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Sekine."}],["$","p",null,{"children":["$","a",null,{"href":"#","className":"hover:text-blue-500 transition-colors","children":"Back to top"}]}]]}]}]]}]}],null] 4:[["$","meta","0",{"charSet":"utf-8"}],["$","title","1",{"children":"Shingoの数学ノート"}],["$","meta","2",{"name":"description","content":"プログラミング言語と機械学習のメモ"}],["$","meta","3",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","link","4",{"rel":"icon","href":"/favicon.ico","type":"image/x-icon","sizes":"256x256"}],["$","meta","5",{"name":"next-size-adjust"}]] b:I[6180,["326","static/chunks/326-ead410bae2047633.js","986","static/chunks/986-f27c5a2c4d841870.js","42","static/chunks/app/%5Bslug%5D/page-419f452b4066bb25.js"],""] d:I[9703,["326","static/chunks/326-ead410bae2047633.js","986","static/chunks/986-f27c5a2c4d841870.js","42","static/chunks/app/%5Bslug%5D/page-419f452b4066bb25.js"],""] c:Tb53a,/*@jsxRuntime automatic @jsxImportSource react*/ const {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0]; const {useMDXComponents: _provideComponents} = arguments[0]; function _createMdxContent(props) { const _components = Object.assign({ p: "p", a: "a", h3: "h3", img: "img", pre: "pre", code: "code", span: "span", blockquote: "blockquote", h2: "h2", ul: "ul", li: "li" }, _provideComponents(), props.components); return _jsxs(_Fragment, { children: [_jsx(_components.p, { children: "前回と今回で、BERTの理論と実装について紹介したいと思います。" }), "\n", _jsx(_components.p, { children: "今回は、BERTの実装について説明していきます。" }), "\n", _jsxs(_components.p, { children: ["BERTの理論を知りたい方は", _jsx(_components.a, { href: "./blog29.html", children: "前回のブログ" }), "を参照してください。"] }), "\n", _jsxs(_components.p, { children: ["実際のコードは", _jsx(_components.a, { href: "https://github.com/Shingo425/NLP/blob/main/src/BERT_tutorial.ipynb", children: "github" }), "に上げてありますので、そちらも参考にしてください。"] }), "\n", _jsx(_components.h3, { children: "実行環境の準備" }), "\n", _jsx(_components.p, { children: "ローカルにNVIDIAのGPUを載せている人は多くはないと思うので、 実行環境はgoogle colaboratoryを想定しています。" }), "\n", _jsx(_components.p, { children: "その場合ランタイムはGPUにしてください。" }), "\n", _jsxs(_components.p, { children: ["また、trainingには以前紹介したyahoo locoのレビューデータを使用します。 まだ取得していない方は", _jsx(_components.a, { href: "./blog28.html", children: "こちらの記事" }), "を参考に取得してください"] }), "\n", _jsx(_components.p, { children: "この記事では、東京のレビューデータ「review_df_yahoo_tokyo.csv」を使用しています。" }), "\n", _jsx(_components.p, { children: "以下のようにファイルをアップロードしてください。" }), "\n", _jsx(_components.p, { children: _jsx(_components.img, { src: "/images/blog30/image1.png", alt: "" }) }), "\n", _jsx(_components.h3, { children: "モジュールのインポート" }), "\n", _jsx(_components.p, { children: "以下をインポートします。" }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: [_jsx(_components.span, { className: "hljs-keyword", children: "import" }), " numpy ", _jsx(_components.span, { className: "hljs-keyword", children: "as" }), " np\n\n\n", _jsx(_components.span, { className: "hljs-keyword", children: "import" }), " pandas ", _jsx(_components.span, { className: "hljs-keyword", children: "as" }), " pd\n", _jsx(_components.span, { className: "hljs-keyword", children: "from" }), " tqdm.notebook\n", _jsx(_components.span, { className: "hljs-keyword", children: "import" }), " tqdm\n", _jsx(_components.span, { className: "hljs-keyword", children: "import" }), " torch\n", _jsx(_components.span, { className: "hljs-keyword", children: "from" }), " sklearn.model_selection\n", _jsx(_components.span, { className: "hljs-keyword", children: "import" }), " train_test_split\n", _jsx(_components.span, { className: "hljs-keyword", children: "import" }), " torch.optim ", _jsx(_components.span, { className: "hljs-keyword", children: "as" }), " optim\n", _jsx(_components.span, { className: "hljs-keyword", children: "import" }), " torch.nn ", _jsx(_components.span, { className: "hljs-keyword", children: "as" }), " nn\n", _jsx(_components.span, { className: "hljs-keyword", children: "import" }), " torch.nn.functional ", _jsx(_components.span, { className: "hljs-keyword", children: "as" }), " F\n"] }) }), "\n", _jsx(_components.p, { children: "haggingfaceのtransformersモジュールを使用します" }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: [_jsx(_components.span, { className: "hljs-comment", children: "# transformersをインストール !pip install transformers !pip install mecab-python3==0.996.2 fugashi ipadic" }), "\n", _jsx(_components.span, { className: "hljs-keyword", children: "from" }), " transformers\n", _jsx(_components.span, { className: "hljs-keyword", children: "import" }), " BertJapaneseTokenizer, BertModel\n"] }) }), "\n", _jsx(_components.p, { children: "準備で上げたデータセットを加工して使用します。" }), "\n", _jsx(_components.p, { children: "今回はlabelをRating > 3ならpositive、Rating < 3ならnegativeとしています。Rating==3は使用しません。" }), "\n", _jsx(_components.p, { children: _jsx(_components.img, { src: "/images/blog30/image3.png", alt: "" }) }), "\n", _jsx(_components.p, { children: "これを用いて、BERTのポジネガ分析をしてみましょう。" }), "\n", _jsx(_components.h3, { children: "tokenizer" }), "\n", _jsx(_components.p, { children: "まずは、tokenizerを定義します。学習済みモデルは、 'cl-tohoku/bert-base-japanese'を使用します。" }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: [_jsx(_components.span, { className: "hljs-comment", children: "# tokenizerの定義とvocabの辞書 model_name = 'cl-tohoku/bert-base-japanese' tokenizer = BertJapaneseTokenizer.from_pretrained(model_name)" }), "\n"] }) }), "\n", _jsx(_components.p, { children: "実際に分割例を見てみます。" }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: [_jsx(_components.span, { className: "hljs-comment", children: "# tokenizerの分割例 text = \"カーネーションが綺麗だった\" tokenizer.tokenize(text)" }), "\n"] }) }), "\n", _jsx(_components.p, { children: _jsx(_components.img, { src: "/images/blog30/image6.png", alt: "" }) }), "\n", _jsx(_components.h3, { children: "Input Embedding" }), "\n", _jsx(_components.p, { children: "次に入力をみていきましょう。" }), "\n", _jsx(_components.p, { children: "入力に必要なのは、Token Embedding(input_id), Segment Embedding(segment_id), attention_maskです。" }), "\n", _jsx(_components.p, { children: "Positional Embeddingは内部でやってくれるので作成する必要はありません。" }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: [_jsx(_components.span, { className: "hljs-comment", children: "# pad_tokenとmax_lengthの設定 " }), "\npad = tokenizer.vocab[", _jsx(_components.span, { className: "hljs-string", children: "\"[PAD]\"" }), "] \nmax_lengths=", _jsx(_components.span, { className: "hljs-number", children: "512" }), " \n\n", _jsx(_components.span, { className: "hljs-comment", children: "# tokenをid化 " }), "\ntoken = [", _jsx(_components.span, { className: "hljs-string", children: "\"[CLS]\"" }), "]+tokenizer.tokenize(text)[:max_lengths-", _jsx(_components.span, { className: "hljs-number", children: "2" }), "]+[", _jsx(_components.span, { className: "hljs-string", children: "\"[SEP]\"" }), "] \ninput_id = tokenizer.convert_tokens_to_ids(token) \n\n", _jsx(_components.span, { className: "hljs-comment", children: "# 1文のみなのでsegment_idは全て0 " }), "\nsegment_id = [", _jsx(_components.span, { className: "hljs-number", children: "0" }), "]*max_lengths \n\n", _jsx(_components.span, { className: "hljs-comment", children: "# input_idが存在する箇所に1を立てる " }), "\nattention_mask = [", _jsx(_components.span, { className: "hljs-number", children: "1" }), "]*", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(input_id)+[", _jsx(_components.span, { className: "hljs-number", children: "0" }), "]*(max_lengths - ", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(input_id)) \ninput_id = input_id+[pad]*(max_lengths-", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(input_id))\n"] }) }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: [_jsx(_components.span, { className: "hljs-built_in", children: "print" }), "(", _jsx(_components.span, { className: "hljs-string", children: "\"text:\"" }), ", text)\n", _jsx(_components.span, { className: "hljs-built_in", children: "print" }), "(", _jsx(_components.span, { className: "hljs-string", children: "\"token:\"" }), ", token)\n", _jsx(_components.span, { className: "hljs-built_in", children: "print" }), "(", _jsx(_components.span, { className: "hljs-string", children: "\"input_id:\"" }), ", input_id[:", _jsx(_components.span, { className: "hljs-number", children: "15" }), "], ", _jsx(_components.span, { className: "hljs-string", children: "\"len:\"" }), ", ", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(input_id))\n", _jsx(_components.span, { className: "hljs-built_in", children: "print" }), "(", _jsx(_components.span, { className: "hljs-string", children: "\"segment_id:\"" }), ", segment_id[:", _jsx(_components.span, { className: "hljs-number", children: "15" }), "], ", _jsx(_components.span, { className: "hljs-string", children: "\"len:\"" }), ", ", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(segment_id))\n", _jsx(_components.span, { className: "hljs-built_in", children: "print" }), "(", _jsx(_components.span, { className: "hljs-string", children: "\"attention_mask:\"" }), ", attention_mask[:", _jsx(_components.span, { className: "hljs-number", children: "15" }), "], ", _jsx(_components.span, { className: "hljs-string", children: "\"len:\"" }), ", ", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(attention_mask))\n"] }) }), "\n", _jsx(_components.p, { children: _jsx(_components.img, { src: "/images/blog30/image2.png", alt: "" }) }), "\n", _jsx(_components.h3, { children: "Data Loader作成" }), "\n", _jsx(_components.p, { children: "上記のinput Embeddingを元に、Datasetクラスを作成します。" }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: [_jsx(_components.span, { className: "hljs-comment", children: "# 上記のinput_dataの作成方法をもとにdataset化" }), "\n", _jsx(_components.span, { className: "hljs-keyword", children: "class" }), " ", _jsx(_components.span, { className: "hljs-title class_", children: "ReviewDataset" }), "(torch.utils.data.Dataset):\n ", _jsx(_components.span, { className: "hljs-keyword", children: "def" }), " ", _jsx(_components.span, { className: "hljs-title function_", children: "__init__" }), "(", _jsx(_components.span, { className: "hljs-params", children: "self, texts, labels=[]" }), "): \n ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".input_ids, ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".segment_ids, ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".attention_masks = [],[],[]\n ", _jsx(_components.span, { className: "hljs-keyword", children: "for" }), " text ", _jsx(_components.span, { className: "hljs-keyword", children: "in" }), " tqdm(texts): \n token = [", _jsx(_components.span, { className: "hljs-string", children: "\"[CLS]\"" }), "]+tokenizer.tokenize(text)[:max_lengths-", _jsx(_components.span, { className: "hljs-number", children: "2" }), "]+[", _jsx(_components.span, { className: "hljs-string", children: "\"[SEP]\"" }), "] \n input_id = tokenizer.convert_tokens_to_ids(token) \n segment_id = [", _jsx(_components.span, { className: "hljs-number", children: "0" }), "]*max_lengths \n attention_mask = [", _jsx(_components.span, { className: "hljs-number", children: "1" }), "]*", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(input_id)+[", _jsx(_components.span, { className: "hljs-number", children: "0" }), "]*(max_lengths - ", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(input_id)) \n input_id = input_id+[pad]*(max_lengths-", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(input_id)) \n ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".input_ids.append(input_id) \n ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".segment_ids.append(segment_id) \n ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".attention_masks.append(attention_mask) \n \n ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".input_ids = np.array(", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".input_ids) \n ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".segment_ids = np.array(", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".segment_ids) \n ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".attention_masks = np.array(", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".attention_masks) \n ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".labels = labels\n\n ", _jsx(_components.span, { className: "hljs-keyword", children: "def" }), " ", _jsx(_components.span, { className: "hljs-title function_", children: "__len__" }), "(", _jsx(_components.span, { className: "hljs-params", children: "self" }), "):\n ", _jsx(_components.span, { className: "hljs-keyword", children: "return" }), " ", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".input_ids)\n\n ", _jsx(_components.span, { className: "hljs-keyword", children: "def" }), " ", _jsx(_components.span, { className: "hljs-title function_", children: "__getitem__" }), "(", _jsx(_components.span, { className: "hljs-params", children: "self, idx" }), "):\n ", _jsx(_components.span, { className: "hljs-keyword", children: "if" }), " ", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".labels):\n ", _jsx(_components.span, { className: "hljs-keyword", children: "return" }), " ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".input_ids[idx], ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".segment_ids[idx], ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".attention_masks[idx], ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".labels[idx]\n ", _jsx(_components.span, { className: "hljs-keyword", children: "else" }), ":\n ", _jsx(_components.span, { className: "hljs-keyword", children: "return" }), " ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".input_ids[idx], ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".segment_ids[idx], ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".attention_masks[idx]\n"] }) }), "\n", _jsx(_components.p, { children: "上記クラスを使って、train_dataloaderとvalid_dataloaderを作成します。" }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: [_jsx(_components.span, { className: "hljs-comment", children: "# datasetとdataloaderの作成" }), "\n", _jsx(_components.span, { className: "hljs-keyword", children: "from" }), " sklearn.model_selection ", _jsx(_components.span, { className: "hljs-keyword", children: "import" }), " train_test_split \n\nbatch_size=", _jsx(_components.span, { className: "hljs-number", children: "8" }), " \nX,y = train_df[", _jsx(_components.span, { className: "hljs-string", children: "\"text\"" }), "].values, train_df[", _jsx(_components.span, { className: "hljs-string", children: "\"label\"" }), "].values \nX_train, X_val, y_train, y_val = train_test_split(X, y, test_size=", _jsx(_components.span, { className: "hljs-number", children: "0.3" }), ", random_state=", _jsx(_components.span, { className: "hljs-number", children: "100" }), ",stratify=y) \ntrain_ds = ReviewDataset(texts=X_train, labels=y_train) \ntrain_dl = torch.utils.data.DataLoader(train_ds, batch_size=batch_size, shuffle=", _jsx(_components.span, { className: "hljs-literal", children: "True" }), ") \n\nval_ds = ReviewDataset(texts=X_val, labels=y_val) \nval_dl = torch.utils.data.DataLoader(val_ds, batch_size=batch_size, shuffle=", _jsx(_components.span, { className: "hljs-literal", children: "False" }), ")\n"] }) }), "\n", _jsx(_components.h3, { children: "modelの作成" }), "\n", _jsx(_components.p, { children: "BERTの2値分類モデルを作成します。" }), "\n", _jsx(_components.p, { children: "[CLS]トークンの特徴量を用いて全結合で2つの値を出力します。" }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: [_jsx(_components.span, { className: "hljs-comment", children: "# Bertの事前学習済みエンコーダーを使用して2値クラス分類器を作成" }), "\n", _jsx(_components.span, { className: "hljs-keyword", children: "class" }), " ", _jsx(_components.span, { className: "hljs-title class_", children: "BertClassification" }), "(nn.Module):\n ", _jsx(_components.span, { className: "hljs-keyword", children: "def" }), " ", _jsx(_components.span, { className: "hljs-title function_", children: "__init__" }), "(", _jsx(_components.span, { className: "hljs-params", children: "self, bert" }), "): \n ", _jsx(_components.span, { className: "hljs-built_in", children: "super" }), "(BertClassification, ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ").__init__() \n ", _jsx(_components.span, { className: "hljs-comment", children: "# BERTモジュール " }), "\n ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".bert = bert \n ", _jsx(_components.span, { className: "hljs-comment", children: "# BERTモデル " }), "\n ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".cls = nn.Linear(in_features=", _jsx(_components.span, { className: "hljs-number", children: "768" }), ", out_features=", _jsx(_components.span, { className: "hljs-number", children: "2" }), ") \n ", _jsx(_components.span, { className: "hljs-comment", children: "# 重み初期化処理 " }), "\n nn.init.normal_(", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".cls.weight, std=", _jsx(_components.span, { className: "hljs-number", children: "0.02" }), ") \n nn.init.normal_(", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".cls.bias, ", _jsx(_components.span, { className: "hljs-number", children: "0" }), ")\n\n ", _jsx(_components.span, { className: "hljs-keyword", children: "def" }), " ", _jsx(_components.span, { className: "hljs-title function_", children: "forward" }), "(", _jsxs(_components.span, { className: "hljs-params", children: ["self, input_ids, token_type_ids=", _jsx(_components.span, { className: "hljs-literal", children: "None" }), ", attention_mask=", _jsx(_components.span, { className: "hljs-literal", children: "None" })] }), "): \n ", _jsx(_components.span, { className: "hljs-comment", children: "# BERTの基本モデル部分の順伝搬 " }), "\n output = ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".bert(input_ids, token_type_ids, attention_mask) \n ", _jsx(_components.span, { className: "hljs-comment", children: "# [CLS]に対応する特徴量を取得します。 " }), "\n pooled_output = output[", _jsx(_components.span, { className: "hljs-number", children: "1" }), "] \n ", _jsx(_components.span, { className: "hljs-comment", children: "# 入力文章の1単語目[CLS]の特徴量を使用して、ポジ・ネガを分類します " }), "\n pooled_output = pooled_output.view(-", _jsx(_components.span, { className: "hljs-number", children: "1" }), ", ", _jsx(_components.span, { className: "hljs-number", children: "768" }), ") ", _jsx(_components.span, { className: "hljs-comment", children: "# sizeを[batch_size, hidden_size]に変換 " }), "\n out = ", _jsx(_components.span, { className: "hljs-variable language_", children: "self" }), ".cls(pooled_output)\n ", _jsx(_components.span, { className: "hljs-keyword", children: "return" }), " out\n"] }) }), "\n", _jsx(_components.p, { children: "「pooled_output=output[1]」の部分がわかりづらいので解説すると、 bertの公式では以下のように記述されています。" }), "\n", _jsxs(_components.blockquote, { children: ["\n", _jsx(_components.p, { children: "A BaseModelOutputWithPoolingAndCrossAttentions (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (BertConfig) and inputs. last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) – Sequence of hidden-states at the output of the last layer of the model. pooler_output (torch.FloatTensor of shape (batch_size, hidden_size)) – Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining." }), "\n"] }), "\n", _jsx(_components.p, { children: "つまり、bertのoutputは以下のようになっています。" }), "\n", _jsx(_components.pre, { children: _jsx(_components.code, { className: "hljs language-python", children: "output=(last_layerの全てのtoken,last_layerの最初のToken(=[CLS]Token))\n" }) }), "\n", _jsx(_components.p, { children: "ということで、output[1]は[CLS]Tokenの特徴量を抽出していることになるわけですね。" }), "\n", _jsx(_components.p, { children: "ちなみに、attentionを可視化したいとか、最終層以外の特徴量も取り出したい等あれば、そういうオプションもあるので、以下を参考にしてください。" }), "\n", _jsx(_components.p, { children: _jsx(_components.a, { href: "https://huggingface.co/transformers/model_doc/bert.html", children: "公式ドキュメント" }) }), "\n", _jsx(_components.h3, { children: "Optimizer等の設定" }), "\n", _jsx(_components.p, { children: "次にOptimizerや、損失関数、gpuを使用するか等の設定を行います。" }), "\n", _jsx(_components.p, { children: "今回は、AdamとCross Entropyを使用します。" }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: ["cuda = torch.cuda.is_available()\n\n", _jsx(_components.span, { className: "hljs-comment", children: "# 学習済みモデルをダウンロード " }), "\nbert = BertModel.from_pretrained(model_name) \nmodel = BertClassification(bert) \n\n", _jsx(_components.span, { className: "hljs-comment", children: "# 高速化 " }), "\ntorch.backends.cudnn.benchmark = ", _jsx(_components.span, { className: "hljs-literal", children: "True" }), "\n\n", _jsx(_components.span, { className: "hljs-keyword", children: "if" }), " cuda:\n model.cuda() \n\n", _jsx(_components.span, { className: "hljs-comment", children: "# optimizerの設定 " }), "\noptimizer = optim.Adam(model.parameters(),lr = ", _jsx(_components.span, { className: "hljs-number", children: "4e-4" }), " ,betas=(", _jsx(_components.span, { className: "hljs-number", children: "0.9" }), ", ", _jsx(_components.span, { className: "hljs-number", children: "0.999" }), ")) \n\n", _jsx(_components.span, { className: "hljs-comment", children: "# 損失関数の設定 " }), "\ncriterion = nn.CrossEntropyLoss()\n"] }) }), "\n", _jsx(_components.p, { children: "また、今回は学習時間を短縮するため、最終層以外のパラメータを固定します。" }), "\n", _jsx(_components.p, { children: "実際のタスクでは、全てのパラメータを学習することも多いです。" }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: [_jsx(_components.span, { className: "hljs-comment", children: "# 最後以外のBertLayerモジュールのパラメータを固定(実際のタスクでは固定させないことも多い)" }), "\n", _jsx(_components.span, { className: "hljs-keyword", children: "for" }), " param ", _jsx(_components.span, { className: "hljs-keyword", children: "in" }), " model.bert.encoder.layer[:-", _jsx(_components.span, { className: "hljs-number", children: "1" }), "].parameters():\n param.requires_grad = ", _jsx(_components.span, { className: "hljs-literal", children: "False" }), "\n"] }) }), "\n", _jsx(_components.h3, { children: "学習" }), "\n", _jsx(_components.p, { children: "ここで学習フェーズです。" }), "\n", _jsx(_components.p, { children: "Pytorchは先ほど作ったdata_loaderを使って、for文でtrainingしていきます。" }), "\n", _jsx(_components.p, { children: "train, validともに1イテレーションごとに記述できるのが良いですね。" }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: [_jsx(_components.span, { className: "hljs-comment", children: "## 学習します。1epochあたり約8分です。 " }), "\nepochs = ", _jsx(_components.span, { className: "hljs-number", children: "2" }), "\n", _jsx(_components.span, { className: "hljs-keyword", children: "for" }), " epoch ", _jsx(_components.span, { className: "hljs-keyword", children: "in" }), " ", _jsx(_components.span, { className: "hljs-built_in", children: "range" }), "(epochs): \n total_loss = ", _jsx(_components.span, { className: "hljs-number", children: "0" }), " \n accuracy = ", _jsx(_components.span, { className: "hljs-number", children: "0" }), " \n model.train()\n ", _jsx(_components.span, { className: "hljs-built_in", children: "print" }), "(", _jsx(_components.span, { className: "hljs-string", children: "\"epoch {} start!\"" }), ".", _jsx(_components.span, { className: "hljs-built_in", children: "format" }), "(epoch+", _jsx(_components.span, { className: "hljs-number", children: "1" }), ")) \n \n ", _jsx(_components.span, { className: "hljs-comment", children: "# train" }), "\n ", _jsx(_components.span, { className: "hljs-keyword", children: "for" }), " iter_num, (input_ids, segment_ids, attention_masks, labels) ", _jsx(_components.span, { className: "hljs-keyword", children: "in" }), " tqdm(", _jsx(_components.span, { className: "hljs-built_in", children: "enumerate" }), "(train_dl),total = ", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(train_dl)): \n optimizer.zero_grad()\n ", _jsx(_components.span, { className: "hljs-keyword", children: "if" }), " cuda: \n input_ids, segment_ids, attention_masks, labels = input_ids.cuda(), segment_ids.cuda(), attention_masks.cuda(), labels.cuda() \n \n ", _jsx(_components.span, { className: "hljs-comment", children: "# forward(順伝搬) " }), "\n outputs = model(input_ids = input_ids, token_type_ids = segment_ids, attention_mask = attention_masks) \n pred_proba = outputs.softmax(dim=", _jsx(_components.span, { className: "hljs-number", children: "1" }), ")[:,", _jsx(_components.span, { className: "hljs-number", children: "1" }), "] \n pred = (pred_proba>=", _jsx(_components.span, { className: "hljs-number", children: "0.5" }), ").", _jsx(_components.span, { className: "hljs-built_in", children: "type" }), "(torch.", _jsx(_components.span, { className: "hljs-built_in", children: "int" }), ") \n loss = criterion(outputs,labels) \n \n ", _jsx(_components.span, { className: "hljs-comment", children: "# backward(逆伝搬) " }), "\n loss.backward() \n optimizer.step() \n total_loss += loss.item() \n accuracy += (pred==labels).", _jsx(_components.span, { className: "hljs-built_in", children: "sum" }), "().item() \n \n ", _jsx(_components.span, { className: "hljs-comment", children: "#50 iterごとにlossとaccuracyを表示" }), "\n ", _jsx(_components.span, { className: "hljs-keyword", children: "if" }), " (iter_num+", _jsx(_components.span, { className: "hljs-number", children: "1" }), ") % ", _jsx(_components.span, { className: "hljs-number", children: "50" }), " == ", _jsx(_components.span, { className: "hljs-number", children: "0" }), ": \n size = batch_size*(iter_num+", _jsx(_components.span, { className: "hljs-number", children: "1" }), ")\n ", _jsx(_components.span, { className: "hljs-built_in", children: "print" }), "(", _jsx(_components.span, { className: "hljs-string", children: "\"{} iter loss:{:.4f} accuracy:{:.4f}\"" }), ".", _jsx(_components.span, { className: "hljs-built_in", children: "format" }), "(iter_num+", _jsx(_components.span, { className: "hljs-number", children: "1" }), ", total_loss/(iter_num+", _jsx(_components.span, { className: "hljs-number", children: "1" }), "), accuracy/size)) \n \n total_loss /= ", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(train_dl) \n accuracy /= ", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(train_ds) \n \n ", _jsx(_components.span, { className: "hljs-comment", children: "# validation " }), "\n val_total_loss = ", _jsx(_components.span, { className: "hljs-number", children: "0" }), " \n val_accuracy = ", _jsx(_components.span, { className: "hljs-number", children: "0" }), " \n model.", _jsx(_components.span, { className: "hljs-built_in", children: "eval" }), "()\n ", _jsx(_components.span, { className: "hljs-keyword", children: "for" }), " input_ids, segment_ids, attention_masks, labels ", _jsx(_components.span, { className: "hljs-keyword", children: "in" }), " tqdm(val_dl):\n ", _jsx(_components.span, { className: "hljs-keyword", children: "if" }), " cuda: \n input_ids, segment_ids, attention_masks, labels = input_ids.cuda(), segment_ids.cuda(), attention_masks.cuda(), labels.cuda() \n ", _jsx(_components.span, { className: "hljs-keyword", children: "with" }), " torch.no_grad(): \n outputs = model(input_ids = input_ids, token_type_ids = segment_ids, attention_mask = attention_masks) \n pred_proba = outputs.softmax(dim=", _jsx(_components.span, { className: "hljs-number", children: "1" }), ")[:,", _jsx(_components.span, { className: "hljs-number", children: "1" }), "] \n pred = (pred_proba>=", _jsx(_components.span, { className: "hljs-number", children: "0.5" }), ").", _jsx(_components.span, { className: "hljs-built_in", children: "type" }), "(torch.", _jsx(_components.span, { className: "hljs-built_in", children: "int" }), ") \n loss = criterion(outputs,labels) \n val_total_loss += loss.item() \n val_accuracy += (pred==labels).", _jsx(_components.span, { className: "hljs-built_in", children: "sum" }), "().item() \n \n val_total_loss /= ", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(val_dl) \n val_accuracy /= ", _jsx(_components.span, { className: "hljs-built_in", children: "len" }), "(val_ds)\n ", _jsx(_components.span, { className: "hljs-built_in", children: "print" }), "(", _jsx(_components.span, { className: "hljs-string", children: "\"epoch{} total loss:{:.4f}, accuracy:{:.4f}, val_total loss:{:.4f}, val_accuracy:{:.4f}\"" }), ".", _jsx(_components.span, { className: "hljs-built_in", children: "format" }), "(epoch+", _jsx(_components.span, { className: "hljs-number", children: "1" }), ",total_loss,accuracy,val_total_loss,val_accuracy)) \n \ntorch.save(model.state_dict(), ", _jsx(_components.span, { className: "hljs-string", children: "'./model.hdf5'" }), ")\n"] }) }), "\n", _jsx(_components.h3, { children: "予測" }), "\n", _jsx(_components.p, { children: "最後に、本当にできたか予測してみましょう。" }), "\n", _jsx(_components.p, { children: "まずは出来上がったモデルをロードします。" }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: ["model.load_state_dict(torch.load(", _jsx(_components.span, { className: "hljs-string", children: "\"./model.hdf5\"" }), "))\n"] }) }), "\n", _jsx(_components.p, { children: "実際に予測してみます。" }), "\n", _jsx(_components.p, { children: "\"今日のランチは最高だった！\"を予測してみます。" }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: ["X_test = [", _jsx(_components.span, { className: "hljs-string", children: "\"今日のランチは最高だった！\"" }), "] \ntest_ds = ReviewDataset(texts=X_test) \ntest_dl = torch.utils.data.DataLoader(test_ds, batch_size=", _jsx(_components.span, { className: "hljs-number", children: "1" }), ", shuffle=", _jsx(_components.span, { className: "hljs-literal", children: "False" }), ")\n\n", _jsx(_components.span, { className: "hljs-keyword", children: "for" }), " input_ids, segment_ids, attention_masks ", _jsx(_components.span, { className: "hljs-keyword", children: "in" }), " test_dl:\n ", _jsx(_components.span, { className: "hljs-keyword", children: "if" }), " cuda: \n input_ids, segment_ids, attention_masks = input_ids.cuda(), segment_ids.cuda(), attention_masks.cuda() \n outputs = model(input_ids = input_ids, token_type_ids = segment_ids, attention_mask = attention_masks) \n pred_proba = outputs.softmax(dim=", _jsx(_components.span, { className: "hljs-number", children: "1" }), ")[:,", _jsx(_components.span, { className: "hljs-number", children: "1" }), "]\n ", _jsx(_components.span, { className: "hljs-built_in", children: "print" }), "(", _jsx(_components.span, { className: "hljs-string", children: "\"入力テキストのポジティブ度: {:.4f}\"" }), ".", _jsx(_components.span, { className: "hljs-built_in", children: "format" }), "(pred_proba.item()))\n"] }) }), "\n", _jsx(_components.p, { children: _jsx(_components.img, { src: "/images/blog30/image4.png", alt: "" }) }), "\n", _jsx(_components.p, { children: "もう一つ予測してみましょう。" }), "\n", _jsx(_components.p, { children: "\"今日のランチは微妙だった。\"を予測してみます。" }), "\n", _jsx(_components.pre, { children: _jsxs(_components.code, { className: "hljs language-python", children: ["X_test = [", _jsx(_components.span, { className: "hljs-string", children: "\"今日のランチは微妙だった。\"" }), "] \ntest_ds = ReviewDataset(texts=X_test) \ntest_dl = torch.utils.data.DataLoader(test_ds, batch_size=", _jsx(_components.span, { className: "hljs-number", children: "1" }), ", shuffle=", _jsx(_components.span, { className: "hljs-literal", children: "False" }), ")\n\n", _jsx(_components.span, { className: "hljs-keyword", children: "for" }), " input_ids, segment_ids, attention_masks ", _jsx(_components.span, { className: "hljs-keyword", children: "in" }), " test_dl:\n ", _jsx(_components.span, { className: "hljs-keyword", children: "if" }), " cuda: \n input_ids, segment_ids, attention_masks = input_ids.cuda(), segment_ids.cuda(), attention_masks.cuda() \n outputs = model(input_ids = input_ids, token_type_ids = segment_ids, attention_mask = attention_masks) \n pred_proba = outputs.softmax(dim=", _jsx(_components.span, { className: "hljs-number", children: "1" }), ")[:,", _jsx(_components.span, { className: "hljs-number", children: "1" }), "]\n ", _jsx(_components.span, { className: "hljs-built_in", children: "print" }), "(", _jsx(_components.span, { className: "hljs-string", children: "\"入力テキストのポジティブ度: {:.4f}\"" }), ".", _jsx(_components.span, { className: "hljs-built_in", children: "format" }), "(pred_proba.item()))\n"] }) }), "\n", _jsx(_components.p, { children: _jsx(_components.img, { src: "/images/blog30/image5.png", alt: "" }) }), "\n", _jsx(_components.p, { children: "うまく予想できてそうですね。" }), "\n", _jsx(_components.h3, { children: "まとめ" }), "\n", _jsx(_components.p, { children: "今回は実際のコードを紹介しました。" }), "\n", _jsx(_components.p, { children: "BERTはoutput layerを変えるだけでclassificationだけでなく、 さまざまなタスクが実行可能なのでぜひ試してみてください！" }), "\n", _jsx(_components.h2, { children: "参考文献" }), "\n", _jsxs(_components.ul, { children: ["\n", _jsx(_components.li, { children: "つくりながら学ぶ! PyTorchによる発展ディープラーニング 小川雄太郎 著" }), "\n"] })] }); } function MDXContent(props = {}) { const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components); return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, { children: _jsx(_createMdxContent, props) })) : _createMdxContent(props); } return { default: MDXContent }; a:["$","div",null,{"className":"container mx-auto px-4 py-8 max-w-7xl","children":["$","div",null,{"className":"flex flex-col md:flex-row gap-8","children":[["$","article",null,{"className":"w-full md:w-[70%] bg-white shadow-lg rounded-lg p-8","children":[["$","header",null,{"className":"mb-8 border-b pb-4","children":[["$","div",null,{"className":"flex items-center gap-2 mb-2","children":[["$","$L6",null,{"href":"/","className":"text-gray-500 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