自学教程：手把手教你使用TensorFlow2实现RNN

class RNN(tf.keras.Model):    def __init__(self, units):        super(RNN, self).__init__()        # 初始化 [b, 64] (b 表示 batch_size)        self.state0 = [tf.zeros([batch_size, units])]        self.state1 = [tf.zeros([batch_size, units])]        # [b, 80] => [b, 80, 100]        self.embedding = tf.keras.layers.Embedding(total_words, embedding_len, input_length=max_review_len)        self.rnn_cell0 = tf.keras.layers.SimpleRNNCell(units=units, dropout=0.2)        self.rnn_cell1 = tf.keras.layers.SimpleRNNCell(units=units, dropout=0.2)        # [b, 80, 100] => [b, 64] => [b, 1]        self.out_layer = tf.keras.layers.Dense(1)    def call(self, inputs, training=None):        """        :param inputs: [b, 80]        :param training:        :return:        """        state0 = self.state0        state1 = self.state1        x = self.embedding(inputs)        for word in tf.unstack(x, axis=1):            out0, state0 = self.rnn_cell0(word, state0, training=training)            out1, state1 = self.rnn_cell1(out0, state1, training=training)        # [b, 64] -> [b, 1]        x = self.out_layer(out1)        prob = tf.sigmoid(x)        return prob

def get_data():    # 获取数据    (X_train, y_train), (X_test, y_test) = tf.keras.datasets.imdb.load_data(num_words=total_words)    # 更改句子长度    X_train = tf.keras.preprocessing.sequence.pad_sequences(X_train, maxlen=max_review_len)    X_test = tf.keras.preprocessing.sequence.pad_sequences(X_test, maxlen=max_review_len)    # 调试输出    print(X_train.shape, y_train.shape)  # (25000, 80) (25000,)    print(X_test.shape, y_test.shape)  # (25000, 80) (25000,)    # 分割训练集    train_db = tf.data.Dataset.from_tensor_slices((X_train, y_train))    train_db = train_db.shuffle(10000).batch(batch_size, drop_remainder=True)    # 分割测试集    test_db = tf.data.Dataset.from_tensor_slices((X_test, y_test))    test_db = test_db.batch(batch_size, drop_remainder=True)    return train_db, test_db

import tensorflow as tfclass RNN(tf.keras.Model):    def __init__(self, units):        super(RNN, self).__init__()        # 初始化 [b, 64]        self.state0 = [tf.zeros([batch_size, units])]        self.state1 = [tf.zeros([batch_size, units])]        # [b, 80] => [b, 80, 100]        self.embedding = tf.keras.layers.Embedding(total_words, embedding_len, input_length=max_review_len)        self.rnn_cell0 = tf.keras.layers.SimpleRNNCell(units=units, dropout=0.2)        self.rnn_cell1 = tf.keras.layers.SimpleRNNCell(units=units, dropout=0.2)        # [b, 80, 100] => [b, 64] => [b, 1]        self.out_layer = tf.keras.layers.Dense(1)    def call(self, inputs, training=None):        """        :param inputs: [b, 80]        :param training:        :return:        """        state0 = self.state0        state1 = self.state1        x = self.embedding(inputs)        for word in tf.unstack(x, axis=1):            out0, state0 = self.rnn_cell0(word, state0, training=training)            out1, state1 = self.rnn_cell1(out0, state1, training=training)        # [b, 64] -> [b, 1]        x = self.out_layer(out1)        prob = tf.sigmoid(x)        return prob# 超参数total_words = 10000  # 文字数量max_review_len = 80  # 句子长度embedding_len = 100  # 词维度batch_size = 1024  # 一次训练的样本数目learning_rate = 0.0001  # 学习率iteration_num = 20  # 迭代次数optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)  # 优化器loss = tf.losses.BinaryCrossentropy(from_logits=True)  # 损失model = RNN(64)# 调试输出summarymodel.build(input_shape=[None, 64])print(model.summary())# 组合model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])def get_data():    # 获取数据    (X_train, y_train), (X_test, y_test) = tf.keras.datasets.imdb.load_data(num_words=total_words)    # 更改句子长度    X_train = tf.keras.preprocessing.sequence.pad_sequences(X_train, maxlen=max_review_len)    X_test = tf.keras.preprocessing.sequence.pad_sequences(X_test, maxlen=max_review_len)    # 调试输出    print(X_train.shape, y_train.shape)  # (25000, 80) (25000,)    print(X_test.shape, y_test.shape)  # (25000, 80) (25000,)    # 分割训练集    train_db = tf.data.Dataset.from_tensor_slices((X_train, y_train))    train_db = train_db.shuffle(10000).batch(batch_size, drop_remainder=True)    # 分割测试集    test_db = tf.data.Dataset.from_tensor_slices((X_test, y_test))    test_db = test_db.batch(batch_size, drop_remainder=True)    return train_db, test_dbif __name__ == "__main__":    # 获取分割的数据集    train_db, test_db = get_data()    # 拟合    model.fit(train_db, epochs=iteration_num, validation_data=test_db, validation_freq=1)