RNN 会根据目前从 1 到 N 的值来预测 N+1 的值。 （LSTM 只是实现 RNN 单元的一种方法。）
简短的回答是：
- train your model using back propagation on your complete sequences [[x11x12,x13],...,[xm1xm2,xm3]]
- run your trained model forward on the start of your sequence [x11x12,x13,...] then sample from the model to predict the rest of your sequence [xm1xm2,xm3,...].
更长的答案是：
您的示例仅显示了模型的初始化。您还需要实现一个训练函数来运行反向传播以及一个预测结果的示例函数。
以下代码片段是混合和匹配的，仅用于说明目的......
对于训练，只需在数据迭代器中使用开始+休息输入完整的序列即可。
例如，在示例代码 tensorflow/models/rnn/ptb_word_lm.py 中，训练循环针对目标（即按一个时间步移动的 input_data）计算一批 input_data 的成本函数
# compute a learning rate decay
session.run(tf.assign(self.learning_rate_variable, learning_rate))
logger.info("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(self.learning_rate_variable)))
"""Runs the model on the given data."""
epoch_size = ((len(training_data) // self.batch_size) - 1) // self.num_steps
costs = 0.0
iters = 0
state = self.initial_state.eval()
for step, (x, y) in enumerate(self.data_iterator(training_data, self.batch_size, self.num_steps)):
# x and y should have shape [batch_size, num_steps]
cost, state, _ = session.run([self.cost_function, self.final_state, self.train_op],
{self.input_data: x,
self.targets: y,
self.initial_state: state})
costs += cost
iters += self.num_steps
请注意，tensorflow/models/rnn/reader.py 中的数据迭代器将输入数据返回为“x”，将目标返回为“y”，仅从 x 向前移动一步。 （您需要创建一个像这样的数据迭代器来打包您的训练序列集。）
def ptb_iterator(raw_data, batch_size, num_steps):
raw_data = np.array(raw_data, dtype=np.int32)
data_len = len(raw_data)
batch_len = data_len // batch_size
data = np.zeros([batch_size, batch_len], dtype=np.int32)
for i in range(batch_size):
data[i] = raw_data[batch_len * i:batch_len * (i + 1)]
epoch_size = (batch_len - 1) // num_steps
if epoch_size == 0:
raise ValueError("epoch_size == 0, decrease batch_size or num_steps")
for i in range(epoch_size):
x = data[:, i*num_steps:(i+1)*num_steps]
y = data[:, i*num_steps+1:(i+1)*num_steps+1]
yield (x, y)
训练后，您可以通过输入序列的开头 start_x=[X1, X2, X3,...] 来向前运行模型以对序列进行预测...此片段假设表示类的二进制值，您必须调整浮点值的采样函数。
def sample(self, sess, num=25, start_x):
# return state tensor with batch size 1 set to zeros, eval
state = self.rnn_layers.zero_state(1, tf.float32).eval()
# run model forward through the start of the sequence
for char in start_x:
# create a 1,1 tensor/scalar set to zero
x = np.zeros((1, 1))
# set to the vocab index
x[0, 0] = char
# fetch: final_state
# input_data = x, initial_state = state
[state] = sess.run([self.final_state], {self.input_data: x, self.initial_state:state})
def weighted_pick(weights):
# an array of cummulative sum of weights
t = np.cumsum(weights)
# scalar sum of tensor
s = np.sum(weights)
# randomly selects a value from the probability distribution
return(int(np.searchsorted(t, np.random.rand(1)*s)))
# PREDICT REST OF SEQUENCE
rest_x = []
# get last character in init
char = start_x[-1]
# sample next num chars in the sequence after init
score = 0.0
for n in xrange(num):
# init input to zeros
x = np.zeros((1, 1))
# lookup character index
x[0, 0] = char
# probs = tf.nn.softmax(self.logits)
# fetch: probs, final_state
# input_data = x, initial_state = state
[probs, state] = sess.run([self.output_layer, self.final_state], {self.input_data: x, self.initial_state:state})
p = probs[0]
logger.info("output=%s" % np.shape(p))
# sample = int(np.random.choice(len(p), p=p))
# select a random value from the probability distribution
sample = weighted_pick(p)
score += p[sample]
# look up the key with the index
logger.debug("sample[%d]=%d" % (n, sample))
pred = self.vocabulary[sample]
logger.debug("pred=%s" % pred)
# add the car to the output
rest_x.append(pred)
# set the next input character
char = pred
return rest_x, score
