32, 70, 105, 108, 101, 115, 32, 70, 111, 114, 32, 80, 111, 116, 101, 110, 116, 105, 97, 108, 32, 83, 101, 110, 105, 111, 114, 32, 78, 111, 116, 101, 115, 32, 79, 102, 102, 101, 114, 105, 110, 103, 32, 83, 105, 122, 101, 32, 78, 111, 116, 32, 68, 105, 115, 99, 108, 111, 115, 101, 100, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent Technologies Q2 GAAP Earnings Per Share $0.32\n",
"<s>Agilent Technologies Q2 GAAP Earnings Per Share $0.32<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 32, 81, 50, 32, 71, 65, 65, 80, 32, 69, 97, 114, 110, 105, 110, 103, 115, 32, 80, 101, 114, 32, 83, 104, 97, 114, 101, 32, 36, 48, 46, 51, 50, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent PD-L1 Assay Receives FDA Approval For Use As Companion Diagnostic\n",
"<s>Agilent PD-L1 Assay Receives FDA Approval For Use As Companion Diagnostic<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 80, 68, 45, 76, 49, 32, 65, 115, 115, 97, 121, 32, 82, 101, 99, 101, 105, 118, 101, 115, 32, 70, 68, 65, 32, 65, 112, 112, 114, 111, 118, 97, 108, 32, 70, 111, 114, 32, 85, 115, 101, 32, 65, 115, 32, 67, 111, 109, 112, 97, 110, 105, 111, 110, 32, 68, 105, 97, 103, 110, 111, 115, 116, 105, 99, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"FDA Approves Nivolumab Plus Ipilimumab For First-Line Metastatic Non-Small Cell Lung Cancer\n",
"<s>FDA Approves Nivolumab Plus Ipilimumab For First-Line Metastatic Non-Small Cell Lung Cancer<\\s>\n",
"[60, 115, 62, 70, 68, 65, 32, 65, 112, 112, 114, 111, 118, 101, 115, 32, 78, 105, 118, 111, 108, 117, 109, 97, 98, 32, 80, 108, 117, 115, 32, 73, 112, 105, 108, 105, 109, 117, 109, 97, 98, 32, 70, 111, 114, 32, 70, 105, 114, 115, 116, 45, 76, 105, 110, 101, 32, 77, 101, 116, 97, 115, 116, 97, 116, 105, 99, 32, 78, 111, 110, 45, 83, 109, 97, 108, 108, 32, 67, 101, 108, 108, 32, 76, 117, 110, 103, 32, 67, 97, 110, 99, 101, 114, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent Gets FDA Approval For Pd-L1 Companion Diagnostic On Dako Omnis\n",
"<s>Agilent Gets FDA Approval For Pd-L1 Companion Diagnostic On Dako Omnis<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 71, 101, 116, 115, 32, 70, 68, 65, 32, 65, 112, 112, 114, 111, 118, 97, 108, 32, 70, 111, 114, 32, 80, 100, 45, 76, 49, 32, 67, 111, 109, 112, 97, 110, 105, 111, 110, 32, 68, 105, 97, 103, 110, 111, 115, 116, 105, 99, 32, 79, 110, 32, 68, 97, 107, 111, 32, 79, 109, 110, 105, 115, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent Technologies Enters Into Amendment No. 3 To Credit Agreement\n",
"<s>Agilent Technologies Enters Into Amendment No. 3 To Credit Agreement<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 32, 69, 110, 116, 101, 114, 115, 32, 73, 110, 116, 111, 32, 65, 109, 101, 110, 100, 109, 101, 110, 116, 32, 78, 111, 46, 32, 51, 32, 84, 111, 32, 67, 114, 101, 100, 105, 116, 32, 65, 103, 114, 101, 101, 109, 101, 110, 116, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent withdraws Q2, FY guidance, cites coronavirus impact\n",
"<s>Agilent withdraws Q2, FY guidance, cites coronavirus impact<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 119, 105, 116, 104, 100, 114, 97, 119, 115, 32, 81, 50, 44, 32, 70, 89, 32, 103, 117, 105, 100, 97, 110, 99, 101, 44, 32, 99, 105, 116, 101, 115, 32, 99, 111, 114, 111, 110, 97, 118, 105, 114, 117, 115, 32, 105, 109, 112, 97, 99, 116, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent Technologies Reports Q1 Non-GAAP Earnings Per Share $0.81\n",
"<s>Agilent Technologies Reports Q1 Non-GAAP Earnings Per Share $0.81<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 32, 82, 101, 112, 111, 114, 116, 115, 32, 81, 49, 32, 78, 111, 110, 45, 71, 65, 65, 80, 32, 69, 97, 114, 110, 105, 110, 103, 115, 32, 80, 101, 114, 32, 83, 104, 97, 114, 101, 32, 36, 48, 46, 56, 49, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Pershing Square Capital Management Lp Raises Sole Share Stake In Agilent Technologies\n",
"<s>Pershing Square Capital Management Lp Raises Sole Share Stake In Agilent Technologies<\\s>\n",
"[60, 115, 62, 80, 101, 114, 115, 104, 105, 110, 103, 32, 83, 113, 117, 97, 114, 101, 32, 67, 97, 112, 105, 116, 97, 108, 32, 77, 97, 110, 97, 103, 101, 109, 101, 110, 116, 32, 76, 112, 32, 82, 97, 105, 115, 101, 115, 32, 83, 111, 108, 101, 32, 83, 104, 97, 114, 101, 32, 83, 116, 97, 107, 101, 32, 73, 110, 32, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent Technologies Announces Twist Bioscience Agrees To Pay $22.5 Million Settlement In Ip Litigation\n",
"<s>Agilent Technologies Announces Twist Bioscience Agrees To Pay $22.5 Million Settlement In Ip Litigation<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 32, 65, 110, 110, 111, 117, 110, 99, 101, 115, 32, 84, 119, 105, 115, 116, 32, 66, 105, 111, 115, 99, 105, 101, 110, 99, 101, 32, 65, 103, 114, 101, 101, 115, 32, 84, 111, 32, 80, 97, 121, 32, 36, 50, 50, 46, 53, 32, 77, 105, 108, 108, 105, 111, 110, 32, 83, 101, 116, 116, 108, 101, 109, 101, 110, 116, 32, 73, 110, 32, 73, 112, 32, 76, 105, 116, 105, 103, 97, 116, 105, 111, 110, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent Technologies Says CEO's FY 2019 Total Compensation Was $12.52 Mln Vs $11.7 Mln In FY 2018\n",
"<s>Agilent Technologies Says CEO's FY 2019 Total Compensation Was $12.52 Mln Vs $11.7 Mln In FY 2018<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 32, 83, 97, 121, 115, 32, 67, 69, 79, 39, 115, 32, 70, 89, 32, 50, 48, 49, 57, 32, 84, 111, 116, 97, 108, 32, 67, 111, 109, 112, 101, 110, 115, 97, 116, 105, 111, 110, 32, 87, 97, 115, 32, 36, 49, 50, 46, 53, 50, 32, 77, 108, 110, 32, 86, 115, 32, 36, 49, 49, 46, 55, 32, 77, 108, 110, 32, 73, 110, 32, 70, 89, 32, 50, 48, 49, 56, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Pershing Square Takes Sole Share Stake In Agilent Technologies\n",
"<s>Pershing Square Takes Sole Share Stake In Agilent Technologies<\\s>\n",
"[60, 115, 62, 80, 101, 114, 115, 104, 105, 110, 103, 32, 83, 113, 117, 97, 114, 101, 32, 84, 97, 107, 101, 115, 32, 83, 111, 108, 101, 32, 83, 104, 97, 114, 101, 32, 83, 116, 97, 107, 101, 32, 73, 110, 32, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Akoya Biosciences, Inc. announced that it has received $50 million in funding from Telegraph Hill Partners, Innovatus Capital Partners, LLC, PJC Capital Management LLC, Agilent Technologies, Inc.\n",
"<s>Akoya Biosciences, Inc. announced that it has received $50 million in funding from Telegraph Hill Partners, Innovatus Capital Partners, LLC, PJC Capital Management LLC, Agilent Technologies, Inc.<\\s>\n",
"[60, 115, 62, 65, 107, 111, 121, 97, 32, 66, 105, 111, 115, 99, 105, 101, 110, 99, 101, 115, 44, 32, 73, 110, 99, 46, 32, 97, 110, 110, 111, 117, 110, 99, 101, 100, 32, 116, 104, 97, 116, 32, 105, 116, 32, 104, 97, 115, 32, 114, 101, 99, 101, 105, 118, 101, 100, 32, 36, 53, 48, 32, 109, 105, 108, 108, 105, 111, 110, 32, 105, 110, 32, 102, 117, 110, 100, 105, 110, 103, 32, 102, 114, 111, 109, 32, 84, 101, 108, 101, 103, 114, 97, 112, 104, 32, 72, 105, 108, 108, 32, 80, 97, 114, 116, 110, 101, 114, 115, 44, 32, 73, 110, 110, 111, 118, 97, 116, 117, 115, 32, 67, 97, 112, 105, 116, 97, 108, 32, 80, 97, 114, 116, 110, 101, 114, 115, 44, 32, 76, 76, 67, 44, 32, 80, 74, 67, 32, 67, 97, 112, 105, 116, 97, 108, 32, 77, 97, 110, 97, 103, 101, 109, 101, 110, 116, 32, 76, 76, 67, 44, 32, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 44, 32, 73, 110, 99, 46, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent Technologies, Inc. Presents at 2019 HR Life Sciences Conference, Dec-04-2019\n",
"<s>Agilent Technologies, Inc. Presents at 2019 HR Life Sciences Conference, Dec-04-2019<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 44, 32, 73, 110, 99, 46, 32, 80, 114, 101, 115, 101, 110, 116, 115, 32, 97, 116, 32, 50, 48, 49, 57, 32, 72, 82, 32, 76, 105, 102, 101, 32, 83, 99, 105, 101, 110, 99, 101, 115, 32, 67, 111, 110, 102, 101, 114, 101, 110, 99, 101, 44, 32, 68, 101, 99, 45, 48, 52, 45, 50, 48, 49, 57, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent Technologies, Inc. Reports Earnings Results for the Fourth Quarter Ended October 31, 2019\n",
"<s>Agilent Technologies, Inc. Reports Earnings Results for the Fourth Quarter Ended October 31, 2019<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 44, 32, 73, 110, 99, 46, 32, 82, 101, 112, 111, 114, 116, 115, 32, 69, 97, 114, 110, 105, 110, 103, 115, 32, 82, 101, 115, 117, 108, 116, 115, 32, 102, 111, 114, 32, 116, 104, 101, 32, 70, 111, 117, 114, 116, 104, 32, 81, 117, 97, 114, 116, 101, 114, 32, 69, 110, 100, 101, 100, 32, 79, 99, 116, 111, 98, 101, 114, 32, 51, 49, 44, 32, 50, 48, 49, 57, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent Technologies Inc. Provides Earnings Guidance for the First Quarter and Full Fiscal Year of 2020\n",
"<s>Agilent Technologies Inc. Provides Earnings Guidance for the First Quarter and Full Fiscal Year of 2020<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 32, 73, 110, 99, 46, 32, 80, 114, 111, 118, 105, 100, 101, 115, 32, 69, 97, 114, 110, 105, 110, 103, 115, 32, 71, 117, 105, 100, 97, 110, 99, 101, 32, 102, 111, 114, 32, 116, 104, 101, 32, 70, 105, 114, 115, 116, 32, 81, 117, 97, 114, 116, 101, 114, 32, 97, 110, 100, 32, 70, 117, 108, 108, 32, 70, 105, 115, 99, 97, 108, 32, 89, 101, 97, 114, 32, 111, 102, 32, 50, 48, 50, 48, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent Companion Diagnostic Expands CE-IVD mark in Europe to include Head and Neck Squamous Cell Carcinoma (HNSCC)\n",
"<s>Agilent Companion Diagnostic Expands CE-IVD mark in Europe to include Head and Neck Squamous Cell Carcinoma (HNSCC)<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 67, 111, 109, 112, 97, 110, 105, 111, 110, 32, 68, 105, 97, 103, 110, 111, 115, 116, 105, 99, 32, 69, 120, 112, 97, 110, 100, 115, 32, 67, 69, 45, 73, 86, 68, 32, 109, 97, 114, 107, 32, 105, 110, 32, 69, 117, 114, 111, 112, 101, 32, 116, 111, 32, 105, 110, 99, 108, 117, 100, 101, 32, 72, 101, 97, 100, 32, 97, 110, 100, 32, 78, 101, 99, 107, 32, 83, 113, 117, 97, 109, 111, 117, 115, 32, 67, 101, 108, 108, 32, 67, 97, 114, 99, 105, 110, 111, 109, 97, 32, 40, 72, 78, 83, 67, 67, 41, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent Technologies Increases Cash Dividend To 18 Cents Per Share\n",
"<s>Agilent Technologies Increases Cash Dividend To 18 Cents Per Share<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 32, 73, 110, 99, 114, 101, 97, 115, 101, 115, 32, 67, 97, 115, 104, 32, 68, 105, 118, 105, 100, 101, 110, 100, 32, 84, 111, 32, 49, 56, 32, 67, 101, 110, 116, 115, 32, 80, 101, 114, 32, 83, 104, 97, 114, 101, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent Technologies, Inc. Announces Quarterly Dividend, Payable on Jan. 22, 2020\n",
"<s>Agilent Technologies, Inc. Announces Quarterly Dividend, Payable on Jan. 22, 2020<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 44, 32, 73, 110, 99, 46, 32, 65, 110, 110, 111, 117, 110, 99, 101, 115, 32, 81, 117, 97, 114, 116, 101, 114, 108, 121, 32, 68, 105, 118, 105, 100, 101, 110, 100, 44, 32, 80, 97, 121, 97, 98, 108, 101, 32, 111, 110, 32, 74, 97, 110, 46, 32, 50, 50, 44, 32, 50, 48, 50, 48, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n",
"Agilent Technologies and Guangzhou Burning Rock Medical Laboratory Co., Ltd. Launch Magnis BR to Innovate Total Process Automation for NGS Testing\n",
"<s>Agilent Technologies and Guangzhou Burning Rock Medical Laboratory Co., Ltd. Launch Magnis BR to Innovate Total Process Automation for NGS Testing<\\s>\n",
"[60, 115, 62, 65, 103, 105, 108, 101, 110, 116, 32, 84, 101, 99, 104, 110, 111, 108, 111, 103, 105, 101, 115, 32, 97, 110, 100, 32, 71, 117, 97, 110, 103, 122, 104, 111, 117, 32, 66, 117, 114, 110, 105, 110, 103, 32, 82, 111, 99, 107, 32, 77, 101, 100, 105, 99, 97, 108, 32, 76, 97, 98, 111, 114, 97, 116, 111, 114, 121, 32, 67, 111, 46, 44, 32, 76, 116, 100, 46, 32, 76, 97, 117, 110, 99, 104, 32, 77, 97, 103, 110, 105, 115, 32, 66, 82, 32, 116, 111, 32, 73, 110, 110, 111, 118, 97, 116, 101, 32, 84, 111, 116, 97, 108, 32, 80, 114, 111, 99, 101, 115, 115, 32, 65, 117, 116, 111, 109, 97, 116, 105, 111, 110, 32, 102, 111, 114, 32, 78, 71, 83, 32, 84, 101, 115, 116, 105, 110, 103, 60, 92, 115, 62]\n",
"\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"for i in range(0,21):\n",
" print(data.headline[i])\n",
" print(text[i])\n",
" print(b_text[i])\n",
" print('\\n'*3)"
]
},
{
"cell_type": "code",
"execution_count": 1113,
"metadata": {},
"outputs": [],
"source": [
"def split_X_y(text):\n",
" X = []\n",
" y = []\n",
" for i in text:\n",
" X.append(i[0:-1])\n",
" y.append(i[1:])\n",
" return X,y"
]
},
{
"cell_type": "code",
"execution_count": 1150,
"metadata": {},
"outputs": [],
"source": [
"X, y = split_X_y(b_text)"
]
},
{
"cell_type": "code",
"execution_count": 1118,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[60, 115, 62, 86, 101, 114, 105, 122, 111, 110, 32, 67, 111, 109, 109, 117, 110, 105, 99, 97, 116, 105, 111, 110, 115, 32, 73, 110, 99, 46, 32, 80, 114, 111, 118, 105, 100, 101, 115, 32, 69, 97, 114, 110, 105, 110, 103, 115, 32, 71, 117, 105, 100, 97, 110, 99, 101, 32, 102, 111, 114, 32, 116, 104, 101, 32, 83, 101, 99, 111, 110, 100, 32, 81, 117, 97, 114, 116, 101, 114, 32, 111, 102, 32, 50, 48, 49, 57, 60, 92, 115]\n",
"<s>Verizon Communications Inc. Provides Earnings Guidance for the Second Quarter of 2019<\\s>\n",
"[115, 62, 86, 101, 114, 105, 122, 111, 110, 32, 67, 111, 109, 109, 117, 110, 105, 99, 97, 116, 105, 111, 110, 115, 32, 73, 110, 99, 46, 32, 80, 114, 111, 118, 105, 100, 101, 115, 32, 69, 97, 114, 110, 105, 110, 103, 115, 32, 71, 117, 105, 100, 97, 110, 99, 101, 32, 102, 111, 114, 32, 116, 104, 101, 32, 83, 101, 99, 111, 110, 100, 32, 81, 117, 97, 114, 116, 101, 114, 32, 111, 102, 32, 50, 48, 49, 57, 60, 92, 115, 62]\n"
]
}
],
"source": [
"#Each sentence is represented as the concatenation of bytes that form its characters in utf-8 encoding.\n",
"num = np.random.randint(0, len(X))\n",
"print(X[num])\n",
"print(text[num])\n",
"print(y[num])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ###########OLD#########################"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check UTF-8 Encoding \n",
"byte_text = []\n",
"for i in text:\n",
" i = i.encode('utf-8')\n",
" byte_text.append(i)\n",
"byte_text[0:5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def encode2bytes(text):\n",
" final_list = []\n",
" for sent in text:\n",
" sent = sent.encode('utf-8')\n",
" temp_list = []\n",
" for char in sent:\n",
" temp_list.append(char)\n",
" final_list.append(temp_list)\n",
" return final_list"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"char2ind = {char: index +1 for index, char in enumerate(chars)} #index +1 to leave 0 for padding\n",
"max_sentence_len = max([len(sentence) for sentence in text])"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"from keras.preprocessing.text import Tokenizer\n",
"# Initialization\n",
"tk = Tokenizer(num_words=None, char_level=True, oov_token='UNK', lower = False)\n",
"# Fitting\n",
"tk.fit_on_texts(text)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# Use char_dict to replace the tk.word_index\n",
"tk.word_index = char2ind \n",
"# Add 'UNK' to the vocabulary \n",
"tk.word_index[tk.oov_token] = max(char2ind.values()) + 1\n",
"\n",
"# invert encoding\n",
"#index2char = {char: index for index, char in tk.word_index.items()}\n",
"chars.append(tk.oov_token)\n",
"chars.insert(0,'')\n",
"index2char = np.array(chars)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{' ': 1, '.': 2, '0': 3, '1': 4, '2': 5, '3': 6, '4': 7, '5': 8, '6': 9, '7': 10, '8': 11, '9': 12, '<': 13, '>': 14, 'A': 15, 'B': 16, 'C': 17, 'D': 18, 'E': 19, 'F': 20, 'G': 21, 'H': 22, 'I': 23, 'J': 24, 'K': 25, 'L': 26, 'M': 27, 'N': 28, 'O': 29, 'P': 30, 'Q': 31, 'R': 32, 'S': 33, 'T': 34, 'U': 35, 'V': 36, 'W': 37, 'X': 38, 'Y': 39, 'Z': 40, '\\\\': 41, 'a': 42, 'b': 43, 'c': 44, 'd': 45, 'e': 46, 'f': 47, 'g': 48, 'h': 49, 'i': 50, 'j': 51, 'k': 52, 'l': 53, 'm': 54, 'n': 55, 'o': 56, 'p': 57, 'q': 58, 'r': 59, 's': 60, 't': 61, 'u': 62, 'v': 63, 'w': 64, 'x': 65, 'y': 66, 'z': 67, 'µ': 68, 'Æ': 69, 'É': 70, 'Ø': 71, 'à': 72, 'á': 73, 'ä': 74, 'å': 75, 'é': 76, 'ê': 77, 'ë': 78, 'í': 79, 'ï': 80, 'ñ': 81, 'ó': 82, 'ö': 83, 'ú': 84, 'ü': 85, 'ē': 86, 'Š': 87, 'UNK': 88}\n",
"\n",
"['' ' ' '.' '0' '1' '2' '3' '4' '5' '6' '7' '8' '9' '<' '>' 'A' 'B' 'C'\n",
" 'D' 'E' 'F' 'G' 'H' 'I' 'J' 'K' 'L' 'M' 'N' 'O' 'P' 'Q' 'R' 'S' 'T' 'U'\n",
" 'V' 'W' 'X' 'Y' 'Z' '\\\\' 'a' 'b' 'c' 'd' 'e' 'f' 'g' 'h' 'i' 'j' 'k' 'l'\n",
" 'm' 'n' 'o' 'p' 'q' 'r' 's' 't' 'u' 'v' 'w' 'x' 'y' 'z' 'µ' 'Æ' 'É' 'Ø'\n",
" 'à' 'á' 'ä' 'å' 'é' 'ê' 'ë' 'í' 'ï' 'ñ' 'ó' 'ö' 'ú' 'ü' 'ē' 'Š' 'UNK']\n"
]
}
],
"source": [
"print(tk.word_index)\n",
"print()\n",
"print(index2char)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"88\n",
"88\n",
"88\n"
]
}
],
"source": [
"print(max(char2ind.values())),print(max(tk.word_index.values())), print(len(tk.word_index));"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"sequences = list()\n",
"for line in text:\n",
" # integer encode line\n",
" encoded_seq = np.array([tk.word_index[char] for char in line])\n",
" #encoded_seq = np.array([ord(char) for char in line])\n",
"\n",
" # store\n",
" sequences.append(encoded_seq)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<s>Agilent Technologies Announces Pricing Of 500 Million Of Senior Notes<\\s>\n",
"[13, 4, 14, 23, 22, 10, 11, 3, 5, 9, 2, 34, 3, 12, 17, 5, 6, 11, 6, 22, 10, 3, 4, 2, 23, 5, 5, 6, 16, 5, 12, 3, 4, 2, 29, 7, 10, 12, 10, 5, 22, 2, 36, 26, 2, 54, 25, 25, 2, 38, 10, 11, 11, 10, 6, 5, 2, 36, 26, 2, 20, 3, 5, 10, 6, 7, 2, 43, 6, 9, 3, 4, 13, 24, 4, 14]\n"
]
}
],
"source": [
"# tranform text characters to unique index\n",
"sequences = tk.texts_to_sequences(text)\n",
"print(text[0])\n",
"print(sequences[0])\n"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"X = []\n",
"y = []\n",
"for i in sequences:\n",
" X.append(i[0:-1])\n",
" y.append(i[1:])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ###########OLD#########################"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Masking is a way to tell sequence-processing layers that certain timesteps in an input are missing, and thus should be skipped when processing the data.\n",
"\n",
"Padding is a special form of masking where the masked steps are at the start or at the beginning of a sequence. Padding comes from the need to encode sequence data into contiguous batches: in order to make all sequences in a batch fit a given standard length, it is necessary to pad or truncate some sequences.\n",
"https://stackoverflow.com/questions/53172852/masking-zero-inputs-in-lstm-in-keras-without-using-embedding"
]
},
{
"cell_type": "code",
"execution_count": 1153,
"metadata": {},
"outputs": [],
"source": [
"X = pad_sequences(X, maxlen = max_sentence_len, padding = 'post')\n",
"y = pad_sequences(y, maxlen = max_sentence_len, padding = 'post')"
]
},
{
"cell_type": "code",
"execution_count": 1121,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<s>Verizon Communications Inc. Provides Earnings Guidance for the Second Quarter of 2019<\\s>\n",
"[ 60 115 62 86 101 114 105 122 111 110 32 67 111 109 109 117 110 105\n",
" 99 97 116 105 111 110 115 32 73 110 99 46 32 80 114 111 118 105\n",
" 100 101 115 32 69 97 114 110 105 110 103 115 32 71 117 105 100 97\n",
" 110 99 101 32 102 111 114 32 116 104 101 32 83 101 99 111 110 100\n",
" 32 81 117 97 114 116 101 114 32 111 102 32 50 48 49 57 60 92\n",
" 115 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
"[115 62 86 101 114 105 122 111 110 32 67 111 109 109 117 110 105 99\n",
" 97 116 105 111 110 115 32 73 110 99 46 32 80 114 111 118 105 100\n",
" 101 115 32 69 97 114 110 105 110 103 115 32 71 117 105 100 97 110\n",
" 99 101 32 102 111 114 32 116 104 101 32 83 101 99 111 110 100 32\n",
" 81 117 97 114 116 101 114 32 111 102 32 50 48 49 57 60 92 115\n",
" 62 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
]
}
],
"source": [
"# Add padding for longer sentences\n",
"print(text[num])\n",
"print(X[num])\n",
"print(y[num])"
]
},
{
"cell_type": "code",
"execution_count": 1122,
"metadata": {},
"outputs": [],
"source": [
"#split input / output\n",
"# X ,y = X_char[:,:-1], X_char[:,1:]\n",
"# print('X: ' , X[0], len(X[0]) ,'\\n', 'y: ', y[0], len(y[0]))"
]
},
{
"cell_type": "code",
"execution_count": 1123,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((17032, 412), (17032, 412))"
]
},
"execution_count": 1123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape, y.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Another Option of Splitting Sequence\n",
"*But misses input/output since we had to pad before splitting*"
]
},
{
"cell_type": "code",
"execution_count": 822,
"metadata": {},
"outputs": [],
"source": [
"padded_seq = pad_sequences(b_text, maxlen = max_sentence_len, padding = 'post')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the `tf.data.Dataset.from_tensor_slices` function to convert a text vector into a stream of character indices."
]
},
{
"cell_type": "code",
"execution_count": 823,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<TensorSliceDataset shapes: (401,), types: tf.int32>\n",
"<s>Agilent Technologies Announces Pricing Of $500 Million Of Senior Notes<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"<s>Agilent Technologies Files For Potential Senior Notes Offering Size Not Disclosed<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"<s>Agilent Technologies Q2 GAAP Earnings Per Share $0.32<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"<s>Agilent PD-L1 Assay Receives FDA Approval For Use As Companion Diagnostic<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"<s>FDA Approves Nivolumab Plus Ipilimumab For First-Line Metastatic Non-Small Cell Lung Cancer<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"<s>Agilent Gets FDA Approval For Pd-L1 Companion Diagnostic On Dako Omnis<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"<s>Agilent Technologies Enters Into Amendment No. 3 To Credit Agreement<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"<s>Agilent withdraws Q2 FY guidance cites coronavirus impact<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"<s>Agilent Technologies Reports Q1 Non-GAAP Earnings Per Share $0.81<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"<s>Pershing Square Capital Management Lp Raises Sole Share Stake In Agilent Technologies<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n"
]
}
],
"source": [
"# Create Training Sequences\n",
"char_dataset = tf.data.Dataset.from_tensor_slices(padded_seq)\n",
"print(char_dataset)\n",
"for i in char_dataset.take(10):\n",
" print(\"\".join(map(chr, i)))"
]
},
{
"cell_type": "code",
"execution_count": 824,
"metadata": {},
"outputs": [],
"source": [
"def create_seq_targets(seq):\n",
" input_txt = seq[:-1]\n",
" target_txt = seq[1:]\n",
" return input_txt, target_txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___________________________________________________________________________________________________________________________________"
]
},
{
"cell_type": "code",
"execution_count": 1335,
"metadata": {},
"outputs": [],
"source": [
"# seq_data = char_dataset.map(create_seq_targets)\n",
"# seq_data\n",
"train_size = len(text) * 50//100\n",
"train_seq_data = tf.data.Dataset.from_tensor_slices((X[:train_size],y[:train_size]))\n",
"test_seq_data = tf.data.Dataset.from_tensor_slices((X[train_size:],y[train_size:]))"
]
},
{
"cell_type": "code",
"execution_count": 1336,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--------------------------------Headline--------------------------------\n",
"[ 60 115 62 65 103 105 108 101 110 116 32 84 101 99 104 110 111 108\n",
" 111 103 105 101 115 32 65 110 110 111 117 110 99 101 115 32 80 114\n",
" 105 99 105 110 103 32 79 102 32 36 53 48 48 32 77 105 108 108\n",
" 105 111 110 32 79 102 32 83 101 110 105 111 114 32 78 111 116 101\n",
" 115 60 92 115 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
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" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0]\n",
"<s>Agilent Technologies Announces Pricing Of $500 Million Of Senior Notes<\\s\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"\n",
"\n",
"[115 62 65 103 105 108 101 110 116 32 84 101 99 104 110 111 108 111\n",
" 103 105 101 115 32 65 110 110 111 117 110 99 101 115 32 80 114 105\n",
" 99 105 110 103 32 79 102 32 36 53 48 48 32 77 105 108 108 105\n",
" 111 110 32 79 102 32 83 101 110 105 111 114 32 78 111 116 101 115\n",
" 60 92 115 62 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
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" 0 0 0 0 0]\n",
"s>Agilent Technologies Announces Pricing Of $500 Million Of Senior Notes<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"--------------------------------Headline--------------------------------\n",
"[ 60 115 62 65 103 105 108 101 110 116 32 84 101 99 104 110 111 108\n",
" 111 103 105 101 115 32 70 105 108 101 115 32 70 111 114 32 80 111\n",
" 116 101 110 116 105 97 108 32 83 101 110 105 111 114 32 78 111 116\n",
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"<s>Agilent Technologies Files For Potential Senior Notes Offering Size Not Disclosed<\\s\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"\n",
"\n",
"[115 62 65 103 105 108 101 110 116 32 84 101 99 104 110 111 108 111\n",
" 103 105 101 115 32 70 105 108 101 115 32 70 111 114 32 80 111 116\n",
" 101 110 116 105 97 108 32 83 101 110 105 111 114 32 78 111 116 101\n",
" 115 32 79 102 102 101 114 105 110 103 32 83 105 122 101 32 78 111\n",
" 116 32 68 105 115 99 108 111 115 101 100 60 92 115 62 0 0 0\n",
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"s>Agilent Technologies Files For Potential Senior Notes Offering Size Not Disclosed<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"--------------------------------Headline--------------------------------\n",
"[ 60 115 62 65 103 105 108 101 110 116 32 84 101 99 104 110 111 108\n",
" 111 103 105 101 115 32 81 50 32 71 65 65 80 32 69 97 114 110\n",
" 105 110 103 115 32 80 101 114 32 83 104 97 114 101 32 36 48 46\n",
" 51 50 60 92 115 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
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" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
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"<s>Agilent Technologies Q2 GAAP Earnings Per Share $0.32<\\s\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"\n",
"\n",
"[115 62 65 103 105 108 101 110 116 32 84 101 99 104 110 111 108 111\n",
" 103 105 101 115 32 81 50 32 71 65 65 80 32 69 97 114 110 105\n",
" 110 103 115 32 80 101 114 32 83 104 97 114 101 32 36 48 46 51\n",
" 50 60 92 115 62 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
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" 0 0 0 0 0]\n",
"s>Agilent Technologies Q2 GAAP Earnings Per Share $0.32<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"--------------------------------Headline--------------------------------\n",
"[ 60 115 62 65 103 105 108 101 110 116 32 80 68 45 76 49 32 65\n",
" 115 115 97 121 32 82 101 99 101 105 118 101 115 32 70 68 65 32\n",
" 65 112 112 114 111 118 97 108 32 70 111 114 32 85 115 101 32 65\n",
" 115 32 67 111 109 112 97 110 105 111 110 32 68 105 97 103 110 111\n",
" 115 116 105 99 60 92 115 0 0 0 0 0 0 0 0 0 0 0\n",
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" 0 0 0 0 0]\n",
"<s>Agilent PD-L1 Assay Receives FDA Approval For Use As Companion Diagnostic<\\s\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"\n",
"\n",
"[115 62 65 103 105 108 101 110 116 32 80 68 45 76 49 32 65 115\n",
" 115 97 121 32 82 101 99 101 105 118 101 115 32 70 68 65 32 65\n",
" 112 112 114 111 118 97 108 32 70 111 114 32 85 115 101 32 65 115\n",
" 32 67 111 109 112 97 110 105 111 110 32 68 105 97 103 110 111 115\n",
" 116 105 99 60 92 115 62 0 0 0 0 0 0 0 0 0 0 0\n",
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" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0]\n",
"s>Agilent PD-L1 Assay Receives FDA Approval For Use As Companion Diagnostic<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"--------------------------------Headline--------------------------------\n",
"[ 60 115 62 70 68 65 32 65 112 112 114 111 118 101 115 32 78 105\n",
" 118 111 108 117 109 97 98 32 80 108 117 115 32 73 112 105 108 105\n",
" 109 117 109 97 98 32 70 111 114 32 70 105 114 115 116 45 76 105\n",
" 110 101 32 77 101 116 97 115 116 97 116 105 99 32 78 111 110 45\n",
" 83 109 97 108 108 32 67 101 108 108 32 76 117 110 103 32 67 97\n",
" 110 99 101 114 60 92 115 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0]\n",
"<s>FDA Approves Nivolumab Plus Ipilimumab For First-Line Metastatic Non-Small Cell Lung Cancer<\\s\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"\n",
"\n",
"[115 62 70 68 65 32 65 112 112 114 111 118 101 115 32 78 105 118\n",
" 111 108 117 109 97 98 32 80 108 117 115 32 73 112 105 108 105 109\n",
" 117 109 97 98 32 70 111 114 32 70 105 114 115 116 45 76 105 110\n",
" 101 32 77 101 116 97 115 116 97 116 105 99 32 78 111 110 45 83\n",
" 109 97 108 108 32 67 101 108 108 32 76 117 110 103 32 67 97 110\n",
" 99 101 114 60 92 115 62 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0]\n",
"s>FDA Approves Nivolumab Plus Ipilimumab For First-Line Metastatic Non-Small Cell Lung Cancer<\\s>\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n"
]
}
],
"source": [
"#Checking OR next(itr(...))\n",
"for input_txt, target_txt in seq_data.take(5):\n",
" print('--------------------------------Headline--------------------------------')\n",
" print(input_txt.numpy())\n",
" print(\"\".join(map(chr, input_txt.numpy())))\n",
"# print(''.join(index2char[input_txt.numpy()]))\n",
" print('\\n')\n",
" print(target_txt.numpy())\n",
" print(\"\".join(map(chr, target_txt.numpy())))\n",
" # There is an extra whitespace!\n",
"# print(''.join(index2char[target_txt.numpy()]))"
]
},
{
"cell_type": "code",
"execution_count": 1356,
"metadata": {},
"outputs": [],
"source": [
"# Batch size\n",
"batch_size = 128\n",
"\n",
"# Buffer size to shuffle the dataset so it doesn't attempt to shuffle\n",
"# the entire sequence in memory. Instead, it maintains a buffer in which it shuffles elements\n",
"buffer_size = 10000\n",
"\n",
"dataset = train_seq_data.shuffle(buffer_size, seed = 42).batch(batch_size, drop_remainder=True)"
]
},
{
"cell_type": "code",
"execution_count": 1357,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<BatchDataset shapes: ((128, 412), (128, 412)), types: (tf.int32, tf.int32)>"
]
},
"execution_count": 1357,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 1358,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(1, 32, 412), dtype=int32, numpy=\n",
"array([[[ 60, 115, 62, ..., 0, 0, 0],\n",
" [ 60, 115, 62, ..., 0, 0, 0],\n",
" [ 60, 115, 62, ..., 0, 0, 0],\n",
" ...,\n",
" [ 60, 115, 62, ..., 0, 0, 0],\n",
" [ 60, 115, 62, ..., 0, 0, 0],\n",
" [ 60, 115, 62, ..., 0, 0, 0]]])>"
]
},
"execution_count": 1358,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_example_batch, train_label_batch = next(iter(dataset.batch(1)))\n",
"train_example_batch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build Language Model"
]
},
{
"cell_type": "code",
"execution_count": 1359,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.losses import sparse_categorical_crossentropy\n",
"#https://datascience.stackexchange.com/questions/41921/sparse-categorical-crossentropy-vs-categorical-crossentropy-keras-accuracy\n",
"def sparse_cat_loss(y_true,y_pred):\n",
" return sparse_categorical_crossentropy(y_true, y_pred, from_logits=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Tips for LSTM Inputs \n",
"- The LSTM input layer must be 3D.\n",
"- The meaning of the 3 input dimensions are: samples, time steps, and features (sequences, sequence_length, characters).\n",
"- The LSTM input layer is defined by the input_shape argument on the first hidden layer.\n",
"- The input_shape argument takes a tuple of two values that define the number of time steps and features.\n",
"- The number of samples is assumed to be 1 or more.\n",
"- The reshape() function on NumPy arrays can be used to reshape your 1D or 2D data to be 3D.\n",
"- The reshape() function takes a tuple as an argument that defines the new shape\n",
"- The LSTM return the entire sequence of outputs for each sample (one vector per timestep per sample), if you set return_sequences=True.\n",
"- Stateful RNN only makes sense if each input sequence in a batch starts exactly where the corresponding sequence in the previous batch left off. Our RNN model is stateless since each sample is different from the other and they dont form a text corpus but are separate headlines.\n",
"\n",
"#### Tips for Embedding Layer\n",
"- Gives relationship between characters.\n",
"- Dense vector representation (n-Dimensional) of float point values. Map(char/byte) to a dense vector.\n",
"- Embeddings are trainable weights/paramaeters by the model equivalent to weights learned by dense layer.\n",
"- In our case each unique character/byte is represented with an N-Dimensional vector of floating point values, where the learned embedding forms a lookup table by \"looking up\" each characters dense vector in the table to encode it.\n",
"- A simple integer encoding of our characters is not efficient for the model to interpret since a linear classifier only learns the weights for a single feature but not the relationship (probability distribution) between each feature(characters) or there encodings.\n",
"- A higher dimensional embedding can capture fine-grained relationships between characters, but takes more data to learn.(256-Dimensions our case)\n"
]
},
{
"cell_type": "code",
"execution_count": 1360,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# stateful=True: preserve hidden(final) state for next batch's intial state\n",
"def create_model(batch_size):\n",
" model = Sequential(name = 'CharLSTM')\n",
" model.add(Embedding(127, 256,batch_input_shape=[batch_size, None], mask_zero=True, name ='EmbedLayer'))\n",
" model.add(Bidirectional(LSTM(1024, return_sequences=True,stateful=False,recurrent_initializer='glorot_uniform'), name = 'BiLSTM'))\n",
" model.add(TimeDistributed(Dense(127, name = 'TimeDistDense')))\n",
" model.compile(optimizer=tf.optimizers.SGD(learning_rate=1e-3), loss = tf.losses.SparseCategoricalCrossentropy(from_logits = True))\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 1361,
"metadata": {},
"outputs": [],
"source": [
"model = create_model(batch_size)"
]
},
{
"cell_type": "code",
"execution_count": 1362,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"CharLSTM\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"EmbedLayer (Embedding) (128, None, 256) 32512 \n",
"_________________________________________________________________\n",
"BiLSTM (Bidirectional) (128, None, 2048) 10493952 \n",
"_________________________________________________________________\n",
"time_distributed_15 (TimeDis (None, None, 127) 260223 \n",
"=================================================================\n",
"Total params: 10,786,687\n",
"Trainable params: 10,786,687\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 1363,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tf.Tensor(\n",
"[ 60 115 62 75 101 115 115 108 101 114 32 84 111 112 97 122 32 77\n",
" 101 108 116 122 101 114 32 38 32 67 104 101 99 107 44 32 76 76\n",
" 80 32 70 105 108 101 115 32 83 104 97 114 101 104 111 108 100 101\n",
" 114 32 67 108 97 115 115 32 65 99 116 105 111 110 32 76 97 119\n",
" 115 117 105 116 32 65 103 97 105 110 115 116 32 66 97 120 116 101\n",
" 114 32 73 110 116 101 114 110 97 116 105 111 110 97 108 32 73 110\n",
" 99 60 92 115 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0], shape=(412,), dtype=int32) \n",
"\n",
"tf.Tensor(\n",
"[115 62 75 101 115 115 108 101 114 32 84 111 112 97 122 32 77 101\n",
" 108 116 122 101 114 32 38 32 67 104 101 99 107 44 32 76 76 80\n",
" 32 70 105 108 101 115 32 83 104 97 114 101 104 111 108 100 101 114\n",
" 32 67 108 97 115 115 32 65 99 116 105 111 110 32 76 97 119 115\n",
" 117 105 116 32 65 103 97 105 110 115 116 32 66 97 120 116 101 114\n",
" 32 73 110 116 101 114 110 97 116 105 111 110 97 108 32 73 110 99\n",
" 60 92 115 62 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0], shape=(412,), dtype=int32)\n"
]
}
],
"source": [
"for input_example_batch, target_example_batch in dataset.take(1):\n",
" print(input_example_batch[0], '\\n')\n",
" print(target_example_batch[0])\n"
]
},
{
"cell_type": "code",
"execution_count": 1371,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(128, 412, 127) <=== (batch_size, sequence_length, byte/character)\n"
]
}
],
"source": [
"for input_example_batch, target_example_batch in dataset.take(1):\n",
"\n",
" # Predict off some random batch\n",
" example_batch_predictions = model(input_example_batch)\n",
"\n",
" # Display the dimensions of the predictions\n",
" print(example_batch_predictions.shape, \" <=== (batch_size, sequence_length, byte/character)\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Logits Predicting Log-Likelihood from Ouput Layer:\n",
"For each character/byte the model looks up the embedding, runs the LSTM one timestep with the embedding as input, and applies the dense layer to generate logits predicting the log-likelihood of the next character/Byte.\n",
"This distribution, for each predicted character/byte, is defined by the logits over the characters(i.e 1-127 Decimal Points bytes)."
]
},
{
"cell_type": "code",
"execution_count": 1374,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(412, 127), dtype=float32, numpy=\n",
"array([[ 0.00267012, -0.00507424, 0.00358143, ..., 0.00626232,\n",
" 0.01144155, -0.00284741],\n",
" [ 0.00349888, -0.00954466, -0.00494754, ..., 0.00844962,\n",
" 0.01703026, 0.00099191],\n",
" [-0.00170235, -0.01700655, -0.00229823, ..., 0.00122966,\n",
" 0.01756719, 0.00086103],\n",
" ...,\n",
" [ 0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ],\n",
" [ 0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ],\n",
" [ 0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ]], dtype=float32)>"
]
},
"execution_count": 1374,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sum(np.where(example_batch_predictions[0][1] < 0, example_batch_predictions[0][1] * -1, example_batch_predictions[0][1])\n",
"example_batch_predictions[0]"
]
},
{
"cell_type": "code",
"execution_count": 1377,
"metadata": {},
"outputs": [],
"source": [
"sampled_indices = tf.random.categorical(example_batch_predictions[0], num_samples=1) + 1"
]
},
{
"cell_type": "code",
"execution_count": 1378,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
},
"tags": []
},
"outputs": [
{
"data": {
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]
},
"execution_count": 1378,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sampled_indices"
]
},
{
"cell_type": "code",
"execution_count": 1379,
"metadata": {},
"outputs": [],
"source": [
"# Reformat to not be a lists of lists\n",
"sampled_indices = tf.squeeze(sampled_indices,axis=-1).numpy()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 1380,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\x12'"
]
},
"execution_count": 1380,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chr(18)"
]
},
{
"cell_type": "code",
"execution_count": 1381,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Given the input seq: \n",
"\n",
"<s>NextEra Energy Partners, LP (NYSE:NEP) completed the acquisition of 39.2% stake in Central Penn Line from Cabot Oil & Gas Corporation (NYSE:COG), WGL Midstream, Inc. and EIF Vega Midstream, LLC.<\\s\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\n",
"412\n",
"\n",
"\n",
"Chars Predictions: \n",
"\n",
"|d?F7;L,5]K+M?5\u001bLS\u000e[e#\u000f6Be*-'$no+d\u0011u1`Z^\u0014W\u0007Y,\u0010 I\u001f?\u001e",
"T\f",
"\f",
"O|j\u0003M\u0002\\Y\u001d",
"}U5\u001d",
"\"\u0006\u0016K)5{idbEzsvm\t2[ma4V\u001f\u0019</MN\u0019*P^\u001f\n",
"G]\u001f[_)[We[\u0010\u00033Qh_<I+\\o}\u0019Z\u00045'7A|\u0017yyUTay\u00105\u001e",
"\u0012\u000ed3}[J\u0011\u0013L\\,9\u0001i\u0016FN\u0005x#[>,s\u0003i{\u0012yJA\"2G\u001ak%1\f",
"O9?ZS?\u001bD\u001c",
"J<\u000b",
"xXjdeY~\u0014\u0019V\"@w^aRVd\u0002'\u001b%2tz.m{=MU\u0011Y\u000b",
"@4%K\n",
"\u0013rc\u000eR}U`7\u0016Wv?tS\u0012o\u0002?HAc2h#\u001d",
"<mw\"<P3}\u0013\u001c",
"ot\"?\u0010W\u0016w<G\n",
"412\n",
"[107 2 66 23 55 14 23 105 43 51 105 16 35 8 118 109 97 18\n",
" 115 84 84 48 107 3 102 6 48 92 12 67 55 88 28 23 88 122\n",
" 13 81 18 12 122 83 12 73 123 126 3 99 54 94 98 53 27 76\n",
" 83 14 91 101 35 15 54 66 101 42 45 39 36 110 111 43 100 17\n",
" 117 49 96 90 94 20 87 7 89 44 16 32 73 31 63 30 84 12\n",
" 12 79 124 106 3 77 2 92 89 29 125 85 53 29 97 8 34 6\n",
" 22 75 41 53 123 105 100 98 69 122 115 118 109 9 50 91 109 97\n",
" 52 86 31 25 60 47 77 78 25 42 80 94 31 13 124 100 63 70\n",
" 55 59 76 44 53 93 75 43 77 70 8 63 10 81 51 96 109 17\n",
" 118 86 15 14 0 50 83 97 51 22 84 6 100 84 68 4 35 74\n",
" 33 19 24 95 22 88 122 44 124 62 86 54 95 84 108 82 0 45\n",
" 62 115 22 46 90 80 37 48 110 7 37 118 124 58 70 104 76 6\n",
" 74 101 100 75 104 115 3 105 123 18 121 74 65 34 50 71 26 107\n",
" 37 49 12 79 57 63 90 83 63 27 68 28 74 60 11 120 88 106\n",
" 100 101 89 126 20 25 86 34 64 119 94 97 82 86 100 2 39 27\n",
" 37 50 116 122 46 109 123 61 77 85 17 21 8 89 11 64 52 37\n",
" 75 13 71 93 31 91 95 41 91 87 101 91 16 3 51 81 104 95\n",
" 60 73 43 92 111 125 25 90 4 53 39 55 65 124 23 121 121 85\n",
" 84 97 121 16 53 30 18 14 100 51 125 91 74 17 19 76 92 44\n",
" 57 1 105 22 70 78 5 120 35 91 62 44 10 103 63 68 79 35\n",
" 103 28 13 74 118 99 14 82 125 85 96 55 22 87 118 63 116 83\n",
" 18 111 2 63 72 65 99 50 104 35 29 60 109 119 34 60 80 51\n",
" 125 19 28 111 116 34 63 16 87 22 119 60 71 13 19 114]\n"
]
}
],
"source": [
"print(\"Given the input seq: \\n\")\n",
"print(\"\".join(map(chr,input_example_batch[0])))\n",
"print(len(\"\".join(map(chr,input_example_batch[0]))))\n",
"print('\\n')\n",
"print(\"Chars Predictions: \\n\")\n",
"print(\"\".join(map(chr,sampled_indices)))\n",
"print(len(\"\".join(map(chr,sampled_indices))))\n",
"print(sampled_indices)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train Language Model"
]
},
{
"cell_type": "code",
"execution_count": 1382,
"metadata": {},
"outputs": [],
"source": [
"# Directory where the checkpoints will be saved\n",
"import os \n",
"checkpoint_dir = './training_checkpoints'\n",
"# Name of the checkpoint files\n",
"checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_{epoch}\")\n",
"\n",
"checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(\n",
" filepath=checkpoint_prefix,\n",
" save_weights_only=True)"
]
},
{
"cell_type": "code",
"execution_count": 1383,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"66/66 - 18825s - loss: 1.0356\n",
"Epoch 2/10\n",
"66/66 - 27071s - loss: 1.0360\n",
"Epoch 3/10\n",
"66/66 - 30669s - loss: 1.0349\n",
"Epoch 4/10\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1383-e86444bc2553>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# https://www.kdnuggets.com/2019/03/train-keras-model-20x-faster-tpu-free.html\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mepochs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mhistory\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muse_multiprocessing\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcheckpoint_callback\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\users\\firo obeid\\anaconda3\\envs\\pushstashenv\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 64\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\firo obeid\\anaconda3\\envs\\pushstashenv\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m 846\u001b[0m batch_size=batch_size):\n\u001b[0;32m 847\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 848\u001b[1;33m \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 849\u001b[0m \u001b[1;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 850\u001b[0m \u001b[1;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\firo obeid\\anaconda3\\envs\\pushstashenv\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 578\u001b[0m \u001b[0mxla_context\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 579\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 580\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 581\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 582\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\firo obeid\\anaconda3\\envs\\pushstashenv\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 609\u001b[0m \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 610\u001b[0m \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 611\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 612\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 613\u001b[0m \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\firo obeid\\anaconda3\\envs\\pushstashenv\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 2418\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2419\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2420\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2421\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2422\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\firo obeid\\anaconda3\\envs\\pushstashenv\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m 1663\u001b[0m if isinstance(t, (ops.Tensor,\n\u001b[0;32m 1664\u001b[0m resource_variable_ops.BaseResourceVariable))),\n\u001b[1;32m-> 1665\u001b[1;33m self.captured_inputs)\n\u001b[0m\u001b[0;32m 1666\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1667\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\firo obeid\\anaconda3\\envs\\pushstashenv\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m 1744\u001b[0m \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1745\u001b[0m return self._build_call_outputs(self._inference_function.call(\n\u001b[1;32m-> 1746\u001b[1;33m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[0;32m 1747\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n\u001b[0;32m 1748\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\firo obeid\\anaconda3\\envs\\pushstashenv\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m 596\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 597\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 598\u001b[1;33m ctx=ctx)\n\u001b[0m\u001b[0;32m 599\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 600\u001b[0m outputs = execute.execute_with_cancellation(\n",
"\u001b[1;32mc:\\users\\firo obeid\\anaconda3\\envs\\pushstashenv\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 59\u001b[0m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[1;32m---> 60\u001b[1;33m inputs, attrs, num_outputs)\n\u001b[0m\u001b[0;32m 61\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"# https://www.kdnuggets.com/2019/03/train-keras-model-20x-faster-tpu-free.html\n",
"epochs = 10\n",
"history = model.fit(dataset, epochs=epochs, use_multiprocessing = True, workers=5, verbose = 2, callbacks=[checkpoint_callback])"
]
},
{
"cell_type": "code",
"execution_count": 1385,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'./training_checkpoints\\\\ckpt_3'"
]
},
"execution_count": 1385,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.train.latest_checkpoint(checkpoint_dir)"
]
},
{
"cell_type": "code",
"execution_count": 1390,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x1a50b34ff28>"
]
},
"execution_count": 1390,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = create_model(batch_size=1)\n",
"\n",
"model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))"
]
},
{
"cell_type": "code",
"execution_count": 1391,
"metadata": {},
"outputs": [],
"source": [
"model.build(tf.TensorShape([1, None]))"
]
},
{
"cell_type": "code",
"execution_count": 1392,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"CharLSTM\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"EmbedLayer (Embedding) (1, None, 256) 32512 \n",
"_________________________________________________________________\n",
"BiLSTM (Bidirectional) (1, None, 2048) 10493952 \n",
"_________________________________________________________________\n",
"time_distributed_17 (TimeDis (None, None, 127) 260223 \n",
"=================================================================\n",
"Total params: 10,786,687\n",
"Trainable params: 10,786,687\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 1406,
"metadata": {},
"outputs": [],
"source": [
"X_test, y_test = next(iter(test_seq_data))"
]
},
{
"cell_type": "code",
"execution_count": 1422,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"55"
]
},
"execution_count": 1422,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(\"\".join(map(chr,X_test.numpy())).strip(chr(0)))"
]
},
{
"cell_type": "code",
"execution_count": 1594,
"metadata": {},
"outputs": [],
"source": [
"new = X[np.random.randint(0,len(data))]"
]
},
{
"cell_type": "code",
"execution_count": 1595,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<s>F5 Networks, Inc. Provides Earnings Guidance for the First Quarter of Fiscal Year 2020 Ending December 31, 2019<\\s\n",
"117\n"
]
}
],
"source": [
"print(\"\".join(map(chr, new)).strip(chr(0)))\n",
"print(len(\"\".join(map(chr, new)).strip(chr(0))))"
]
},
{
"cell_type": "code",
"execution_count": 1588,
"metadata": {},
"outputs": [],
"source": [
"prediction = model(new.reshape(1,-1))"
]
},
{
"cell_type": "code",
"execution_count": 1589,
"metadata": {},
"outputs": [],
"source": [
"# prediction.numpy()[:,54:,:] \n",
"prediction = prediction[-1,:,:]"
]
},
{
"cell_type": "code",
"execution_count": 1591,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"s~P'PPsjjjs!!s!!\\!!!!!\\!!!!!!!!!!!f!f!!!\u0010ooff!!s!!!!!f\n",
"o!!\u001e",
"FffffQM:fIfff!eseHU\u0010\u0010\u0010ssss.\u0010bD!!!!fffff!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
"97\n"
]
}
],
"source": [
"print(\"\".join(map(chr,np.argmax(prediction, axis = 1) + 1)))\n",
"print(len(\"s~P'PPsjjjs!!s!!\\!!!!!\\!!!!!!!!!!!f!f!!!\u0010ooff!!s!!!!!fo!!\u001e",
"FffffQM:fIfff!eseHU\u0010\u0010\u0010ssss.\u0010bD!!!!fffff\"))"
]
},
{
"cell_type": "code",
"execution_count": 1529,
"metadata": {},
"outputs": [],
"source": [
"p_i = np.zeros((prediction.shape))\n",
"for i in range(0, 412):\n",
" p = np.exp(prediction[i])/np.sum(np.exp(prediction[i]))\n",
" p_i[i] = p"
]
},
{
"cell_type": "code",
"execution_count": 1530,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([125, 125, 70, 70, 101, 91, 32, 32, 118, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 114, 32, 32, 91, 101, 101, 32,\n",
" 114, 64, 101, 101, 101, 101, 15, 101, 15, 99, 110, 110, 97,\n",
" 101, 101, 101, 101, 109, 32, 32, 32, 32, 32, 7, 101, 32,\n",
" 32, 32, 100, 71, 101, 95, 32, 101, 32, 32, 32, 101, 32,\n",
" 32, 32, 32, 32, 32, 32, 101, 32, 37, 114, 101, 125, 125,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
" 32, 32, 32, 32, 32, 32, 32, 32, 32], dtype=int64)"
]
},
"execution_count": 1530,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.argmax(p_i, axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 1534,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'~~GGf\\\\!!w!!!!!!!!!!s!!\\\\ff!sAffff\\x10f\\x10doobffffn!!!!!\\x08f!!!eHf`!f!!!f!!!!!!!f!&sf~~!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!'"
]
},
"execution_count": 1534,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\".join(map(chr,np.argmax(p_i, axis = 1) + 1))"
]
},
{
"cell_type": "code",
"execution_count": 1463,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(412, 127)"
]
},
"execution_count": 1463,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prediction.shape"
]
},
{
"cell_type": "code",
"execution_count": 870,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"479/479 - 10507s - loss: 1.2081 - accuracy: 8.6585e-04\n"
]
},
{
"data": {
"text/plain": [
"[1.2080851793289185, 0.0008658546721562743]"
]
},
"execution_count": 870,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.evaluate(dataset, verbose=2, use_multiprocessing=1)"
]
},
{
"cell_type": "code",
"execution_count": 948,
"metadata": {},
"outputs": [],
"source": [
"model = create_model(batch_size=128)\n",
"\n",
"model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))\n",
"\n",
"model.build(tf.TensorShape([1, None]))"
]
},
{
"cell_type": "code",
"execution_count": 1647,
"metadata": {},
"outputs": [],
"source": [
"def generate_text(model, start_string):\n",
" # Evaluation step (generating text using the learned model)\n",
"\n",
" # Number of characters to generate\n",
" num_generate = 100\n",
"\n",
" # Converting our start string to numbers (vectorizing)\n",
" input_eval = [ord(s) for s in start_string]\n",
" input_eval = tf.expand_dims(input_eval, 0)\n",
"\n",
" # Empty string to store our results\n",
" text_generated = []\n",
"\n",
" # Low temperatures results in more predictable text.\n",
" # Higher temperatures results in more surprising text.\n",
" # Experiment to find the best setting.\n",
" temperature = 1.0\n",
"\n",
" # Here batch size == 1\n",
" model.reset_states()\n",
" for i in range(num_generate):\n",
" predictions = model(input_eval)\n",
" # remove the batch dimension\n",
" predictions = tf.squeeze(predictions, 0)\n",
"\n",
" # using a categorical distribution to predict the character returned by the model\n",
" predictions = predictions / temperature\n",
" predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()\n",
"\n",
" # We pass the predicted character as the next input to the model\n",
" # along with the previous hidden state\n",
" input_eval = tf.expand_dims([predicted_id], 0)\n",
"\n",
" text_generated.append(chr(predicted_id))\n",
"\n",
" return (start_string + ''.join(text_generated))"
]
},
{
"cell_type": "code",
"execution_count": 1648,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"u\\F;q7z'^7XZFB]B2mLpx~f\u0012\u001fa-'?\u0014\u000e(*Q+u^4K\u001d",
"MC#\u00046]]:\u0007a.LS\u000b",
"8jO\u0004\n",
"$[\u001bf*J\u00071\u0017B#\u001a\u0013\n",
"\u0017$ILq&\u0003c\n"
]
}
],
"source": [
"print(generate_text(model, start_string=\"<s>\"))"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"how much for the maple syrup 20.99 Thats ricidulous\n"
]
}
],
"source": [
"\n",
"\n",
"strs = \"how much for the maple syrup? $%20.99? That's ”˚‑|–—=_ricidulous!!!???|||\"\n",
"\n",
"nstr = re.sub('[!,*)@=#-({|}_‑–?^;:{|}˚~\\t\\n“—’”/_]',r'',strs)\n",
"print(nstr)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save Trained Vectors of Embeddings and Model Weights"
]
},
{
"cell_type": "code",
"execution_count": 1646,
"metadata": {},
"outputs": [],
"source": [
"\n",
"import io, csv\n",
"\n",
"# save model weights\n",
"e = model.layers[0]\n",
"weights = e.get_weights()[0]\n",
"weights.shape # shape: (characters/bytes, embedding_dim) -->(127,256)\n",
"\n",
"# save embeddings.\n",
"out_v = io.open('vecs.tsv', 'w', encoding='utf-8')\n",
"out_m = io.open('meta.tsv', 'w', encoding='utf-8')\n",
"tsv_writer = csv.writer(out_m, delimiter='\\t')\n",
"\n",
"# for char in map(chr, (i for i in range(1,255+1))):\n",
"# print(char)\n",
"for i in range(0,127):\n",
" vec = weights[i] \n",
" tsv_writer.writerow(str(chr(i+1)))\n",
"# out_m.write(chr(i+1), lineterminator='\\n')# skip 0, it's padding.255 last vector\n",
" out_v.write('\\t'.join([str(x) for x in vec]) + \"\\n\")\n",
"out_v.close()\n",
"out_m.close()\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Stock Direction Prediction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, the output of a RNN layer contains a single vector per sample. This vector is the RNN cell output corresponding to the last timestep, containing information about the entire input sequence. The shape of this output is (batch_size, units) where units corresponds to the units argument passed to the layer's constructor."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def direction_model(batch_size):\n",
" model = Sequential(name = 'RNNStocks')\n",
" model.add(Embedding(iinput_dim = 127, output_dim = 256,\n",
" batch_input_shape=[batch_size, None], \n",
" weights=[embedding_matrix],trainable=False, name ='EmbedLayer'))\n",
" model.add(Bidirectional(LSTM(1024, return_sequences=False,stateful=False,recurrent_initializer='glorot_uniform'), name = 'BiLSTM'))\n",
" #final state encodes full representation of passed headine\n",
" model.add(Dense(512, activation = 'LeakyReLU',name = 'FullConnected'))\n",
" model.add(Dense(1, name='Output', activation='sigmoid'))\n",
" model.compile(optimizer=tf.optimizers.ADAM(learning_rate=1e-3), loss = 'binary_crossentropy')\n",
" return model"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.15"
}
},
"nbformat": 4,
"nbformat_minor": 4
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|