LSTM
CELL LSTM循环神经网络单元
有两个记忆参数
self.state0 = [tf.zeros([batch_size, units]), tf.zeros([batch_size, units])] # 层1 记忆参数
import numpy as np
import tensorflow as tf
from tensorflow import keras
print(tf.__version__, np.__version__)
# 设置随机种子
tf.random.set_seed(22)
tf.random.set_seed(22)
# GPU自增长
gpus = tf.config.experimental.list_physical_devices('gpu')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
# 设置参数
total_words = 10000 # 所有单词类型
max_review_len = 80 # 句子最大长度
batch_size = 64
# 数据加载 新闻分类
(x_train, y_train), (x_test, y_test) = keras.datasets.imdb.load_data(num_words=total_words)
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)
# 数据集 整理
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)
# 最常出现的单词 独热编码长度
embedding_len = 100
# 定义RNN类
class MyRNN(tf.keras.Model):
def __init__(self, units):
super(MyRNN, self).__init__()
# [b, 64] c 和 h的参数
self.state0 = [tf.zeros([batch_size, units]), tf.zeros([batch_size, units])] # 层1 记忆参数
self.state1 = [tf.zeros([batch_size, units]), tf.zeros([batch_size, units])] # 层2 记忆参数
# embedding层 [b, 80] ==> [b, 80, 100]
self.embedding = tf.keras.layers.Embedding(total_words,
embedding_len,
input_length=max_review_len)
# 简单循环神经网络单元 [b, 80, 100] ==> h_dim:64
# self.rnn_cell0 = tf.keras.layers.SimpleRNNCell(units=units, dropout=0.5)
# self.rnn_cell1 = tf.keras.layers.SimpleRNNCell(units=units, dropout=0.5)
self.rnn_cell0 = tf.keras.layers.LSTMCell(units=units, dropout=0.5)
self.rnn_cell1 = tf.keras.layers.LSTMCell(units=units, dropout=0.5)
# 全连接层 预测结果 [b, 64] ==> [b, 1] 好评与坏评
self.outlayer = tf.keras.layers.Dense(1)
# 堆叠网络
def call(self, inputs, training=None, **kwargs):
# inputs:[b, 80]
# training:默认为 训练模式
# 输入层
x = inputs
# [b, 80] ==> [b, 80, 100] predict
# embedding层
x = self.embedding(x)
# rnn [b, 80, 100] ==> [b, 64]
# 记忆参数
state0 = self.state0
state1 = self.state1
# batch个句子 循环取第i个单词(80个单词) 处理
for word in tf.unstack(x, axis=1): # word[b, 100] [b, axis=1, 100]
# h1 = x* wxh + h0 * whh
out0, state0 = self.rnn_cell0(word, state0, training)
out1, state1 = self.rnn_cell1(out0, state1, training)
# 输出层
# out:[b, 64] ==> [b, 1]
x = self.outlayer(out1)
# 概率输出
prob = tf.sigmoid(x)
return prob
def main():
units = 64 # 循环神经网络 宽度
epochs = 4 # 总循环次数
import time
t0 = time.time()
# 创建对象
model = MyRNN(units)
# 编译模型
model.compile(optimizer=tf.keras.optimizers.Adam(0.001),
loss=tf.losses.BinaryCrossentropy(),
metrics=['accuracy'],
experimental_run_tf_function=False)
# 训练模型
model.fit(db_train, epochs=epochs, validation_data=db_test)
# 评估模型
model.evaluate(db_test)
t1 = time.time()
print('total time cost:', t1 - t0)
if __name__ == '__main__':
main()
keras
import numpy as np
import tensorflow as tf
from tensorflow import keras
print(tf.__version__, np.__version__)
# 随机种子
tf.random.set_seed(22)
tf.random.set_seed(22)
# GPU自增长
gpus = tf.config.experimental.list_physical_devices('gpu')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
# 设置参数
total_words = 10000 # 所有单词类型
max_review_len = 80 # 句子最大长度
batch_size = 128
# 数据加载 新闻分类
(x_train, y_train), (x_test, y_test) = keras.datasets.imdb.load_data(num_words=total_words)
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)
# 数据集 整理
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)
# 最常出现的单词 独热编码长度
embedding_len = 100
class MyRNN(tf.keras.Model):
def __init__(self, units):
super(MyRNN, self).__init__()
# embedding 层
# [b, 80] ==> [b, 80, 100]
self.embedding = tf.keras.layers.Embedding(total_words,
embedding_len,
input_length=max_review_len)
# 循环神经网络层 [b, 80, 100] ==> h_dim:64
self.rnn = keras.Sequential([
tf.keras.layers.LSTM(units, dropout=0.5, return_sequences=True, unroll=True),
tf.keras.layers.LSTM(units, dropout=0.5, unroll=True)
])
# 全连接层 [b, 64] ==> [b, 1]
self.outlayer = tf.keras.layers.Dense(1)
# 前向传播
def call(self, inputs, training=None, **kwargs):
# inputs:[b, 80]
# training:默认为 训练模式
x = inputs
# embedding 层
# [b, 80] ==> [b, 80, 100]
x = self.embedding(x)
# 循环神经网络层
x = self.rnn(x)
# 全连接 输出层
# out:[b, 64] ==> [b, 1]
x = self.outlayer(x)
prob = tf.sigmoid(x)
return prob
def main():
units = 64 # 循环神经网络宽度
epochs = 5 # 总循环次数
# 4 : 83.29%
# 5: 82.57%
import time
t0 = time.time()
# 创建网络
model = MyRNN(units)
# 编译网络
model.compile(optimizer=tf.keras.optimizers.Adam(0.001),
loss=tf.losses.BinaryCrossentropy(),
metrics=['accuracy'],
experimental_run_tf_function=False)
# 训练网络
model.fit(db_train, epochs=epochs, validation_data=db_test)
# 评估网络
model.evaluate(db_test)
t1 = time.time()
print('total time cost:', t1 - t0)
if __name__ == '__main__':
main()
GRU
CELL GRU循环神经网络单元
import numpy as np
import tensorflow as tf
from tensorflow import keras
print(tf.__version__, np.__version__)
# 设置随机种子
tf.random.set_seed(22)
tf.random.set_seed(22)
# GPU自增长
gpus = tf.config.experimental.list_physical_devices('gpu')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
# 设置参数
total_words = 10000 # 所有单词类型
max_review_len = 80 # 句子最大长度
batch_size = 64
# 数据加载 新闻分类
(x_train, y_train), (x_test, y_test) = keras.datasets.imdb.load_data(num_words=total_words)
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)
# 数据集 整理
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)
# 最常出现的单词 独热编码长度
embedding_len = 100
# 定义RNN类
class MyRNN(tf.keras.Model):
def __init__(self, units):
super(MyRNN, self).__init__()
# [b, 64] c 和 h的参数
self.state0 = [tf.zeros([batch_size, units])] # 层1 记忆参数
self.state1 = [tf.zeros([batch_size, units])] # 层2 记忆参数
# embedding层 [b, 80] ==> [b, 80, 100]
self.embedding = tf.keras.layers.Embedding(total_words,
embedding_len,
input_length=max_review_len)
# 简单循环神经网络单元 [b, 80, 100] ==> h_dim:64
# self.rnn_cell0 = tf.keras.layers.SimpleRNNCell(units=units, dropout=0.5)
# self.rnn_cell1 = tf.keras.layers.SimpleRNNCell(units=units, dropout=0.5)
self.rnn_cell0 = tf.keras.layers.GRUCell(units=units, dropout=0.5)
self.rnn_cell1 = tf.keras.layers.GRUCell(units=units, dropout=0.5)
# 全连接层 预测结果 [b, 64] ==> [b, 1] 好评与坏评
self.outlayer = tf.keras.layers.Dense(1)
# 堆叠网络
def call(self, inputs, training=None, **kwargs):
# inputs:[b, 80]
# training:默认为 训练模式
# 输入层
x = inputs
# [b, 80] ==> [b, 80, 100] predict
# embedding层
x = self.embedding(x)
# rnn [b, 80, 100] ==> [b, 64]
# 记忆参数
state0 = self.state0
state1 = self.state1
# batch个句子 循环取第i个单词(80个单词) 处理
for word in tf.unstack(x, axis=1): # word[b, 100] [b, axis=1, 100]
# h1 = x* wxh + h0 * whh
out0, state0 = self.rnn_cell0(word, state0, training)
out1, state1 = self.rnn_cell1(out0, state1, training)
# 输出层
# out:[b, 64] ==> [b, 1]
x = self.outlayer(out1)
# 概率输出
prob = tf.sigmoid(x)
return prob
def main():
units = 64 # 循环神经网络 宽度
epochs = 4 # 总循环次数
# 4: 83.04%
import time
t0 = time.time()
# 创建对象
model = MyRNN(units)
# 编译模型
model.compile(optimizer=tf.keras.optimizers.Adam(0.001),
loss=tf.losses.BinaryCrossentropy(),
metrics=['accuracy'],
experimental_run_tf_function=False)
# 训练模型
model.fit(db_train, epochs=epochs, validation_data=db_test)
# 评估模型
model.evaluate(db_test)
t1 = time.time()
print('total time cost:', t1 - t0)
if __name__ == '__main__':
main()
keras
import numpy as np
import tensorflow as tf
from tensorflow import keras
print(tf.__version__, np.__version__)
# 随机种子
tf.random.set_seed(22)
tf.random.set_seed(22)
# GPU自增长
gpus = tf.config.experimental.list_physical_devices('gpu')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
# 设置参数
total_words = 10000 # 所有单词类型
max_review_len = 80 # 句子最大长度
batch_size = 128
# 数据加载 新闻分类
(x_train, y_train), (x_test, y_test) = keras.datasets.imdb.load_data(num_words=total_words)
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)
# 数据集 整理
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)
# 最常出现的单词 独热编码长度
embedding_len = 100
class MyRNN(tf.keras.Model):
def __init__(self, units):
super(MyRNN, self).__init__()
# embedding 层
# [b, 80] ==> [b, 80, 100]
self.embedding = tf.keras.layers.Embedding(total_words,
embedding_len,
input_length=max_review_len)
# 循环神经网络层 [b, 80, 100] ==> h_dim:64
self.rnn = keras.Sequential([
tf.keras.layers.GRU(units, dropout=0.5, return_sequences=True, unroll=True),
tf.keras.layers.GRU(units, dropout=0.5, unroll=True)
])
# 全连接层 [b, 64] ==> [b, 1]
self.outlayer = tf.keras.layers.Dense(1)
# 前向传播
def call(self, inputs, training=None, **kwargs):
# inputs:[b, 80]
# training:默认为 训练模式
x = inputs
# embedding 层
# [b, 80] ==> [b, 80, 100]
x = self.embedding(x)
# 循环神经网络层
x = self.rnn(x)
# 全连接 输出层
# out:[b, 64] ==> [b, 1]
x = self.outlayer(x)
prob = tf.sigmoid(x)
return prob
def main():
units = 64 # 循环神经网络宽度
epochs = 4 # 总循环次数
# LSTM: 4: 83.29%
# LSTM: 5: 82.57%
# GRU 4: 83.53%
import time
t0 = time.time()
# 创建网络
model = MyRNN(units)
# 编译网络
model.compile(optimizer=tf.keras.optimizers.Adam(0.001),
loss=tf.losses.BinaryCrossentropy(),
metrics=['accuracy'],
experimental_run_tf_function=False)
# 训练网络
model.fit(db_train, epochs=epochs, validation_data=db_test)
# 评估网络
model.evaluate(db_test)
t1 = time.time()
print('total time cost:', t1 - t0)
if __name__ == '__main__':
main()