{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "U2ll54gJt3hR"
},
"source": [
"# 머신 러닝 교과서 3판"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f6CoM_jTt3hS"
},
"source": [
"# 16장 - 순환 신경망으로 순차 데이터 모델링 (1/2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FAhOxXi8t3hT"
},
"source": [
"**아래 링크를 통해 이 노트북을 주피터 노트북 뷰어(nbviewer.jupyter.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.**\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ivB0ZSFGt3hT"
},
"source": [
"### 목차"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "57dQGeStt3hT"
},
"source": [
"- 순차 데이터 소개\n",
" - 순차 데이터 모델링: 순서를 고려한다\n",
" - 시퀀스 표현\n",
" - 시퀀스 모델링의 종류\n",
"- 시퀀스 모델링을 위한 RNN\n",
" - RNN 반복 구조 이해하기\n",
" - RNN의 활성화 출력 계산\n",
" - 은닉 순환과 출력 순환\n",
" - 긴 시퀀스 학습의 어려움\n",
" - LSTM 셀\n",
"- 텐서플로로 시퀀스 모델링을 위한 RNN 구현하기\n",
" - 첫 번째 프로젝트: IMDb 영화 리뷰 감성 분석\n",
" - 영화 리뷰 데이터 준비\n",
" - 문장 인코딩을 위한 임베딩 층\n",
" - RNN 모델 만들기\n",
" - 감성 분석 작업을 위한 RNN 모델 만들기"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-31T14:52:56.448296Z",
"iopub.status.busy": "2020-12-31T14:52:56.447399Z",
"iopub.status.idle": "2020-12-31T14:52:56.450102Z",
"shell.execute_reply": "2020-12-31T14:52:56.450840Z"
},
"id": "SHqvUk9kt3hT"
},
"outputs": [],
"source": [
"from IPython.display import Image"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Wr5StFyQt3hT"
},
"source": [
"# 순차 데이터 소개\n",
"## 순차 데이터 모델링: 순서를 고려한다\n",
"## 시퀀스 표현"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
},
"execution": {
"iopub.execute_input": "2020-12-31T14:52:56.456190Z",
"iopub.status.busy": "2020-12-31T14:52:56.455553Z",
"iopub.status.idle": "2020-12-31T14:52:56.463431Z",
"shell.execute_reply": "2020-12-31T14:52:56.464269Z"
},
"id": "k1c-6p52t3hU",
"outputId": "47d2fa04-ac6f-40f4-90d8-036ab92a396a"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 2
}
],
"source": [
"Image(url='https://git.io/JLdVm', width=700)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QL3g5J07t3hU"
},
"source": [
"## 시퀀스 모델링의 종류"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 427
},
"execution": {
"iopub.execute_input": "2020-12-31T14:52:56.470205Z",
"iopub.status.busy": "2020-12-31T14:52:56.469239Z",
"iopub.status.idle": "2020-12-31T14:52:56.510740Z",
"shell.execute_reply": "2020-12-31T14:52:56.510046Z"
},
"id": "Qe4WgH2Ot3hU",
"outputId": "614daf60-6fac-4308-98f5-283ecb4ad73f"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 3
}
],
"source": [
"Image(url='https://git.io/JLdVO', width=700)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z5XrGPuht3hU"
},
"source": [
"# 시퀀스 모델링을 위한 RNN\n",
"## RNN 반복 구조 이해하기"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 312
},
"execution": {
"iopub.execute_input": "2020-12-31T14:52:56.516239Z",
"iopub.status.busy": "2020-12-31T14:52:56.515511Z",
"iopub.status.idle": "2020-12-31T14:52:56.521165Z",
"shell.execute_reply": "2020-12-31T14:52:56.520502Z"
},
"id": "YrVhGKS0t3hV",
"outputId": "3f34feae-2b13-4944-ea51-77b0b2e8d9c8"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 4
}
],
"source": [
"Image(url='https://git.io/JLdV3', width=700)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 614
},
"execution": {
"iopub.execute_input": "2020-12-31T14:52:56.525160Z",
"iopub.status.busy": "2020-12-31T14:52:56.524521Z",
"iopub.status.idle": "2020-12-31T14:52:56.530736Z",
"shell.execute_reply": "2020-12-31T14:52:56.531285Z"
},
"id": "_L2SXQ0Bt3hV",
"outputId": "3eb49f57-80f3-4b17-df2b-4015ac2d3c4b"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 5
}
],
"source": [
"Image(url='https://git.io/JLdVs', width=700)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Dv07BsIEt3hV"
},
"source": [
"## RNN의 활성화 출력 계산"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"execution": {
"iopub.execute_input": "2020-12-31T14:52:56.535282Z",
"iopub.status.busy": "2020-12-31T14:52:56.534647Z",
"iopub.status.idle": "2020-12-31T14:52:56.540928Z",
"shell.execute_reply": "2020-12-31T14:52:56.540327Z"
},
"id": "KxnXQiRSt3hV",
"outputId": "a753ba40-5be7-4a78-d8a0-ac3f79b7141f"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 6
}
],
"source": [
"Image(url='https://git.io/JLdVC', width=700)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 373
},
"execution": {
"iopub.execute_input": "2020-12-31T14:52:56.544851Z",
"iopub.status.busy": "2020-12-31T14:52:56.544209Z",
"iopub.status.idle": "2020-12-31T14:52:56.551185Z",
"shell.execute_reply": "2020-12-31T14:52:56.551727Z"
},
"id": "c7_mK8qVt3hV",
"outputId": "f30e36d3-6213-4629-f4a0-1f746008d384"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 7
}
],
"source": [
"Image(url='https://git.io/JLdVW', width=700)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "u8Jumkd0t3hW"
},
"source": [
"## 은닉 순환과 출력 순환"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 603
},
"execution": {
"iopub.execute_input": "2020-12-31T14:52:56.555805Z",
"iopub.status.busy": "2020-12-31T14:52:56.555162Z",
"iopub.status.idle": "2020-12-31T14:52:56.561794Z",
"shell.execute_reply": "2020-12-31T14:52:56.562334Z"
},
"id": "iuGEUtnYt3hW",
"outputId": "e4f61e63-ae24-48eb-dcf4-8b038fa424e7"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 8
}
],
"source": [
"Image(url='https://git.io/JLdV8', width=700)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T14:52:56.568669Z",
"iopub.status.busy": "2020-12-31T14:52:56.568012Z",
"iopub.status.idle": "2020-12-31T14:52:58.384001Z",
"shell.execute_reply": "2020-12-31T14:52:58.383269Z"
},
"id": "Wvo3vxr8t3hW",
"outputId": "145b1fca-232a-44a8-ee58-e67b448f87d5"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"W_xh 크기: (5, 2)\n",
"W_oo 크기: (2, 2)\n",
"b_h 크기: (2,)\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"tf.random.set_seed(1)\n",
"\n",
"rnn_layer = tf.keras.layers.SimpleRNN(\n",
" units=2, use_bias=True,\n",
" return_sequences=True)\n",
"rnn_layer.build(input_shape=(None, None, 5))\n",
"\n",
"w_xh, w_oo, b_h = rnn_layer.weights\n",
"\n",
"print('W_xh 크기:', w_xh.shape)\n",
"print('W_oo 크기:', w_oo.shape)\n",
"print('b_h 크기:', b_h.shape)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T14:52:58.395104Z",
"iopub.status.busy": "2020-12-31T14:52:58.394214Z",
"iopub.status.idle": "2020-12-31T14:52:58.414734Z",
"shell.execute_reply": "2020-12-31T14:52:58.414063Z"
},
"id": "THMnNQoEt3hW",
"outputId": "e9dca2d2-a0e4-4c66-ee3e-ffb1b055923d",
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"타임 스텝 0 =>\n",
" 입력 : [[1. 1. 1. 1. 1.]]\n",
" 은닉 : [[0.96635884 0.9771539 ]]\n",
" 출력 (수동) : [[0.7470998 0.7518313]]\n",
" SimpleRNN 출력 : [0.7470998 0.7518313]\n",
"\n",
"타임 스텝 1 =>\n",
" 입력 : [[2. 2. 2. 2. 2.]]\n",
" 은닉 : [[1.9327177 1.9543078]]\n",
" 출력 (수동) : [[0.99347186 0.89415705]]\n",
" SimpleRNN 출력 : [0.99347186 0.89415705]\n",
"\n",
"타임 스텝 2 =>\n",
" 입력 : [[3. 3. 3. 3. 3.]]\n",
" 은닉 : [[2.8990765 2.9314618]]\n",
" 출력 (수동) : [[0.99937034 0.9767568 ]]\n",
" SimpleRNN 출력 : [0.99937034 0.9767568 ]\n",
"\n"
]
}
],
"source": [
"x_seq = tf.convert_to_tensor(\n",
" [[1.0]*5, [2.0]*5, [3.0]*5],\n",
" dtype=tf.float32)\n",
"\n",
"\n",
"## SimepleRNN의 출력:\n",
"output = rnn_layer(tf.reshape(x_seq, shape=(1, 3, 5)))\n",
"\n",
"## 수동으로 출력 계산하기:\n",
"out_man = []\n",
"for t in range(len(x_seq)):\n",
" xt = tf.reshape(x_seq[t], (1, 5))\n",
" print('타임 스텝 {} =>'.format(t))\n",
" print(' 입력 :', xt.numpy())\n",
"\n",
" ht = tf.matmul(xt, w_xh) + b_h\n",
" print(' 은닉 :', ht.numpy())\n",
"\n",
" if t>0:\n",
" prev_o = out_man[t-1]\n",
" else:\n",
" prev_o = tf.zeros(shape=(ht.shape))\n",
"\n",
" ot = ht + tf.matmul(prev_o, w_oo)\n",
" ot = tf.math.tanh(ot)\n",
" out_man.append(ot)\n",
" print(' 출력 (수동) :', ot.numpy())\n",
" print(' SimpleRNN 출력 :'.format(t), output[0][t].numpy())\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oTJJZpjUt3hX"
},
"source": [
"## 긴 시퀀스 학습의 어려움"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 352
},
"execution": {
"iopub.execute_input": "2020-12-31T14:52:58.419575Z",
"iopub.status.busy": "2020-12-31T14:52:58.418902Z",
"iopub.status.idle": "2020-12-31T14:52:58.424237Z",
"shell.execute_reply": "2020-12-31T14:52:58.423554Z"
},
"id": "BrsADvYht3hX",
"outputId": "c6320490-adcb-474f-e1f2-f68c0f9f31eb"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 11
}
],
"source": [
"Image(url='https://git.io/JLdV4', width=700)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RtDltN0gt3hX"
},
"source": [
"## LSTM 셀"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 382
},
"execution": {
"iopub.execute_input": "2020-12-31T14:52:58.429035Z",
"iopub.status.busy": "2020-12-31T14:52:58.428310Z",
"iopub.status.idle": "2020-12-31T14:52:58.438957Z",
"shell.execute_reply": "2020-12-31T14:52:58.439507Z"
},
"id": "6YpVdbeTt3hX",
"outputId": "c7dea33e-cef8-401d-815b-213b6e2a3195"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 12
}
],
"source": [
"Image(url='https://git.io/JLdVR', width=700)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qNOM8cBit3hX"
},
"source": [
"# 텐서플로로 시퀀스 모델링을 위한 RNN 구현하기\n",
"## 첫 번째 프로젝트: IMDb 영화 리뷰 감성 분석\n",
"### 영화 리뷰 데이터 준비"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-31T14:52:58.443781Z",
"iopub.status.busy": "2020-12-31T14:52:58.443146Z",
"iopub.status.idle": "2020-12-31T14:52:58.776050Z",
"shell.execute_reply": "2020-12-31T14:52:58.775357Z"
},
"id": "EDdVCNv5t3hY"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import tensorflow_datasets as tfds\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gUgiOmnUv7DQ",
"outputId": "2f905bde-20fd-4815-e246-76aaee72f0d2"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2023-11-10 06:02:41-- https://github.com/rickiepark/python-machine-learning-book-3rd-edition/raw/master/ch08/movie_data.csv.gz\n",
"Resolving github.com (github.com)... 192.30.255.112\n",
"Connecting to github.com (github.com)|192.30.255.112|:443... connected.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: https://raw.githubusercontent.com/rickiepark/python-machine-learning-book-3rd-edition/master/ch08/movie_data.csv.gz [following]\n",
"--2023-11-10 06:02:41-- https://raw.githubusercontent.com/rickiepark/python-machine-learning-book-3rd-edition/master/ch08/movie_data.csv.gz\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 26521894 (25M) [application/octet-stream]\n",
"Saving to: ‘../ch08/movie_data.csv.gz’\n",
"\n",
"../ch08/movie_data. 100%[===================>] 25.29M 142MB/s in 0.2s \n",
"\n",
"2023-11-10 06:02:41 (142 MB/s) - ‘../ch08/movie_data.csv.gz’ saved [26521894/26521894]\n",
"\n"
]
}
],
"source": [
"# 코랩에서 실행하는 경우 다음 코드를 실행하세요.\n",
"!mkdir ../ch08\n",
"!wget https://github.com/rickiepark/python-machine-learning-book-3rd-edition/raw/master/ch08/movie_data.csv.gz -O ../ch08/movie_data.csv.gz"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-31T14:52:58.786349Z",
"iopub.status.busy": "2020-12-31T14:52:58.785414Z",
"iopub.status.idle": "2020-12-31T14:53:00.001551Z",
"shell.execute_reply": "2020-12-31T14:53:00.002307Z"
},
"id": "9HcKmMPmt3hY"
},
"outputs": [],
"source": [
"import os\n",
"import gzip\n",
"import shutil\n",
"\n",
"\n",
"with gzip.open('../ch08/movie_data.csv.gz', 'rb') as f_in, open('movie_data.csv', 'wb') as f_out:\n",
" shutil.copyfileobj(f_in, f_out)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"execution": {
"iopub.execute_input": "2020-12-31T14:53:00.009102Z",
"iopub.status.busy": "2020-12-31T14:53:00.008204Z",
"iopub.status.idle": "2020-12-31T14:53:00.445362Z",
"shell.execute_reply": "2020-12-31T14:53:00.444609Z"
},
"id": "I-9BuHr_t3hY",
"outputId": "d7acb197-d14b-4a11-92e4-7b9c337cfc7d"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" review sentiment\n",
"49995 OK, lets start with the best. the building. al... 0\n",
"49996 The British 'heritage film' industry is out of... 0\n",
"49997 I don't even know where to begin on this one. ... 0\n",
"49998 Richard Tyler is a little boy who is scared of... 0\n",
"49999 I waited long to watch this movie. Also becaus... 1"
],
"text/html": [
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" \n",
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"
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"
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"
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]
},
"metadata": {},
"execution_count": 16
}
],
"source": [
"df = pd.read_csv('movie_data.csv', encoding='utf-8')\n",
"\n",
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T14:53:00.520021Z",
"iopub.status.busy": "2020-12-31T14:53:00.519118Z",
"iopub.status.idle": "2020-12-31T14:53:00.531067Z",
"shell.execute_reply": "2020-12-31T14:53:00.530180Z"
},
"id": "kpF_RvlVt3hY",
"outputId": "2aae24b2-2372-4a08-a0db-a182de46fcbc"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"b'In 1974, the teenager Martha Moxley (Maggie Grace)' 1\n",
"b'OK... so... I really like Kris Kristofferson and h' 0\n",
"b'***SPOILER*** Do not read this, if you think about' 0\n"
]
}
],
"source": [
"# 단계 1: 데이터셋 만들기\n",
"target = df.pop('sentiment')\n",
"\n",
"ds_raw = tf.data.Dataset.from_tensor_slices(\n",
" (df.values, target.values))\n",
"\n",
"## 확인:\n",
"for ex in ds_raw.take(3):\n",
" tf.print(ex[0].numpy()[0][:50], ex[1])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8IBmW08tt3hY"
},
"source": [
" * **훈련/검증/테스트 분할**"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-31T14:53:00.537026Z",
"iopub.status.busy": "2020-12-31T14:53:00.536266Z",
"iopub.status.idle": "2020-12-31T14:53:00.540439Z",
"shell.execute_reply": "2020-12-31T14:53:00.539876Z"
},
"id": "89xqs9SXt3hZ"
},
"outputs": [],
"source": [
"tf.random.set_seed(1)\n",
"\n",
"ds_raw = ds_raw.shuffle(\n",
" 50000, reshuffle_each_iteration=False)\n",
"\n",
"ds_raw_test = ds_raw.take(25000)\n",
"ds_raw_train_valid = ds_raw.skip(25000)\n",
"ds_raw_train = ds_raw_train_valid.take(20000)\n",
"ds_raw_valid = ds_raw_train_valid.skip(20000)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8eB8MAjOt3hZ"
},
"source": [
" * **Tokenizer와 Encoder**\n",
" * `tfds.deprecated.text.Tokenizer`: https://www.tensorflow.org/datasets/api_docs/python/tfds/deprecated/text/Tokenizer\n",
" * `tfds.deprecated.text.TokenTextEncoder`: https://www.tensorflow.org/datasets/api_docs/python/tfds/deprecated/text/TokenTextEncoder"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hzP-YuF3t3hZ"
},
"source": [
" * **시퀀스 인코딩: 각 시퀀스에서 마지막 100개 원소만 유지하기**"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T14:53:00.547157Z",
"iopub.status.busy": "2020-12-31T14:53:00.546406Z",
"iopub.status.idle": "2020-12-31T14:53:04.613801Z",
"shell.execute_reply": "2020-12-31T14:53:04.612898Z"
},
"id": "zH3IFCust3hZ",
"outputId": "8c237c2c-d946-483a-ca49-4741c9d9b1fa",
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"어휘 사전 크기: 87007\n"
]
}
],
"source": [
"## 단계 2: 고유 토큰(단어) 찾기\n",
"from collections import Counter\n",
"\n",
"try:\n",
" tokenizer = tfds.features.text.Tokenizer()\n",
"except AttributeError:\n",
" tokenizer = tfds.deprecated.text.Tokenizer()\n",
"\n",
"token_counts = Counter()\n",
"\n",
"for example in ds_raw_train:\n",
" tokens = tokenizer.tokenize(example[0].numpy()[0])\n",
" token_counts.update(tokens)\n",
"\n",
"print('어휘 사전 크기:', len(token_counts))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T14:53:04.657429Z",
"iopub.status.busy": "2020-12-31T14:53:04.637210Z",
"iopub.status.idle": "2020-12-31T14:53:04.747652Z",
"shell.execute_reply": "2020-12-31T14:53:04.746754Z"
},
"id": "yzd68Ikut3hZ",
"outputId": "5971fe2f-422a-498a-cffe-5e57ed7592ff"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[232, 9, 270, 1123]"
]
},
"metadata": {},
"execution_count": 20
}
],
"source": [
"## 단계 3: 고유 토큰을 정수로 인코딩하기\n",
"try:\n",
" encoder = tfds.features.text.TokenTextEncoder(token_counts)\n",
"except AttributeError:\n",
" encoder = tfds.deprecated.text.TokenTextEncoder(token_counts)\n",
"\n",
"example_str = 'This is an example!'\n",
"encoder.encode(example_str)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-31T14:53:04.753242Z",
"iopub.status.busy": "2020-12-31T14:53:04.752412Z",
"iopub.status.idle": "2020-12-31T14:53:04.755655Z",
"shell.execute_reply": "2020-12-31T14:53:04.754832Z"
},
"id": "o2_YKm8bt3ha"
},
"outputs": [],
"source": [
"## 단계 3-A: 변환 함수 정의하기\n",
"def encode(text_tensor, label):\n",
" text = text_tensor.numpy()[0]\n",
" encoded_text = encoder.encode(text)\n",
" return encoded_text, label\n",
"\n",
"## 단계 3-B: 인코딩 함수를 텐서플로 연산으로 감싸기\n",
"def encode_map_fn(text, label):\n",
" return tf.py_function(encode, inp=[text, label],\n",
" Tout=(tf.int64, tf.int64))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T14:53:04.766645Z",
"iopub.status.busy": "2020-12-31T14:53:04.766238Z",
"iopub.status.idle": "2020-12-31T14:53:05.315769Z",
"shell.execute_reply": "2020-12-31T14:53:05.316485Z"
},
"id": "frJTGpBxt3ha",
"outputId": "e2263c3c-1b49-4d14-84d9-8504be743b7b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"시퀀스 길이: (24,)\n",
"시퀀스 길이: (179,)\n",
"시퀀스 길이: (262,)\n",
"시퀀스 길이: (535,)\n",
"시퀀스 길이: (130,)\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(,\n",
" )"
]
},
"metadata": {},
"execution_count": 22
}
],
"source": [
"ds_train = ds_raw_train.map(encode_map_fn)\n",
"ds_valid = ds_raw_valid.map(encode_map_fn)\n",
"ds_test = ds_raw_test.map(encode_map_fn)\n",
"\n",
"tf.random.set_seed(1)\n",
"for example in ds_train.shuffle(1000).take(5):\n",
" print('시퀀스 길이:', example[0].shape)\n",
"\n",
"example"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JoiIum3jt3ha"
},
"source": [
" * **batch() 대 padded_batch()**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YdUNeW8rt3ha"
},
"source": [
"```python\n",
"\n",
"# 에러 발생\n",
"BATCH_SIZE = 32\n",
"train_data = all_encoded_data.batch(BATCH_SIZE)\n",
"\n",
"next(iter(train_data))\n",
"\n",
"# 이 코드는 에러를 발생시킵니다\n",
"# 이 데이터셋에는 .batch()를 적용할 수 없습니다\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T14:53:05.323061Z",
"iopub.status.busy": "2020-12-31T14:53:05.322063Z",
"iopub.status.idle": "2020-12-31T14:53:05.568975Z",
"shell.execute_reply": "2020-12-31T14:53:05.568087Z"
},
"id": "tXrml4TEt3ha",
"outputId": "eb620d07-148e-4aa7-a453-57c3d188465d"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"개별 샘플 크기: (119,)\n",
"개별 샘플 크기: (688,)\n",
"개별 샘플 크기: (308,)\n",
"개별 샘플 크기: (204,)\n",
"개별 샘플 크기: (326,)\n",
"개별 샘플 크기: (240,)\n",
"개별 샘플 크기: (127,)\n",
"개별 샘플 크기: (453,)\n",
"배치 차원: (4, 688)\n",
"배치 차원: (4, 453)\n"
]
}
],
"source": [
"## 일부 데이터 추출하기\n",
"ds_subset = ds_train.take(8)\n",
"for example in ds_subset:\n",
" print('개별 샘플 크기:', example[0].shape)\n",
"\n",
"## 배치 데이터 만들기\n",
"ds_batched = ds_subset.padded_batch(\n",
" 4, padded_shapes=([-1], []))\n",
"\n",
"for batch in ds_batched:\n",
" print('배치 차원:', batch[0].shape)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-31T14:53:05.575398Z",
"iopub.status.busy": "2020-12-31T14:53:05.574415Z",
"iopub.status.idle": "2020-12-31T14:53:05.579834Z",
"shell.execute_reply": "2020-12-31T14:53:05.579134Z"
},
"id": "a2A9ol3Jt3hb"
},
"outputs": [],
"source": [
"## 배치 데이터 만들기\n",
"train_data = ds_train.padded_batch(\n",
" 32, padded_shapes=([-1],[]))\n",
"\n",
"valid_data = ds_valid.padded_batch(\n",
" 32, padded_shapes=([-1],[]))\n",
"\n",
"test_data = ds_test.padded_batch(\n",
" 32, padded_shapes=([-1],[]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qUybBb3zt3hb"
},
"source": [
"### 문장 인코딩을 위한 임베딩 층\n",
"\n",
" * `input_dim`: 단어 개수, 즉 최대 정수 인덱스 + 1.\n",
" * `output_dim`:\n",
" * `input_length`: (패딩된) 시퀀스 길이\n",
" * 예를 들어, `'This is an example' -> [0, 0, 0, 0, 0, 0, 3, 1, 8, 9]` \n",
" => input_lenght는 10\n",
"\n",
" * 이 층을 호출할 때 입력으로 정수값을 받습니다. 임베딩 층이 정수를 `[output_dim]` 크기의 실수 벡터로 변환합니다\n",
" * 입력 크기가 `[BATCH_SIZE]`이면 출력 크기는 `[BATCH_SIZE, output_dim]`가 됩니다\n",
" * 입력 크기가 `[BATCH_SIZE, 10]`이면 출력 크기는 `[BATCH_SIZE, 10, output_dim]`가 됩니다"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 579
},
"execution": {
"iopub.execute_input": "2020-12-31T14:53:05.584719Z",
"iopub.status.busy": "2020-12-31T14:53:05.584041Z",
"iopub.status.idle": "2020-12-31T14:53:05.593015Z",
"shell.execute_reply": "2020-12-31T14:53:05.592348Z"
},
"id": "8i2mrfBWt3hb",
"outputId": "8d546e94-e451-48ea-846a-bf626baa3875"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 25
}
],
"source": [
"Image(url='https://git.io/JLdV0', width=700)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T14:53:05.601333Z",
"iopub.status.busy": "2020-12-31T14:53:05.600692Z",
"iopub.status.idle": "2020-12-31T14:53:05.618008Z",
"shell.execute_reply": "2020-12-31T14:53:05.617346Z"
},
"id": "esPfmEMMt3hb",
"outputId": "efbd4692-b5cd-4528-ea7f-fcd8ce0c9e4a"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" embed-layer (Embedding) (None, 20, 6) 600 \n",
" \n",
"=================================================================\n",
"Total params: 600 (2.34 KB)\n",
"Trainable params: 600 (2.34 KB)\n",
"Non-trainable params: 0 (0.00 Byte)\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"from tensorflow.keras.layers import Embedding\n",
"\n",
"\n",
"model = tf.keras.Sequential()\n",
"\n",
"model.add(Embedding(input_dim=100,\n",
" output_dim=6,\n",
" input_length=20,\n",
" name='embed-layer'))\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rqwKaKkqt3hb"
},
"source": [
"### RNN 모델 만들기\n",
"\n",
"* **케라스 RNN 층:**\n",
" * `tf.keras.layers.SimpleRNN(units, return_sequences=False)`\n",
" * `tf.keras.layers.LSTM(..)`\n",
" * `tf.keras.layers.GRU(..)`\n",
" * `tf.keras.layers.Bidirectional()`\n",
"\n",
"* **`return_sequenes=?` 결정하기**\n",
" * 다층 RNN이면 마지막 층을 제외하고 모든 RNN 층을 `return_sequenes=True`로 지정합니다\n",
" * 마지막 RNN 층은 문제의 종류에 따라 결정됩니다:\n",
" * 다대다: -> `return_sequences=True`\n",
" * 다대일: -> `return_sequenes=False`"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T14:53:05.626167Z",
"iopub.status.busy": "2020-12-31T14:53:05.625414Z",
"iopub.status.idle": "2020-12-31T14:53:05.706279Z",
"shell.execute_reply": "2020-12-31T14:53:05.707065Z"
},
"id": "oy4iAXCHt3hc",
"outputId": "efc37584-a02c-47e0-9a63-1b42574dcff9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_1\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" embedding (Embedding) (None, None, 32) 32000 \n",
" \n",
" simple_rnn_1 (SimpleRNN) (None, None, 32) 2080 \n",
" \n",
" simple_rnn_2 (SimpleRNN) (None, 32) 2080 \n",
" \n",
" dense (Dense) (None, 1) 33 \n",
" \n",
"=================================================================\n",
"Total params: 36193 (141.38 KB)\n",
"Trainable params: 36193 (141.38 KB)\n",
"Non-trainable params: 0 (0.00 Byte)\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"## SimpleRNN 층으로 RNN 모델 만들기\n",
"from tensorflow.keras import Sequential\n",
"from tensorflow.keras.layers import Embedding\n",
"from tensorflow.keras.layers import SimpleRNN\n",
"from tensorflow.keras.layers import Dense\n",
"\n",
"model = Sequential()\n",
"model.add(Embedding(1000, 32))\n",
"model.add(SimpleRNN(32, return_sequences=True))\n",
"model.add(SimpleRNN(32))\n",
"model.add(Dense(1))\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T14:53:05.715529Z",
"iopub.status.busy": "2020-12-31T14:53:05.714882Z",
"iopub.status.idle": "2020-12-31T14:53:06.010097Z",
"shell.execute_reply": "2020-12-31T14:53:06.009262Z"
},
"id": "hP_dd6GUt3hc",
"outputId": "7bfeac8b-4c0a-4f08-c9c9-319a473c544b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_2\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" embedding_1 (Embedding) (None, None, 32) 320000 \n",
" \n",
" lstm (LSTM) (None, None, 32) 8320 \n",
" \n",
" lstm_1 (LSTM) (None, 32) 8320 \n",
" \n",
" dense_1 (Dense) (None, 1) 33 \n",
" \n",
"=================================================================\n",
"Total params: 336673 (1.28 MB)\n",
"Trainable params: 336673 (1.28 MB)\n",
"Non-trainable params: 0 (0.00 Byte)\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"## LSTM 층으로 RNN 모델 만들기\n",
"from tensorflow.keras.layers import LSTM\n",
"\n",
"\n",
"model = Sequential()\n",
"model.add(Embedding(10000, 32))\n",
"model.add(LSTM(32, return_sequences=True))\n",
"model.add(LSTM(32))\n",
"model.add(Dense(1))\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T14:53:06.020415Z",
"iopub.status.busy": "2020-12-31T14:53:06.018360Z",
"iopub.status.idle": "2020-12-31T14:53:06.272493Z",
"shell.execute_reply": "2020-12-31T14:53:06.271598Z"
},
"id": "6UQfBf4yt3hc",
"outputId": "6b2f8565-d4cc-4482-8212-00ff65a2c487"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_3\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" embedding_2 (Embedding) (None, None, 32) 320000 \n",
" \n",
" gru (GRU) (None, None, 32) 6336 \n",
" \n",
" gru_1 (GRU) (None, 32) 6336 \n",
" \n",
" dense_2 (Dense) (None, 1) 33 \n",
" \n",
"=================================================================\n",
"Total params: 332705 (1.27 MB)\n",
"Trainable params: 332705 (1.27 MB)\n",
"Non-trainable params: 0 (0.00 Byte)\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"## GRU 층으로 RNN 모델 만들기\n",
"from tensorflow.keras.layers import GRU\n",
"\n",
"model = Sequential()\n",
"model.add(Embedding(10000, 32))\n",
"model.add(GRU(32, return_sequences=True))\n",
"model.add(GRU(32))\n",
"model.add(Dense(1))\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TFSW6uDst3hc"
},
"source": [
"### 감성 분석 작업을 위한 RNN 모델 만들기"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T14:53:06.298278Z",
"iopub.status.busy": "2020-12-31T14:53:06.297321Z",
"iopub.status.idle": "2020-12-31T15:31:18.726835Z",
"shell.execute_reply": "2020-12-31T15:31:18.727434Z"
},
"id": "FszX7_Qft3hc",
"outputId": "54c4c6cf-8923-4986-d7ca-45a67ab8c3fb"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_4\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" embed-layer (Embedding) (None, None, 20) 1740180 \n",
" \n",
" bidir-lstm (Bidirectional) (None, 128) 43520 \n",
" \n",
" dense_3 (Dense) (None, 64) 8256 \n",
" \n",
" dense_4 (Dense) (None, 1) 65 \n",
" \n",
"=================================================================\n",
"Total params: 1792021 (6.84 MB)\n",
"Trainable params: 1792021 (6.84 MB)\n",
"Non-trainable params: 0 (0.00 Byte)\n",
"_________________________________________________________________\n",
"Epoch 1/10\n",
"625/625 [==============================] - 145s 220ms/step - loss: 0.6439 - accuracy: 0.5989 - val_loss: 0.6189 - val_accuracy: 0.6930\n",
"Epoch 2/10\n",
"625/625 [==============================] - 44s 71ms/step - loss: 0.4293 - accuracy: 0.8034 - val_loss: 0.4391 - val_accuracy: 0.8138\n",
"Epoch 3/10\n",
"625/625 [==============================] - 45s 72ms/step - loss: 0.2220 - accuracy: 0.9161 - val_loss: 0.4540 - val_accuracy: 0.8336\n",
"Epoch 4/10\n",
"625/625 [==============================] - 45s 71ms/step - loss: 0.1189 - accuracy: 0.9625 - val_loss: 0.5119 - val_accuracy: 0.8344\n",
"Epoch 5/10\n",
"625/625 [==============================] - 45s 71ms/step - loss: 0.0674 - accuracy: 0.9798 - val_loss: 0.5506 - val_accuracy: 0.8444\n",
"Epoch 6/10\n",
"625/625 [==============================] - 46s 74ms/step - loss: 0.0503 - accuracy: 0.9854 - val_loss: 0.6473 - val_accuracy: 0.8124\n",
"Epoch 7/10\n",
"625/625 [==============================] - 45s 72ms/step - loss: 0.0418 - accuracy: 0.9875 - val_loss: 0.7181 - val_accuracy: 0.8300\n",
"Epoch 8/10\n",
"625/625 [==============================] - 44s 70ms/step - loss: 0.0475 - accuracy: 0.9854 - val_loss: 0.8262 - val_accuracy: 0.7650\n",
"Epoch 9/10\n",
"625/625 [==============================] - 45s 72ms/step - loss: 0.0304 - accuracy: 0.9910 - val_loss: 0.7237 - val_accuracy: 0.8272\n",
"Epoch 10/10\n",
"625/625 [==============================] - 44s 70ms/step - loss: 0.0156 - accuracy: 0.9958 - val_loss: 0.8161 - val_accuracy: 0.8180\n",
"782/782 [==============================] - 34s 44ms/step - loss: 0.7992 - accuracy: 0.8179\n",
"테스트 정확도: 81.79%\n"
]
}
],
"source": [
"embedding_dim = 20\n",
"vocab_size = len(token_counts) + 2\n",
"\n",
"tf.random.set_seed(1)\n",
"\n",
"## 모델 생성\n",
"bi_lstm_model = tf.keras.Sequential([\n",
" tf.keras.layers.Embedding(\n",
" input_dim=vocab_size,\n",
" output_dim=embedding_dim,\n",
" name='embed-layer'),\n",
"\n",
" tf.keras.layers.Bidirectional(\n",
" tf.keras.layers.LSTM(64, name='lstm-layer'),\n",
" name='bidir-lstm'),\n",
"\n",
" tf.keras.layers.Dense(64, activation='relu'),\n",
"\n",
" tf.keras.layers.Dense(1, activation='sigmoid')\n",
"])\n",
"\n",
"bi_lstm_model.summary()\n",
"\n",
"## 컴파일과 훈련:\n",
"bi_lstm_model.compile(\n",
" optimizer=tf.keras.optimizers.Adam(1e-3),\n",
" loss=tf.keras.losses.BinaryCrossentropy(from_logits=False),\n",
" metrics=['accuracy'])\n",
"\n",
"history = bi_lstm_model.fit(\n",
" train_data,\n",
" validation_data=valid_data,\n",
" epochs=10)\n",
"\n",
"## 테스트 데이터에서 평가\n",
"test_results= bi_lstm_model.evaluate(test_data)\n",
"print('테스트 정확도: {:.2f}%'.format(test_results[1]*100))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-31T15:31:18.733388Z",
"iopub.status.busy": "2020-12-31T15:31:18.732717Z",
"iopub.status.idle": "2020-12-31T15:31:19.095760Z",
"shell.execute_reply": "2020-12-31T15:31:19.096539Z"
},
"id": "yAYzzBm2t3hd",
"outputId": "afb55e94-bfc6-46b3-9612-c2549788fdef",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
" saving_api.save_model(\n"
]
}
],
"source": [
"if not os.path.exists('models'):\n",
" os.mkdir('models')\n",
"\n",
"\n",
"bi_lstm_model.save('models/Bidir-LSTM-full-length-seq.h5')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bOGhajp5t3hd"
},
"source": [
" * **짧은 시퀀스에 SimpleRNN 적용하기**"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-31T15:31:19.114411Z",
"iopub.status.busy": "2020-12-31T15:31:19.113501Z",
"iopub.status.idle": "2020-12-31T15:31:19.116108Z",
"shell.execute_reply": "2020-12-31T15:31:19.115368Z"
},
"id": "TDbCSxpIt3hd"
},
"outputs": [],
"source": [
"def preprocess_datasets(\n",
" ds_raw_train,\n",
" ds_raw_valid,\n",
" ds_raw_test,\n",
" max_seq_length=None,\n",
" batch_size=32):\n",
"\n",
" ## 단계 1: (데이터셋 만들기 이미 완료)\n",
" ## 단계 2: 고유 토큰 찾기\n",
" try:\n",
" tokenizer = tfds.features.text.Tokenizer()\n",
" except AttributeError:\n",
" tokenizer = tfds.deprecated.text.Tokenizer()\n",
"\n",
" token_counts = Counter()\n",
"\n",
" for example in ds_raw_train:\n",
" tokens = tokenizer.tokenize(example[0].numpy()[0])\n",
" if max_seq_length is not None:\n",
" tokens = tokens[-max_seq_length:]\n",
" token_counts.update(tokens)\n",
"\n",
" print('어휘 사전 크기:', len(token_counts))\n",
"\n",
"\n",
" ## 단계 3: 텍스트 인코딩하기\n",
" try:\n",
" encoder = tfds.features.text.TokenTextEncoder(token_counts)\n",
" except AttributeError:\n",
" encoder = tfds.deprecated.text.TokenTextEncoder(token_counts)\n",
"\n",
" def encode(text_tensor, label):\n",
" text = text_tensor.numpy()[0]\n",
" encoded_text = encoder.encode(text)\n",
" if max_seq_length is not None:\n",
" encoded_text = encoded_text[-max_seq_length:]\n",
" return encoded_text, label\n",
"\n",
" def encode_map_fn(text, label):\n",
" return tf.py_function(encode, inp=[text, label],\n",
" Tout=(tf.int64, tf.int64))\n",
"\n",
" ds_train = ds_raw_train.map(encode_map_fn)\n",
" ds_valid = ds_raw_valid.map(encode_map_fn)\n",
" ds_test = ds_raw_test.map(encode_map_fn)\n",
"\n",
" ## 단계 4: 배치 데이터 만들기\n",
" train_data = ds_train.padded_batch(\n",
" batch_size, padded_shapes=([-1],[]))\n",
"\n",
" valid_data = ds_valid.padded_batch(\n",
" batch_size, padded_shapes=([-1],[]))\n",
"\n",
" test_data = ds_test.padded_batch(\n",
" batch_size, padded_shapes=([-1],[]))\n",
"\n",
" return (train_data, valid_data,\n",
" test_data, len(token_counts))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-31T15:31:19.128046Z",
"iopub.status.busy": "2020-12-31T15:31:19.127068Z",
"iopub.status.idle": "2020-12-31T15:31:19.129259Z",
"shell.execute_reply": "2020-12-31T15:31:19.129831Z"
},
"id": "JVuxsNTRt3hd"
},
"outputs": [],
"source": [
"def build_rnn_model(embedding_dim, vocab_size,\n",
" recurrent_type='SimpleRNN',\n",
" n_recurrent_units=64,\n",
" n_recurrent_layers=1,\n",
" bidirectional=True):\n",
"\n",
" tf.random.set_seed(1)\n",
"\n",
" # 모델 생성\n",
" model = tf.keras.Sequential()\n",
"\n",
" model.add(\n",
" Embedding(\n",
" input_dim=vocab_size,\n",
" output_dim=embedding_dim,\n",
" name='embed-layer')\n",
" )\n",
"\n",
" for i in range(n_recurrent_layers):\n",
" return_sequences = (i < n_recurrent_layers-1)\n",
"\n",
" if recurrent_type == 'SimpleRNN':\n",
" recurrent_layer = SimpleRNN(\n",
" units=n_recurrent_units,\n",
" return_sequences=return_sequences,\n",
" name='simprnn-layer-{}'.format(i))\n",
" elif recurrent_type == 'LSTM':\n",
" recurrent_layer = LSTM(\n",
" units=n_recurrent_units,\n",
" return_sequences=return_sequences,\n",
" name='lstm-layer-{}'.format(i))\n",
" elif recurrent_type == 'GRU':\n",
" recurrent_layer = GRU(\n",
" units=n_recurrent_units,\n",
" return_sequences=return_sequences,\n",
" name='gru-layer-{}'.format(i))\n",
"\n",
" if bidirectional:\n",
" recurrent_layer = Bidirectional(\n",
" recurrent_layer, name='bidir-'+recurrent_layer.name)\n",
"\n",
" model.add(recurrent_layer)\n",
"\n",
" model.add(tf.keras.layers.Dense(64, activation='relu'))\n",
" model.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n",
"\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T15:31:19.135494Z",
"iopub.status.busy": "2020-12-31T15:31:19.134630Z",
"iopub.status.idle": "2020-12-31T15:31:23.127554Z",
"shell.execute_reply": "2020-12-31T15:31:23.127922Z"
},
"id": "WaDu41Pht3hd",
"outputId": "a17a23a2-71f2-4e3f-b69a-1104061d7d01"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"어휘 사전 크기: 58063\n",
"Model: \"sequential_5\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" embed-layer (Embedding) (None, None, 20) 1161300 \n",
" \n",
" bidir-simprnn-layer-0 (Bid (None, 128) 10880 \n",
" irectional) \n",
" \n",
" dense_5 (Dense) (None, 64) 8256 \n",
" \n",
" dense_6 (Dense) (None, 1) 65 \n",
" \n",
"=================================================================\n",
"Total params: 1180501 (4.50 MB)\n",
"Trainable params: 1180501 (4.50 MB)\n",
"Non-trainable params: 0 (0.00 Byte)\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"from tensorflow.keras.layers import Bidirectional\n",
"\n",
"\n",
"batch_size = 32\n",
"embedding_dim = 20\n",
"max_seq_length = 100\n",
"\n",
"train_data, valid_data, test_data, n = preprocess_datasets(\n",
" ds_raw_train, ds_raw_valid, ds_raw_test,\n",
" max_seq_length=max_seq_length,\n",
" batch_size=batch_size\n",
")\n",
"\n",
"\n",
"vocab_size = n + 2\n",
"\n",
"rnn_model = build_rnn_model(\n",
" embedding_dim, vocab_size,\n",
" recurrent_type='SimpleRNN',\n",
" n_recurrent_units=64,\n",
" n_recurrent_layers=1,\n",
" bidirectional=True)\n",
"\n",
"rnn_model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T15:31:23.140595Z",
"iopub.status.busy": "2020-12-31T15:31:23.140192Z",
"iopub.status.idle": "2020-12-31T15:37:30.416144Z",
"shell.execute_reply": "2020-12-31T15:37:30.416906Z"
},
"id": "Q43zdoSVt3hd",
"outputId": "c258a2c7-4693-42e9-f40a-be48d8e241a3"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/10\n",
"625/625 [==============================] - 179s 282ms/step - loss: 0.6877 - accuracy: 0.5437 - val_loss: 0.5733 - val_accuracy: 0.7176\n",
"Epoch 2/10\n",
"625/625 [==============================] - 138s 221ms/step - loss: 0.4811 - accuracy: 0.7659 - val_loss: 0.4919 - val_accuracy: 0.7892\n",
"Epoch 3/10\n",
"625/625 [==============================] - 138s 221ms/step - loss: 0.2658 - accuracy: 0.8935 - val_loss: 0.6333 - val_accuracy: 0.6632\n",
"Epoch 4/10\n",
"625/625 [==============================] - 138s 221ms/step - loss: 0.1993 - accuracy: 0.9232 - val_loss: 0.5770 - val_accuracy: 0.8108\n",
"Epoch 5/10\n",
"625/625 [==============================] - 137s 219ms/step - loss: 0.1453 - accuracy: 0.9499 - val_loss: 0.6287 - val_accuracy: 0.7758\n",
"Epoch 6/10\n",
"625/625 [==============================] - 138s 221ms/step - loss: 0.0857 - accuracy: 0.9726 - val_loss: 0.7515 - val_accuracy: 0.7644\n",
"Epoch 7/10\n",
"625/625 [==============================] - 138s 220ms/step - loss: 0.0500 - accuracy: 0.9849 - val_loss: 0.9449 - val_accuracy: 0.6744\n",
"Epoch 8/10\n",
"625/625 [==============================] - 142s 227ms/step - loss: 0.1503 - accuracy: 0.9416 - val_loss: 1.0362 - val_accuracy: 0.6554\n",
"Epoch 9/10\n",
"625/625 [==============================] - 138s 220ms/step - loss: 0.1151 - accuracy: 0.9556 - val_loss: 0.8802 - val_accuracy: 0.7124\n",
"Epoch 10/10\n",
"625/625 [==============================] - 139s 222ms/step - loss: 0.0735 - accuracy: 0.9754 - val_loss: 0.7908 - val_accuracy: 0.7338\n"
]
}
],
"source": [
"rnn_model.compile(optimizer=tf.keras.optimizers.Adam(1e-3),\n",
" loss=tf.keras.losses.BinaryCrossentropy(from_logits=False),\n",
" metrics=['accuracy'])\n",
"\n",
"\n",
"history = rnn_model.fit(\n",
" train_data,\n",
" validation_data=valid_data,\n",
" epochs=10)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T15:37:30.422212Z",
"iopub.status.busy": "2020-12-31T15:37:30.421332Z",
"iopub.status.idle": "2020-12-31T15:37:51.102354Z",
"shell.execute_reply": "2020-12-31T15:37:51.102953Z"
},
"id": "WaiatV_It3he",
"outputId": "6c705491-256d-4505-bcd0-066be6041a1f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"782/782 [==============================] - 33s 42ms/step - loss: 0.7589 - accuracy: 0.7413\n"
]
}
],
"source": [
"results = rnn_model.evaluate(test_data)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T15:37:51.108896Z",
"iopub.status.busy": "2020-12-31T15:37:51.108181Z",
"iopub.status.idle": "2020-12-31T15:37:51.112037Z",
"shell.execute_reply": "2020-12-31T15:37:51.111337Z"
},
"id": "x6QyEwQSt3he",
"outputId": "539fbf50-b1eb-483a-c79a-2a0d9004b2d7"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"테스트 정확도: 74.13%\n"
]
}
],
"source": [
"print('테스트 정확도: {:.2f}%'.format(results[1]*100))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KjPp6Koit3he"
},
"source": [
"## 연습문제:\n",
"\n",
"### 전체 길이를 사용한 시퀀스에 단방향 SimpleRNN 적용하기"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T15:37:51.118653Z",
"iopub.status.busy": "2020-12-31T15:37:51.118013Z",
"iopub.status.idle": "2020-12-31T15:37:55.390222Z",
"shell.execute_reply": "2020-12-31T15:37:55.389585Z"
},
"id": "VDMMjV1xt3he",
"outputId": "230055c9-fe09-437d-e816-467812a56d66"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"어휘 사전 크기: 87007\n",
"Model: \"sequential_6\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" embed-layer (Embedding) (None, None, 20) 1740180 \n",
" \n",
" simprnn-layer-0 (SimpleRNN (None, 64) 5440 \n",
" ) \n",
" \n",
" dense_7 (Dense) (None, 64) 4160 \n",
" \n",
" dense_8 (Dense) (None, 1) 65 \n",
" \n",
"=================================================================\n",
"Total params: 1749845 (6.68 MB)\n",
"Trainable params: 1749845 (6.68 MB)\n",
"Non-trainable params: 0 (0.00 Byte)\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"batch_size = 32\n",
"embedding_dim = 20\n",
"max_seq_length = None\n",
"\n",
"train_data, valid_data, test_data, n = preprocess_datasets(\n",
" ds_raw_train, ds_raw_valid, ds_raw_test,\n",
" max_seq_length=max_seq_length,\n",
" batch_size=batch_size\n",
")\n",
"\n",
"\n",
"vocab_size = n + 2\n",
"\n",
"rnn_model = build_rnn_model(\n",
" embedding_dim, vocab_size,\n",
" recurrent_type='SimpleRNN',\n",
" n_recurrent_units=64,\n",
" n_recurrent_layers=1,\n",
" bidirectional=False)\n",
"\n",
"rnn_model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T15:37:55.402376Z",
"iopub.status.busy": "2020-12-31T15:37:55.401627Z",
"iopub.status.idle": "2020-12-31T15:59:01.339172Z",
"shell.execute_reply": "2020-12-31T15:59:01.338258Z"
},
"id": "eR8OJjstt3he",
"outputId": "c29dcb50-7070-4396-a942-02210edef6a8"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/10\n",
"625/625 [==============================] - 584s 932ms/step - loss: 0.7014 - accuracy: 0.5005 - val_loss: 0.6964 - val_accuracy: 0.4984\n",
"Epoch 2/10\n",
"625/625 [==============================] - 527s 843ms/step - loss: 0.6991 - accuracy: 0.4997 - val_loss: 0.6929 - val_accuracy: 0.5126\n",
"Epoch 3/10\n",
"625/625 [==============================] - 525s 841ms/step - loss: 0.6967 - accuracy: 0.4943 - val_loss: 0.6929 - val_accuracy: 0.5126\n",
"Epoch 4/10\n",
"625/625 [==============================] - 521s 833ms/step - loss: 0.6956 - accuracy: 0.4969 - val_loss: 0.6930 - val_accuracy: 0.5128\n",
"Epoch 5/10\n",
"625/625 [==============================] - 523s 837ms/step - loss: 0.6949 - accuracy: 0.4981 - val_loss: 0.6929 - val_accuracy: 0.5136\n",
"Epoch 6/10\n",
"625/625 [==============================] - 524s 839ms/step - loss: 0.6944 - accuracy: 0.5021 - val_loss: 0.6927 - val_accuracy: 0.5138\n",
"Epoch 7/10\n",
"625/625 [==============================] - 521s 834ms/step - loss: 0.6931 - accuracy: 0.5062 - val_loss: 0.6922 - val_accuracy: 0.5136\n",
"Epoch 8/10\n",
"625/625 [==============================] - 526s 841ms/step - loss: 0.6913 - accuracy: 0.5061 - val_loss: 0.6918 - val_accuracy: 0.5134\n",
"Epoch 9/10\n",
"625/625 [==============================] - 532s 851ms/step - loss: 0.6862 - accuracy: 0.5076 - val_loss: 0.6953 - val_accuracy: 0.5144\n",
"Epoch 10/10\n",
"625/625 [==============================] - 539s 862ms/step - loss: 0.6813 - accuracy: 0.5101 - val_loss: 0.7008 - val_accuracy: 0.5090\n"
]
}
],
"source": [
"rnn_model.compile(optimizer=tf.keras.optimizers.Adam(1e-3),\n",
" loss=tf.keras.losses.BinaryCrossentropy(from_logits=False),\n",
" metrics=['accuracy'])\n",
"\n",
"history = rnn_model.fit(\n",
" train_data,\n",
" validation_data=valid_data,\n",
" epochs=10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-P9l6HEHt3he"
},
"source": [
"# 부록"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bAw6wJ0jt3hf"
},
"source": [
"### A -- 데이터셋을 만드는 다른 방법: tensorflow_datasets 사용하기"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 849,
"referenced_widgets": [
"2aa9fec7496843228cfac256f0fbeddd",
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"fb5d61bda65a4897946d4508fe79eab9",
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"adfc506cb94c409f9c9cbcfbefd633b4",
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},
"execution": {
"iopub.execute_input": "2020-12-31T15:59:01.345251Z",
"iopub.status.busy": "2020-12-31T15:59:01.344304Z",
"iopub.status.idle": "2020-12-31T16:00:03.995238Z",
"shell.execute_reply": "2020-12-31T16:00:03.994443Z"
},
"id": "Bm4eEIM7t3hf",
"outputId": "6aea6b4a-14dd-492e-a95c-1c4343d43fe0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"tfds.core.DatasetInfo(\n",
" name='imdb_reviews',\n",
" full_name='imdb_reviews/plain_text/1.0.0',\n",
" description=\"\"\"\n",
" Large Movie Review Dataset. This is a dataset for binary sentiment\n",
" classification containing substantially more data than previous benchmark\n",
" datasets. We provide a set of 25,000 highly polar movie reviews for training,\n",
" and 25,000 for testing. There is additional unlabeled data for use as well.\n",
" \"\"\",\n",
" config_description=\"\"\"\n",
" Plain text\n",
" \"\"\",\n",
" homepage='http://ai.stanford.edu/~amaas/data/sentiment/',\n",
" data_dir=PosixGPath('/tmp/tmp0ldve2zatfds'),\n",
" file_format=tfrecord,\n",
" download_size=80.23 MiB,\n",
" dataset_size=Unknown size,\n",
" features=FeaturesDict({\n",
" 'label': ClassLabel(shape=(), dtype=int64, num_classes=2),\n",
" 'text': Text(shape=(), dtype=string),\n",
" }),\n",
" supervised_keys=('text', 'label'),\n",
" disable_shuffling=False,\n",
" splits={\n",
" 'test': ,\n",
" 'train': ,\n",
" 'unsupervised': ,\n",
" },\n",
" citation=\"\"\"@InProceedings{maas-EtAl:2011:ACL-HLT2011,\n",
" author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},\n",
" title = {Learning Word Vectors for Sentiment Analysis},\n",
" booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},\n",
" month = {June},\n",
" year = {2011},\n",
" address = {Portland, Oregon, USA},\n",
" publisher = {Association for Computational Linguistics},\n",
" pages = {142--150},\n",
" url = {http://www.aclweb.org/anthology/P11-1015}\n",
" }\"\"\",\n",
")\n",
"Downloading and preparing dataset 80.23 MiB (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Dl Completed...: 0 url [00:00, ? url/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "2aa9fec7496843228cfac256f0fbeddd"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Dl Size...: 0 MiB [00:00, ? MiB/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "a454303a6eda459988b23015272ed260"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating splits...: 0%| | 0/3 [00:00, ? splits/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "6dbceadbef5c496f9bcdf4b4a83f1ae2"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating train examples...: 0%| | 0/25000 [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "12184356e65546fba10a52162f1d27e8"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Shuffling /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteCHCH46/imdb_reviews-train.tfrecord…"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "96b73cdc3f45492f95c369f03ab48344"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating test examples...: 0%| | 0/25000 [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "35b5de06c7184fdcb9c45cd1f67b672d"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Shuffling /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteCHCH46/imdb_reviews-test.tfrecord*…"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8b32a71bc2354dccb643ac9dc118d8fd"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating unsupervised examples...: 0%| | 0/50000 [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "5b259e307826421e936aa9797d1734e2"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Shuffling /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteCHCH46/imdb_reviews-unsupervised.t…"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "acce8bd6116c456bb67a2c46935a1677"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Dataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dict_keys([Split('train'), Split('test'), Split('unsupervised')])"
]
},
"metadata": {},
"execution_count": 40
}
],
"source": [
"imdb_bldr = tfds.builder('imdb_reviews')\n",
"print(imdb_bldr.info)\n",
"\n",
"imdb_bldr.download_and_prepare()\n",
"\n",
"datasets = imdb_bldr.as_dataset(shuffle_files=False)\n",
"\n",
"datasets.keys()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-31T16:00:04.000279Z",
"iopub.status.busy": "2020-12-31T16:00:03.999377Z",
"iopub.status.idle": "2020-12-31T16:00:04.002868Z",
"shell.execute_reply": "2020-12-31T16:00:04.001894Z"
},
"id": "YHn1LXgkt3hf"
},
"outputs": [],
"source": [
"imdb_train = datasets['train']\n",
"imdb_train = datasets['test']"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2THlQCVWt3hf"
},
"source": [
"### B -- Tokenizer와 Encoder\n",
"\n",
" * `tfds.deprecated.text.Tokenizer`: https://www.tensorflow.org/datasets/api_docs/python/tfds/deprecated/text/Tokenizer\n",
" * `tfds.deprecated.text.TokenTextEncoder`: https://www.tensorflow.org/datasets/api_docs/python/tfds/deprecated/text/TokenTextEncoder"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T16:00:04.012615Z",
"iopub.status.busy": "2020-12-31T16:00:04.011509Z",
"iopub.status.idle": "2020-12-31T16:00:04.015817Z",
"shell.execute_reply": "2020-12-31T16:00:04.015226Z"
},
"id": "tZrY4Fcnt3hf",
"outputId": "02b9a3fe-1da2-4006-9544-37331a65d2b2"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"[1, 4, 2, 3]\n",
"[1, 4, 2, 3, 5, 5, 5, 5, 5, 5]\n"
]
}
],
"source": [
"vocab_set = {'a', 'b', 'c', 'd'}\n",
"encoder = tfds.deprecated.text.TokenTextEncoder(vocab_set)\n",
"print(encoder)\n",
"\n",
"print(encoder.encode(b'a b c d, , : .'))\n",
"\n",
"print(encoder.encode(b'a b c d e f g h i z'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NY6z1aTit3hg"
},
"source": [
"### C -- 케라스로 텍스트 전처리하기"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T16:00:04.023781Z",
"iopub.status.busy": "2020-12-31T16:00:04.022913Z",
"iopub.status.idle": "2020-12-31T16:00:04.028002Z",
"shell.execute_reply": "2020-12-31T16:00:04.027228Z"
},
"id": "LRO2xhDKt3hg",
"outputId": "ef032f92-85a3-4167-d04f-c8436a0fb115"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[1, 2, 3, 4], [5, 6, 7, 8]]\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0, 0, 0, 0, 0, 0, 1, 2, 3, 4],\n",
" [0, 0, 0, 0, 0, 0, 5, 6, 7, 8]], dtype=int32)"
]
},
"metadata": {},
"execution_count": 43
}
],
"source": [
"TOP_K = 200\n",
"MAX_LEN = 10\n",
"\n",
"tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=TOP_K)\n",
"\n",
"tokenizer.fit_on_texts(['this is an example', 'je suis en forme '])\n",
"sequences = tokenizer.texts_to_sequences(['this is an example', 'je suis en forme '])\n",
"print(sequences)\n",
"\n",
"tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=MAX_LEN)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T16:00:04.036271Z",
"iopub.status.busy": "2020-12-31T16:00:04.035409Z",
"iopub.status.idle": "2020-12-31T16:00:15.234946Z",
"shell.execute_reply": "2020-12-31T16:00:15.234078Z"
},
"id": "vePZ3UTot3hg",
"outputId": "5ba96d38-f6ce-4269-dc03-06a634ec91ee",
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"25000\n",
"(25000, 500)\n"
]
}
],
"source": [
"TOP_K = 20000\n",
"MAX_LEN = 500\n",
"\n",
"tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=TOP_K)\n",
"\n",
"tokenizer.fit_on_texts(\n",
" [example['text'].numpy().decode('utf-8')\n",
" for example in imdb_train])\n",
"\n",
"x_train = tokenizer.texts_to_sequences(\n",
" [example['text'].numpy().decode('utf-8')\n",
" for example in imdb_train])\n",
"\n",
"print(len(x_train))\n",
"\n",
"\n",
"x_train_padded = tf.keras.preprocessing.sequence.pad_sequences(\n",
" x_train, maxlen=MAX_LEN)\n",
"\n",
"print(x_train_padded.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9FYRIc-St3hg"
},
"source": [
"### D -- 임베딩"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2020-12-31T16:00:15.250708Z",
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"name": "stdout",
"text": [
"[[0.0285978206 -0.0122499242 -0.0394328944 -0.0145259611]\n",
" [-0.016297698 0.0204829238 0.0123164877 0.0303565152]\n",
" [-0.0233924985 -0.0107878074 -0.00653867796 0.0383420847]\n",
" [0.0457952954 -0.0139975436 0.000184893608 0.0398626]\n",
" [-0.00592473894 0.0485283621 0.0443233289 -0.005304683]\n",
" [-0.0153851733 0.0370714702 -0.0447837822 0.0241911896]]\n",
"TensorShape([6, 4])\n",
"[[0.0285978206 -0.0122499242 -0.0394328944 -0.0145259611]]\n"
]
}
],
"source": [
"from tensorflow.keras.layers import Embedding\n",
"\n",
"\n",
"tf.random.set_seed(1)\n",
"embed = Embedding(input_dim=100, output_dim=4)\n",
"\n",
"inp_arr = np.array([1, 98, 5, 6, 67, 45])\n",
"tf.print(embed(inp_arr))\n",
"tf.print(embed(inp_arr).shape)\n",
"\n",
"tf.print(embed(np.array([1])))"
]
}
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