使用TensorFlow实现RNN
使用Cell实现
# 以cell方式实现RNN
# %%
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)
# 避免输出无关调试信息
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('dataset shape')
print(x_train.shape, len(x_train[0]), y_train.shape)
print(x_test.shape, len(x_test[0]), y_test.shape)
# %%
print('dataset x_train[0]: ', 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)
# %%
# 调整特殊词汇位置
word_index = {k: (v + 3) for k, v in word_index.items()}
word_index[""] = 0
word_index[""] = 1
word_index[""] = 2 # unknown
word_index[""] = 3
# 翻转编码表
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
def decode_review(text):
# dict.get(key, default=None)
# default -- 如果指定键的值不存在时，返回该默认值。
return ' '.join([reverse_word_index.get(i, '?') for i in text])
# 编码-->句子
print(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batch_size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):
# units [b, 64]
super(MyRNN, self).__init__()
# [b, 64]，构建Cell初始化状态向量，重复使用
self.state0 = [tf.zeros([batch_size, units])]
self.state1 = [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.SimpleRNNCell(units, dropout=0.5)
self.rnn_cell1 = layers.SimpleRNNCell(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):
# 测试阶段 training=false
x = inputs # [b, 80]
# embedding: [b, 80] => [b, 80, 100]
x = self.embedding(x)
# rnn cell compute,[b, 80, 100] => [b, 64]
# 初始化隐藏层状态
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)
# 第二层的输入为第一层的输出
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(learning_rate=1e-3),
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RNN
# %%
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 = 512 # 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[""] = 0
word_index[""] = 1
word_index[""] = 2 # unknown
word_index[""] = 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batch_size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):
# Layer方式构建多层网络
# 该方式不需要设置RNN初始状态
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 使用Sequential管理两层RNN
# return_sequences=True 使该层输出作为下一层输入
self.rnn = keras.Sequential([
layers.SimpleRNN(units, dropout=0.5, return_sequences=True),
layers.SimpleRNN(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]
# Layer方式不用手动管理隐藏层状态
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 = 64 # RNN状态向量长度f
epochs = 5 # 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()
