RNN与情感分类问题实战-加载IMDB数据集

目录

* Sentiment Analysis
<https://www.cnblogs.com/nickchen121/p/10963848.html#sentiment-analysis>
* Two approaches
<https://www.cnblogs.com/nickchen121/p/10963848.html#two-approaches>
* Single layer
<https://www.cnblogs.com/nickchen121/p/10963848.html#single-layer>
* Multi-layers
<https://www.cnblogs.com/nickchen121/p/10963848.html#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()

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