introduce
Recurrent Neural Network RNN Practical Sentiment Classification:
Next, we start to use keras in tensorflow to build a simple RNN model. The data uses the dataset of emotional text that comes with tensorflow.keras.
the code
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 # 用到的通用的单词数量(超过部分,全部做相同标记)
max_review_len = 80 # 每个句子的长度(单词数)
embedding_len = 100 # 每个单词用100维度的向量表示
(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) # dorp最后一个batch
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
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): # 在第一维度展开(展开每个单词) 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()