我对TensorFlow很新，我过去几周一直在学习，但这是我的第一个RNN .

我的目的是预测一个非常简单的函数的下一个值，在这种情况下是一条线 .

我预计损失将很快降至0，预测将变得准确 . 然而，无论如何，损失似乎毫无意义，它基本上跟随任何数据波动（在这种情况下，一条线，它上升和上升） .

我正在发布整个代码（简短和基本的）因为我认为我要么缺少一些非常微不足道的东西，要么我在这里误解了一些关键概念 .

import numpy as np
import tensorflow as tf

num_steps = 6
state_size = 4
batch_size = 5
data_width = 600

def get_next_batch():
    def f(x): return x*2.867

    for k in range(0,data_width,num_steps):
        rx,ry = np.zeros([batch_size,num_steps]),np.zeros([batch_size,num_steps])
        for t in range(batch_size):
            rx[t] = [(k+i)+(t*data_width) for i in range(num_steps)]
            ry[t] = [f(h) for h in rx[t]]
        yield rx,ry

X = tf.placeholder(tf.float32,[batch_size,num_steps])
Y_= tf.placeholder(tf.float32,[batch_size,num_steps])

rnn_inputs = tf.unstack(tf.reshape(X,[batch_size,-1,1]),axis=1)

cell = tf.contrib.rnn.BasicRNNCell(state_size)
init_state = tf.zeros([batch_size,state_size])
rnn_outputs,final_state = tf.contrib.rnn.static_rnn(cell,rnn_inputs,initial_state=init_state)

W = tf.Variable(tf.truncated_normal([state_size,1],stddev=0.1))
b = tf.Variable(tf.zeros(1))

Y = [tf.matmul(p,W)+b for p in rnn_outputs]

loss = tf.losses.mean_squared_error(labels=tf.unstack(tf.reshape(Y_,[batch_size,-1,1]),axis=1),predictions=Y)

train_step = tf.train.AdagradOptimizer(0.3).minimize(loss)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)

    f = np.zeros([batch_size,state_size])

    for batch_X,batch_Y in get_next_batch():
        e,f,_ = sess.run([loss,final_state,train_step],feed_dict = { X:batch_X,Y_:batch_Y,init_state:f })

        print(e)
tensorflow rnn