HTTP/2 200 content-type: application/octet-stream x-guploader-uploadid: ABgVH89SryA-JnKPrUoGbFQ5SdfZP_ZyhXLDpMS98Ob8vqcb6bjJatKh96P0UYEF-o8eU4Nu expires: Fri, 18 Jul 2025 04:09:34 GMT date: Fri, 18 Jul 2025 03:09:34 GMT cache-control: public, max-age=3600 last-modified: Fri, 16 Aug 2024 09:16:02 GMT etag: "f099dc70ba73a3026642fe9a0512e414" x-goog-generation: 1723799762788397 x-goog-metageneration: 1 x-goog-stored-content-encoding: identity x-goog-stored-content-length: 739846 x-goog-hash: crc32c=WheNUg== x-goog-hash: md5=8JnccLpzowJmQv6aBRLkFA== x-goog-storage-class: MULTI_REGIONAL accept-ranges: bytes content-length: 739846 server: UploadServer alt-svc: h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 { "cells": [ { "cell_type": "markdown", "metadata": { "id": "DweYe9FcbMK_" }, "source": [ "##### Copyright 2018 The TensorFlow Authors.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2024-08-16T07:37:52.034458Z", "iopub.status.busy": "2024-08-16T07:37:52.033883Z", "iopub.status.idle": "2024-08-16T07:37:52.037637Z", "shell.execute_reply": "2024-08-16T07:37:52.037019Z" }, "id": "AVV2e0XKbJeX" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "sZfSvVcDo6GQ" }, "source": [ "# Load text" ] }, { "cell_type": "markdown", "metadata": { "id": "giK0nMbZFnoR" }, "source": [ "\n", " \n", " \n", " \n", " \n", "

\n", " View on TensorFlow.org\n", "

\n", " Run in Google Colab\n", "

\n", " View source on GitHub\n", "

\n", " Download notebook\n", "

" ] }, { "cell_type": "markdown", "metadata": { "id": "dwlfPb11GH8J" }, "source": [ "This tutorial demonstrates two ways to load and preprocess text.\n", "\n", "- First, you will use Keras utilities and preprocessing layers. These include `tf.keras.utils.text_dataset_from_directory` to turn data into a `tf.data.Dataset` and `tf.keras.layers.TextVectorization` for data standardization, tokenization, and vectorization. If you are new to TensorFlow, you should start with these.\n", "- Then, you will use lower-level utilities like `tf.data.TextLineDataset` to load text files, `tf.lookup` for custom in-model lookup tables, and [TensorFlow Text](https://www.tensorflow.org/text) APIs, such as `text.UnicodeScriptTokenizer` and `text.case_fold_utf8`, to preprocess the data for finer-grain control." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:37:52.040916Z", "iopub.status.busy": "2024-08-16T07:37:52.040707Z", "iopub.status.idle": "2024-08-16T07:38:20.049731Z", "shell.execute_reply": "2024-08-16T07:38:20.048732Z" }, "id": "sa6IKWvADqH7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting tensorflow-text==2.13.*\r\n", " Downloading tensorflow_text-2.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.0 kB)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement 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install \"tensorflow-text==2.13.*\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:20.054147Z", "iopub.status.busy": "2024-08-16T07:38:20.053866Z", "iopub.status.idle": "2024-08-16T07:38:23.220823Z", "shell.execute_reply": "2024-08-16T07:38:23.220141Z" }, "id": "baYFZMW_bJHh" }, "outputs": [], "source": [ "import collections\n", "import pathlib\n", "\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf\n", "\n", "from tensorflow.keras import layers\n", "from tensorflow.keras import losses\n", "from tensorflow.keras import utils\n", "from tensorflow.keras.layers import TextVectorization\n", "\n", "import tensorflow_datasets as tfds\n", "import tensorflow_text as tf_text" ] }, { "cell_type": "markdown", "metadata": { "id": "Az-d_K5_HQ5k" }, "source": [ "## Example 1: Predict the tag for a Stack Overflow question\n", "\n", "As a first example, you will download a dataset of programming questions from Stack Overflow. Each question (_\"How do I sort a dictionary by value?\"_) is labeled with exactly one tag (`Python`, `CSharp`, `JavaScript`, or `Java`). Your task is to develop a model that predicts the tag for a question. This is an example of multi-class classification—an important and widely applicable kind of machine learning problem." ] }, { "cell_type": "markdown", "metadata": { "id": "bU4PRq7Z81B0" }, "source": [ "To implement this task, you'll start with the simplest tools:\n", "\n", "* `keras.utils.text_datasaet_from_directory`: for loading text-file examples.\n", "* `keras.layers.TextVectorization`: for converting strings to token indices.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "tjC3yLa5IjP7" }, "source": [ "### Download and explore the dataset\n", "\n", "Begin by downloading the Stack Overflow dataset using `tf.keras.utils.get_file`, and exploring the directory structure:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:23.226266Z", "iopub.status.busy": "2024-08-16T07:38:23.224828Z", "iopub.status.idle": "2024-08-16T07:38:26.625037Z", "shell.execute_reply": "2024-08-16T07:38:26.624334Z" }, "id": "8ELgzA6SHTuV" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/stack_overflow_16k.tar.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 8192/6053168 [..............................] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "4202496/6053168 [===================>..........] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "6053168/6053168 [==============================] - 0s 0us/step\n" ] } ], "source": [ "data_url = 'https://storage.googleapis.com/download.tensorflow.org/data/stack_overflow_16k.tar.gz'\n", "\n", "dataset_dir = utils.get_file(\n", " origin=data_url,\n", " untar=True,\n", " cache_dir='stack_overflow',\n", " cache_subdir='')\n", "\n", "dataset_dir = pathlib.Path(dataset_dir).parent" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:26.629184Z", "iopub.status.busy": "2024-08-16T07:38:26.628650Z", "iopub.status.idle": "2024-08-16T07:38:26.635600Z", "shell.execute_reply": "2024-08-16T07:38:26.635035Z" }, "id": "jIrPl5fUH2gb" }, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/tmp/.keras/stack_overflow_16k.tar.gz'),\n", " PosixPath('/tmp/.keras/README.md'),\n", " PosixPath('/tmp/.keras/train'),\n", " PosixPath('/tmp/.keras/test')]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(dataset_dir.iterdir())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:26.639206Z", "iopub.status.busy": "2024-08-16T07:38:26.638658Z", "iopub.status.idle": "2024-08-16T07:38:26.642979Z", "shell.execute_reply": "2024-08-16T07:38:26.642410Z" }, "id": "fEoV7YByJoWQ" }, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/tmp/.keras/train/java'),\n", " PosixPath('/tmp/.keras/train/csharp'),\n", " PosixPath('/tmp/.keras/train/python'),\n", " PosixPath('/tmp/.keras/train/javascript')]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_dir = dataset_dir/'train'\n", "list(train_dir.iterdir())" ] }, { "cell_type": "markdown", "metadata": { "id": "3mxAN17MhEh0" }, "source": [ "The `train/csharp`, `train/java`, `train/python` and `train/javascript` directories contain many text files, each of which is a Stack Overflow question.\n", "\n", "Print an example file and inspect the data:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:26.646237Z", "iopub.status.busy": "2024-08-16T07:38:26.645783Z", "iopub.status.idle": "2024-08-16T07:38:26.649575Z", "shell.execute_reply": "2024-08-16T07:38:26.648900Z" }, "id": "Go1vTSGdJu08" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "why does this blank program print true x=true.def stupid():. x=false.stupid().print x\n", "\n" ] } ], "source": [ "sample_file = train_dir/'python/1755.txt'\n", "\n", "with open(sample_file) as f:\n", " print(f.read())" ] }, { "cell_type": "markdown", "metadata": { "id": "deWBTkpJiO7D" }, "source": [ "### Load the dataset\n", "\n", "Next, you will load the data off-disk and prepare it into a format suitable for training. To do so, you will use the `tf.keras.utils.text_dataset_from_directory` utility to create a labeled `tf.data.Dataset`. If you're new to `tf.data`, it's a powerful collection of tools for building input pipelines. (Learn more in the [tf.data: Build TensorFlow input pipelines](../../guide/data.ipynb) guide.)\n", "\n", "The `tf.keras.utils.text_dataset_from_directory` API expects a directory structure as follows:\n", "\n", "```\n", "train/\n", "...csharp/\n", "......1.txt\n", "......2.txt\n", "...java/\n", "......1.txt\n", "......2.txt\n", "...javascript/\n", "......1.txt\n", "......2.txt\n", "...python/\n", "......1.txt\n", "......2.txt\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "Dyl6JTAjlbQV" }, "source": [ "When running a machine learning experiment, it is a best practice to divide your dataset into three splits: [training](https://developers.google.com/machine-learning/glossary#training_set), [validation](https://developers.google.com/machine-learning/glossary#validation_set), and [test](https://developers.google.com/machine-learning/glossary#test-set).\n", "\n", "The Stack Overflow dataset has already been divided into training and test sets, but it lacks a validation set.\n", "\n", "Create a validation set using an 80:20 split of the training data by using `tf.keras.utils.text_dataset_from_directory` with `validation_split` set to `0.2` (i.e. 20%):" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:26.653024Z", "iopub.status.busy": "2024-08-16T07:38:26.652478Z", "iopub.status.idle": "2024-08-16T07:38:27.535592Z", "shell.execute_reply": "2024-08-16T07:38:27.534910Z" }, "id": "qqyliMw8N-az" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 8000 files belonging to 4 classes.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Using 6400 files for training.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2024-08-16 07:38:27.487373: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n", "Skipping registering GPU devices...\n" ] } ], "source": [ "batch_size = 32\n", "seed = 42\n", "\n", "raw_train_ds = utils.text_dataset_from_directory(\n", " train_dir,\n", " batch_size=batch_size,\n", " validation_split=0.2,\n", " subset='training',\n", " seed=seed)" ] }, { "cell_type": "markdown", "metadata": { "id": "DMI_gPLfloD7" }, "source": [ "As the previous cell output suggests, there are 8,000 examples in the training folder, of which you will use 80% (or 6,400) for training. You will learn in a moment that you can train a model by passing a `tf.data.Dataset` directly to `Model.fit`.\n", "\n", "First, iterate over the dataset and print out a few examples, to get a feel for the data.\n", "\n", "Note: To increase the difficulty of the classification problem, the dataset author replaced occurrences of the words *Python*, *CSharp*, *JavaScript*, or *Java* in the programming question with the word *blank*." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:27.539433Z", "iopub.status.busy": "2024-08-16T07:38:27.538881Z", "iopub.status.idle": "2024-08-16T07:38:27.564780Z", "shell.execute_reply": "2024-08-16T07:38:27.564167Z" }, "id": "_JMTyZ6Glt_C" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Question: b'\"my tester is going to the wrong constructor i am new to programming so if i ask a question that can be easily fixed, please forgive me. my program has a tester class with a main. when i send that to my regularpolygon class, it sends it to the wrong constructor. i have two constructors. 1 without perameters..public regularpolygon(). {. mynumsides = 5;. mysidelength = 30;. }//end default constructor...and my second, with perameters. ..public regularpolygon(int numsides, double sidelength). {. mynumsides = numsides;. mysidelength = sidelength;. }// end constructor...in my tester class i have these two lines:..regularpolygon shape = new regularpolygon(numsides, sidelength);. shape.menu();...numsides and sidelength were declared and initialized earlier in the testing class...so what i want to happen, is the tester class sends numsides and sidelength to the second constructor and use it in that class. but it only uses the default constructor, which therefor ruins the whole rest of the program. can somebody help me?..for those of you who want to see more of my code: here you go..public double vertexangle(). {. system.out.println(\"\"the vertex angle method: \"\" + mynumsides);// prints out 5. system.out.println(\"\"the vertex angle method: \"\" + mysidelength); // prints out 30.. double vertexangle;. vertexangle = ((mynumsides - 2.0) / mynumsides) * 180.0;. return vertexangle;. }//end method vertexangle..public void menu().{. system.out.println(mynumsides); // prints out what the user puts in. system.out.println(mysidelength); // prints out what the user puts in. gotographic();. calcr(mynumsides, mysidelength);. calcr(mynumsides, mysidelength);. print(); .}// end menu...this is my entire tester class:..public static void main(string[] arg).{. int numsides;. double sidelength;. scanner keyboard = new scanner(system.in);.. system.out.println(\"\"welcome to the regular polygon program!\"\");. system.out.println();.. system.out.print(\"\"enter the number of sides of the polygon ==> \"\");. numsides = keyboard.nextint();. system.out.println();.. system.out.print(\"\"enter the side length of each side ==> \"\");. sidelength = keyboard.nextdouble();. system.out.println();.. regularpolygon shape = new regularpolygon(numsides, sidelength);. shape.menu();.}//end main...for testing it i sent it numsides 4 and sidelength 100.\"\\n'\n", "Label: 1\n", "Question: b'\"blank code slow skin detection this code changes the color space to lab and using a threshold finds the skin area of an image. but it\\'s ridiculously slow. i don\\'t know how to make it faster ? ..from colormath.color_objects import *..def skindetection(img, treshold=80, color=[255,20,147]):.. print img.shape. res=img.copy(). for x in range(img.shape[0]):. for y in range(img.shape[1]):. rgbimg=rgbcolor(img[x,y,0],img[x,y,1],img[x,y,2]). labimg=rgbimg.convert_to(\\'lab\\', debug=false). if (labimg.lab_l > treshold):. res[x,y,:]=color. else: . res[x,y,:]=img[x,y,:].. return res\"\\n'\n", "Label: 3\n", "Question: b'\"option and validation in blank i want to add a new option on my system where i want to add two text files, both rental.txt and customer.txt. inside each text are id numbers of the customer, the videotape they need and the price...i want to place it as an option on my code. right now i have:...add customer.rent return.view list.search.exit...i want to add this as my sixth option. say for example i ordered a video, it would display the price and would let me confirm the price and if i am going to buy it or not...here is my current code:.. import blank.io.*;. import blank.util.arraylist;. import static blank.lang.system.out;.. public class rentalsystem{. static bufferedreader input = new bufferedreader(new inputstreamreader(system.in));. static file file = new file(\"\"file.txt\"\");. static arraylist<string> list = new arraylist<string>();. static int rows;.. public static void main(string[] args) throws exception{. introduction();. system.out.print(\"\"nn\"\");. login();. system.out.print(\"\"nnnnnnnnnnnnnnnnnnnnnn\"\");. introduction();. string repeat;. do{. loadfile();. system.out.print(\"\"nwhat do you want to do?nn\"\");. system.out.print(\"\"n - - - - - - - - - - - - - - - - - - - - - - -\"\");. system.out.print(\"\"nn | 1. add customer | 2. rent return |n\"\");. system.out.print(\"\"n - - - - - - - - - - - - - - - - - - - - - - -\"\");. system.out.print(\"\"nn | 3. view list | 4. search |n\"\");. system.out.print(\"\"n - - - - - - - - - - - - - - - - - - - - - - -\"\");. system.out.print(\"\"nn | 5. exit |n\"\");. system.out.print(\"\"n - - - - - - - - - -\"\");. system.out.print(\"\"nnchoice:\"\");. int choice = integer.parseint(input.readline());. switch(choice){. case 1:. writedata();. break;. case 2:. rentdata();. break;. case 3:. viewlist();. break;. case 4:. search();. break;. case 5:. system.out.println(\"\"goodbye!\"\");. system.exit(0);. default:. system.out.print(\"\"invalid choice: \"\");. break;. }. system.out.print(\"\"ndo another task? [y/n] \"\");. repeat = input.readline();. }while(repeat.equals(\"\"y\"\"));.. if(repeat!=\"\"y\"\") system.out.println(\"\"ngoodbye!\"\");.. }.. public static void writedata() throws exception{. system.out.print(\"\"nname: \"\");. string cname = input.readline();. system.out.print(\"\"address: \"\");. string add = input.readline();. system.out.print(\"\"phone no.: \"\");. string pno = input.readline();. system.out.print(\"\"rental amount: \"\");. string ramount = input.readline();. system.out.print(\"\"tapenumber: \"\");. string tno = input.readline();. system.out.print(\"\"title: \"\");. string title = input.readline();. system.out.print(\"\"date borrowed: \"\");. string dborrowed = input.readline();. system.out.print(\"\"due date: \"\");. string ddate = input.readline();. createline(cname, add, pno, ramount,tno, title, dborrowed, ddate);. rentdata();. }.. public static void createline(string name, string address, string phone , string rental, string tapenumber, string title, string borrowed, string due) throws exception{. filewriter fw = new filewriter(file, true);. fw.write(\"\"nname: \"\"+name + \"\"naddress: \"\" + address +\"\"nphone no.: \"\"+ phone+\"\"nrentalamount: \"\"+rental+\"\"ntape no.: \"\"+ tapenumber+\"\"ntitle: \"\"+ title+\"\"ndate borrowed: \"\"+borrowed +\"\"ndue date: \"\"+ due+\"\":rn\"\");. fw.close();. }.. public static void loadfile() throws exception{. try{. list.clear();. fileinputstream fstream = new fileinputstream(file);. bufferedreader br = new bufferedreader(new inputstreamreader(fstream));. rows = 0;. while( br.ready()). {. list.add(br.readline());. rows++;. }. br.close();. } catch(exception e){. system.out.println(\"\"list not yet loaded.\"\");. }. }.. public static void viewlist(){. system.out.print(\"\"n~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\"\");. system.out.print(\"\" |list of all costumers|\"\");. system.out.print(\"\"~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\"\");. for(int i = 0; i <rows; i++){. system.out.println(list.get(i));. }. }. public static void rentdata()throws exception. { system.out.print(\"\"n~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\"\");. system.out.print(\"\" |rent data list|\"\");. system.out.print(\"\"~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\"\");. system.out.print(\"\"nenter customer name: \"\");. string cname = input.readline();. system.out.print(\"\"date borrowed: \"\");. string dborrowed = input.readline();. system.out.print(\"\"due date: \"\");. string ddate = input.readline();. system.out.print(\"\"return date: \"\");. string rdate = input.readline();. system.out.print(\"\"rent amount: \"\");. string ramount = input.readline();.. system.out.print(\"\"you pay:\"\"+ramount);... }. public static void search()throws exception. { system.out.print(\"\"n~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\"\");. system.out.print(\"\" |search costumers|\"\");. system.out.print(\"\"~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\"\");. system.out.print(\"\"nenter costumer name: \"\");. string cname = input.readline();. boolean found = false;.. for(int i=0; i < rows; i++){. string temp[] = list.get(i).split(\"\",\"\");.. if(cname.equals(temp[0])){. system.out.println(\"\"search result:nyou are \"\" + temp[0] + \"\" from \"\" + temp[1] + \"\".\"\"+ temp[2] + \"\".\"\"+ temp[3] + \"\".\"\"+ temp[4] + \"\".\"\"+ temp[5] + \"\" is \"\" + temp[6] + \"\".\"\"+ temp[7] + \"\" is \"\" + temp[8] + \"\".\"\");. found = true;. }. }.. if(!found){. system.out.print(\"\"no results.\"\");. }.. }.. public static boolean evaluate(string uname, string pass){. if (uname.equals(\"\"admin\"\")&&pass.equals(\"\"12345\"\")) return true;. else return false;. }.. public static string login()throws exception{. bufferedreader input=new bufferedreader(new inputstreamreader(system.in));. int counter=0;. do{. system.out.print(\"\"username:\"\");. string uname =input.readline();. system.out.print(\"\"password:\"\");. string pass =input.readline();.. boolean accept= evaluate(uname,pass);.. if(accept){. break;. }else{. system.out.println(\"\"incorrect username or password!\"\");. counter ++;. }. }while(counter<3);.. if(counter !=3) return \"\"login successful\"\";. else return \"\"login failed\"\";. }. public static void introduction() throws exception{.. system.out.println(\"\" - - - - - - - - - - - - - - - - - - - - - - - - -\"\");. system.out.println(\"\" ! r e n t a l !\"\");. system.out.println(\"\" ! ~ ~ ~ ~ ~ ! ================= ! ~ ~ ~ ~ ~ !\"\");. system.out.println(\"\" ! s y s t e m !\"\");. system.out.println(\"\" - - - - - - - - - - - - - - - - - - - - - - - - -\"\");. }..}\"\\n'\n", "Label: 1\n", "Question: b'\"exception: dynamic sql generation for the updatecommand is not supported against a selectcommand that does not return any key i dont know what is the problem this my code : ..string nomtable;..datatable listeetablissementtable = new datatable();.datatable listeinteretstable = new datatable();.dataset ds = new dataset();.sqldataadapter da;.sqlcommandbuilder cmdb;..private void listeinterets_click(object sender, eventargs e).{. nomtable = \"\"listeinteretstable\"\";. d.cnx.open();. da = new sqldataadapter(\"\"select nome from offices\"\", d.cnx);. ds = new dataset();. da.fill(ds, nomtable);. datagridview1.datasource = ds.tables[nomtable];.}..private void sauvgarder_click(object sender, eventargs e).{. d.cnx.open();. cmdb = new sqlcommandbuilder(da);. da.update(ds, nomtable);. d.cnx.close();.}\"\\n'\n", "Label: 0\n", "Question: b'\"parameter with question mark and super in blank, i\\'ve come across a method that is formatted like this:..public final subscription subscribe(final action1<? super t> onnext, final action1<throwable> onerror) {.}...in the first parameter, what does the question mark and super mean?\"\\n'\n", "Label: 1\n", "Question: b'call two objects wsdl the first time i got a very strange wsdl. ..i would like to call the object (interface - invoicecheck_out) do you know how?....i would like to call the object (variable) do you know how?..try to call (it`s ok)....try to call (how call this?)\\n'\n", "Label: 0\n", "Question: b\"how to correctly make the icon for systemtray in blank using icon sizes of any dimension for systemtray doesn't look good overall. .what is the correct way of making icons for windows system tray?..screenshots: https://imgur.com/zsibwn9..icon: https://imgur.com/vsh4zo8\\n\"\n", "Label: 0\n", "Question: b'\"is there a way to check a variable that exists in a different script than the original one? i\\'m trying to check if a variable, which was previously set to true in 2.py in 1.py, as 1.py is only supposed to continue if the variable is true...2.py..import os..completed = false..#some stuff here..completed = true...1.py..import 2 ..if completed == true. #do things...however i get a syntax error at ..if completed == true\"\\n'\n", "Label: 3\n", "Question: b'\"blank control flow i made a number which asks for 2 numbers with blank and responds with the corresponding message for the case. how come it doesnt work for the second number ? .regardless what i enter for the second number , i am getting the message \"\"your number is in the range 0-10\"\"...using system;.using system.collections.generic;.using system.linq;.using system.text;..namespace consoleapplication1.{. class program. {. static void main(string[] args). {. string myinput; // declaring the type of the variables. int myint;.. string number1;. int number;... console.writeline(\"\"enter a number\"\");. myinput = console.readline(); //muyinput is a string which is entry input. myint = int32.parse(myinput); // myint converts the string into an integer.. if (myint > 0). console.writeline(\"\"your number {0} is greater than zero.\"\", myint);. else if (myint < 0). console.writeline(\"\"your number {0} is less than zero.\"\", myint);. else. console.writeline(\"\"your number {0} is equal zero.\"\", myint);.. console.writeline(\"\"enter another number\"\");. number1 = console.readline(); . number = int32.parse(myinput); .. if (number < 0 || number == 0). console.writeline(\"\"your number {0} is less than zero or equal zero.\"\", number);. else if (number > 0 && number <= 10). console.writeline(\"\"your number {0} is in the range from 0 to 10.\"\", number);. else. console.writeline(\"\"your number {0} is greater than 10.\"\", number);.. console.writeline(\"\"enter another number\"\");.. }. } .}\"\\n'\n", "Label: 0\n", "Question: b'\"credentials cannot be used for ntlm authentication i am getting org.apache.commons.httpclient.auth.invalidcredentialsexception: credentials cannot be used for ntlm authentication: exception in eclipse..whether it is possible mention eclipse to take system proxy settings directly?..public class httpgetproxy {. private static final string proxy_host = \"\"proxy.****.com\"\";. private static final int proxy_port = 6050;.. public static void main(string[] args) {. httpclient client = new httpclient();. httpmethod method = new getmethod(\"\"https://kodeblank.org\"\");.. hostconfiguration config = client.gethostconfiguration();. config.setproxy(proxy_host, proxy_port);.. string username = \"\"*****\"\";. string password = \"\"*****\"\";. credentials credentials = new usernamepasswordcredentials(username, password);. authscope authscope = new authscope(proxy_host, proxy_port);.. client.getstate().setproxycredentials(authscope, credentials);.. try {. client.executemethod(method);.. if (method.getstatuscode() == httpstatus.sc_ok) {. string response = method.getresponsebodyasstring();. system.out.println(\"\"response = \"\" + response);. }. } catch (ioexception e) {. e.printstacktrace();. } finally {. method.releaseconnection();. }. }.}...exception:... dec 08, 2017 1:41:39 pm . org.apache.commons.httpclient.auth.authchallengeprocessor selectauthscheme. info: ntlm authentication scheme selected. dec 08, 2017 1:41:39 pm org.apache.commons.httpclient.httpmethoddirector executeconnect. severe: credentials cannot be used for ntlm authentication: . org.apache.commons.httpclient.usernamepasswordcredentials. org.apache.commons.httpclient.auth.invalidcredentialsexception: credentials . cannot be used for ntlm authentication: . enter code here . org.apache.commons.httpclient.usernamepasswordcredentials. at org.apache.commons.httpclient.auth.ntlmscheme.authenticate(ntlmscheme.blank:332). at org.apache.commons.httpclient.httpmethoddirector.authenticateproxy(httpmethoddirector.blank:320). at org.apache.commons.httpclient.httpmethoddirector.executeconnect(httpmethoddirector.blank:491). at org.apache.commons.httpclient.httpmethoddirector.executewithretry(httpmethoddirector.blank:391). at org.apache.commons.httpclient.httpmethoddirector.executemethod(httpmethoddirector.blank:171). at org.apache.commons.httpclient.httpclient.executemethod(httpclient.blank:397). at org.apache.commons.httpclient.httpclient.executemethod(httpclient.blank:323). at httpgetproxy.main(httpgetproxy.blank:31). dec 08, 2017 1:41:39 pm org.apache.commons.httpclient.httpmethoddirector processproxyauthchallenge. info: failure authenticating with ntlm @proxy.****.com:6050\"\\n'\n", "Label: 1\n" ] } ], "source": [ "for text_batch, label_batch in raw_train_ds.take(1):\n", " for i in range(10):\n", " print(\"Question: \", text_batch.numpy()[i])\n", " print(\"Label:\", label_batch.numpy()[i])" ] }, { "cell_type": "markdown", "metadata": { "id": "jCZGl4Q5l2sS" }, "source": [ "The labels are `0`, `1`, `2` or `3`. To check which of these correspond to which string label, you can inspect the `class_names` property on the dataset:\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:27.568256Z", "iopub.status.busy": "2024-08-16T07:38:27.567689Z", "iopub.status.idle": "2024-08-16T07:38:27.571697Z", "shell.execute_reply": "2024-08-16T07:38:27.571064Z" }, "id": "gIpCS7YjmGkj" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Label 0 corresponds to csharp\n", "Label 1 corresponds to java\n", "Label 2 corresponds to javascript\n", "Label 3 corresponds to python\n" ] } ], "source": [ "for i, label in enumerate(raw_train_ds.class_names):\n", " print(\"Label\", i, \"corresponds to\", label)" ] }, { "cell_type": "markdown", "metadata": { "id": "oUsdn-37qol9" }, "source": [ "Next, you will create a validation and a test set using `tf.keras.utils.text_dataset_from_directory`. You will use the remaining 1,600 reviews from the training set for validation.\n", "\n", "Note: When using the `validation_split` and `subset` arguments of `tf.keras.utils.text_dataset_from_directory`, make sure to either specify a random seed or pass `shuffle=False`, so that the validation and training splits have no overlap." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:27.575055Z", "iopub.status.busy": "2024-08-16T07:38:27.574449Z", "iopub.status.idle": "2024-08-16T07:38:27.856339Z", "shell.execute_reply": "2024-08-16T07:38:27.855568Z" }, "id": "x7m6sCWJQuYt" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 8000 files belonging to 4 classes.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Using 1600 files for validation.\n" ] } ], "source": [ "# Create a validation set.\n", "raw_val_ds = utils.text_dataset_from_directory(\n", " train_dir,\n", " batch_size=batch_size,\n", " validation_split=0.2,\n", " subset='validation',\n", " seed=seed)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:27.859731Z", "iopub.status.busy": "2024-08-16T07:38:27.859427Z", "iopub.status.idle": "2024-08-16T07:38:28.146799Z", "shell.execute_reply": "2024-08-16T07:38:28.145902Z" }, "id": "BXMZc7fMQwKE" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 8000 files belonging to 4 classes.\n" ] } ], "source": [ "test_dir = dataset_dir/'test'\n", "\n", "# Create a test set.\n", "raw_test_ds = utils.text_dataset_from_directory(\n", " test_dir,\n", " batch_size=batch_size)" ] }, { "cell_type": "markdown", "metadata": { "id": "NHuAF8hYfP5Z" }, "source": [ "### Configure the datasets for performance\n", "\n", "These are two important methods you should use when loading data to make sure that I/O does not become blocking.\n", "\n", "- `Dataset.cache` keeps data in memory after it's loaded off-disk. This will ensure the dataset does not become a bottleneck while training your model. If your dataset is too large to fit into memory, you can also use this method to create a performant on-disk cache, which is more efficient to read than many small files.\n", "- `Dataset.prefetch` overlaps data preprocessing and model execution while training.\n", "\n", "You can learn more about both methods, as well as how to cache data to disk in the *Prefetching* section of the [Better performance with the tf.data API](../../guide/data_performance.ipynb) guide." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:28.150372Z", "iopub.status.busy": "2024-08-16T07:38:28.149858Z", "iopub.status.idle": "2024-08-16T07:38:28.157813Z", "shell.execute_reply": "2024-08-16T07:38:28.157236Z" }, "id": "h14NxXx13WRD" }, "outputs": [], "source": [ "raw_train_ds = raw_train_ds.cache().prefetch(buffer_size=tf.data.AUTOTUNE)\n", "raw_val_ds = raw_val_ds.cache().prefetch(buffer_size=tf.data.AUTOTUNE)\n", "raw_test_ds = raw_test_ds.prefetch(buffer_size=tf.data.AUTOTUNE)" ] }, { "cell_type": "markdown", "metadata": { "id": "8Ph91k8n3yPB" }, "source": [ "This first example will keep things simple by integrating the text processing into the model. But you may be able to increase performance by moving the text processing into the dataset pipeline, this is demonstrated in [Example 2](#example2)" ] }, { "cell_type": "markdown", "metadata": { "id": "Xdt-ATrGRGDL" }, "source": [ "### Prepare the dataset for training" ] }, { "cell_type": "markdown", "metadata": { "id": "N6fRti45Rlj8" }, "source": [ "Next, you will standardize, tokenize, and vectorize the data using the `tf.keras.layers.TextVectorization` layer.\n", "\n", "- _Standardization_ refers to preprocessing the text, typically to remove punctuation or HTML elements to simplify the dataset.\n", "- _Tokenization_ refers to splitting strings into tokens (for example, splitting a sentence into individual words by splitting on whitespace).\n", "- _Vectorization_ refers to converting tokens into numbers so they can be fed into a neural network.\n", "\n", "All of these tasks can be accomplished with this layer. (You can learn more about each of these in the `tf.keras.layers.TextVectorization` API docs.)\n", "\n", "Note that:\n", "\n", "- The default standardization converts text to lowercase and removes punctuation (`standardize='lower_and_strip_punctuation'`).\n", "- The default tokenizer splits on whitespace (`split='whitespace'`).\n", "- The default vectorization mode is `'int'` (`output_mode='int'`). This outputs integer indices (one per token). This mode can be used to build models that take word order into account. You can also use other modes—like `'binary'`—to build [bag-of-words](https://developers.google.com/machine-learning/glossary#bag-of-words) models.\n", "\n", "You will build two models to learn more about standardization, tokenization, and vectorization with `TextVectorization`:\n", "\n", "- First, you will use the `'binary'` vectorization mode to build a bag-of-words model.\n", "- Then, you will use the `'int'` mode with a 1D ConvNet." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:28.161649Z", "iopub.status.busy": "2024-08-16T07:38:28.161204Z", "iopub.status.idle": "2024-08-16T07:38:28.174627Z", "shell.execute_reply": "2024-08-16T07:38:28.174046Z" }, "id": "voaC43rZR0jc" }, "outputs": [], "source": [ "VOCAB_SIZE = 10000\n", "\n", "binary_vectorize_layer = TextVectorization(\n", " max_tokens=VOCAB_SIZE,\n", " output_mode='binary')" ] }, { "cell_type": "markdown", "metadata": { "id": "ifDPFxuf2Hfz" }, "source": [ "For the `'int'` mode, in addition to maximum vocabulary size, you need to set an explicit maximum sequence length (`MAX_SEQUENCE_LENGTH`), which will cause the layer to pad or truncate sequences to exactly `output_sequence_length` values:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:28.178053Z", "iopub.status.busy": "2024-08-16T07:38:28.177599Z", "iopub.status.idle": "2024-08-16T07:38:28.184430Z", "shell.execute_reply": "2024-08-16T07:38:28.183861Z" }, "id": "XWsY01Zl2aRe" }, "outputs": [], "source": [ "MAX_SEQUENCE_LENGTH = 250\n", "\n", "int_vectorize_layer = TextVectorization(\n", " max_tokens=VOCAB_SIZE,\n", " output_mode='int',\n", " output_sequence_length=MAX_SEQUENCE_LENGTH)" ] }, { "cell_type": "markdown", "metadata": { "id": "ts6h9b5atD-Y" }, "source": [ "Next, call `TextVectorization.adapt` to fit the state of the preprocessing layer to the dataset. This will cause the model to build an index of strings to integers.\n", "\n", "Note: It's important to only use your training data when calling `TextVectorization.adapt`, as using the test set would leak information." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:28.187871Z", "iopub.status.busy": "2024-08-16T07:38:28.187427Z", "iopub.status.idle": "2024-08-16T07:38:29.603145Z", "shell.execute_reply": "2024-08-16T07:38:29.602288Z" }, "id": "yTXsdDEqSf9e" }, "outputs": [], "source": [ "# Make a text-only dataset (without labels), then call `TextVectorization.adapt`.\n", "train_text = raw_train_ds.map(lambda text, labels: text)\n", "binary_vectorize_layer.adapt(train_text)\n", "int_vectorize_layer.adapt(train_text)" ] }, { "cell_type": "markdown", "metadata": { "id": "XKVO6Jg7Sls0" }, "source": [ "Print the result of using these layers to preprocess data:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:29.607796Z", "iopub.status.busy": "2024-08-16T07:38:29.607132Z", "iopub.status.idle": "2024-08-16T07:38:29.625246Z", "shell.execute_reply": "2024-08-16T07:38:29.624451Z" }, "id": "Vi_sElMiSmXe" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Question: tf.Tensor(b'\"unit testing of setters and getters teacher wanted us to do a comprehensive unit test. for me, this will be the first time that i use junit. i am confused about testing set and get methods. do you think should i test them? if the answer is yes; is this code enough for testing?.. public void testsetandget(){. int a = 10;. class firstclass = new class();. firstclass.setvalue(10);. int value = firstclass.getvalue();. assert.asserttrue(\"\"error\"\", value==a);. }...in my code, i think if there is an error, we can\\'t know that the error is deriving because of setter or getter.\"\\n', shape=(), dtype=string)\n", "Label: tf.Tensor(1, shape=(), dtype=int32)\n" ] } ], "source": [ "# Retrieve a batch (of 32 reviews and labels) from the dataset.\n", "text_batch, label_batch = next(iter(raw_train_ds))\n", "first_question, first_label = text_batch[0], label_batch[0]\n", "print(\"Question:\", first_question)\n", "print(\"Label:\", first_label)" ] }, { "cell_type": "markdown", "metadata": { "id": "a2nTgt9e1KjK" }, "source": [ "The binary vectorization layer returns a multi-hot vector, with a 1 in the location for each token that was in the input string." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:29.629128Z", "iopub.status.busy": "2024-08-16T07:38:29.628578Z", "iopub.status.idle": "2024-08-16T07:38:29.791317Z", "shell.execute_reply": "2024-08-16T07:38:29.790492Z" }, "id": "UGukZoYv2v3v" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'binary' vectorized question: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 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"execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"'binary' vectorized question:\",\n", " list(binary_vectorize_layer(first_question).numpy()))\n", "\n", "plt.plot(binary_vectorize_layer(first_question).numpy())\n", "plt.xlim(0,1000)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:29.795041Z", "iopub.status.busy": "2024-08-16T07:38:29.794455Z", "iopub.status.idle": "2024-08-16T07:38:29.810557Z", "shell.execute_reply": "2024-08-16T07:38:29.809674Z" }, "id": "Lu07FsIw2yH5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'int' vectorized question: [1011 773 9 2456 8 1863 2362 690 1267 4 40 5 1 1011\n", " 196 12 74 13 72 33 2 98 105 14 3 70 9611 3\n", " 34 888 202 773 107 8 41 242 40 58 291 90 3 196\n", " 191 10 2 182 6 668 6 13 30 1187 12 773 22 42\n", " 1 28 5 140 29 5213 15 29 1 28 51 1 1 1\n", " 7 23 30 3 291 10 67 6 32 65 185 166 102 14\n", " 2 65 6 1 193 9 2784 45 2410 0 0 0 0 0\n", " 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\n", " 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\n", " 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\n", " 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\n", " 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\n", " 0 0 0 0 0 0 0 0 0 0 0 0]\n" ] } ], "source": [ "print(\"'int' vectorized question:\",\n", " int_vectorize_layer(first_question).numpy())" ] }, { "cell_type": "markdown", "metadata": { "id": "wgjeF9PdS7tN" }, "source": [ "As shown above, `TextVectorization`'s `'binary'` mode returns an array denoting which tokens exist at least once in the input, while the `'int'` mode replaces each token by an integer, thus preserving their order.\n", "\n", "You can lookup the token (string) that each integer corresponds to by calling `TextVectorization.get_vocabulary` on the layer:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:29.814267Z", "iopub.status.busy": "2024-08-16T07:38:29.813670Z", "iopub.status.idle": "2024-08-16T07:38:29.870188Z", "shell.execute_reply": "2024-08-16T07:38:29.869449Z" }, "id": "WpBnTZilS8wt" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1289 ---> roman\n", "313 ---> source\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Vocabulary size: 10000\n" ] } ], "source": [ "print(\"1289 ---> \", int_vectorize_layer.get_vocabulary()[1289])\n", "print(\"313 ---> \", int_vectorize_layer.get_vocabulary()[313])\n", "print(\"Vocabulary size: {}\".format(len(int_vectorize_layer.get_vocabulary())))" ] }, { "cell_type": "markdown", "metadata": { "id": "NYGb7z_bfpGm" }, "source": [ "### Train the model\n", "\n", "It's time to create your neural network.\n", "\n", "For the `'binary'` vectorized data, define a simple bag-of-words linear model, then configure and train it:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:29.873734Z", "iopub.status.busy": "2024-08-16T07:38:29.873238Z", "iopub.status.idle": "2024-08-16T07:38:30.014685Z", "shell.execute_reply": "2024-08-16T07:38:30.013567Z" }, "id": "L9D0Gngw8Cwr" }, "outputs": [ { "data": { "image/png": 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S0lJcvXoVMpkMS5cudcqy72e83cSmTp0q+rqFpd/DBICePXti586d6NSpk5szdJ3w8HAcPHgQ/fv3N5u2cuVKq58s2/8eJhHh8OHDTsvp3nvisERk4ftb7MHmzn1iwYIFWLBggdvWd7/g7ca82X37CZMxxhhzJi6YjDHGmARcMBljjDEJuGAyxhhjEnDBZIwxxiSwOkr2fniaP2NMGj7e3Ye39b3LasHMzc11Zx6MMQ8oKirCP/7xDz7e3cD0E3jtf6mIeafMzEyL7VYL5qRJk1yWDGPMe/zjH//g490Ntm7dCoDPrfcCawWT72EyxhhjEnDBZIwxxiTggskYY4xJwAWTMcYYk4AL5l0KDAw0+8WL1atXezoth9xPfWHsfrBx40bR8Wjp570A4OLFi3j66adRV1eH6upq0TyDBg1CU1OT2Twd42QyGQYPHuzqLjnFokWLrI7sbv/zXjKZDMOGDXPaerlg3qX6+nr8/PPPAIDU1FQQ0T37awv3U18Yu598+OGHVn/eq6SkBIMHD0ZSUhI6deqELl26gIhQXFwsTJ83b57ZfKa4oqIihIaGgohw5MgRl/fFGWbOnInFixfjr3/9q9m09j/v5ePj49T1csF8wAQGBiI2NtbTaTBmlaf3UU+v3x51dXWYMGECnnnmGcyZM8dsulKpRGhoKNauXYvNmzd7IEPX6NOnD3bs2IEVK1Zgy5YtblsvF0zGGLtHrVq1CpWVlVi2bJnF6f7+/ti0aRPkcjmysrJQVlbm5gxdR6fTIT09HS+99BJaWlrcsk4umIwxdg8iIqxfvx5Dhw5FZGSk1bjk5GQsXboURqMRGRkZFu9n3qsmTpyIK1euYNeuXW5ZHxdMF8nLyxPdeP7111+RmZmJ4OBghIaGIiUlBefPnxfiV69eLcR2794dxcXFSExMRFBQEAICApCQkIBDhw4J8cuXLxfi218+2rt3r9DepUsXs+U3NDTg0KFDQoyvr9WHPdnU0tKC3NxcjBkzBuHh4VCpVIiOjsY777yDtrY2AEBtba3ZwILly5cL87dvT09PF5at1+uRnZ2Nnj17ws/PD127dkVaWhpKSkqsbuMzZ85g0qRJCA0NFdqqq6sd7h+zrqamBvPnz0efPn3g5+eHkJAQjBs3DgcOHBBinL2P3ovHiCuVlpaiqqoKOp3OZuyrr76KpKQkHD16FHPnzpW0fCnvsb3nORMpx7cUAwcOBAB8/fXXds3nMOogNzeXLDSzO/j5558JAKWmpppNS01NFaYVFhZSfX097du3j1QqFQ0ZMsQsXqfTkVqtpuHDhwvxxcXFNGDAAPLz86OCggJRvFqtpqeeespsOTExMRQaGmrWbi1eSl86ys/PJwD0+uuv0/Xr10mv19O7775LcrmcFixYIIpNTk4muVxO586dM1vO8OHDadOmTcL/y8vL6aGHHqKwsDDatWsXGY1GOn78OMXHx5O/vz8VFhaK5jdt4/j4eDpw4AA1NDTQ4cOHycfHh/R6vc1+PMgcOd4rKiqoV69eFBYWRvn5+WQwGOjMmTOUlpZGMpmMPv74Y1G8s/dRTx8jCQkJ1LlzZyoqKrIaY0l6ejqlp6fbNc+GDRsIAH344YdWp73++usW5y0uLiaNRiP8X6/Xk1arJQC0ceNGob2oqMhsO9j7HttznrP3+L4Tg8FAACguLs7idB8fHxo6dKjk5ZkAoNzcXPP2jg1cMO0npWDm5+eL2tPT0wmA2Qldp9MRAPr5559F7UePHiUApNPpRO2eLpgjR440a586dSopFAoyGAxC29dff00AaPbs2aLYgwcPUlRUFN26dUtomzZtGgEQFVGi2wexUqmkmJgYUbtpG+/evdtmzkzMkeN9+vTpBIA2b94sam9qaqLIyEhSqVRUWVkptLuiYHryGImPj6eQkBC7TuxEzi+Yq1atIgC0Zs0ai/N2LJhEt4ujQqEgtVpNp06dEto6bgd732N7znP2Ht+2yGQyevjhhy1Oc3bB5EuybjJkyBDR/7VaLQCgvLzcLFatVguXGkyio6MRGRmJ0tJSVFRUuC5RO6SkpIguz5jodDo0NzfjxIkTQltSUhKio6Px2WefoaamRmh/8803MXfuXCgUCqEtLy8PcrkcKSkpouWGh4ejf//++PHHH3HlyhWz9T7xxBPO6BazYceOHQCA8ePHi9qVSiUSExPR2Njo8ktknjxGCgoKcP36dQwfPtxl65DCdC+y/bFjy7Bhw7B69Wo0NDQgIyMDjY2NFuMcfY+lnOccPb6t8fX1tdoPZ+OC6SYajUb0fz8/PwAQ7vW1FxwcbHEZ3bp1AwBcu3bNydk5xmAwYNmyZYiOjkZISIhwH+Pll18GANy4cUMUP2/ePNy4cQMffPABAKCsrAzfffcd/ud//keIuXnzJgwGA9ra2qDRaMzuf/70008AgLNnz5rlo1arXdVV9v+Z3h9/f38EBQWZTQ8LCwMAVFZWujSPe+UYcSV/f38AQHNzs13zZWdnIzMzE8ePH7f4VZS7eY9tnefu5vi2pqWlBSqVSnL83eCC6YVqampw+6qAmOkkYDopAIBcLsetW7fMYmtray0u25k/XjthwgTk5ORg5syZKCsrQ1tbG4hI+N2/jn2YMmUKwsLC8P777+PmzZt46623MG3aNISEhAgxSqUSwcHB8PX1RXNzs/AF5I6vhIQEp/WDSadUKqHRaNDU1ASj0Wg2vaqqCsDtTwsmrthH75VjxJUiIiIA3P7D1V7r169Hv3798Mknn2DDhg2iaY68x1I5+/iuq6sDEQnbwtW4YHqhpqYm4SkdJseOHUN5eTl0Op1o54iIiMDVq1dFsZWVlbh06ZLFZQcEBIhOHv369cO6devsys/X1xcnTpzAoUOHEB4ejuzsbHTt2lU40Vi7PKJUKjF79mxcu3YNb731FjZt2oQXX3zRLC4tLQ0tLS2iEY8mK1euRI8ePdz2vStmbuLEiQBgNpT/5s2b2L9/P1QqFZKTk4V2V+yj3n6MuMPjjz8OAHZdvjQJDAzE9u3boVarhSs+7dn7HtvDmce36X01bQtX44LphTQaDV555RUUFRWhoaEBR44cwdSpU+Hn54d33nlHFJuUlITy8nK8//77qK+vx/nz5/Hiiy+K/sJu73e/+x3Kyspw+fJlFBUV4cKFC4iLi7M7Rx8fH4wcORKVlZV48803UV1djcbGRhw4cAAfffSR1flmz54NlUqFpUuXYvTo0Xj44YfNYt544w306dMHf/rTn7Bnzx4YDAZcv34da9euxWuvvYbVq1d77VD/B8Ebb7yBXr16Yd68edi5cyeMRiPKysrwxz/+ERUVFXjnnXeEy3aAa/ZRTx4jo0aNQmhoKA4fPuzoJnQKnU6Hbt26obS01KH5+/fvj7Vr11qcZu97bA97j++pU6dCJpPhl19+MVuW6WsoSUlJDuVit46jgHiUrH3UajUBEL3efPNNKioqMmtfsmQJEZFZ+/jx44Xl6XQ6ioqKopMnT1JycjIFBQWRSqWi+Ph4OnjwoNn6a2tracaMGRQREUEqlYpiY2OpuLiYYmJihOUvXLhQiD99+jTFxcWRWq0mrVYrGmFnqS/WXqdOnSK9Xk9ZWVmk1WpJoVBQWFgYTZ8+nRYtWiTEWRrxNnPmTAJA//nPf6xu15qaGpo/fz717t2bFAoFde3alZKSkmjfvn1CjKVtzPuufRw93qurq2nevHnUq1cvUigUpNFoKDk5mfbv328W68x9lMizxwgRUVxcnFeMkiUieuWVV8jX15euXr0qtOn1erNj4k4jT2fNmmVxtLCU99jR85yU49tk1KhRFBgYSC0tLWbTMjIyzEbZt8dfK7nPmU4G97NPPvnE7qHjzDXuxeP9Xj1GXFEwa2trKSoqirKyspyRotf57bffSKVS0YwZM8ymlZSUkEwmM/vqS3v8tRJ2z/voo48wf/58T6fB2D1Po9EgPz8f27Ztw5o1azydjlMREbKzs9GpUyfk5OSIpl24cAFpaWlYvHgxJk+e7LacuGAyl1u/fj0mTpyI+vp6fPTRR/jtt98wadIkT6fF2D1j1qxZVn8Pc9CgQThy5Aj27NmDuro6D2TnGlVVVbhw4QL2799vNiJ37dq1WLFiBVasWGE2X/vfw2xtbXVqTlwwvYTpOZalpaW4evUqZDIZli5d6um0nCYvLw8hISH48MMP8cUXX/CgHWa3+/0YsWTq1Kmir1tY+j1MAOjZsyd27tyJTp06uTlD1wkPD8fBgwfRv39/s2krV660+smy/e9hEpFTB2fxWctLLFiw4L79seYZM2ZgxowZnk6D3ePu52OE3Rv4EyZjjDEmARdMxhhjTAIumIwxxpgEXDAZY4wxCawO+tmyZYs782CMeUBRUREAPt7dwfTMV97W9y6rBTMzM9OdeTDGPIiPd/fhbX3vkv3/xwAxxryE6aEO/EmEMe/C9zAZY4wxCbhgMsYYYxJwwWSMMcYk4ILJGGOMScAFkzHGGJOACyZjjDEmARdMxhhjTAIumIwxxpgEXDAZY4wxCbhgMsYYYxJwwWSMMcYk4ILJGGOMScAFkzHGGJOACyZjjDEmARdMxhhjTAIumIwxxpgEXDAZY4wxCbhgMsYYYxJwwWSMMcYk4ILJGGOMScAFkzHGGJOACyZjjDEmARdMxhhjTAIumIwxxpgEXDAZY4wxCbhgMsYYYxJwwWSMMcYk4ILJGGOMScAFkzHGGJOACyZjjDEmARdMxhhjTAIumIwxxpgEXDAZY4wxCXw9nQBjD7Lvv/8eRUVForbTp08DAFauXClqHz58OEaMGOG23BhjYjIiIk8nwdiDav/+/Rg9ejQUCgXkcssXfNra2tDc3Ixvv/0WiYmJbs6QMWbCBZMxD2pra0N4eDj0ev0d47p06YLKykr4+Pi4KTPGWEd8D5MxD5LL5ZgyZQr8/Pysxvj5+WHq1KlcLBnzMC6YjHnYs88+i1u3blmdfuvWLTz77LNuzIgxZglfkmXMC/Ts2RMXL160OE2r1eLixYuQyWRuzoox1h5/wmTMCzz33HNQKBRm7QqFAtOnT+diyZgX4E+YjHmB06dP47/+678sTjt+/Dj69+/v5owYYx3xJ0zGvMCjjz6K/v37m32SfOyxx7hYMuYluGAy5iWef/550UhYhUKBadOmeTAjxlh7fEmWMS9x+fJlPPTQQzAdkjKZDBcuXEDPnj09mxhjDAB/wmTMa2i1WgwdOhRyuRxyuRxDhw7lYsmYF+GCyZgXee655yCTySCXy/Hcc895Oh3GWDt8SZYxL1JdXY3w8HAAQHl5Obp16+bhjBhjJjYLJn//izHG2IPA1udHST/vNW/ePAwfPtwpCTF2r3n77bcBAH/5y1/csr7vv/8eMpkMcXFxblmfN8nMzOTzDXO7oqIi/OMf/7AZJ+kTZm5uLiZNmuS05Bi7l2RkZAAAtm7d6pb1GY1GAEBQUJBb1udN+HzDPGHLli3IzMx0zidMxpj7PIiFkrF7AY+SZYwxxiTggskYY4xJwAWTMcYYk4ALJmOMuUF1dTVkMpnwGjRoEJqammzGyWQyDB482AMZs464YD7A6uvr8cgjjyAlJcXTqXhVLq70oPSTmevSpQuICMXFxQCAkpISzJs3z2pcUVERQkNDQUQ4cuSIu9NlFrikYAYGBiI2NtYVi/bK9TqTO/tARGhra0NbW5tb1nenvrk7F0+5l/p5PxxP3kqpVCI0NBRr167F5s2bPZ0Ok4i/VvIACwoKwvnz5z2dBgDvysWVHpR+sjvz9/fHpk2b8Pvf/x5ZWVmIiYlB3759PZ0Ws4EvyTLGmAckJydj6dKlMBqNyMjIsHg/k3kXpxbM1atXQyaToaGhAYcOHRJuWPv6ij/I6vV6ZGdno2fPnvDz80PXrl2RlpaGkpISISY2NlZ003vq1KkAgNGjR4vaa2trJa/XmtraWrOb7MuXLwcAtLS0iNrT09Pt6odJTU0N5s+fjz59+kCpVKJ79+4YPXo0PvvsMzQ2NkruQ73PuQEAACAASURBVPvl+Pn5ISQkBOPGjcOBAweEmLy8PFHOZ86cwaRJkxAaGiq0rV+/XhTT/mANDg422x6ml1wux5UrV4Rtk5ubizFjxiA8PBwqlQrR0dF45513RJccbfWtY74dTxyO9PnXX39FZmYmgoODERoaipSUFI9/srPWT3tzN21PmUyG7t27o7i4GImJiQgKCkJAQAASEhJw6NAhIX758uVCfPtLrHv37hXau3TpYrZ8R48nJt2rr76KpKQkHD16FHPnzpU0jyuPB3vOaQ8ksgEA5ebm2goTUavV9NRTT1mcVl5eTg899BCFhYXRrl27yGg00vHjxyk+Pp78/f2psLBQiC0pKSG1Wk06nY7q6+uJiKipqYmGDh1Kmzdvtmu9UowdO5bkcjmdO3fObNrw4cPp888/d6gfFRUV1KtXLwoPD6f8/Hyqq6ujyspKysnJIQD09ttvS+qDaTlhYWGUn59PBoOBzpw5Q2lpaSSTyejjjz8WxaemphIAio+PpwMHDlBDQwMdPnyYfHx8SK/Xi2IaGxuF+TQaDRmNRtGyXnvtNQJAr7/+utCWn58vtF2/fp30ej29++67JJfLacGCBWb523p/LOXiaJ9TU1OpsLCQ6uvrad++faRSqWjIkCFW130n6enplJ6e7tC8lljqZ/t2qbnrdDpSq9U0fPhwIb64uJgGDBhAfn5+VFBQIIq3tv1jYmIoNDTUrN3W+5WQkECdO3emoqIiqV23yZHzzb2muLiYNBqN8H+9Xk9arZYA0MaNG4X2oqIis/fFlceDPee0+01ubi5JKIfk9oI5bdo0AkCbNm0StVdUVJBSqaSYmBhR+5YtWwgApaWlUVtbG02bNo1eeeUVu9crxbfffksAaPbs2aL2gwcPUo8ePai5udmhfkyfPt3qdhw7dqzkgmlaTsc/FpqamigyMpJUKhVVVlYK7aaDZffu3Vb7LKVg5ubmkkwmo+nTp4vmzc/Pp5EjR5otc+rUqaRQKMhgMIjaHSmYjvY5Pz9fFJ+enk4AhD8U7OHugik1d51ORwDo559/FrUfPXqUAJBOpxO1O7tgxsfHU0hIiFNPpA9iwSS6XRwVCgWp1Wo6deqU0NbxfXHl8WDvufl+IrVguv0eZl5eHuRyudmw+vDwcPTv3x8//vijcMkPuP3g6yVLluDLL79EbGwsampqkJOT45LcEhMTMWjQIHz22WeoqakR2t98803MmzdPdEnKnn7s2LEDADBu3Dizde7Zs8fi0HJLTMsZP368qF2pVCIxMRGNjY34+uuvzeZ74oknJC3fpLa2FoGBgQCAH374AdOmTcOIESOwdu1aUVxKSoroMpCJTqdDc3MzTpw4Ydd6LXG0z0OGDBH9X6vVArj9G5Pezp7c1Wo1Bg4cKGqLjo5GZGQkSktLUVFR4bI8CwoKcP36df5lEScYNmwYVq9ejYaGBmRkZKCxsdFinCuPB3vPzQ8itxbMmzdvwmAwoK2tDRqNxuz+2E8//QQAOHv2rGi+nJwcDB06FIWFhcjIyIBc7rq0X3rpJdy4cQMffPABAKCsrAzff/89ZsyY4VA/TLH+/v539VBtW8sJCwsDAFRWVppNU6vVDq3z0qVLSE1NhVarxZdffgk/Pz/RdIPBgGXLliE6OhohISFC/19++WUAwI0bNxxar8nd9Fmj0Yj+b8r9Xvg6hz25BwcHW1yG6Yenr1275uTsmKtkZ2cjMzMTx48fx5w5c8ymu/J4cPTc/KBxSeWx9qPTSqUSwcHB8PX1RXNzM+j2JWGzV0JCgmi+goICGAwGREdHY/bs2SgtLbVrvfbIzMyEVqvF+++/j5s3b+Ktt97CzJkzRTuoPf1QKpXQaDRoamoSfrbpTu607e60nKqqKgC3/xp0BqPRiJSUFDQ3N2Pnzp3o3LmzWcyECROQk5ODmTNnoqysDG1tbSAi4fcjqcNP5dj7/ri7z/eimpoaiz9JZCqUpsIJAHK5HLdu3TKLra2ttbhs/vF491u/fj369euHTz75BBs2bBBNc+Xx4Oi5+UHjkoIZEBAgOjD79euHdevWAQDS0tLQ0tIiGsVnsnLlSvTo0QMtLS1C2y+//IIXXngB27dvx1dffQWVSoXU1FTo9Xq71iuVr68vXnzxRVy7dg1vvfUWvvjiC2RnZ5vF2dOPiRMnAgB2795tFjto0CDRDxPfqQ+m5ezatUu0jJs3b2L//v1QqVRITk62q7+WtLa2YvLkyTh9+jS2b98u+n5Yeno68vLy0NraikOHDiE8PBzZ2dno2rWrcIK1djnJkffHXX2+VzU1NQlPjjE5duwYysvLodPpEBERIbRHRETg6tWrotjKykpcunTJ4rKdcTwx+wQGBmL79u1Qq9XCVa72XHk82HtufiDZuskJB27Cjx07ljQaDV26dIkKCwvJ19eXTp48SUREVVVV1KdPH+rduzft3r2bamtrqaamhj766CMKCAgQrctoNNKAAQPo3//+t9BWUFBACoWCRowYQbdu3ZK8XnvU1dWRRqMhmUxGzz//vMUYe/phGtkWERFBO3fupLq6Orp8+TLNmjWLwsLC6OLFi5L60HGEXF1dnWiE3Lp160Q5WhtcYitm7ty5BIA+/fRTs/hnnnmGduzYQUREo0aNIgC0atUq0uv1dOPGDfruu++oR48eBID27dsnmtfW+yNllKyjfV64cKHFATJSuHvQj9TcdTodaTQaSkxMlDRKds6cOQSA3nvvPTIajXTu3DmaNGkSRUVFWRz0Y+v94lGyjrE06KejjRs3EgCbo2SdeTzYc06733h0lOzp06cpLi6O1Go1abVaWrNmjWh6TU0NzZ8/n3r37k0KhYK6du1KSUlJohPsn//8ZwIgvI4dO0Z6vV7UBoBycnIkr9ceL7/8MgGg0tJSqzFS+mFSXV1N8+bNo169epFCoaCIiAiaPHkylZWVieJs9aHjcjQaDSUnJ9P+/fuFmKKiIrPt1HFn2LFjh9n0KVOm0JEjRyzO2/5lKph6vZ6ysrJIq9WSQqGgsLAwmj59Oi1atEiIbT+yzlrfrOVyt31esmQJEZFZ+/jx462+p5Y4q2Ba66ejuet0OoqKiqKTJ09ScnIyBQUFkUqlovj4eDp48KDZ+mtra2nGjBkUERFBKpWKYmNjqbi4mGJiYoTlL1y4UIi3tS/GxcXxKFk7WDp/3Wnk6axZsyz+IePK48Gec9r9RGrBlBFZuAHSjkwmQ25uLiZNmnSnMMbuWxkZGQCArVu3ejgTsYEDB6K6uvq+GrnI5xvmCVu2bEFmZqbF8QDt8aPxGGOMMQm4YDLGGGMSPBAF09pzUdu//va3v3k6TcYkMT3rtbS0FFevXoVMJsPSpUs9nRZj970H4mnKtq5LM3YvWbBgARYsWODpNBh74DwQnzAZY4yxu8UFkzHGGJOACyZjjDEmARdMxhhjTAJJg36KiopcnQdjXsv0YIAtW7Z4OJMHA59vmLtJ3eckPemHMcYYu9/Z+kaFpE+Y/Kgq9iDz1kfj3Y/40XjME0yPxrOF72EyxhhjEnDBZIwxxiTggskYY4xJwAWTMcYYk4ALJmOMMSaBywrmwIEDJf1KiOm1fPlyV6ViUUlJiVkODz/8sFlcbW2tWRxjzPtcvHgRTz/9NOrq6lBdXS06ZgcNGoSmpiazeTrGyWQyDB482APZu8bu3bvRt29f+Pra/kJESUkJxo8fj+DgYAQFBWH06NE4dOiQ18cvWrQIubm5NvvnDC79hLl161YQkfDKysoCAOzZs0fULmU4r7MNHDgQRIQXXngBALBkyRKcO3fOLC44OBhEhKeffhorV668L375pL6+Ho888ghSUlI8nYpX5cLuXSUlJRg8eDCSkpLQqVMndOnSBUSE4uJiYfq8efPM5jPFFRUVITQ0FESEI0eOuDt9pzt//jyefvppLF68GFVVVTbjf/jhBzz55JMICgrCqVOn8Msvv6B3794YOXIkvvnmG6+OnzlzJhYvXoy//vWvdmwhB5ENACg3N9dWmBmdTkdbt24VtWVlZREA2rNnj6g9MzOTcnJy7F6HVGq1mp566imL0w4ePEgASKvVUmtrq8WYqqoq8vf3p/Lyco/k6Gx1dXXUu3dvGjdunFvWd6e+uTsXR6Snp1N6erqn0xBx5/7izvU7cr4xGAzUvXt3ysrKMptWXFxMSqWSQkNDCQB9/vnnFpdRVFREoaGhDuXsjZ599ll64403qLm5maKiosjHx8dqbGtrK/Xv358iIiLoxo0bQntLSwv169ePtFotNTU1eW08EVFJSQnJZDKHahURUW5uLkkoh+SyT5glJSVIT0+XFPvFF1947Adwn3rqKTzyyCO4fPkyvv32W4sx//rXvzB69GhERES4OTvXCAoKwvnz57F7925Pp+JVubB706pVq1BZWYlly5ZZnO7v749NmzZBLpcjKysLZWVlbs7Q/f75z39i0aJFki7Ffv/99zhx4gTS09OhUqmEdh8fHzz77LO4fPkydu7c6bXxAKDT6ZCeno6XXnoJLS0tEreS/XjQD4Dp06cDAD799FOL0z/99FP893//txszYoxJQURYv349hg4disjISKtxycnJWLp0KYxGIzIyMizez7yftC80tnz33XcAYPHeralt//79XhtvMnHiRFy5cgW7du0ym+YsXlUw9Xo9srOz0bNnT/j5+aFr165IS0tDSUmJEBMbGyu6QT916lQAwOjRo0XttbW1WL16NWQyGRoaGnDo0CFhWse/up5//nnI5XLk5eWhtrZWNO2HH37AtWvXMGHCBLvyNKmpqcH8+fPRp08fKJVKdO/eHaNHj8Znn32GxsZGyTm2X46fnx9CQkIwbtw4HDhwQIjJy8sTbYMzZ85g0qRJCA0NFdrWr18viml/4ggODrY6KEsulwsPIW9paUFubi7GjBmD8PBwqFQqREdH45133kFbW5uwPFt965hvx5OYI33+9ddfkZmZieDgYISGhiIlJQXnz5+3tLu5nJT8ly9fLuQeGxsrtO/du1do79Kli9Bua5uapstkMnTv3h3FxcVITExEUFAQAgICkJCQIBo44ez1u1tpaSmqqqqg0+lsxr766qtISkrC0aNHMXfuXEnLd+U+aM95xJVOnz4NAOjevbvZtKioKAAQfSr3tniTgQMHAgC+/vprs2lOY+uaLRy8h2mJtXuYRETl5eX00EMPUVhYGO3atYuMRiMdP36c4uPjyd/fnwoLC4XYkpISUqvVpNPpqL6+noiImpqaaOjQobR582azZUu535KUlEQA6IMPPjDLed68eQ7lWVFRQb169aLw8HDKz8+nuro6qqyspJycHAJAb7/9tqQcTcsJCwuj/Px8MhgMdObMGUpLSyOZTEYff/yxKD41NZUAUHx8PB04cIAaGhro8OHD5OPjQ3q9XhTT2NgozKfRaMhoNIqW9dprrxEAev3114W2/Px8oe369euk1+vp3XffJblcTgsWLDDL39b2t5SLo31OTU2lwsJCqq+vp3379pFKpaIhQ4ZYXbcUjtzDtDd/a9soJibG4r01W9tUp9ORWq2m4cOHC9ujuLiYBgwYQH5+flRQUODS9SckJFDnzp2pqKjIaowl9p5vNmzYYLZ/tldcXEwajUb4v16vJ61WSwBo48aNQrule5iu3AftOY/cLVv3MMeMGUMA6PDhw2bTzp49SwDod7/7ndfGmxgMBgJAcXFxVvtqjdR7mF5TMKdNm0YAaNOmTaL2iooKUiqVFBMTI2rfsmULAaC0tDRqa2ujadOm0SuvvGJxvVIK5ubNmwmAaMe+ceMGaTQaOnr0qEN5Tp8+3er2Gzt2rOSCaVpOxz8GmpqaKDIyklQqFVVWVgrtpgN39+7dVvsrpWDm5uaSTCaj6dOni+bNz8+nkSNHmi1z6tSppFAoyGAwiNodKZiO9jk/P18Un56eTgCEPxQc4UjBtDd/VxRMAPTzzz+L2o8ePUoASKfTSVqeo+uPj4+nkJAQu0/89p5vVq1aRQBozZo1Fqd3LJhEt4ujQqEgtVpNp06dEto69tOV+6C957u7cTcFs6ysjACI8vG2+PZkMhk9/PDDVvtqjccH/dgrLy8Pcrnc7OsF4eHh6N+/P3788UfhkiBw+xcklixZgi+//BKxsbGoqalBTk6Ow+v/wx/+gODgYBQXF+PEiRMAgC+//BIPP/wwoqOjHcpzx44dAIBx48aZrW/Pnj0Wh7lbYlrO+PHjRe1KpRKJiYlobGy0eBniiSeekLR8k9raWgQGBgK4fSl62rRpGDFiBNauXSuKS0lJEV2SMtHpdGhubha2391wtM9DhgwR/V+r1QIAysvL7zoneziavzOp1WrhMpVJdHQ0IiMjUVpaioqKCpetu6CgANevX8fw4cNdtg4AwmV8hUIheZ5hw4Zh9erVaGhoQEZGBhobGy3GuXIftPd850rBwcEAgIaGBrNppjZTjDfGt+fr62v1/XQGryiYN2/ehMFgQFtbGzQajdn9s59++gkAcPbsWdF8OTk5GDp0KAoLC5GRkQG53PHu+Pv7Y/LkyQCATz75RPj3T3/6k0N5mmL9/f0RFBTkcF62lhMWFgYAqKysNJumVqsdWuelS5eQmpoKrVaLL7/8En5+fqLpBoMBy5YtQ3R0NEJCQoT+v/zyywCAGzduOLRek7vps0ajEf3flHv7e6uudjf5O5O1k0q3bt0AANeuXXPp+t3B398fANDc3GzXfNnZ2cjMzMTx48cxZ84cs+mu3AcdPd+5yqOPPgoAFgv01atXAQB9+/b12vj2Wlpa7BrwZC+vKJhKpRLBwcHw9fVFc3Oz6KEG7V8JCQmi+QoKCmAwGBAdHY3Zs2ejtLTU4vKlPp3HNBJ248aNOHfuHIqKivDss886lKdSqYRGo0FTUxOMRqPNdVvL0dZyTF9KDg8Pl9RHW4xGI1JSUtDc3IydO3eic+fOZjETJkxATk4OZs6cibKyMrS1tYGI8PbbbwMw/xFWe5+O5O4+O5sj+cvlcty6dcsstuMgNBMp27SmpsbigzZMhdJUOF21fncwfdXLYDDYPe/69evRr18/fPLJJ9iwYYNomiv3QUfPd65iWs+PP/5oNs3UlpiY6LXxJnV1dSAil379zysKJgCkpaWhpaXF4qOPVq5ciR49eoi+X/PLL7/ghRdewPbt2/HVV19BpVIhNTUVer3ebP6AgADRyaBfv35Yt26dWdwTTzyBxx57DNeuXcOUKVOQmpqKkJAQh/OcOHEiAFj8juGgQYPwl7/8RVKOpuV0HC598+ZN7N+/HyqVCsnJyWbrsFdraysmT56M06dPY/v27aK/4tLT05GXl4fW1lYcOnQI4eHhyM7ORteuXYWTp7VLIVK3f3vu6rOr2Jt/RESE8NezSWVlJS5dumRx+VK2aVNTk/CkG5Njx46hvLwcOp1OdGJxxfrd4fHHHwdg+dOILYGBgdi+fTvUajU++OADs+mu3AftPd+5Unx8PB577DFs27ZNNFK9tbUVX3zxBbRareiytLfFm5j2X9M+4RK2bnLCTYN+qqqqqE+fPtS7d2/avXs31dbWUk1NDX300UcUEBAgysFoNNKAAQPo3//+t9BWUFBACoWCRowYQbdu3RIte+zYsaTRaOjSpUtUWFhIvr6+dPLkSYs5mgYRAKCvv/76rvI0jbKLiIignTt3Ul1dHV2+fJlmzZpFYWFhdPHiRUk5dhytV1dXJxqtt27dOlGOlgbRdGQpZu7cuQSAPv30U7P4Z555hnbs2EFERKNGjSIAtGrVKtLr9XTjxg367rvvqEePHgSA9u3bZ9f2lzJK1tE+L1y40OLgF3s4Y5SsrfznzJlDAOi9994jo9FI586do0mTJlFUVJTFQTe2tqlOpyONRkOJiYmSRsk6e/3uGiXb1tZG3bp1szoAydKgn442btxIAGyOknXmPmjPeYSIaMqUKQSALly4YHObdGRr0A/R7UFP/v7+NHnyZKqoqKDq6mrKysoiX19f2rt3r9fHExF9/vnnBEA4T9nDq0bJfvrpp0IRav/q+BWGmpoamj9/PvXu3ZsUCgV17dqVkpKSRCfgP//5z6JlHDt2jPR6vdmy2z9q7/Tp0xQXF0dqtZq0Wq3VEXVEtw8SX1/fOz4qT0qeJtXV1TRv3jzq1asXKRQKioiIoMmTJ1NZWZkozlaOHZej0WgoOTmZ9u/fL8QUFRVZ3M7t7dixw2z6lClT6MiRIxbnbf8y7Yh6vZ6ysrJIq9WSQqGgsLAwmj59Oi1atEiIbT+KzVrfrOVyt31esmQJEZFZ+/jx462+73fi6KPxpORvUltbSzNmzKCIiAhSqVQUGxtLxcXFFBMTI+S/cOFCId7W/qLT6SgqKopOnjxJycnJFBQURCqViuLj4+ngwYMuX39cXJxbRskSEb3yyivk6+tLV69eFdosnRPuNPJ01qxZFv8wcOU+aM95ZNSoURQYGEgtLS2Stonpq1+WXh2/DmPy008/0bhx46hTp04UGBhIo0aNsriveGt8RkYGRUVFmX1gksKrCiZj9zJvfJasLaaCea9x5HxTW1tLUVFRFp8lez/47bffSKVS0YwZMzyditcyPUvW0vfwpbjnvlbCGGOO0Gg0yM/Px7Zt27BmzRpPp+NURITs7Gx06tTprr42dz+7cOEC0tLSsHjxYuGbDq7CBZMxds8bNGgQjhw5gj179qCurs7T6ThNVVUVLly4gP3793vtqHBPW7t2LVasWIEVK1a4fF1cMBm7j5ie9VpaWoqrV69CJpN57JeA3K1nz57YuXMnOnXq5OlUnCY8PBwHDx5E//79PZ2K11q5cqXLP1maeOaJyYwxl1iwYAEWLFjg6TQYuy/xJ0zGGGNMAi6YjDHGmARcMBljjDEJuGAyxhhjEsiILDyduX2ATIZhw4ZZ/PVrxh4Ehw8fBnD7Z6GYa23bto3PN8ztrly5gsOHD1v8sYL2bBbMjIwMpybGGLuzY8eOAYDod1gZY663devWO063WTAZY+41adIkAMCWLVs8nAljrD2+h8kYY4xJwAWTMcYYk4ALJmOMMSYBF0zGGGNMAi6YjDHGmARcMBljjDEJuGAyxhhjEnDBZIwxxiTggskYY4xJwAWTMcYYk4ALJmOMMSYBF0zGGGNMAi6YjDHGmARcMBljjDEJuGAyxhhjEnDBZIwxxiTggskYY4xJwAWTMcYYk4ALJmOMMSYBF0zGGGNMAi6YjDHGmARcMBljjDEJuGAyxhhjEnDBZIwxxiTggskYY4xJwAWTMcYYk4ALJmOMMSYBF0zGGGNMAi6YjDHGmARcMBljjDEJuGAyxhhjEnDBZIwxxiTggskYY4xJICMi8nQSjD2o/vWvf+Hvf/87Wltbhbbq6moAQJcuXYQ2Hx8fzJ8/H88//7zbc2SM3cYFkzEPKisrQ79+/STFnjlzBn379nVxRowxa/iSLGMe1LdvX+h0OshkMqsxMpkMOp2OiyVjHsYFkzEPe/755+Hj42N1uq+vL6ZNm+bGjBhjlvAlWcY8rLy8HFqtFm1tbRany2QyXL58GVFRUW7OjDHWHn/CZMzDIiMj8eSTT0IuNz8c5XI5nnrqKS6WjHkBLpiMeYHnnnvOYrtMJuORsYx5Cb4ky5gX+O233xAWFobm5mZRu6+vLyorKxEaGuqhzBhjJvwJkzEvEBISgjFjxogG//j4+CA5OZmLJWNeggsmY15i6tSpooE/RISpU6d6MCPGWHt8SZYxL3Hjxg2EhoaiqakJAODv74/q6mqo1WoPZ8YYA/gTJmNeIyAgABMnToRCoYBCocDEiRO5WDLmRbhgMuZF/vjHP6K5uRnNzc344x//6Ol0GGPt+Ho6AU/bsmWLp1NgTNDa2oqAgAAQEerq6nj/ZF5l0qRJnk7Box74e5h3eoYnY4yx//OAlwv+hAkAubm5D/xfTncjIyMDALB161YPZ3J/+M9//gOZTIYRI0aYTZPJZLy/MrfbsmULMjMzPZ2Gx3HBZMzLxMXFeToFxpgFXDAZ8zKWninLGPM8PjIZY4wxCbhgMsYYYxJwwWSMMcYk4ILpRF988QVkMhlkMhn8/f09nQ5jIhcvXsTTTz+Nuro6VFdXC/uqTCbDoEGDhEfytdcxTiaTYfDgwR7I3jV2796Nvn37wtfX9nCOkpISjB8/HsHBwQgKCsLo0aNx6NAhr49ftGgRcnNzbfaP2cYF04kmT54MIkJiYqKnU7ln1dfX45FHHkFKSoqnU7mvlJSUYPDgwUhKSkKnTp3QpUsXEBGKi4uF6fPmzTObzxRXVFSE0NBQEBGOHDni7vSd7vz583j66aexePFiVFVV2Yz/4Ycf8OSTTyIoKAinTp3CL7/8gt69e2PkyJH45ptvvDp+5syZWLx4Mf7617/asYWYRfSAA0C5ublOXWZiYiIplUqnLtObpaenU3p6ulOWVVdXR71796Zx48Y5ZXmupFar6amnnnLrOh3ZXw0GA3Xv3p2ysrLMphUXF5NSqaTQ0FACQJ9//rnFZRQVFVFoaKhDOXujZ599lt544w1qbm6mqKgo8vHxsRrb2tpK/fv3p4iICLpx44bQ3tLSQv369SOtVktNTU1eG09EVFJSQjKZzOFzXW5uLnG5IOJPmMyrBAUF4fz589i9e7enU7lvrFq1CpWVlVi2bJnF6f7+/ti0aRPkcjmysrJQVlbm5gzd75///CcWLVok6VLs999/jxMnTiA9PR0qlUpo9/HxwbPPPovLly9j586dXhsPADqdDunp6XjppZfQ0tIicSuxjrhgMnYfIyKsX78eQ4cORWRkpNW45ORkLF26FEajERkZGRbvZ95P2hcaW7777jsAsHjv1tS2f/9+r403mThxIq5cuYJdu3aZTWPScMG8C6dPn8Yf/vAHaDQaqNVqxMXF4eDBg1bj9Xo9srOz0bNnT/j5+aFr165IS0tDSUmJEJOXlycaYPHrr78iZzKdDgAAFlFJREFUMzMTwcHBCA0NRUpKCs6fPy9a7s2bN7Fs2TI8+uijCAgIQOfOnTFhwgR89dVXaG1ttTsHT+nYd9NJ295tsnr1aiG2e/fuKC4uRmJiIoKCghAQEICEhATR4Ijly5cL8bGxsUL73r17hfYuXbqYLb+hoQGHDh0SYqR8WnG30tJSVFVVQafT2Yx99dVXkZSUhKNHj2Lu3LmSll9TU4P58+ejT58+8PPzQ0hICMaNG4cDBw4IMY7s04D37KunT58GAHTv3t1sWlRUFACIPpV7W7zJwIEDAQBff/212TQmkaevCXsaHLyHefbsWQoODqaoqCj65ptvyGg00tGjRykpKYl69uxpdg+zvLycHnroIQoLC6Ndu3aR0Wik48ePU3x8PPn7+1NhYaEoPjU1lQBQamoqFRYWUn19Pe3bt49UKhUNGTJEFDtjxgzSaDT0zTff0I0bN6iyspIWLFhAAOjAgQMO5yCVM+9hEv1f3xsbGy22S9kmREQ6nY7UajUNHz5ciC8uLqYBAwaQn58fFRQUiOKt3ZOMiYmxeP/O1j3MhIQE6ty5MxUVFUntuk327q8bNmwgAPT6669bnF5cXEwajUb4v16vJ61WSwBo48aNQrule5gVFRXUq1cvCgsLo/z8fDIYDHTmzBlKS0sjmUxGH3/8sSjenvfPVfuqJbbuYY4ZM4YA0OHDh82mnT17lgDQ7373O6+NNzEYDASA4uLirPbVGr6HedsDvwUcLZgZGRkEgLZt2yZqv3r1KimVSrOCOW3aNAJAmzZtErVXVFSQUqmkmJgYUbvp5JKfny9qT09PJwCk1+uFtl69etGTTz5plmPfvn1FBdPeHKRyd8GUsk2IbhdMAPTzzz+L2o8ePUoASKfTidqdXTDj4+MpJCTEqSd3e/fXVatWEQBas2aNxekdCybR7eKoUChIrVbTqVOnhLaO22D69OkEgDZv3ixqb2pqosjISFKpVFRZWSm02/P+uWpfteRuCmZZWRkBEOXjbfHtyWQyevjhh6321RoumLfxJVkH7d27F8Dtez/tRUZGom/fvmbxeXl5kMvlZl+XCA8PR//+/fHjjz/iypUrZvMNGTJE9H+tVgsAKC8vF9rGjh2LwsJC/M///A8OHz4sXIY9c+YMRo4cedc5eBsp28RErVYLl6JMoqOjERkZidLSUlRUVLgsz4KCAly/fh3Dhw932TpsMV3WVigUkucZNmwYVq9ejYaGBmRkZKCxsdFi3I4dOwAA48ePF7UrlUokJiaisbHR4uU/Ke+fN+2rwcHBAICGhgazaaY2U4w3xrfn6+tr9f1ktnHBdMDNmzdhNBrh7++PwMBAs+ndunUzizcYDGhra4NGozH7IvhPP/0EADh79qzZsjQajej/fn5+AIC2tjahbc2aNfjXv/6FCxcuIDExEZ06dcLYsWOFE9rd5uBtpGwTE2snDtN7dO3aNSdn511MD9Bobm62a77s7GxkZmbi+PHjmDNnjtl00/7k7++PoKAgs+lhYWEAgMrKSrNptt4/b9tXH330UQCwWKCvXr0KAKI/kr0tvr2Wlha7BjwxMS6YDlAqlQgKCkJTUxPq6+vNpl+/ft0sPjg4GL6+vmhubgbdvhRu9kpISHAoH5lMhueeew7ffvstamtrkZeXByJCWloa/v73v7slB29VU1Nj8UdvTYWy/R83crkct27dMoutra21uOx74cfHIyIiAAAGg8HuedevX49+/frhk08+wYYNG0TTlEolNBoNmpqaYDQazeY1PQwgPDzc7vV6275qWs+PP/5oNs3U1v5hJd4Wb1JXVwciEvYJZj8umA4aN24cgP+7NGtSXV2NM2fOmMWnpaWhpaXF4qOrVq5ciR49ejj8/ajg4GBh5JxCocCYMWOEkYnth5C7Mgdv1dTUJDzNxuTYsWMoLy+HTqcTnTwiIiKEv9BNKisrcenSJYvLDggIEBXYfv36Yd26dU7M/u49/vjjACx/GrElMDAQ27dvh1qtxgcffGA2feLEiQBg9jWFmzdvYv/+/VCpVGa3LKTypn01Pj4ejz32GLZt2yb6uk1rayu++OILaLVa0WVpb4s3Me3bpn2COcCtd0y9EBwc9HPu3Dnq3LmzaJTsiRMnKDk5mbp162Y26Keqqor69OlDvXv3pt27d1NtbS3V1NTQRx99RAEBAWY5WBv4snDhQrOBLBqNhuLj46m0tJSampqoqqqK/va3vxEAWr58ucM5SOXuQT9StgnR7UE/Go2GEhMTJY2SnTNnDgGg9957j4xGI507d44mTZpEUVFRFgf9jB07ljQaDV26dIkKCwvJ19eXTp48KUz3hlGybW1t1K1bN6uDkywN+ulo48aNBMDmKNm6ujrRKNl169aJ4u15/+zdV6dMmUIA6MKFCza3SUe2Bv0Q3R705O/vT5MnT6aKigqqrq6mrKws8vX1pb1793p9PBHR559/TgBox44dErfM/+FBP7c98FvA0YJJRHTmzBn6wx/+QJ06dRKGxu/cuZMSExMJAAGgF154QYivqamh+fPnU+/evf9fe/ceFFX5xgH8e4gFloVdBBGR+CXqZA7ZZugIJYPAxOp4YSQIK5vKwUgtYpQyFG1GJINhmnEmmkEdptEcw2wgV8fKSP9AlwmUS2ZISWVcxQt3ucXz+4PZM6671NmFw3J5PjP7B+95znkfDu/Zh3P2PWdJoVCQt7c3RUVF0blz58QYg8Egrmt87dq1S8z1wdeqVauIaOixV4mJibRgwQJydXUlT09PCg4OpkOHDtHg4KBJzlJysNZoFcyCggKz3/GVV16xaZ8QDRVMPz8/unbtGul0OnJ3dyelUklhYWFUXFxs1n9rayslJCSQr68vKZVKWrZsGZWWllJQUJC4/R07dojx1dXVFBoaSiqVivz9/c1mooaGhtp9liwR0c6dO8nR0ZHq6+vFtpaWFrN9928zTzdv3mzxn4bbt29TcnIyBQQEkEKhII1GQzqdjoqKisQYW/9+1ozViIgIcnNzo4GBAUn7RK/Xm/VtfD18O4zRlStXaOXKlaRWq8nNzY0iIiIsjqPxGh8XF0d+fn7U19c3bMxwuGAOmfJ7YCQFkw0Z7TPM0WIsmJOJLeO1tbWV/Pz8LD5LdjK4d+8eKZVKSkhIsHcq45bxWbIP3wIkFRfMIfwZJmOTnEajgV6vx8mTJ5GTk2PvdEYVESEpKQlqtRrp6en2Tmdcqq2tRUxMDFJTU7F+/Xp7pzOhccFkbApYtGgRysrKcPbsWbS3t9s7nVHT3NyM2tpaFBUV2TQjdyrIzc1FRkYGMjIy7J3KhMcFk006xme9VlZWor6+HoIgIC0tzd5p2d3s2bNx+vRpqNVqe6cyambOnIni4mIEBgbaO5VxKzMzk88sR8n4e1o0YyOUkpKClJQUe6fBGJtk+AyTMcYYk4ALJmOMMSYBF0zGGGNMAi6YjDHGmAQCkYUnU08hgiAgODjY4reXM2lKSkoADH0tFJPXyZMnebyyMVdXV4eSkhKLX2QwlfAZJmOMMSYBn2EKAvLz8/Hiiy/aO5UJKy4uDgDw1Vdf2TmTyY/HK7OHEydOID4+ns8w7Z0AY4wxNhFwwWSMMcYk4ILJGGOMScAFkzHGGJOAC6aV3NzcIAiCycvBwQHTpk2DVqvFli1bcPnyZXunydiY++uvv7B27Vq0t7fj9u3bJsfIokWL0NPTY7bOw3GCIGDx4sV2yF5ea9euhSAI2Ldvn9myDz74APn5+XbIilmLC6aVOjs7UV5eDgCIjo4GEaG/vx/V1dXYu3cvqqursXjxYrzxxhvo7u62c7aMjY2KigosXrwYUVFRUKvVmD59OogIpaWl4vLk5GSz9YxxBoMBXl5eICKUlZWNdfqyOnLkCPR6/bDLN23ahNTUVOzevXsMs2K24II5Ch555BH4+PggOjoaP/74I95//318/vnneOmll6b8NGx7cXNzw7Jly6Zs/2Opvb0da9aswQsvvIC3337bbLmzszO8vLyQm5uL48eP2yFD+2loaEBycjJeffXVYWPmzp2LgoICZGRk4MSJE2OYHbMWF0wZfPzxx1i6dClOnTqFL7/80t7pMCarrKwsNDU1Yc+ePRaXu7i44NixY3BwcEBiYiJqamrGOEP72bRpE+Li4hAVFfWvcVqtFrGxsdi+fTsGBgbGKDtmLS6YMhAEQfxP+7PPPrNzNozJh4hw+PBhLF26FLNmzRo2TqfTIS0tDR0dHYiLi7P4eeZkk5eXh19++QXZ2dmS4tetW4e6ujqcOXNG5syYrbhgysR4Oa6kpAT9/f1ie0tLC5KSkjB79mw4OTnB29sbMTExqKioEGMKCwtNJkH8+eefiI+Ph4eHB7y8vLB69WrcuHHDpL/e3l7s2bMHTzzxBFxdXeHp6Yk1a9bg1KlT+Oeff0xipeQwFu7cuYNt27Zh7ty5cHJywrRp07By5UqcP39ejNm3b5+4Hx68xPntt9+K7dOnTxfbs7OzIQgCurq6cPHiRTHG0dHRZLkgCHj00UdRWlqKyMhIuLu7w9XVFeHh4bh48aJs/U82lZWVaG5uhlar/c/YDz/8EFFRUaiqqsI777wjaftSxogtxwsg73FQV1eH7du3Iy8vD+7u7pLWefrppwEA33333Yj7ZzKhKQ4A5efnW7VOeXk5AaDo6OhhY+7fv08ACAA1NDQQEVFDQwM99thj5OPjQ2fOnKGOjg66evUqhYWFkYuLC126dMlkG9HR0WI/ly5dos7OTjp37hwplUpasmSJSWxCQgJpNBr6/vvvqbu7m5qamiglJYUA0Pnz58U4a3OQIjY2lmJjY61ap7GxkQICAsjHx4f0ej21tbXR9evXKSYmhgRBoEOHDpnEq1Qqeu6558y2ExQURF5eXmbtw8UbabVaUqlUFBISIu7b0tJSeuqpp8jJyYkuXLgga//h4eHk6elJBoNh2BhLbBmvcjp69CgBoI8++sji8tLSUtJoNOLPLS0t5O/vTwDoiy++ENsNBoPZfrR2jFhzvMhxHDxIp9PRli1bxJ+N+yk9PX3Yddra2ggAhYaGjqhvOeTn5xOXC6IpvwfkKpjd3d1mBfO1114jAHTs2DGT2MbGRnJ2dqagoCCTduMbgF6vN2mPjY0lANTS0iK2BQQE0LPPPmuWx+OPP25SMK3NQQpbCubrr79OAOj48eMm7T09PTRr1ixSKpXU1NQktstRMAFQeXm5SXtVVRUBIK1WK2l7tvYfFhZG06ZNs/qNebwVzKysLAJAOTk5Fpc/XDCJhoqjQqEglUpFv/76q9j28H60doxYc7zIcRwYHTx4kObMmUOdnZ1im5SCSUQkCALNmzfP5r7lwgVzCF+SlUljYyMAQKFQiJfsCgsL4eDggNWrV5vEzpw5E4GBgbh8+TLq6urMtrVkyRKTn/39/QEMzcAzWrFiBS5duoQ333wTJSUl4mXY69evY/ny5WKcrTmMtoKCAgDAqlWrTNqdnZ0RGRmJ+/fvy35pSqVSiZfBjBYuXIhZs2ahsrJS/BvK4cKFC7h79y5CQkJk62MsGD+LVCgUktcJDg5GdnY2urq6EBcXh/v371uMs3WMSDle5DoObt68iffeew95eXlQqVRWr+/o6Djs/mD2xwVTJsXFxQCAkJAQKBQK9Pb2oq2tDYODg9BoNGY3a1+5cgUA8Ntvv5ltS6PRmPzs5OQEABgcHBTbcnJycOTIEdTW1iIyMhJqtRorVqwQ33QAjCiH0WTMw8XFxeLnOz4+PgCApqYmWfPw8PCw2D5jxgwAwK1bt2TtfzJwcXEBAJPP6aVISkpCfHw8rl69avFWlJGMkf86XuQ8DvR6Pdra2rB8+XKTbRpvK9m9e7fY9vvvv5utPzAwAKVSaXW/bGxwwZTB4OAgcnJyAABbt24FMPRfsYeHBxwdHdHf3w8auhxu9goPD7epT+NB+cMPP6C1tRWFhYUgIsTExOCTTz4ZkxykcnZ2hkajQU9PDzo6OsyWNzc3Axj6b9/IwcEBfX19ZrGtra0W+xAE4T/zuHPnjsX7ZI2F0lg45ep/MvD19QUAtLW1Wb3u4cOHMX/+fOTl5eHo0aMmy2wZI1LJeRxs3brV4raMv196errYNm/ePJN129vbQUTiPmXjDxdMGaSmpuKnn37CunXrxO+KBICYmBgMDAyYzMI0yszMxP/+9z+b78Hy8PBAdXU1gKHLY88//7w4e/DBaepy5mCNdevWAYDZFPre3l4UFRVBqVRCp9OJ7b6+vqivrzeJbWpqws2bNy1u39XV1aTAzZ8/HwcPHjSJ6enpEZ9EY/Tzzz+joaEBWq3W5I1Ljv4ngyeffBIAbLp86ebmhq+//hoqlcri7VfWjhFrjJfj4EHG8WXcp2z84YI5CgYHB3Hr1i188803iIyMRFZWFjZu3Ihjx46ZnGns378fc+fOxcaNG3H27Fm0tbXh7t27yM3Nxd69e5GdnT2i2w/eeustVFVVobe3F7du3UJWVhaICBEREWOWg1T79+9HQEAAkpOTcfr0aXR0dKCmpgYvv/wyGhsbceDAAfGyGwBERUWhoaEBn376KTo7O3Hjxg28++67JmeBD3rmmWdQU1ODv//+GwaDAbW1tQgNDTWJ0Wg02LlzJwwGA7q6ulBWVoYNGzbAyckJBw4cMIkd7f4jIiLg5eWFkpISW3fhuKDVajFjxgxUVlbatH5gYCByc3MtLrN2jFjD2uNgw4YNEAQBf/zxh039SWG8neW/HnLA7GhMphaNY7By1qFKpRJnvxpfgiCQRqOhhQsX0ubNm+ny5cvDrn/nzh3atm0bzZkzhxQKBXl7e1NUVBSdO3dOjDEYDGZ97Nq1S8z3wdeqVauIiKiiooISExNpwYIF5OrqSp6enhQcHEyHDh2iwcFBq3Owhi2zZImIbt++TcnJyRQQEEAKhYI0Gg3pdDoqKioyi21tbaWEhATy9fUlpVJJy5Yto9LSUgoKChL3xY4dO8T46upqCg0NJZVKRf7+/mazOLVaLfn5+dG1a9dIp9ORu7s7KZVKCgsLo+LiYtn7Dw0NnRSzZImIdu7cSY6OjlRfXy+2tbS0mI3Vf5t5unnzZouzjaWMEVuOFyLrjoOIiAhyc3OjgYEBq/ZNYmKiWQ4ASKfTmcXGxcWRn58f9fX1WdXHWOBZskOm/B4Yj29AE42tBdOejAVzohmP47W1tZX8/PwoMTHR3qnI4t69e6RUKikhIUG2PioqKkgQBLNbaMYLLphD+JIsY2xENBoN9Ho9Tp48KU52myyICElJSVCr1UhPT5elj9raWsTExCA1NRXr16+XpQ82OrhgMsZGbNGiRSgrK8PZs2fR3t5u73RGTXNzM2pra1FUVGTTjFwpcnNzkZGRgYyMDFm2z0YPF0w2pRif9VpZWYn6+noIgoC0tDR7pzUpzJ49G6dPn4ZarbZ3KqNm5syZKC4uRmBgoGx9ZGZm8pnlBDE5nwjN2DBSUlKQkpJi7zQYYxMQn2EyxhhjEnDBZIwxxiTggskYY4xJwAWTMcYYk4ALJmOMMSaBQGTh6xqmkKnyrRKMMTZSU7xc8G0l+fn59k6BMcbYBDDlzzAZY4wxKfgzTMYYY0wCLpiMMcaYBFwwGWOMMQkcAXxl7yQYY4yx8e7/TNR5Xeoli1YAAAAASUVORK5CYII=", "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "binary_model = tf.keras.Sequential([\n", " binary_vectorize_layer,\n", " layers.Dense(4)])\n", "\n", "binary_model.compile(\n", " loss=losses.SparseCategoricalCrossentropy(from_logits=True),\n", " optimizer='adam',\n", " metrics=['accuracy'])\n", "\n", "tf.keras.utils.plot_model(binary_model, show_shapes=True)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:30.019087Z", "iopub.status.busy": "2024-08-16T07:38:30.018510Z", "iopub.status.idle": "2024-08-16T07:38:39.277341Z", "shell.execute_reply": "2024-08-16T07:38:39.276652Z" }, "id": "n12Cnhtc_bct" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/200 [..............................] - ETA: 1:25 - loss: 1.4060 - accuracy: 0.2500" ] }, { "name": 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "195/200 [============================>.] - ETA: 0s - loss: 0.2926 - accuracy: 0.9502" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "200/200 [==============================] - 1s 4ms/step - loss: 0.2921 - accuracy: 0.9500 - val_loss: 0.4903 - val_accuracy: 0.8431\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "bin_history = binary_model.fit(\n", " raw_train_ds, validation_data=raw_val_ds, epochs=10)\n", "\n", "print()" ] }, { "cell_type": "markdown", "metadata": { "id": "EwidD-SwNIkz" }, "source": [ "Next, you will use the `'int'` vectorized layer to build a 1D ConvNet:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:39.281072Z", "iopub.status.busy": "2024-08-16T07:38:39.280490Z", "iopub.status.idle": "2024-08-16T07:38:39.285774Z", "shell.execute_reply": "2024-08-16T07:38:39.285167Z" }, "id": "nxLCInw7AUFr" }, "outputs": [], "source": [ "def create_model(vocab_size, num_labels, vectorizer=None):\n", " my_layers =[]\n", " if vectorizer is not None:\n", " my_layers = [vectorizer]\n", "\n", " my_layers.extend([\n", " layers.Embedding(vocab_size, 64, mask_zero=True),\n", " layers.Dropout(0.5),\n", " layers.Conv1D(64, 5, padding=\"valid\", activation=\"relu\", strides=2),\n", " layers.GlobalMaxPooling1D(),\n", " layers.Dense(num_labels)\n", " ])\n", "\n", " model = tf.keras.Sequential(my_layers)\n", " return model" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:39.288930Z", "iopub.status.busy": "2024-08-16T07:38:39.288547Z", "iopub.status.idle": "2024-08-16T07:38:39.457876Z", "shell.execute_reply": "2024-08-16T07:38:39.457052Z" }, "id": "_igpRY7p8NTK" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# `vocab_size` is `VOCAB_SIZE + 1` since `0` is used additionally for padding.\n", "int_model = create_model(vocab_size=VOCAB_SIZE + 1, num_labels=4, vectorizer=int_vectorize_layer)\n", "\n", "tf.keras.utils.plot_model(int_model, show_shapes=True)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:38:39.461597Z", "iopub.status.busy": "2024-08-16T07:38:39.461280Z", "iopub.status.idle": "2024-08-16T07:39:00.327925Z", "shell.execute_reply": "2024-08-16T07:39:00.327234Z" }, "id": "C4UA3h8GAUFs" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/200 [..............................] - ETA: 2:08 - loss: 1.4045 - accuracy: 0.1875" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5/200 [..............................] - ETA: 2s - loss: 1.4005 - accuracy: 0.1750 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 10/200 [>.............................] - ETA: 2s - loss: 1.3919 - accuracy: 0.2250" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 16/200 [=>............................] - ETA: 2s - loss: 1.3845 - accuracy: 0.2793" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "200/200 [==============================] - 2s 10ms/step - loss: 0.7792 - accuracy: 0.6737 - val_loss: 0.6409 - val_accuracy: 0.7419\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 3/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/200 [..............................] - ETA: 1s - loss: 0.6403 - accuracy: 0.7188" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7/200 [>.............................] - ETA: 1s - loss: 0.7084 - accuracy: 0.7188" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "200/200 [==============================] - 2s 10ms/step - loss: 0.2987 - accuracy: 0.9044 - val_loss: 0.4819 - val_accuracy: 0.8206\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 6/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/200 [..............................] - ETA: 1s - loss: 0.1769 - accuracy: 1.0000" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7/200 [>.............................] - ETA: 1s - loss: 0.2796 - accuracy: 0.9062" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "200/200 [==============================] - 2s 10ms/step - loss: 0.1126 - accuracy: 0.9702 - val_loss: 0.5629 - val_accuracy: 0.8069\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 9/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/200 [..............................] - ETA: 1s - loss: 0.0646 - accuracy: 1.0000" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7/200 [>.............................] - ETA: 1s - loss: 0.0818 - accuracy: 0.9821" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "199/200 [============================>.] - ETA: 0s - loss: 0.0565 - accuracy: 0.9881" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "200/200 [==============================] - 2s 10ms/step - loss: 0.0563 - accuracy: 0.9881 - val_loss: 0.6394 - val_accuracy: 0.8037\n" ] } ], "source": [ "\n", "int_model.compile(\n", " loss=losses.SparseCategoricalCrossentropy(from_logits=True),\n", " optimizer='adam',\n", " metrics=['accuracy'])\n", "int_history = int_model.fit(raw_train_ds, validation_data=raw_val_ds, epochs=10)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:00.331608Z", "iopub.status.busy": "2024-08-16T07:39:00.331325Z", "iopub.status.idle": "2024-08-16T07:39:00.574476Z", "shell.execute_reply": "2024-08-16T07:39:00.573743Z" }, "id": "hHTVwxNAovn7" }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'CE/token')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "loss = plt.plot(bin_history.epoch, bin_history.history['loss'], label='bin-loss')\n", "plt.plot(bin_history.epoch, bin_history.history['val_loss'], '--', color=loss[0].get_color(), label='bin-val_loss')\n", "\n", "loss = plt.plot(int_history.epoch, int_history.history['loss'], label='int-loss')\n", "plt.plot(int_history.epoch, int_history.history['val_loss'], '--', color=loss[0].get_color(), label='int-val_loss')\n", "\n", "plt.legend()\n", "plt.xlabel('Epoch')\n", "plt.ylabel('CE/token')" ] }, { "cell_type": "markdown", "metadata": { "id": "0kHgPE_YwHvp" }, "source": [ "You are nearly ready to train your model.\n", "\n", "As a final preprocessing step, you will apply the `TextVectorization` layers you created earlier to the training, validation, and test sets:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:00.577885Z", "iopub.status.busy": "2024-08-16T07:39:00.577611Z", "iopub.status.idle": "2024-08-16T07:39:00.848952Z", "shell.execute_reply": "2024-08-16T07:39:00.848312Z" }, "id": "46LeHmnD55wJ" }, "outputs": [], "source": [ "binary_train_ds = raw_train_ds.map(lambda x,y: (binary_vectorize_layer(x), y))\n", "binary_val_ds = raw_val_ds.map(lambda x,y: (binary_vectorize_layer(x), y))\n", "binary_test_ds = raw_test_ds.map(lambda x,y: (binary_vectorize_layer(x), y))\n", "\n", "int_train_ds = raw_train_ds.map(lambda x,y: (int_vectorize_layer(x), y))\n", "int_val_ds = raw_val_ds.map(lambda x,y: (int_vectorize_layer(x), y))\n", "int_test_ds = raw_test_ds.map(lambda x,y: (int_vectorize_layer(x), y))" ] }, { "cell_type": "markdown", "metadata": { "id": "NZo_OTxD3ItL" }, "source": [ "### Export the model" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:00.852730Z", "iopub.status.busy": "2024-08-16T07:39:00.852460Z", "iopub.status.idle": "2024-08-16T07:39:01.239999Z", "shell.execute_reply": "2024-08-16T07:39:01.239369Z" }, "id": "HYteWp9fykbP" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: bin.tf/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: bin.tf/assets\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saved artifact at 'bin.tf'. The following endpoints are available:\n", "\n", "* Endpoint 'serve'\n", " Args:\n", " args_0: string Tensor, shape=(None,)\n", " Returns:\n", " float32 Tensor, shape=(None, 4)\n" ] } ], "source": [ "binary_model.export('bin.tf')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:01.243490Z", "iopub.status.busy": "2024-08-16T07:39:01.243194Z", "iopub.status.idle": "2024-08-16T07:39:01.349369Z", "shell.execute_reply": "2024-08-16T07:39:01.348698Z" }, "id": "OPD0WE_vy-dT" }, "outputs": [], "source": [ "loaded = tf.saved_model.load('bin.tf')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:01.352726Z", "iopub.status.busy": "2024-08-16T07:39:01.352465Z", "iopub.status.idle": "2024-08-16T07:39:01.510527Z", "shell.execute_reply": "2024-08-16T07:39:01.509767Z" }, "id": "xFoBg53DzE2s" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/1 [==============================] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1/1 [==============================] - 0s 114ms/step\n" ] }, { "data": { "text/plain": [ "array([[-0.11534691, 0.01791038, -0.58859897, 0.3795665 ]],\n", " dtype=float32)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "binary_model.predict(['How do you sort a list?'])" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:01.513631Z", "iopub.status.busy": "2024-08-16T07:39:01.513379Z", "iopub.status.idle": "2024-08-16T07:39:01.549446Z", "shell.execute_reply": "2024-08-16T07:39:01.548822Z" }, "id": "Cx5aIBm3zVQ0" }, "outputs": [ { "data": { "text/plain": [ "array([[-0.11534691, 0.01791038, -0.58859897, 0.3795665 ]],\n", " dtype=float32)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loaded.serve(tf.constant(['How do you sort a list?'])).numpy()" ] }, { "cell_type": "markdown", "metadata": { "id": "0QDVfii_4slI" }, "source": [ "\n", "Including the text preprocessing logic inside your model enables you to export a model for production that simplifies deployment, and reduces the potential for [train/test skew](https://developers.google.com/machine-learning/guides/rules-of-ml#training-serving_skew).\n", "\n", "There is a performance difference to keep in mind when choosing where to apply `tf.keras.layers.TextVectorization`. Using it outside of your model enables you to do asynchronous CPU processing and buffering of your data when training on GPU. So, if you're training your model on the GPU, you probably want to go with this option to get the best performance while developing your model, then switch to including the `TextVectorization` layer inside your model when you're ready to prepare for deployment.\n", "\n", "Visit the [Save and load models](../keras/save_and_load.ipynb) tutorial to learn more about saving models." ] }, { "cell_type": "markdown", "metadata": { "id": "p4cvuFzavTRy" }, "source": [ "\n", "\n", "## Example 2: Predict the author of Iliad translations" ] }, { "cell_type": "markdown", "metadata": { "id": "fOlJ22508RIe" }, "source": [ "The following provides an example of using `tf.data.TextLineDataset` to load examples from text files, and [TensorFlow Text](https://www.tensorflow.org/text) to preprocess the data. You will use three different English translations of the same work, Homer's Iliad, and train a model to identify the translator given a single line of text." ] }, { "cell_type": "markdown", "metadata": { "id": "cu2ruJMG9vnY" }, "source": [ "To implement this task you'll use some lower level tools.\n", "\n", "* You'll use `tf.data.TextLineDataset` to load text-lines from files.\n", "* You'll implement your own version of `keras.layers.TextVectorization` using:\n", " * `text.UnicodeScriptTokenizer` - to convert strings to tokens.\n", " * `tf.lookup.StaticVocabularyTable` - to convert tokens to integer ids.\n", "* You'll maximize performance by placing the text processing in the dataset pipeline, so it can run in parallel with the model training." ] }, { "cell_type": "markdown", "metadata": { "id": "-pCgKbOSk7kU" }, "source": [ "### Download and explore the dataset\n", "\n", "The texts of the three translations are by:\n", "\n", "- [William Cowper](https://en.wikipedia.org/wiki/William_Cowper): [text](https://storage.googleapis.com/download.tensorflow.org/data/illiad/cowper.txt)\n", "- [Edward, Earl of Derby](https://en.wikipedia.org/wiki/Edward_Smith-Stanley,_14th_Earl_of_Derby): [text](https://storage.googleapis.com/download.tensorflow.org/data/illiad/derby.txt)\n", "- [Samuel Butler](https://en.wikipedia.org/wiki/Samuel_Butler_%28novelist%29): [text](https://storage.googleapis.com/download.tensorflow.org/data/illiad/butler.txt)\n", "\n", "The text files used in this tutorial have undergone some typical preprocessing tasks like removing document header and footer, line numbers and chapter titles.\n", "\n", "Download these lightly munged files locally:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:01.553209Z", "iopub.status.busy": "2024-08-16T07:39:01.552926Z", "iopub.status.idle": "2024-08-16T07:39:02.026596Z", "shell.execute_reply": "2024-08-16T07:39:02.025966Z" }, "id": "4YlKQthEYlFw" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/illiad/cowper.txt\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 8192/815980 [..............................] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "815980/815980 [==============================] - 0s 0us/step\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/illiad/derby.txt\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 8192/809730 [..............................] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "809730/809730 [==============================] - 0s 0us/step\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/illiad/butler.txt\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 8192/807992 [..............................] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "807992/807992 [==============================] - 0s 0us/step\n" ] }, { "data": { "text/plain": [ "[PosixPath('/home/kbuilder/.keras/datasets/flower_photos.tar'),\n", " PosixPath('/home/kbuilder/.keras/datasets/butler.txt'),\n", " PosixPath('/home/kbuilder/.keras/datasets/fashion-mnist'),\n", " PosixPath('/home/kbuilder/.keras/datasets/cowper.txt'),\n", " PosixPath('/home/kbuilder/.keras/datasets/flower_photos.tgz'),\n", " PosixPath('/home/kbuilder/.keras/datasets/mnist.npz'),\n", " PosixPath('/home/kbuilder/.keras/datasets/HIGGS.csv.gz'),\n", " PosixPath('/home/kbuilder/.keras/datasets/derby.txt'),\n", " PosixPath('/home/kbuilder/.keras/datasets/facades.tar.gz'),\n", " PosixPath('/home/kbuilder/.keras/datasets/train.csv'),\n", " PosixPath('/home/kbuilder/.keras/datasets/jena_climate_2009_2016.csv'),\n", " PosixPath('/home/kbuilder/.keras/datasets/iris_test.csv'),\n", " PosixPath('/home/kbuilder/.keras/datasets/cats_and_dogs_filtered'),\n", " PosixPath('/home/kbuilder/.keras/datasets/jena_climate_2009_2016.csv.zip'),\n", " PosixPath('/home/kbuilder/.keras/datasets/heart.csv'),\n", " PosixPath('/home/kbuilder/.keras/datasets/facades'),\n", " PosixPath('/home/kbuilder/.keras/datasets/cifar-10-batches-py'),\n", " PosixPath('/home/kbuilder/.keras/datasets/kandinsky5.jpg'),\n", " PosixPath('/home/kbuilder/.keras/datasets/320px-Felis_catus-cat_on_snow.jpg'),\n", " PosixPath('/home/kbuilder/.keras/datasets/iris_training.csv'),\n", " PosixPath('/home/kbuilder/.keras/datasets/cats_and_dogs.zip'),\n", " PosixPath('/home/kbuilder/.keras/datasets/194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg'),\n", " PosixPath('/home/kbuilder/.keras/datasets/Red_sunflower'),\n", " PosixPath('/home/kbuilder/.keras/datasets/cifar-10-batches-py.tar.gz'),\n", " PosixPath('/home/kbuilder/.keras/datasets/YellowLabradorLooking_new.jpg'),\n", " PosixPath('/home/kbuilder/.keras/datasets/flower_photos')]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DIRECTORY_URL = 'https://storage.googleapis.com/download.tensorflow.org/data/illiad/'\n", "FILE_NAMES = ['cowper.txt', 'derby.txt', 'butler.txt']\n", "\n", "for name in FILE_NAMES:\n", " text_dir = utils.get_file(name, origin=DIRECTORY_URL + name)\n", "\n", "parent_dir = pathlib.Path(text_dir).parent\n", "list(parent_dir.iterdir())" ] }, { "cell_type": "markdown", "metadata": { "id": "M8PHK5J_cXE5" }, "source": [ "### Load the dataset\n", "\n", "Previously, with `tf.keras.utils.text_dataset_from_directory` all contents of a file were treated as a single example. Here, you will use `tf.data.TextLineDataset`, which is designed to create a `tf.data.Dataset` from a text file where each example is a line of text from the original file. `TextLineDataset` is useful for text data that is primarily line-based (for example, poetry or error logs).\n", "\n", "Iterate through these files, loading each one into its own dataset. Each example needs to be individually labeled, so use `Dataset.map` to apply a labeler function to each one. This will iterate over every example in the dataset, returning (`example, label`) pairs." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:02.029896Z", "iopub.status.busy": "2024-08-16T07:39:02.029637Z", "iopub.status.idle": "2024-08-16T07:39:02.032909Z", "shell.execute_reply": "2024-08-16T07:39:02.032278Z" }, "id": "YIIWIdPXgk7I" }, "outputs": [], "source": [ "def labeler(example, index):\n", " return example, tf.cast(index, tf.int64)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:02.035882Z", "iopub.status.busy": "2024-08-16T07:39:02.035649Z", "iopub.status.idle": "2024-08-16T07:39:02.107778Z", "shell.execute_reply": "2024-08-16T07:39:02.107189Z" }, "id": "8Ajx7AmZnEg3" }, "outputs": [], "source": [ "labeled_data_sets = []\n", "\n", "for i, file_name in enumerate(FILE_NAMES):\n", " lines_dataset = tf.data.TextLineDataset(str(parent_dir/file_name))\n", " labeled_dataset = lines_dataset.map(lambda ex: labeler(ex, i))\n", " labeled_data_sets.append(labeled_dataset)" ] }, { "cell_type": "markdown", "metadata": { "id": "wPOsVK1e9NGM" }, "source": [ "Next, you'll combine these labeled datasets into a single dataset using `Dataset.concatenate`, and shuffle it with `Dataset.shuffle`:\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:02.111017Z", "iopub.status.busy": "2024-08-16T07:39:02.110793Z", "iopub.status.idle": "2024-08-16T07:39:02.113927Z", "shell.execute_reply": "2024-08-16T07:39:02.113329Z" }, "id": "6jAeYkTIi9-2" }, "outputs": [], "source": [ "BUFFER_SIZE = 50000\n", "BATCH_SIZE = 64\n", "VALIDATION_SIZE = 5000" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:02.116888Z", "iopub.status.busy": "2024-08-16T07:39:02.116477Z", "iopub.status.idle": "2024-08-16T07:39:02.122585Z", "shell.execute_reply": "2024-08-16T07:39:02.121918Z" }, "id": "Qd544E-Sh63L" }, "outputs": [], "source": [ "all_labeled_data = labeled_data_sets[0]\n", "for labeled_dataset in labeled_data_sets[1:]:\n", " all_labeled_data = all_labeled_data.concatenate(labeled_dataset)\n", "\n", "all_labeled_data = all_labeled_data.shuffle(\n", " BUFFER_SIZE, reshuffle_each_iteration=False)" ] }, { "cell_type": "markdown", "metadata": { "id": "r4JEHrJXeG5k" }, "source": [ "Print out a few examples as before. The dataset hasn't been batched yet, hence each entry in `all_labeled_data` corresponds to one data point:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:02.125644Z", "iopub.status.busy": "2024-08-16T07:39:02.125422Z", "iopub.status.idle": "2024-08-16T07:39:02.780213Z", "shell.execute_reply": "2024-08-16T07:39:02.779563Z" }, "id": "gywKlN0xh6u5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sentence: b'and Proto, Pherusa and Dynamene, Dexamene, Amphinome and Callianeira,'\n", "Label: 2\n", "Sentence: b\"Far from my sire and friends, in Lemnos' isle.\"\n", "Label: 1\n", "Sentence: b'Sarpedon, as a lion on a herd:'\n", "Label: 1\n", "Sentence: b'shook the dust from out of their manes, and bore their chariot swiftly'\n", "Label: 2\n", "Sentence: b'with ease, had not Phoebus Apollo been angry, and in the guise of'\n", "Label: 2\n", "Sentence: b'As may assuage Achilles, and prevail'\n", "Label: 0\n", "Sentence: b'should have been hurled in vain, and withdrew under cover of his men.'\n", "Label: 2\n", "Sentence: b'drove straight at Patroclus. Patroclus then sprang from his chariot to'\n", "Label: 2\n", "Sentence: b\"Me, Menela\\xc3\\xbcs, by Minerva's aid,\"\n", "Label: 0\n", "Sentence: b'As he spoke Patroclus put on his armour. First he greaved his legs with'\n", "Label: 2\n" ] } ], "source": [ "for text, label in all_labeled_data.take(10):\n", " print(\"Sentence: \", text.numpy())\n", " print(\"Label:\", label.numpy())" ] }, { "cell_type": "markdown", "metadata": { "id": "5rrpU2_sfDh0" }, "source": [ "### Prepare the dataset for training\n", "\n", "Instead of using `tf.keras.layers.TextVectorization` to preprocess the text dataset, you will now use the lower-level TensorFlow Text APIs to standardize and tokenize the data, build a vocabulary and use `tf.lookup.StaticVocabularyTable` to map tokens to integers to feed to the model. (Learn more about [TensorFlow Text](https://www.tensorflow.org/text)).\n", "\n", "Define a function to convert the text to lower-case and tokenize it:\n", "\n", "- TensorFlow Text provides various tokenizers. In this example, you will use the `text.UnicodeScriptTokenizer` to tokenize the dataset.\n", "- You will use `Dataset.map` to apply the tokenization to the dataset." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:02.784098Z", "iopub.status.busy": "2024-08-16T07:39:02.783442Z", "iopub.status.idle": "2024-08-16T07:39:02.788029Z", "shell.execute_reply": "2024-08-16T07:39:02.787427Z" }, "id": "gX6opNxnjgkH" }, "outputs": [], "source": [ "class MyTokenizer(tf.keras.layers.Layer):\n", " def __init__(self):\n", " super().__init__()\n", " self.tokenizer = tf_text.UnicodeScriptTokenizer()\n", "\n", " def call(self, text):\n", " lower_case = tf_text.case_fold_utf8(text)\n", " result = self.tokenizer.tokenize(lower_case)\n", " # If you pass a batch of strings, it will return a RaggedTensor.\n", " if isinstance(result, tf.RaggedTensor):\n", " # Convert to dense 0-padded.\n", " result = result.to_tensor()\n", " return result" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:02.790842Z", "iopub.status.busy": "2024-08-16T07:39:02.790591Z", "iopub.status.idle": "2024-08-16T07:39:02.795793Z", "shell.execute_reply": "2024-08-16T07:39:02.795166Z" }, "id": "v4DpQW-Y12rm" }, "outputs": [], "source": [ "tokenizer = MyTokenizer()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:02.798686Z", "iopub.status.busy": "2024-08-16T07:39:02.798432Z", "iopub.status.idle": "2024-08-16T07:39:04.317794Z", "shell.execute_reply": "2024-08-16T07:39:04.317139Z" }, "id": "vzUrAzOq31QL" }, "outputs": [ { "data": { "text/plain": [ "<_MapDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenized_ds = all_labeled_data.map(lambda text, label: (tokenizer(text), label))\n", "tokenized_ds" ] }, { "cell_type": "markdown", "metadata": { "id": "jx4Q2i8XLV7o" }, "source": [ "You can iterate over the dataset and print out a few tokenized examples:\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:04.321387Z", "iopub.status.busy": "2024-08-16T07:39:04.321139Z", "iopub.status.idle": "2024-08-16T07:39:05.365479Z", "shell.execute_reply": "2024-08-16T07:39:05.364787Z" }, "id": "S0cWZcWjkQAc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tf.Tensor(\n", "[b'and' b'proto' b',' b'pherusa' b'and' b'dynamene' b',' b'dexamene' b','\n", " b'amphinome' b'and' b'callianeira' b','], shape=(13,), dtype=string)\n", "\n", "tf.Tensor(2, shape=(), dtype=int64)\n" ] } ], "source": [ "for tokens, label in tokenized_ds.take(1):\n", " break\n", "\n", "print(tokens)\n", "print()\n", "print(label)" ] }, { "cell_type": "markdown", "metadata": { "id": "JPd4PsskJ_Xt" }, "source": [ "Next, you will build a vocabulary by sorting tokens by frequency and keeping the top `VOCAB_SIZE` tokens:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:05.369637Z", "iopub.status.busy": "2024-08-16T07:39:05.368989Z", "iopub.status.idle": "2024-08-16T07:39:08.220551Z", "shell.execute_reply": "2024-08-16T07:39:08.219830Z" }, "id": "FqYVv8LMnHKW" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First five vocab entries: [b',', b'the', b'and', b\"'\", b'of']\n", "\n" ] } ], "source": [ "tokenized_ds = tokenized_ds.cache().prefetch(tf.data.AUTOTUNE)\n", "\n", "vocab_count = collections.Counter()\n", "for toks, labels in tokenized_ds.ragged_batch(1000):\n", " toks = tf.reshape(toks, [-1])\n", " for tok in toks.numpy():\n", " vocab_count[tok] += 1\n", "\n", "vocab = [tok for tok, count in vocab_count.most_common(VOCAB_SIZE)]\n", "\n", "print(\"First five vocab entries:\", vocab[:5])\n", "print()" ] }, { "cell_type": "markdown", "metadata": { "id": "PyKSsaNAKi17" }, "source": [ "To convert the tokens into integers, use the `vocab` set to create a `tf.lookup.StaticVocabularyTable`. You will map tokens to integers with `0` reserved for padding, and `n+1` reserved to denote an out-of-vocabulary (OOV) token." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:08.224150Z", "iopub.status.busy": "2024-08-16T07:39:08.223881Z", "iopub.status.idle": "2024-08-16T07:39:08.229053Z", "shell.execute_reply": "2024-08-16T07:39:08.228421Z" }, "id": "UYB3t-jUhdYB" }, "outputs": [], "source": [ "class MyVocabTable(tf.keras.layers.Layer):\n", " def __init__(self, vocab):\n", " super().__init__()\n", " self.keys = [''] + vocab\n", " self.values = range(len(self.keys))\n", "\n", " self.init = tf.lookup.KeyValueTensorInitializer(\n", " self.keys, self.values, key_dtype=tf.string, value_dtype=tf.int64)\n", "\n", " num_oov_buckets = 1\n", "\n", " self.table = tf.lookup.StaticVocabularyTable(self.init, num_oov_buckets)\n", "\n", " def call(self, x):\n", " result = self.table.lookup(x)\n", " return result" ] }, { "cell_type": "markdown", "metadata": { "id": "laUBoirZ55Sp" }, "source": [ "Try it on a dummy vocabulary:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:08.232094Z", "iopub.status.busy": "2024-08-16T07:39:08.231858Z", "iopub.status.idle": "2024-08-16T07:39:08.244193Z", "shell.execute_reply": "2024-08-16T07:39:08.243562Z" }, "id": "kCBo2yFHD7y62" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vocab_table = MyVocabTable(['a','b','c'])\n", "vocab_table(tf.constant([''] + list('abcdefghi')))" ] }, { "cell_type": "markdown", "metadata": { "id": "yvXh1oGo6CEq" }, "source": [ "Create one for the real vocabulary:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:08.247247Z", "iopub.status.busy": "2024-08-16T07:39:08.247017Z", "iopub.status.idle": "2024-08-16T07:39:08.316167Z", "shell.execute_reply": "2024-08-16T07:39:08.315577Z" }, "id": "zPsRw1UD6u-q" }, "outputs": [], "source": [ "vocab_table = MyVocabTable(vocab)" ] }, { "cell_type": "markdown", "metadata": { "id": "Z5F-EiBpOADE" }, "source": [ "Finally, define a layer to standardize, tokenize and vectorize the dataset using the tokenizer and lookup table:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:08.319221Z", "iopub.status.busy": "2024-08-16T07:39:08.318988Z", "iopub.status.idle": "2024-08-16T07:39:08.357355Z", "shell.execute_reply": "2024-08-16T07:39:08.356764Z" }, "id": "8ClQM4xxpo-s" }, "outputs": [], "source": [ "preprocess_text = tf.keras.Sequential([\n", " tokenizer,\n", " vocab_table\n", "])" ] }, { "cell_type": "markdown", "metadata": { "id": "v6S5Qyabi-vo" }, "source": [ "You can try this on a single example to print the output:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:08.361004Z", "iopub.status.busy": "2024-08-16T07:39:08.360615Z", "iopub.status.idle": "2024-08-16T07:39:08.948698Z", "shell.execute_reply": "2024-08-16T07:39:08.947964Z" }, "id": "jgxPZaxUuTbk" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sentence: b'and Proto, Pherusa and Dynamene, Dexamene, Amphinome and Callianeira,'\n", "Vectorized sentence: [ 3 7805 1 7806 3 7807 1 7808 1 7809 3 9790 1]\n" ] } ], "source": [ "example_text, example_label = next(iter(all_labeled_data))\n", "print(\"Sentence: \", example_text.numpy())\n", "vectorized_text = preprocess_text(example_text)\n", "print(\"Vectorized sentence: \", vectorized_text.numpy())" ] }, { "cell_type": "markdown", "metadata": { "id": "p9qHM0v8k_Mg" }, "source": [ "Now create a dataset pipeline that will process the text on the fly using `Dataset.map`:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:08.952107Z", "iopub.status.busy": "2024-08-16T07:39:08.951853Z", "iopub.status.idle": "2024-08-16T07:39:11.045650Z", "shell.execute_reply": "2024-08-16T07:39:11.044929Z" }, "id": "KmQVsAgJ-RM0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ids: [ 3 7805 1 7806 3 7807 1 7808 1 7809 3 9790 1]\n", "Label: 2\n" ] } ], "source": [ "all_encoded_data = all_labeled_data.map(lambda text, labels:(preprocess_text(text), labels))\n", "\n", "for ids, label in all_encoded_data.take(1):\n", " break\n", "\n", "print(\"Ids: \", ids.numpy())\n", "print(\"Label: \", label.numpy())" ] }, { "cell_type": "markdown", "metadata": { "id": "_YZToSXSm0qr" }, "source": [ "### Split the dataset into training and test sets\n" ] }, { "cell_type": "markdown", "metadata": { "id": "itxIJwkrUXgv" }, "source": [ "The Keras `TextVectorization` layer also batches and pads the vectorized data. Padding is required because the examples inside of a batch need to be the same size and shape, but the examples in these datasets are not all the same size—each line of text has a different number of words.\n", "\n", "`tf.data.Dataset` supports splitting and padded-batching datasets:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:11.049605Z", "iopub.status.busy": "2024-08-16T07:39:11.049072Z", "iopub.status.idle": "2024-08-16T07:39:11.060856Z", "shell.execute_reply": "2024-08-16T07:39:11.060268Z" }, "id": "r-rmbijQh6bf" }, "outputs": [], "source": [ "train_data = all_encoded_data.skip(VALIDATION_SIZE).shuffle(BUFFER_SIZE).padded_batch(BATCH_SIZE)\n", "validation_data = all_encoded_data.take(VALIDATION_SIZE).padded_batch(BATCH_SIZE)" ] }, { "cell_type": "markdown", "metadata": { "id": "m-wmFq8uW1zS" }, "source": [ "Now, `validation_data` and `train_data` are not collections of (`example, label`) pairs, but collections of batches. Each batch is a pair of (*many examples*, *many labels*) represented as arrays.\n", "\n", "To illustrate this:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:11.064618Z", "iopub.status.busy": "2024-08-16T07:39:11.063972Z", "iopub.status.idle": "2024-08-16T07:39:12.153800Z", "shell.execute_reply": "2024-08-16T07:39:12.153056Z" }, "id": "kMslWfuwoqpB" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Text batch shape: (64, 18)\n", "Label batch shape: (64,)\n", "First text example: tf.Tensor(\n", "[ 3 7805 1 7806 3 7807 1 7808 1 7809 3 9790 1 0\n", " 0 0 0 0], shape=(18,), dtype=int64)\n", "First label example: tf.Tensor(2, shape=(), dtype=int64)\n" ] } ], "source": [ "sample_text, sample_labels = next(iter(validation_data))\n", "print(\"Text batch shape: \", sample_text.shape)\n", "print(\"Label batch shape: \", sample_labels.shape)\n", "print(\"First text example: \", sample_text[0])\n", "print(\"First label example: \", sample_labels[0])" ] }, { "cell_type": "markdown", "metadata": { "id": "h44Ox11OYLP-" }, "source": [ "Configure the datasets for better performance as before:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:12.157374Z", "iopub.status.busy": "2024-08-16T07:39:12.157110Z", "iopub.status.idle": "2024-08-16T07:39:12.161840Z", "shell.execute_reply": "2024-08-16T07:39:12.161325Z" }, "id": "BpT0b_7mYRXV" }, "outputs": [], "source": [ "train_data = train_data.prefetch(tf.data.AUTOTUNE)\n", "validation_data = validation_data.prefetch(tf.data.AUTOTUNE)" ] }, { "cell_type": "markdown", "metadata": { "id": "K8SUhGFNsmRi" }, "source": [ "### Train the model\n", "\n", "You can train a model on this dataset as before:" ] }, { "cell_type": "markdown", "metadata": { "id": "UI4I6_Sa0vWu" }, "source": [ "Since this text vectorization adds `0` for padding and `n+1` for out-of-vocabulary (OOV) tokens, the vocabulary size has increased by two:" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:12.165453Z", "iopub.status.busy": "2024-08-16T07:39:12.165008Z", "iopub.status.idle": "2024-08-16T07:39:12.214495Z", "shell.execute_reply": "2024-08-16T07:39:12.213805Z" }, "id": "ClUFxHh47dtJ" }, "outputs": [], "source": [ "model = create_model(vocab_size=VOCAB_SIZE+2, num_labels=3)\n", "\n", "model.compile(\n", " optimizer='adam',\n", " loss=losses.SparseCategoricalCrossentropy(from_logits=True),\n", " metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:12.217476Z", "iopub.status.busy": "2024-08-16T07:39:12.217250Z", "iopub.status.idle": "2024-08-16T07:39:12.381354Z", "shell.execute_reply": "2024-08-16T07:39:12.380501Z" }, "id": "xHpqtP6b7fPl" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.keras.utils.plot_model(model, show_shapes=True)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:39:12.385562Z", "iopub.status.busy": "2024-08-16T07:39:12.384959Z", "iopub.status.idle": "2024-08-16T07:40:39.331474Z", "shell.execute_reply": "2024-08-16T07:40:39.330620Z" }, "id": "QJgI1pow2YR9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/Unknown - 23s 23s/step - loss: 1.0808 - accuracy: 0.5312" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8/Unknown - 23s 8ms/step - loss: 1.0767 - accuracy: 0.4121" ] }, { "name": "stdout", "output_type": "stream", 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"stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 54/Unknown - 23s 6ms/step - loss: 1.0054 - accuracy: 0.4340" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 65/Unknown - 23s 6ms/step - loss: 0.9876 - accuracy: 0.4620" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 75/Unknown - 23s 6ms/step - loss: 0.9712 - accuracy: 0.4777" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 86/Unknown - 23s 6ms/step - loss: 0.9528 - 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "697/697 [==============================] - ETA: 0s - loss: 0.3642 - accuracy: 0.8489" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "697/697 [==============================] - 28s 10ms/step - loss: 0.3642 - accuracy: 0.8489 - val_loss: 0.3574 - val_accuracy: 0.8472\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 3/3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/697 [..............................] - ETA: 4:17:33 - loss: 0.3495 - accuracy: 0.8438" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "697/697 [==============================] - 29s 9ms/step - loss: 0.2910 - accuracy: 0.8808 - val_loss: 0.3527 - val_accuracy: 0.8506\n" ] } ], "source": [ "history = model.fit(train_data, validation_data=validation_data, epochs=3)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:40:39.335650Z", "iopub.status.busy": "2024-08-16T07:40:39.335104Z", "iopub.status.idle": "2024-08-16T07:40:42.737281Z", "shell.execute_reply": "2024-08-16T07:40:42.736418Z" }, "id": "KTPCYf_Jh6TH" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/Unknown - 1s 1s/step - loss: 0.3171 - accuracy: 0.8906" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "79/79 [==============================] - 3s 29ms/step - loss: 0.3527 - accuracy: 0.8506\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loss: 0.35271939635276794\n", "Accuracy: 85.06%\n" ] } ], "source": [ "metrics = model.evaluate(validation_data, return_dict=True)\n", "\n", "print(\"Loss: \", metrics['loss'])\n", "print(\"Accuracy: {:2.2%}\".format(metrics['accuracy']))" ] }, { "cell_type": "markdown", "metadata": { "id": "_knIsO-r4pHb" }, "source": [ "### Export the model" ] }, { "cell_type": "markdown", "metadata": { "id": "FEuMLJA_Xiwo" }, "source": [ "To make the model capable of taking raw strings as input, pack both the text processor and the model into a `keras.Sequential`:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:40:42.741240Z", "iopub.status.busy": "2024-08-16T07:40:42.740696Z", "iopub.status.idle": "2024-08-16T07:40:42.823780Z", "shell.execute_reply": "2024-08-16T07:40:42.822946Z" }, "id": "ZpFRX39rt6UL" }, "outputs": [], "source": [ "export_model = tf.keras.Sequential([\n", " preprocess_text,\n", " model\n", "])" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:40:42.826973Z", "iopub.status.busy": "2024-08-16T07:40:42.826726Z", "iopub.status.idle": "2024-08-16T07:40:42.883733Z", "shell.execute_reply": "2024-08-16T07:40:42.882923Z" }, "id": "G-Cvd27y4qwt" }, "outputs": [], "source": [ "export_model.compile(\n", " loss=losses.SparseCategoricalCrossentropy(from_logits=False),\n", " optimizer='adam',\n", " metrics=['accuracy'])" ] }, { "cell_type": "markdown", "metadata": { "id": "AmU7iV9vtnfY" }, "source": [ "This model you can run directly on batches of strings:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:40:42.886971Z", "iopub.status.busy": "2024-08-16T07:40:42.886724Z", "iopub.status.idle": "2024-08-16T07:40:42.897020Z", "shell.execute_reply": "2024-08-16T07:40:42.896214Z" }, "id": "UVwz4FADul8p" }, "outputs": [ { "data": { "text/plain": [ "<_PrefetchDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a test dataset of raw strings.\n", "test_ds = all_labeled_data.take(VALIDATION_SIZE).batch(BATCH_SIZE)\n", "test_ds = test_ds.cache().prefetch(tf.data.AUTOTUNE)\n", "test_ds" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:40:42.900203Z", "iopub.status.busy": "2024-08-16T07:40:42.899592Z", "iopub.status.idle": "2024-08-16T07:40:48.500011Z", "shell.execute_reply": "2024-08-16T07:40:48.499088Z" }, "id": "Pyg0B4zsc-UD" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/Unknown - 5s 5s/step - loss: 1.2408 - accuracy: 0.8906" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7/Unknown - 5s 9ms/step - loss: 1.0326 - accuracy: 0.8549" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 13/Unknown - 5s 9ms/step - loss: 0.7683 - accuracy: 0.8678" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 20/Unknown - 5s 8ms/step - loss: 0.9390 - accuracy: 0.8641" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 55/Unknown - 5s 8ms/step - loss: 1.0255 - accuracy: 0.8489" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 61/Unknown - 5s 8ms/step - loss: 1.0324 - accuracy: 0.8496" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 68/Unknown - 5s 8ms/step - loss: 1.0353 - accuracy: 0.8497" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 75/Unknown - 5s 8ms/step - loss: 1.0337 - accuracy: 0.8506" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "79/79 [==============================] - 5s 9ms/step - loss: 1.0300 - accuracy: 0.8506\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loss: 1.0299931764602661\n", "Accuracy: 85.06%\n" ] } ], "source": [ "loss, accuracy = export_model.evaluate(test_ds)\n", "\n", "print(\"Loss: \", loss)\n", "print(\"Accuracy: {:2.2%}\".format(accuracy))" ] }, { "cell_type": "markdown", "metadata": { "id": "jenLg-ANtusp" }, "source": [ "Use `saved_model.save` to export it.\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:40:48.503742Z", "iopub.status.busy": "2024-08-16T07:40:48.503451Z", "iopub.status.idle": "2024-08-16T07:41:00.344927Z", "shell.execute_reply": "2024-08-16T07:41:00.344152Z" }, "id": "RrxPxqXYwp5t" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: export.tf/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: export.tf/assets\n" ] } ], "source": [ "tf.saved_model.save(export_model, 'export.tf')" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:00.352927Z", "iopub.status.busy": "2024-08-16T07:41:00.352633Z", "iopub.status.idle": "2024-08-16T07:41:03.744375Z", "shell.execute_reply": "2024-08-16T07:41:03.743618Z" }, "id": "wdpOqe2nxTDP" }, "outputs": [], "source": [ "loaded = tf.saved_model.load('export.tf')" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:03.748358Z", "iopub.status.busy": "2024-08-16T07:41:03.748094Z", "iopub.status.idle": "2024-08-16T07:41:03.821474Z", "shell.execute_reply": "2024-08-16T07:41:03.820891Z" }, "id": "sFvoII8lnpyi" }, "outputs": [ { "data": { "text/plain": [ "array([[-3.2765586 , -0.37794957, 3.0517902 ]], dtype=float32)" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "export_model(tf.constant(['The field bristled with the long and deadly spears which they bore.'])).numpy()" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:03.824737Z", "iopub.status.busy": "2024-08-16T07:41:03.824504Z", "iopub.status.idle": "2024-08-16T07:41:04.404459Z", "shell.execute_reply": "2024-08-16T07:41:04.403712Z" }, "id": "nig380T3siW_" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-08-16 07:41:04.103166: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:933] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/sequential_2/StatefulPartitionedCall/my_tokenizer/UnicodeScriptTokenize/RaggedConcat/assert_equal_1/Assert/AssertGuard/branch_executed/_42\n" ] }, { "data": { "text/plain": [ "array([[-3.2765586 , -0.37794957, 3.0517902 ]], dtype=float32)" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loaded(tf.constant(['The field bristled with the long and deadly spears which they bore.'])).numpy()" ] }, { "cell_type": "markdown", "metadata": { "id": "o6Mm0Y9QYQwE" }, "source": [ "The loss and accuracy for the model on encoded validation set and the exported model on the raw validation set are the same, as expected." ] }, { "cell_type": "markdown", "metadata": { "id": "Stk2BP8GE-qo" }, "source": [ "### Run inference on new data" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:04.408476Z", "iopub.status.busy": "2024-08-16T07:41:04.408203Z", "iopub.status.idle": "2024-08-16T07:41:06.198375Z", "shell.execute_reply": "2024-08-16T07:41:06.197646Z" }, "id": "-w1fQGJPD2Yh" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/1 [==============================] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1/1 [==============================] - 2s 2s/step\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Question: Join'd to th' Ionians with their flowing robes,\n", "Predicted label: 1\n", "Question: the allies, and his armour flashed about him so that he seemed to all\n", "Predicted label: 2\n", "Question: And with loud clangor of his arms he fell.\n", "Predicted label: 0\n" ] } ], "source": [ "inputs = [\n", " \"Join'd to th' Ionians with their flowing robes,\", # Label: 1\n", " \"the allies, and his armour flashed about him so that he seemed to all\", # Label: 2\n", " \"And with loud clangor of his arms he fell.\", # Label: 0\n", "]\n", "\n", "predicted_scores = export_model.predict(inputs)\n", "predicted_labels = tf.math.argmax(predicted_scores, axis=1)\n", "\n", "for input, label in zip(inputs, predicted_labels):\n", " print(\"Question: \", input)\n", " print(\"Predicted label: \", label.numpy())" ] }, { "cell_type": "markdown", "metadata": { "id": "9eA8TVdnA-3L" }, "source": [ "## Download more datasets using TensorFlow Datasets (TFDS)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "2QFSxfZ3Vqsn" }, "source": [ "You can download many more datasets from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview).\n", "\n", "In this example, you will use the [IMDB Large Movie Review dataset](https://www.tensorflow.org/datasets/catalog/imdb_reviews) to train a model for sentiment classification:" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:06.202360Z", "iopub.status.busy": "2024-08-16T07:41:06.201707Z", "iopub.status.idle": "2024-08-16T07:41:06.993039Z", "shell.execute_reply": "2024-08-16T07:41:06.992294Z" }, "id": "NzC65LOaVw0B" }, "outputs": [], "source": [ "# Training set.\n", "train_ds = tfds.load(\n", " 'imdb_reviews',\n", " split='train[:80%]',\n", " batch_size=BATCH_SIZE,\n", " shuffle_files=True,\n", " as_supervised=True)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:06.996887Z", "iopub.status.busy": "2024-08-16T07:41:06.996634Z", "iopub.status.idle": "2024-08-16T07:41:07.450468Z", "shell.execute_reply": "2024-08-16T07:41:07.449721Z" }, "id": "XKGkgPBkFh0k" }, "outputs": [], "source": [ "# Validation set.\n", "val_ds = tfds.load(\n", " 'imdb_reviews',\n", " split='train[80%:]',\n", " batch_size=BATCH_SIZE,\n", " shuffle_files=True,\n", " as_supervised=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "BQjf3YZAb5Ne" }, "source": [ "Print a few examples:" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:07.454909Z", "iopub.status.busy": "2024-08-16T07:41:07.454258Z", "iopub.status.idle": "2024-08-16T07:41:07.896516Z", "shell.execute_reply": "2024-08-16T07:41:07.895825Z" }, "id": "Bq1w8MnfWt2C" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Review: b\"Instead, go to the zoo, buy some peanuts and feed 'em to the monkeys. Monkeys are funny. People with amnesia who don't say much, just sit there with vacant eyes are not all that funny.

Black comedy? There isn't a black person in it, and there isn't one funny thing in it either.

Walmart buys these things up somehow and puts them on their dollar rack. It's labeled Unrated. I think they took out the topless scene. They may have taken out other stuff too, who knows? All we know is that whatever they took out, isn't there any more.

The acting seemed OK to me. There's a lot of unfathomables tho. It's supposed to be a city? It's supposed to be a big lake? If it's so hot in the church people are fanning themselves, why are they all wearing coats?\"\n", "Label: 0\n", "Review: b'Well, was Morgan Freeman any more unusual as God than George Burns? This film sure was better than that bore, \"Oh, God\". I was totally engrossed and LMAO all the way through. Carrey was perfect as the out of sorts anchorman wannabe, and Aniston carried off her part as the frustrated girlfriend in her usual well played performance. I, for one, don\\'t consider her to be either ugly or untalented. I think my favorite scene was when Carrey opened up the file cabinet thinking it could never hold his life history. See if you can spot the file in the cabinet that holds the events of his bathroom humor: I was rolling over this one. Well written and even better played out, this comedy will go down as one of this funnyman\\'s best.'\n", "Label: 1\n", "Review: b'I remember stumbling upon this special while channel-surfing in 1965. I had never heard of Barbra before. When the show was over, I thought \"This is probably the best thing on TV I will ever see in my life.\" 42 years later, that has held true. There is still nothing so amazing, so honestly astonishing as the talent that was displayed here. You can talk about all the super-stars you want to, this is the most superlative of them all!

You name it, she can do it. Comedy, pathos, sultry seduction, ballads, Barbra is truly a story-teller. Her ability to pull off anything she attempts is legendary. But this special was made in the beginning, and helped to create the legend that she quickly became. In spite of rising so far in such a short time, she has fulfilled the promise, revealing more of her talents as she went along. But they are all here from the very beginning. You will not be disappointed in viewing this.'\n", "Label: 1\n", "Review: b\"Firstly, I would like to point out that people who have criticised this film have made some glaring errors. Anything that has a rating below 6/10 is clearly utter nonsense.

Creep is an absolutely fantastic film with amazing film effects. The actors are highly believable, the narrative thought provoking and the horror and graphical content extremely disturbing.

There is much mystique in this film. Many questions arise as the audience are revealed to the strange and freakish creature that makes habitat in the dark rat ridden tunnels. How was 'Craig' created and what happened to him?

A fantastic film with a large chill factor. A film with so many unanswered questions and a film that needs to be appreciated along with others like 28 Days Later, The Bunker, Dog Soldiers and Deathwatch.

Look forward to more of these fantastic films!!\"\n", "Label: 1\n", "Review: b\"I'm sorry but I didn't like this doc very much. I can think of a million ways it could have been better. The people who made it obviously don't have much imagination. The interviews aren't very interesting and no real insight is offered. The footage isn't assembled in a very informative way, either. It's too bad because this is a movie that really deserves spellbinding special features. One thing I'll say is that Isabella Rosselini gets more beautiful the older she gets. All considered, this only gets a '4.'\"\n", "Label: 0\n" ] } ], "source": [ "for review_batch, label_batch in val_ds.take(1):\n", " for i in range(5):\n", " print(\"Review: \", review_batch[i].numpy())\n", " print(\"Label: \", label_batch[i].numpy())" ] }, { "cell_type": "markdown", "metadata": { "id": "q-lVaukyb75k" }, "source": [ "You can now preprocess the data and train a model as before.\n", "\n", "Note: You will use `tf.keras.losses.BinaryCrossentropy` instead of `tf.keras.losses.SparseCategoricalCrossentropy` for your model, since this is a binary classification problem." ] }, { "cell_type": "markdown", "metadata": { "id": "ciz2CxAsZw3Z" }, "source": [ "### Prepare the dataset for training" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:07.900669Z", "iopub.status.busy": "2024-08-16T07:41:07.900019Z", "iopub.status.idle": "2024-08-16T07:41:09.728300Z", "shell.execute_reply": "2024-08-16T07:41:09.727538Z" }, "id": "UzT_t9ihZLH4" }, "outputs": [], "source": [ "vectorize_layer = TextVectorization(\n", " max_tokens=VOCAB_SIZE,\n", " output_mode='int',\n", " output_sequence_length=MAX_SEQUENCE_LENGTH)\n", "\n", "# Make a text-only dataset (without labels), then call `TextVectorization.adapt`.\n", "train_text = train_ds.map(lambda text, labels: text)\n", "vectorize_layer.adapt(train_text)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:09.732712Z", "iopub.status.busy": "2024-08-16T07:41:09.732028Z", "iopub.status.idle": "2024-08-16T07:41:09.735983Z", "shell.execute_reply": "2024-08-16T07:41:09.735398Z" }, "id": "zz-Xrd_ZZ4tB" }, "outputs": [], "source": [ "def vectorize_text(text, label):\n", " text = tf.expand_dims(text, -1)\n", " return vectorize_layer(text), label" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:09.739242Z", "iopub.status.busy": "2024-08-16T07:41:09.738636Z", "iopub.status.idle": "2024-08-16T07:41:09.835418Z", "shell.execute_reply": "2024-08-16T07:41:09.834834Z" }, "id": "ycn0Itd6g5aF" }, "outputs": [], "source": [ "train_ds = train_ds.map(vectorize_text)\n", "val_ds = val_ds.map(vectorize_text)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:09.838943Z", "iopub.status.busy": "2024-08-16T07:41:09.838418Z", "iopub.status.idle": "2024-08-16T07:41:09.844579Z", "shell.execute_reply": "2024-08-16T07:41:09.843977Z" }, "id": "jc11jQTlZ5lj" }, "outputs": [], "source": [ "# Configure datasets for performance as before.\n", "train_ds = train_ds.cache().prefetch(tf.data.AUTOTUNE)\n", "val_ds = val_ds.cache().prefetch(tf.data.AUTOTUNE)" ] }, { "cell_type": "markdown", "metadata": { "id": "SQzoYkaGZ82Z" }, "source": [ "### Create, configure and train the model" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:09.848050Z", "iopub.status.busy": "2024-08-16T07:41:09.847592Z", "iopub.status.idle": "2024-08-16T07:41:09.900016Z", "shell.execute_reply": "2024-08-16T07:41:09.899462Z" }, "id": "B9IOTLkyZ-a7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_5\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Layer (type) Output Shape Param # \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "=================================================================\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " embedding_2 (Embedding) (None, None, 64) 640000 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " dropout_2 (Dropout) (None, None, 64) 0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " conv1d_2 (Conv1D) (None, None, 64) 20544 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " global_max_pooling1d_2 (Gl (None, 64) 0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " obalMaxPooling1D) \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " dense_3 (Dense) (None, 1) 65 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "=================================================================\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Total params: 660609 (2.52 MB)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Trainable params: 660609 (2.52 MB)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Non-trainable params: 0 (0.00 Byte)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n" ] } ], "source": [ "model = create_model(vocab_size=VOCAB_SIZE, num_labels=1)\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:09.908323Z", "iopub.status.busy": "2024-08-16T07:41:09.908078Z", "iopub.status.idle": "2024-08-16T07:41:09.917529Z", "shell.execute_reply": "2024-08-16T07:41:09.916957Z" }, "id": "xLnDs5dhaBAk" }, "outputs": [], "source": [ "model.compile(\n", " loss=losses.BinaryCrossentropy(from_logits=True),\n", " optimizer='adam',\n", " metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:09.920616Z", "iopub.status.busy": "2024-08-16T07:41:09.920393Z", "iopub.status.idle": "2024-08-16T07:41:20.605445Z", "shell.execute_reply": "2024-08-16T07:41:20.604731Z" }, "id": "rq59QpNzaDMa" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/313 [..............................] - ETA: 4:37 - loss: 0.6916 - accuracy: 0.5156" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6/313 [..............................] - ETA: 3s - loss: 0.6932 - accuracy: 0.4922 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 12/313 [>.............................] - ETA: 3s - loss: 0.6924 - accuracy: 0.4818" ] }, { 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model.fit(train_ds, validation_data=val_ds, epochs=3)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:20.609044Z", "iopub.status.busy": "2024-08-16T07:41:20.608788Z", "iopub.status.idle": "2024-08-16T07:41:20.789381Z", "shell.execute_reply": "2024-08-16T07:41:20.788669Z" }, "id": "gCMWCEtyaEbR" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/79 [..............................] - ETA: 0s - loss: 0.2093 - accuracy: 0.9219" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "26/79 [========>.....................] - ETA: 0s - loss: 0.3243 - accuracy: 0.8612" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "51/79 [==================>...........] - ETA: 0s - loss: 0.3186 - accuracy: 0.8569" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "76/79 [===========================>..] - ETA: 0s - loss: 0.3142 - accuracy: 0.8596" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "79/79 [==============================] - 0s 2ms/step - loss: 0.3165 - accuracy: 0.8584\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loss: 0.3165397047996521\n", "Accuracy: 85.84%\n" ] } ], "source": [ "loss, accuracy = model.evaluate(val_ds)\n", "\n", "print(\"Loss: \", loss)\n", "print(\"Accuracy: {:2.2%}\".format(accuracy))" ] }, { "cell_type": "markdown", "metadata": { "id": "jGtqLXVnaaFy" }, "source": [ "### Export the model" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:20.792905Z", "iopub.status.busy": "2024-08-16T07:41:20.792634Z", "iopub.status.idle": "2024-08-16T07:41:20.850931Z", "shell.execute_reply": "2024-08-16T07:41:20.850331Z" }, "id": "yE9WZARZaZr1" }, "outputs": [], "source": [ "export_model = tf.keras.Sequential(\n", " [vectorize_layer, model,\n", " layers.Activation('sigmoid')])\n", "\n", "export_model.compile(\n", " loss=losses.SparseCategoricalCrossentropy(from_logits=False),\n", " optimizer='adam',\n", " metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:41:20.854440Z", "iopub.status.busy": "2024-08-16T07:41:20.853886Z", "iopub.status.idle": "2024-08-16T07:41:20.995063Z", "shell.execute_reply": "2024-08-16T07:41:20.994363Z" }, "id": "bhF8tDH-afoC" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/1 [==============================] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1/1 [==============================] - 0s 100ms/step\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Question: This is a fantastic movie.\n", "Predicted label: 1\n", "Question: This is a bad movie.\n", "Predicted label: 0\n", "Question: This movie was so bad that it was good.\n", "Predicted label: 0\n", "Question: I will never say yes to watching this movie.\n", "Predicted label: 1\n" ] } ], "source": [ "# 0 --> negative review\n", "# 1 --> positive review\n", "inputs = [\n", " \"This is a fantastic movie.\",\n", " \"This is a bad movie.\",\n", " \"This movie was so bad that it was good.\",\n", " \"I will never say yes to watching this movie.\",\n", "]\n", "\n", "predicted_scores = export_model.predict(inputs)\n", "predicted_labels = [int(round(x[0])) for x in predicted_scores]\n", "\n", "for input, label in zip(inputs, predicted_labels):\n", " print(\"Question: \", input)\n", " print(\"Predicted label: \", label)" ] }, { "cell_type": "markdown", "metadata": { "id": "q1KSXDFPWiPN" }, "source": [ "## Conclusion\n", "\n", "This tutorial demonstrated several ways to load and preprocess text. As a next step, you can explore additional text preprocessing [TensorFlow Text](https://www.tensorflow.org/text) tutorials, such as:\n", "\n", "- [BERT Preprocessing with TF Text](https://www.tensorflow.org/text/guide/bert_preprocessing_guide)\n", "- [Tokenizing with TF Text](https://www.tensorflow.org/text/guide/tokenizers)\n", "- [Subword tokenizers](https://www.tensorflow.org/text/guide/subwords_tokenizer)\n", "\n", "You can also find new datasets on [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). And, to learn more about `tf.data`, check out the guide on [building input pipelines](../../guide/data.ipynb)." ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "private_outputs": true, "provenance": [ { "file_id": "1XHr3IzoXn_Si4HSDpY43hdTirjwMDvON", "timestamp": 1694267031282 }, { "file_id": "https://github.com/tensorflow/docs/blob/4daf8a1645ddb4dd97ef31dca2e456ffaedbc8a1/site/en/tutorials/load_data/text.ipynb", "timestamp": 1694092268316 } ], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "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": "3.9.19" } }, "nbformat": 4, "nbformat_minor": 0 }