使用TensorFlow实现LSTM

ゝ一世哀愁。 2022-09-08 13:52 182阅读 0赞

# 使用TensorFlow实现LSTM #

## 使用Cell实现 ##

# 以Cell方式实现LSTM
    # %%
    import os
    
    import numpy as np
    import tensorflow as tf
    from tensorflow import keras
    from tensorflow.keras import layers, losses, optimizers, Sequential
    
    # 指定GPU
    os.environ["CUDA_VISIBLE_DEVICES"] = "1"
    
    tf.random.set_seed(22)
    np.random.seed(22)
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    assert tf.__version__.startswith('2.')
    
    batch_size = 128  # 批量大小
    total_words = 10000  # 词汇表大小N_vocab
    max_review_len = 80  # 句子最大长度s，大于的句子部分将截断，小于的将填充
    embedding_len = 100  # 词向量特征长度f
    # 加载IMDB数据集，此处的数据采用数字编码，一个数字代表一个单词
    (x_train, y_train), (x_test, y_test) = keras.datasets.imdb.load_data(num_words=total_words)
    print(x_train.shape, len(x_train[0]), y_train.shape)
    print(x_test.shape, len(x_test[0]), y_test.shape)
    # %%
    print(x_train[0])
    # %%
    # 数字编码表
    word_index = keras.datasets.imdb.get_word_index()
    # for k,v in word_index.items():
    #     print(k,v)
    # %%
    word_index = {k: (v + 3) for k, v in word_index.items()}
    word_index["<PAD>"] = 0
    word_index["<START>"] = 1
    word_index["<UNK>"] = 2  # unknown
    word_index["<UNUSED>"] = 3
    # 翻转编码表
    reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
    
    
    def decode_review(text):
        return ' '.join([reverse_word_index.get(i, '?') for i in text])
    
    
    decode_review(x_train[8])
    
    # %%
    
    # x_train:[b, 80]
    # x_test: [b, 80]
    # 截断和填充句子，使得等长，此处长句子保留句子后面的部分，短句子在前面填充
    x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen=max_review_len)
    x_test = keras.preprocessing.sequence.pad_sequences(x_test, maxlen=max_review_len)
    # 构建数据集，打散，批量，并丢掉最后一个不够batchsz的batch
    db_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
    db_train = db_train.shuffle(1000).batch(batch_size, drop_remainder=True)
    db_test = tf.data.Dataset.from_tensor_slices((x_test, y_test))
    db_test = db_test.batch(batch_size, drop_remainder=True)
    print('x_train shape:', x_train.shape, tf.reduce_max(y_train), tf.reduce_min(y_train))
    print('x_test shape:', x_test.shape)
    
    
    # %%
    
    class MyRNN(keras.Model):
        # Cell方式构建多层网络
        def __init__(self, units):
            super(MyRNN, self).__init__()
            # [b, 64]，构建Cell初始化状态向量，重复使用
            # 与RNN不同 LSTM有两个输出 隐藏层状态也为两个
            self.state0 = [tf.zeros([batch_size, units]), tf.zeros([batch_size, units])]
            self.state1 = [tf.zeros([batch_size, units]), tf.zeros([batch_size, units])]
            # 词向量编码 [b, 80] => [b, 80, 100]
            self.embedding = layers.Embedding(total_words, embedding_len,
                                              input_length=max_review_len)
            # 构建2个Cell
            self.rnn_cell0 = layers.LSTMCell(units, dropout=0.5)
            self.rnn_cell1 = layers.LSTMCell(units, dropout=0.5)
            # 构建分类网络，用于将CELL的输出特征进行分类，2分类
            # [b, 80, 100] => [b, 64] => [b, 1]
            self.out_layer = Sequential([
                layers.Dense(units),
                layers.Dropout(rate=0.5),
                layers.ReLU(),
                layers.Dense(1)])
    
        def call(self, inputs, training=None, mask=None):
            x = inputs  # [b, 80]
            # embedding: [b, 80] => [b, 80, 100]
            x = self.embedding(x)
            # rnn cell compute,[b, 80, 100] => [b, 64]
            state0 = self.state0
            state1 = self.state1
            out1 = None
            for word in tf.unstack(x, axis=1):  # word: [b, 100]
                out0, state0 = self.rnn_cell0(word, state0, training)
                out1, state1 = self.rnn_cell1(out0, state1, training)
            # 末层最后一个输出作为分类网络的输入: [b, 64] => [b, 1]
            x = self.out_layer(out1, training)
            # p(y is pos|x)
            prob = tf.sigmoid(x)
    
            return prob
    
    
    def main():
        units = 64  # RNN状态向量长度f
        epochs = 50  # 训练epochs
    
        model = MyRNN(units)
        # 装配
        model.compile(optimizer=optimizers.RMSprop(0.001),
                      loss=losses.BinaryCrossentropy(),
                      metrics=['accuracy'])
        # 训练和验证
        model.fit(db_train, epochs=epochs, validation_data=db_test)
        # 测试
        model.evaluate(db_test)
    
    
    if __name__ == '__main__':
        main()

## 使用Layer实现 ##

# 以Layer方式实现LSTM
    # %%
    import os
    
    import numpy as np
    import tensorflow as tf
    from tensorflow import keras
    from tensorflow.keras import layers, losses, optimizers, Sequential
    
    # 指定GPU
    os.environ["CUDA_VISIBLE_DEVICES"] = "2"
    
    tf.random.set_seed(22)
    np.random.seed(22)
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    assert tf.__version__.startswith('2.')
    
    batch_size = 128  # 批量大小
    total_words = 10000  # 词汇表大小N_vocab
    max_review_len = 80  # 句子最大长度s，大于的句子部分将截断，小于的将填充
    embedding_len = 100  # 词向量特征长度f
    # 加载IMDB数据集，此处的数据采用数字编码，一个数字代表一个单词
    (x_train, y_train), (x_test, y_test) = keras.datasets.imdb.load_data(num_words=total_words)
    print(x_train.shape, len(x_train[0]), y_train.shape)
    print(x_test.shape, len(x_test[0]), y_test.shape)
    # %%
    print(x_train[0])
    # %%
    # 数字编码表
    word_index = keras.datasets.imdb.get_word_index()
    # for k,v in word_index.items():
    #     print(k,v)
    # %%
    word_index = {k: (v + 3) for k, v in word_index.items()}
    word_index["<PAD>"] = 0
    word_index["<START>"] = 1
    word_index["<UNK>"] = 2  # unknown
    word_index["<UNUSED>"] = 3
    # 翻转编码表
    reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
    
    
    def decode_review(text):
        return ' '.join([reverse_word_index.get(i, '?') for i in text])
    
    
    decode_review(x_train[8])
    
    # %%
    
    # x_train:[b, 80]
    # x_test: [b, 80]
    # 截断和填充句子，使得等长，此处长句子保留句子后面的部分，短句子在前面填充
    x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen=max_review_len)
    x_test = keras.preprocessing.sequence.pad_sequences(x_test, maxlen=max_review_len)
    # 构建数据集，打散，批量，并丢掉最后一个不够batchsz的batch
    db_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
    db_train = db_train.shuffle(1000).batch(batch_size, drop_remainder=True)
    db_test = tf.data.Dataset.from_tensor_slices((x_test, y_test))
    db_test = db_test.batch(batch_size, drop_remainder=True)
    print('x_train shape:', x_train.shape, tf.reduce_max(y_train), tf.reduce_min(y_train))
    print('x_test shape:', x_test.shape)
    
    
    # %%
    
    class MyRNN(keras.Model):
        # Cell方式构建多层网络
        def __init__(self, units):
            super(MyRNN, self).__init__()
            # 词向量编码 [b, 80] => [b, 80, 100]
            self.embedding = layers.Embedding(total_words, embedding_len,
                                              input_length=max_review_len)
            # 构建RNN
            self.rnn = keras.Sequential([
                layers.LSTM(units, dropout=0.5, return_sequences=True),
                layers.LSTM(units, dropout=0.5)
            ])
            # 构建分类网络，用于将CELL的输出特征进行分类，2分类
            # [b, 80, 100] => [b, 64] => [b, 1]
            self.out_layer = Sequential([
                layers.Dense(32),
                layers.Dropout(rate=0.5),
                layers.ReLU(),
                layers.Dense(1)])
    
        def call(self, inputs, training=None, mask=None):
            x = inputs  # [b, 80]
            # embedding: [b, 80] => [b, 80, 100]
            x = self.embedding(x)
            # rnn cell compute,[b, 80, 100] => [b, 64]
            x = self.rnn(x)
            # 末层最后一个输出作为分类网络的输入: [b, 64] => [b, 1]
            x = self.out_layer(x, training)
            # p(y is pos|x)
            prob = tf.sigmoid(x)
    
            return prob
    
    
    def main():
        units = 32  # RNN状态向量长度f
        epochs = 50  # 训练epochs
    
        model = MyRNN(units)
        # 装配
        model.compile(optimizer=optimizers.Adam(0.001),
                      loss=losses.BinaryCrossentropy(),
                      metrics=['accuracy'])
        # 训练和验证
        model.fit(db_train, epochs=epochs, validation_data=db_test)
        # 测试
        model.evaluate(db_test)
    
    
    if __name__ == '__main__':
        main()