代码例子：
import tensorflow as tf class TCNNConfig(object): # class TCNNConfig(filename): """CNN配置参数""" embedding_dim = 8 # 词向量维度 seq_length = 3 # 序列长度 num_classes = 2 # 类别数 num_filters = 1 # 卷积核数目 kernel_size = 2 # 卷积核尺寸 vocab_size = 50 # 字典大小 # vocab_size = 5000 # 字典大小 hidden_dim = 10 # 全连接层神经元 dropout_keep_prob = 0.5 # dropout保留比例 learning_rate = 1e-3 # 学习率 batch_size = 1 # 每批训练大小 num_epochs = 1 # 总迭代轮次 print_per_batch = 10 # 每多少轮输出一次结果 save_per_batch = 10 # 每多少轮存入tensorboard class TextCNN(object): """文本分类，CNN模型""" def __init__(self, config): self.config = config # 三个待输入的数据 self.input_x = tf.placeholder(tf.int32, [None, self.config.seq_length], name='input_x') self.input_y = tf.placeholder(tf.float32, [None, self.config.num_classes], name='input_y') self.keep_prob = tf.placeholder(tf.float32, name='keep_prob') self.cnn() def cnn(self): """CNN模型""" # 词向量映射 with tf.device('/cpu:0'): embedding = tf.get_variable('embedding', [self.config.vocab_size, self.config.embedding_dim]) embedding_inputs = tf.nn.embedding_lookup(embedding, self.input_x) # self.embedding_inputs = embedding_inputs with tf.name_scope("cnn"): # CNN layer conv = tf.layers.conv1d(embedding_inputs, self.config.num_filters, self.config.kernel_size, name='conv') # self._conv = conv # global max pooling layer gmp = tf.reduce_max(conv, reduction_indices=[1], name='gmp') with tf.name_scope("score"): # 全连接层，后面接dropout以及relu激活 fc = tf.layers.dense(gmp, self.config.hidden_dim, name='fc1') fc = tf.contrib.layers.dropout(fc, self.keep_prob) fc = tf.nn.relu(fc) # 分类器 self.logits = tf.layers.dense(fc, self.config.num_classes, name='fc2') self.y_pred_cls = tf.argmax(tf.nn.softmax(self.logits), 1) # 预测类别 with tf.name_scope("optimize"): # 损失函数，交叉熵 cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=self.logits, labels=self.input_y) self.loss = tf.reduce_mean(cross_entropy) # 优化器 self.optim = tf.train.AdamOptimizer(learning_rate=self.config.learning_rate).minimize(self.loss) with tf.name_scope("accuracy"): # 准确率 correct_pred = tf.equal(tf.argmax(self.input_y, 1), self.y_pred_cls) self.acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) model = TextCNN(TCNNConfig()) import os tensorboard_dir = 'D:/学习/Tensorflow/dev/test/tensorboard/textcnn' if not os.path.exists(tensorboard_dir): os.makedirs(tensorboard_dir) tf.summary.scalar("loss", model.loss) tf.summary.scalar("accuracy", model.acc) merged_summary = tf.summary.merge_all() writer = tf.summary.FileWriter(tensorboard_dir) # 创建session session = tf.Session() session.run(tf.global_variables_initializer()) writer.add_graph(session.graph) for step in range(1): session.run(model.loss, feed_dict={model.input_x: [[1, 2, 3]], model.input_y: [[0, 1]], model.keep_prob: 0.5})在anaconda prompt命令行中输入：tensorboard --logdir=D:\学习\Tensorflow\dev\test\tensorboard\textcnn
C:\Users\zkq> tensorboard --logdir=D:\学习\Tensorflow\dev\test\tensorboard\textcnn
然后使用chrome浏览器，输入：http://ip:6006
网络结构如下图所示。
