和单向LSTM搭建的步骤是一样的：
- 设定超参数（隐藏向量的维度，学习率，输入输出维度等）
- 定义输入输出placeholder
- 定义网络结构，写好网络计算语句（dynamic_run等，用于计算时生成LSTM单元的输出状态）
- 定义全连接层的权重和偏差，用于将LSTM单元状态的输出转换成类别未规范化概率
- 计算输出的未规范化概率
- 定义softmax层
- 定义损失
- 定义训练优化器和优化操作
- sess.run()
下面给出代码：
learning_rate = 0.1
state_size = 128 # hidden layer num of features
n_classes = 19
n_features = 23
#输入
x = tf.placeholder(tf.float32, [None, None, n_features], name='input_placeholder') #batch_size, time_step, feat_len
y = tf.placeholder(tf.float32, [None, None, n_classes], name='labels_placeholder') #batch_size, time_step, n_classes
batch_size = tf.placeholder(tf.int32, (), name='batch_size')
time_steps = tf.placeholder(tf.int32, (), name='times_step')
#双向rnn
lstm_fw_cell = tf.contrib.rnn.BasicLSTMCell(state_size)
lstm_bw_cell = tf.contrib.rnn.BasicLSTMCell(state_size)
init_fw = lstm_fw_cell.zero_state(batch_size, dtype=tf.float32)
init_bw = lstm_bw_cell.zero_state(batch_size, dtype=tf.float32)
weights = tf.get_variable("weights", [2 * state_size, n_classes], dtype=tf.float32, #注意这里的维度
initializer = tf.random_normal_initializer(mean=0, stddev=1))
biases = tf.get_variable("biases", [n_classes], dtype=tf.float32,
initializer = tf.random_normal_initializer(mean=0, stddev=1))
outputs, final_states = tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell,
lstm_bw_cell,
x,
initial_state_fw = init_fw,
initial_state_bw = init_bw)
outputs = tf.concat(outputs, 2) #将前向和后向的状态连接起来
state_out = tf.matmul(tf.reshape(outputs, [-1, 2 * state_size]), weights) + biases #注意这里的维度
logits = tf.reshape(state_out, [batch_size, time_steps, n_classes])
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=y)) #计算交叉熵
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) #优化方法
train_op = optimizer.minimize(loss_op)
#进行softmax计算
probs = tf.nn.softmax(logits, -1) #-1也是默认值，表示在最后一维进行运算
predict = tf.argmax(probs, -1)#最大的概率在最后一维的哪一列，从0计数
#维度变为 batch_size * time_step
def train_network(num_epochs = 100):
with tf.Session() as sess:
sess.run(tf.global_variables_initializer()) #初始化variable
intervel = 5
for epoch in range(num_epochs):
#开始训练
for idx, (time_step, inputs, labels, idxes) in enumerate(get_dump_seq_data(1)):
_= sess.run([train_op],
feed_dict = {x: inputs,
y:labels,
batch_size:len(inputs),
time_steps: time_step})
print("epoch %d train done" % epoch)
#这一轮训练完毕，计算损失值和准确率
if epoch % intervel == 0 and epoch > 1:
#训练集误差
acc_record, total_df, total_acc, loss = compute_accuracy(sess, 1) #这里是我自定义的函数，与整个架构关系不大
#验证集误差
acc_record_dev, total_df_dev, total_acc_dev, loss_dev = compute_accuracy(sess, 0)
print("train_acc: %.6f, train_loss: %.6f; dev_acc: %.6f, dev_loss: %.6f" % (total_acc, loss, total_acc_dev, loss_dev))
print("- " * 50)
if num_epochs - epoch <= intervel:
return acc_record, total_df, acc_record_dev, total_df_dev
