From 781afc337a9357cb09fe9a039d5c9ff65f3a2708 Mon Sep 17 00:00:00 2001
From: MaoXianxin <qq1044467857@gmail.com>
Date: Thu, 22 Jul 2021 21:05:52 +0800
Subject: [PATCH] text classification with RNN

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+本教程的目的是带领大家学会用 RNN 进行文本分类
+
+本次用到的数据集是 IMDB，一共有 50000 条电影评论，其中 25000 条是训练集，另外 25000 条是测试集
+
+首先我们需要加载数据集，可以通过 TFDS 很简单的把数据集下载过来，如下代码所示
+
+```
+dataset, info = tfds.load('imdb_reviews', with_info=True, as_supervised=True)
+
+train_dataset, test_dataset = dataset['train'], dataset['test']
+
+train_dataset.element_spec
+```
+
+接下来我们需要创建 text encoder，可以通过 tf.keras.layers.experimental.preprocessing.TextVectorization 实现，如下代码所示
+
+```
+VOCAB_SIZE = 1000
+encoder = tf.keras.layers.experimental.preprocessing.TextVectorization(
+    max_tokens=VOCAB_SIZE
+)
+encoder.adapt(train_dataset.map(lambda text, label: text))
+```
+
+接下来我们需要搭建模型，下图是模型结构图
+
+![](https://maoxianxin1996.oss-accelerate.aliyuncs.com/codechina1/20210722205501.png)
+
+对应的代码如下所示
+
+```
+model = tf.keras.Sequential([
+    encoder,
+    tf.keras.layers.Embedding(
+        input_dim=len(encoder.get_vocabulary()),
+        output_dim=64,
+        # Use masking to handle the variable sequence lengths
+        mask_zero=True),
+    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),
+    tf.keras.layers.Dense(64, activation='relu'),
+    tf.keras.layers.Dense(1)
+])
+
+model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
+              optimizer=tf.keras.optimizers.Adam(1e-4),
+              metrics=['accuracy'])
+```
+
+到这一步，我们就可以开始训练了，以及训练后进行模型评估
+
+```
+history = model.fit(train_dataset, epochs=10,
+                    validation_data=test_dataset,
+                    validation_steps=30)
+
+test_loss, test_acc = model.evaluate(test_dataset)
+
+print('Test Loss:', test_loss)
+print('Test Accuracy:', test_acc)
+```
+
+![](https://maoxianxin1996.oss-accelerate.aliyuncs.com/codechina1/20210722205743.png)
+
+上面是训练的结果记录图
+
+代码地址: https://codechina.csdn.net/csdn_codechina/enterprise_technology/-/blob/master/text_classification_rnn.ipynb
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