參考以下這篇文章學習，從上層模組結構到底層**都分析得很清晰
直接從模型構建過程開始，rnn的核心單元是乙個可沿時間序列展開的cell，一般先定義模型中要使用到的cell結構:
lstm_cell = tf.nn
.rnn_cell.basiclstmcell(size, forget_bias=0.0, state_is_tuple=true)
if is_training and config.keep_prob < 1:
lstm_cell = tf.nn
lstm_cell, output_keep_prob=config.keep_prob)
cell = tf.nn
.rnn_cell.multirnncell([lstm_cell] * config.num_layers, state_is_tuple=true)
with tf.device("/cpu:0"):
embedding = tf.get_variable(
"embedding", [vocab_size, size], dtype=data_type())
inputs = tf.nn
.embedding_lookup(embedding, input_.input_data)
設定cell的輸入輸出
outputs, state = tf.nn
.rnn(cell, inputs, initial_state=self._initial_state)
output = tf.reshape(tf.concat(1, outputs), [-1, size])
softmax_w = tf.get_variable(
"softmax_w", [size, vocab_size], dtype=data_type())
softmax_b = tf.get_variable("softmax_b", [vocab_size], dtype=data_type())
logits = tf.matmul(output, softmax_w) + softmax_b
loss = tf.nn
.seq2seq.sequence_loss_by_example(
[logits],
[tf.reshape(input_.targets, [-1])],
[tf.ones([batch_size * num_steps], dtype=data_type())])
self._cost = cost = tf.reduce_sum(loss) / batch_size
np.exp(costs / iters)接下來是梯度傳遞函式，與其它的nn一樣：
self._lr = tf.variable(0.0, trainable=false)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars),
config.max_grad_norm)
optimizer = tf.train
.gradientdescentoptimizer(self._lr)
zip(grads, tvars),
global_step=tf.contrib
.framework
.get_or_create_global_step())
官網例子**：
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