深度学习之神经网络(CNNRNNGAN)算法原理加实战 lstm代码
# 构建计算图——LSTM模型 # embedding # LSTM # fc # train_op # 训练流程代码 # 数据集封装 # api: next_batch(batch_size) # 词表封装: # api: sentence2id(text_sentence): 句子转换id # 类别的封装: # api: category2id(text_category). import tensorflow as tf import os import sys import numpy as np import math tf.logging.set_verbosity(tf.logging.INFO) print("ok1") # 定义数据超参数 def get_default_params(): return tf.contrib.training.HParams( num_embedding_size = 16, # 词的embedding长度 num_timesteps = 50, # lstm步长,一个句子词的个数 num_lstm_nodes = [32, 32], num_lstm_layers = 2, num_fc_nodes = 32, batch_size = 100, clip_lstm_grads = 1.0, # 梯度上限 learning