Bidirectional LSTM on IMDB

<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1"> <meta name="description" content="Keras documentation"> <meta name="author" content="Keras Team"> <link rel="shortcut icon" href="https://keras.io/img/favicon.ico"> <link rel="canonical" href="https://keras.io/examples/nlp/bidirectional_lstm_imdb/" />  <meta property="og:title" content="Keras documentation: Bidirectional LSTM on IMDB"> <meta property="og:image" content="https://keras.io/img/logo-k-keras-wb.png"> <meta name="twitter:title" content="Keras documentation: Bidirectional LSTM on IMDB"> <meta name="twitter:image" content="https://keras.io/img/k-keras-social.png"> <meta name="twitter:card" content="summary"> <title>Bidirectional LSTM on IMDB</title>  <link href="/css/bootstrap.min.css" rel="stylesheet">  <link href="https://fonts.googleapis.com/css2?family=Open+Sans:wght@400;600;700;800&display=swap" rel="stylesheet">  <link href="/css/docs.css" rel="stylesheet"> <link href="/css/monokai.css" rel="stylesheet">  <script>(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-5DNGF4N'); </script> <script> (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o), m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m) })(window,document,'script','https://www.google-analytics.com/analytics.js','ga'); ga('create', 'UA-175165319-128', 'auto'); ga('send', 'pageview'); </script>  <script async defer src="https://buttons.github.io/buttons.js"></script> </head> <body>  <noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-5DNGF4N" height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>  <div class='k-page'> <div class="k-nav" id="nav-menu"> <a href='/'><img src='/img/logo-small.png' class='logo-small' /></a> <div class="nav flex-column nav-pills" role="tablist" aria-orientation="vertical"> <a class="nav-link" href="/about/" role="tab" aria-selected="">About Keras</a> <a class="nav-link" href="/getting_started/" role="tab" aria-selected="">Getting started</a> <a class="nav-link" href="/guides/" role="tab" aria-selected="">Developer guides</a> <a class="nav-link" href="/api/" role="tab" aria-selected="">Keras 3 API documentation</a> <a class="nav-link" href="/2.18/api/" role="tab" aria-selected="">Keras 2 API documentation</a> <a class="nav-link active" href="/examples/" role="tab" aria-selected="">Code examples</a> <a class="nav-sublink" href="/examples/vision/">Computer Vision</a> <a class="nav-sublink active" href="/examples/nlp/">Natural Language Processing</a> <a class="nav-sublink2" href="/examples/nlp/text_classification_from_scratch/">Text classification from scratch</a> <a class="nav-sublink2" href="/examples/nlp/active_learning_review_classification/">Review Classification using Active Learning</a> <a class="nav-sublink2" href="/examples/nlp/fnet_classification_with_keras_hub/">Text Classification using FNet</a> <a class="nav-sublink2" href="/examples/nlp/multi_label_classification/">Large-scale multi-label text classification</a> <a class="nav-sublink2" href="/examples/nlp/text_classification_with_transformer/">Text classification with Transformer</a> <a class="nav-sublink2" href="/examples/nlp/text_classification_with_switch_transformer/">Text classification with Switch Transformer</a> <a class="nav-sublink2" href="/examples/nlp/tweet-classification-using-tfdf/">Text classification using Decision Forests and pretrained embeddings</a> <a class="nav-sublink2" href="/examples/nlp/pretrained_word_embeddings/">Using pre-trained word embeddings</a> <a class="nav-sublink2 active" href="/examples/nlp/bidirectional_lstm_imdb/">Bidirectional LSTM on IMDB</a> <a class="nav-sublink2" href="/examples/nlp/data_parallel_training_with_keras_hub/">Data Parallel Training with KerasHub and tf.distribute</a> <a class="nav-sublink2" href="/examples/nlp/neural_machine_translation_with_keras_hub/">English-to-Spanish translation with KerasHub</a> <a class="nav-sublink2" href="/examples/nlp/neural_machine_translation_with_transformer/">English-to-Spanish translation with a sequence-to-sequence Transformer</a> <a class="nav-sublink2" href="/examples/nlp/lstm_seq2seq/">Character-level recurrent sequence-to-sequence model</a> <a class="nav-sublink2" href="/examples/nlp/multimodal_entailment/">Multimodal entailment</a> <a class="nav-sublink2" href="/examples/nlp/ner_transformers/">Named Entity Recognition using Transformers</a> <a class="nav-sublink2" href="/examples/nlp/text_extraction_with_bert/">Text Extraction with BERT</a> <a class="nav-sublink2" href="/examples/nlp/addition_rnn/">Sequence to sequence learning for performing number addition</a> <a class="nav-sublink2" href="/examples/nlp/semantic_similarity_with_keras_hub/">Semantic Similarity with KerasHub</a> <a class="nav-sublink2" href="/examples/nlp/semantic_similarity_with_bert/">Semantic Similarity with BERT</a> <a class="nav-sublink2" href="/examples/nlp/sentence_embeddings_with_sbert/">Sentence embeddings using Siamese RoBERTa-networks</a> <a class="nav-sublink2" href="/examples/nlp/masked_language_modeling/">End-to-end Masked Language Modeling with BERT</a> <a class="nav-sublink2" href="/examples/nlp/abstractive_summarization_with_bart/">Abstractive Text Summarization with BART</a> <a class="nav-sublink2" href="/examples/nlp/pretraining_BERT/">Pretraining BERT with Hugging Face Transformers</a> <a class="nav-sublink2" href="/examples/nlp/parameter_efficient_finetuning_of_gpt2_with_lora/">Parameter-efficient fine-tuning of GPT-2 with LoRA</a> <a class="nav-sublink2" href="/examples/nlp/mlm_training_tpus/">Training a language model from scratch with 🤗 Transformers and TPUs</a> <a class="nav-sublink2" href="/examples/nlp/multiple_choice_task_with_transfer_learning/">MultipleChoice Task with Transfer Learning</a> <a class="nav-sublink2" href="/examples/nlp/question_answering/">Question Answering with Hugging Face Transformers</a> <a class="nav-sublink2" href="/examples/nlp/t5_hf_summarization/">Abstractive Summarization with Hugging Face Transformers</a> <a class="nav-sublink" href="/examples/structured_data/">Structured Data</a> <a class="nav-sublink" href="/examples/timeseries/">Timeseries</a> <a class="nav-sublink" href="/examples/generative/">Generative Deep Learning</a> <a class="nav-sublink" href="/examples/audio/">Audio Data</a> <a class="nav-sublink" href="/examples/rl/">Reinforcement Learning</a> <a class="nav-sublink" href="/examples/graph/">Graph Data</a> <a class="nav-sublink" href="/examples/keras_recipes/">Quick Keras Recipes</a> <a class="nav-link" href="/keras_tuner/" role="tab" aria-selected="">KerasTuner: Hyperparameter Tuning</a> <a class="nav-link" href="/keras_hub/" role="tab" aria-selected="">KerasHub: Pretrained Models</a> <a class="nav-link" href="/keras_cv/" role="tab" aria-selected="">KerasCV: Computer Vision Workflows</a> <a class="nav-link" href="/keras_nlp/" role="tab" aria-selected="">KerasNLP: Natural Language Workflows</a> </div> </div> <div class='k-main'> <div class='k-main-top'> <script> function displayDropdownMenu() { e = document.getElementById("nav-menu"); if (e.style.display == "block") { e.style.display = "none"; } else { e.style.display = "block"; document.getElementById("dropdown-nav").style.display = "block"; } } function resetMobileUI() { if (window.innerWidth <= 840) { document.getElementById("nav-menu").style.display = "none"; document.getElementById("dropdown-nav").style.display = "block"; } else { document.getElementById("nav-menu").style.display = "block"; document.getElementById("dropdown-nav").style.display = "none"; } var navmenu = document.getElementById("nav-menu"); var menuheight = navmenu.clientHeight; var kmain = document.getElementById("k-main-id"); kmain.style.minHeight = (menuheight + 100) + 'px'; } window.onresize = resetMobileUI; window.addEventListener("load", (event) => { resetMobileUI() }); </script> <div id='dropdown-nav' onclick="displayDropdownMenu();"> <svg viewBox="-20 -20 120 120" width="60" height="60"> <rect width="100" height="20"></rect> <rect y="30" width="100" height="20"></rect> <rect y="60" width="100" height="20"></rect> </svg> </div> <form class="bd-search d-flex align-items-center k-search-form" id="search-form"> <input type="search" class="k-search-input" id="search-input" placeholder="Search Keras documentation..." aria-label="Search Keras documentation..." autocomplete="off"> <button class="k-search-btn"> <svg width="13" height="13" viewBox="0 0 13 13"><title>search</title><path d="m4.8495 7.8226c0.82666 0 1.5262-0.29146 2.0985-0.87438 0.57232-0.58292 0.86378-1.2877 0.87438-2.1144 0.010599-0.82666-0.28086-1.5262-0.87438-2.0985-0.59352-0.57232-1.293-0.86378-2.0985-0.87438-0.8055-0.010599-1.5103 0.28086-2.1144 0.87438-0.60414 0.59352-0.8956 1.293-0.87438 2.0985 0.021197 0.8055 0.31266 1.5103 0.87438 2.1144 0.56172 0.60414 1.2665 0.8956 2.1144 0.87438zm4.4695 0.2115 3.681 3.6819-1.259 1.284-3.6817-3.7 0.0019784-0.69479-0.090043-0.098846c-0.87973 0.76087-1.92 1.1413-3.1207 1.1413-1.3553 0-2.5025-0.46363-3.4417-1.3909s-1.4088-2.0686-1.4088-3.4239c0-1.3553 0.4696-2.4966 1.4088-3.4239 0.9392-0.92727 2.0864-1.3969 3.4417-1.4088 1.3553-0.011889 2.4906 0.45771 3.406 1.4088 0.9154 0.95107 1.379 2.0924 1.3909 3.4239 0 1.2126-0.38043 2.2588-1.1413 3.1385l0.098834 0.090049z"></path></svg> </button> </form> <script> var form = document.getElementById('search-form'); form.onsubmit = function(e) { e.preventDefault(); var query = document.getElementById('search-input').value; window.location.href = '/search.html?query=' + query; return False } </script> </div> <div class='k-main-inner' id='k-main-id'> <div class='k-location-slug'> ► <a href='/examples/'>Code examples</a> / <a href='/examples/nlp/'>Natural Language Processing</a> / Bidirectional LSTM on IMDB </div> <div class='k-content'> <h1 id="bidirectional-lstm-on-imdb">Bidirectional LSTM on IMDB</h1> Author: <a href="https://twitter.com/fchollet">fchollet</a> Date created: 2020/05/03 Last modified: 2020/05/03 Description: Train a 2-layer bidirectional LSTM on the IMDB movie review sentiment classification dataset. <div class='example_version_banner keras_3'>ⓘ This example uses Keras 3</div> <img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> <a href="https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/nlp/ipynb/bidirectional_lstm_imdb.ipynb">View in Colab</a> •<img class="k-inline-icon" src="https://github.com/favicon.ico"/> <a href="https://github.com/keras-team/keras-io/blob/master/examples/nlp/bidirectional_lstm_imdb.py">GitHub source</a> <hr /> <h2 id="setup">Setup</h2> <div class="codehilite"><pre><code>import numpy as np import keras from keras import layers max_features = 20000 # Only consider the top 20k words maxlen = 200 # Only consider the first 200 words of each movie review </code></pre></div> <hr /> <h2 id="build-the-model">Build the model</h2> <div class="codehilite"><pre><code># Input for variable-length sequences of integers inputs = keras.Input(shape=(None,), dtype="int32") # Embed each integer in a 128-dimensional vector x = layers.Embedding(max_features, 128)(inputs) # Add 2 bidirectional LSTMs x = layers.Bidirectional(layers.LSTM(64, return_sequences=True))(x) x = layers.Bidirectional(layers.LSTM(64))(x) # Add a classifier outputs = layers.Dense(1, activation="sigmoid")(x) model = keras.Model(inputs, outputs) model.summary() </code></pre></div> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">Model: "functional_1" </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ │ input_layer (InputLayer) │ (None, None) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ embedding (Embedding) │ (None, None, 128) │ 2,560,000 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ bidirectional (Bidirectional) │ (None, None, 128) │ 98,816 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ bidirectional_1 (Bidirectional) │ (None, 128) │ 98,816 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ dense (Dense) │ (None, 1) │ 129 │ └─────────────────────────────────┴───────────────────────────┴────────────┘ </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Total params: 2,757,761 (10.52 MB) </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Trainable params: 2,757,761 (10.52 MB) </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Non-trainable params: 0 (0.00 B) </pre> <hr /> <h2 id="load-the-imdb-movie-review-sentiment-data">Load the IMDB movie review sentiment data</h2> <div class="codehilite"><pre><code>(x_train, y_train), (x_val, y_val) = keras.datasets.imdb.load_data( num_words=max_features ) print(len(x_train), "Training sequences") print(len(x_val), "Validation sequences") # Use pad_sequence to standardize sequence length: # this will truncate sequences longer than 200 words and zero-pad sequences shorter than 200 words. x_train = keras.utils.pad_sequences(x_train, maxlen=maxlen) x_val = keras.utils.pad_sequences(x_val, maxlen=maxlen) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz 17464789/17464789 ━━━━━━━━━━━━━━━━━━━━ 0s 0us/step 25000 Training sequences 25000 Validation sequences </code></pre></div> </div> <hr /> <h2 id="train-and-evaluate-the-model">Train and evaluate the model</h2> You can use the trained model hosted on <a href="https://huggingface.co/keras-io/bidirectional-lstm-imdb">Hugging Face Hub</a> and try the demo on <a href="https://huggingface.co/spaces/keras-io/bidirectional_lstm_imdb">Hugging Face Spaces</a>. <div class="codehilite"><pre><code>model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]) model.fit(x_train, y_train, batch_size=32, epochs=2, validation_data=(x_val, y_val)) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 1/2 782/782 ━━━━━━━━━━━━━━━━━━━━ 61s 75ms/step - accuracy: 0.7540 - loss: 0.4697 - val_accuracy: 0.8269 - val_loss: 0.4202 Epoch 2/2 782/782 ━━━━━━━━━━━━━━━━━━━━ 54s 69ms/step - accuracy: 0.9151 - loss: 0.2263 - val_accuracy: 0.8428 - val_loss: 0.3650 <keras.src.callbacks.history.History at 0x7f3efd663850> </code></pre></div> </div> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#bidirectional-lstm-on-imdb'>Bidirectional LSTM on IMDB</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#setup'>Setup</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#build-the-model'>Build the model</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#load-the-imdb-movie-review-sentiment-data'>Load the IMDB movie review sentiment data</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#train-and-evaluate-the-model'>Train and evaluate the model</a> </div> </div> </div> </div> </div> </body> <footer style="float: left; width: 100%; padding: 1em; border-top: solid 1px #bbb;"> <a href="https://policies.google.com/terms">Terms</a> | <a href="https://policies.google.com/privacy">Privacy</a> </footer> </html>

CINXE.COM

Bidirectional LSTM on IMDB