目录
- Sentiment Analysis
- Two approaches
- Single layer
- Multi-layers
Sentiment Analysis
Two approaches
-
SimpleRNNCell
single layer
multi-layers
RNNCell
Single layer
import os
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
tf.random.set_seed(22)
np.random.seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')
batchsz = 128
# the most frequest words
total_words = 10000
max_review_len = 80
embedding_len = 100
(x_train,
y_train), (x_test,
y_test) = keras.datasets.imdb.load_data(num_words=total_words)
# x_train:[b, 80]
# x_test: [b, 80]
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)
db_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
db_train = db_train.shuffle(1000).batch(batchsz, drop_remainder=True)
db_test = tf.data.Dataset.from_tensor_slices((x_test, y_test))
db_test = db_test.batch(batchsz, 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):
def __init__(self, units):
super(MyRNN, self).__init__()
# [b, 64]
self.state0 = [tf.zeros([batchsz, units])]
self.state1 = [tf.zeros([batchsz, units])]
# transform text to embedding representation
# [b, 80] => [b, 80, 100]
self.embedding = layers.Embedding(total_words,
embedding_len,
input_length=max_review_len)
# [b, 80, 100] , h_dim: 64
# RNN: cell1 ,cell2, cell3
# SimpleRNN，units=64表示100个向量转成64个初始的状态
self.rnn_cell0 = layers.SimpleRNNCell(units, dropout=0.5)
self.rnn_cell1 = layers.SimpleRNNCell(units, dropout=0.5)
# fc, [b, 80, 100] => [b, 64] => [b, 1]
self.outlayer = layers.Dense(1)
def call(self, inputs, training=None):
"""
net(x) net(x, training=True) :train mode
net(x, training=False): test
:param inputs: [b, 80]
:param training:
:return:
"""
# [b, 80]
x = inputs
# embedding: [b, 80] => [b, 80, 100]
x = self.embedding(x)
# rnn cell compute
# [b, 80, 100] => [b, 64]
state0 = self.state0
state1 = self.state1
for word in tf.unstack(x, axis=1): # word: [b, 100]
# h1 = x*wxh+h0*whh
# out0: [b, 64]
out0, state0 = self.rnn_cell0(word, state0, training)
# out1: [b, 64]
out1, state1 = self.rnn_cell1(out0, state1, training)
# out: [b, 64] => [b, 1]
x = self.outlayer(out1)
# p(y is pos|x)
prob = tf.sigmoid(x)
return prob
def main():
units = 64
epochs = 4
model = MyRNN(units)
model.compile(optimizer=keras.optimizers.Adam(0.001),
loss=tf.losses.BinaryCrossentropy(),
metrics=['accuracy'])
model.fit(db_train, epochs=epochs, validation_data=db_test)
model.evaluate(db_test)
if __name__ == '__main__':
main()
Multi-layers
import os
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
tf.random.set_seed(22)
np.random.seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')
batchsz = 128
# the most frequest words
total_words = 10000 # 编码10000个单词
max_review_len = 80 # 句子长度80
embedding_len = 100
(x_train,
y_train), (x_test,
y_test) = keras.datasets.imdb.load_data(num_words=total_words)
# x_train:[b, 80]
# x_test: [b, 80]
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)
db_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
# drop_remainder，丢弃最后一个大小不合适的batch
db_train = db_train.shuffle(1000).batch(batchsz, drop_remainder=True)
db_test = tf.data.Dataset.from_tensor_slices((x_test, y_test))
db_test = db_test.batch(batchsz, 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):
def __init__(self, units):
super(MyRNN, self).__init__()
# transform text to embedding representation
# [b, 80] => [b, 80, 100] # embedding_len=100表示一个单词为100的向量
self.embedding = layers.Embedding(total_words,
embedding_len,
input_length=max_review_len)
# [b, 80, 100] , h_dim: 64
self.rnn = keras.Sequential([
layers.SimpleRNN(units,
dropout=0.5,
return_sequences=True,
unroll=True),
layers.SimpleRNN(units, dropout=0.5, unroll=True)
])
# fc, [b, 80, 100] => [b, 64] => [b, 1] # 得到分类结果
self.outlayer = layers.Dense(1)
def call(self, inputs, training=None):
"""
net(x) net(x, training=True) :train mode
net(x, training=False): test
:param inputs: [b, 80]
:param training: 计算过程是train还是test
:return:
"""
# [b, 80]
x = inputs
# embedding: [b, 80] => [b, 80, 100]
x = self.embedding(x)
# rnn cell compute
# x: [b, 80, 100] => [b, 64]
x = self.rnn(x)
# out: [b, 64] => [b, 1]
x = self.outlayer(x)
# p(y is pos|x)
prob = tf.sigmoid(x)
return prob
def main():
units = 64
epochs = 4
model = MyRNN(units)
model.compile(optimizer=keras.optimizers.Adam(0.001),
loss=tf.losses.BinaryCrossentropy(),
metrics=['accuracy'])
model.fit(db_train, epochs=epochs, validation_data=db_test)
model.evaluate(db_test)
if __name__ == '__main__':
main()