RNN-深度学习循环神经网络情感分类模型搭建

import os os.environ['TF_CPP_MIN_LOG_LEVEL']='2' 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) 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 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: """ /* 开发不易，整理也不易，如需要详细的说明文档和程序，以及完整的数据集，训练好的模型，或者进一步开发， 可加作者新联系方式咨询，WX：Q3101759565，QQ：3101759565 */ 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'],experimental_run_tf_function=False) model.fit(db_train, epochs=epochs, validation_data=db_test) model.evaluate(db_test) if __name__ == '__main__': main()