你好，我拷贝了整个代码，运行的时候，没反应？也没生成图形，也没提示错误，不知道是怎么回事呢？

我自己用处理的数据进行训练,没用提供的数据.

我想问下代码里,训练时x传入7列,是不是不对啊,第7列应该是label吧.应该只用传入6列吧,是不是?

另外想问下预测的部分,如果我想用前90%进行训练,,后10%进行预测,应该怎么写啊.

用了未来数据了。。。你实际上是用今天和后两天的数据在预测今天的数据。

训练集的由于数据量比较多，在归一化时接近真实分布的均值和方差，但是测试集样本量比较少，这样计算的测试集的均值和方差肯定会有误差的。但是我对整体归一化的效果并没有分开归一化的效果好，能请教一下原因吗？

data=df.iloc[:,2:10].values #取第3-10列 获取了8列数据，为什么input_size=7呢

训练的时候，报错：
Input 'y' of 'Mul' Op has type float32 that does not match type int32 of argument 'x'.

关于这行代码：loss=tf.reduce_mean(tf.nn.nce_loss(nce_weights, nce_bias, embed, train_labels, num_sampled, num_classes=vocabulary_size))

我想是tensorflow的版本问题还是什么原因？

那个stock_predict.py，import tensorflow as tf都没有，后面使用tf怎么能成功？还有那个rnn是哪里来的？
第二个stock_predict_2.py，会发生越界。请问能跑的代码是哪个版本？

input=tf.reshape(X,[-1,input_size])
input_rnn=tf.matmul(input,w_in)+b_in
input_rnn=tf.reshape(input_rnn,[-1,time_step,rnn_unit])

output_rnn,final_states=tf.nn.dynamic_rnn(cell, input_rnn,initial_state=init_state, dtype=tf.float32)

综合网上教程，我觉的dynamic_cnn中的input_rnn维度应该是[-1,time_step,input_size],tensorflow中是封装好的（参考：https://www.cnblogs.com/zyly/p/9029591.html），但作者您自己写了input 的w 和b，将input_rnn的维度改成了[-1,time_step,rnn_unit]，感觉有点奇怪。

我自己写的代码如下：
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)
print("start")
print(tf.version)
print(mnist)

n_inputs=28 #input_size
max_time=28 #也即time_step
lstm_size=100 #num_units
n_classes=10
batch_size=50
n_batch=mnist.train.num_examples //batch_size

x=tf.placeholder(tf.float32,[None,784])
y=tf.placeholder(tf.float32,[None,10])

w=tf.Variable(tf.truncated_normal([lstm_size,n_classes],stddev=0.1))
b=tf.Variable(tf.constant(0.1,shape=[n_classes]))

def RNN(X,w,b):
inputs=tf.reshape(X,[-1,max_time,n_inputs])
lstm_cell=tf.contrib.rnn.BasicLSTMCell(lstm_size)
outputs,final_state=tf.nn.dynamic_rnn(lstm_cell,inputs,dtype=tf.float32)
#final_state[0]=cell state
#final_state[1]=hidden state
results=tf.nn.softmax(tf.matmul(final_state[1],w)+b)
return results

prediction=RNN(x,w,b)
cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #hinton建议设置为1e-3，代表初始学习率

correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

n=0
with tf.Session() as sess:
init=tf.global_variables_initializer()
sess.run(init)
for epoch in range(6):
print("n:",n)
n+=1
for batch in range(n_batch):
batch_xs,batch_ys=mnist.train.next_batch(batch_size)
sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
print("epoch:",epoch)

    acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
    print(str(epoch)+" times,accuracy:"+str(acc))

print("over")

如果要进行预测 预测后的数据应该进行归一化还原才是真实数据 但是我们数据是预测的 怎么知道预测数据的std 和 mean呢
stock预测第一部分的代码 进行了后期预测 但是没有还原的操作啊 是不是有问题

module_file = tf.train.latest_checkpoint()
示例中怎么没有参数，无法运行。。。
这个方法的定义是：
def latest_checkpoint(checkpoint_dir, latest_filename=None):
"""Finds the filename of latest saved checkpoint file.

Args:
checkpoint_dir: Directory where the variables were saved.
latest_filename: Optional name for the protocol buffer file that
contains the list of most recent checkpoint filenames.
See the corresponding argument to Saver.save().

Returns:
The full path to the latest checkpoint or None if no checkpoint was found.
"""

Pick the latest checkpoint based on checkpoint state.

ckpt = get_checkpoint_state(checkpoint_dir, latest_filename)
if ckpt and ckpt.model_checkpoint_path:
# Look for either a V2 path or a V1 path, with priority for V2.
v2_path = _prefix_to_checkpoint_path(ckpt.model_checkpoint_path,
saver_pb2.SaverDef.V2)
v1_path = _prefix_to_checkpoint_path(ckpt.model_checkpoint_path,
saver_pb2.SaverDef.V1)
if file_io.get_matching_files(v2_path) or file_io.get_matching_files(
v1_path):
return ckpt.model_checkpoint_path
else:
logging.error("Couldn't match files for checkpoint %s",
ckpt.model_checkpoint_path)
return None

ValueError: Variable rnn/basic_lstm_cell/weights already exists, disallowed. Did you mean to set reuse=True in VarScope

直接使用了吗？

Attempt to have a second RNNCell use the weights of a variable scope that already has weights: 'rnn/basic_lstm_cell'; and the cell was not constructed as BasicLSTMCell(..., reuse=True). To share the weights of an RNNCell, simply reuse it in your second calculation, or create a new one with the argument reuse=True.
我查了stackoverflow，按照上面问题将rnn.BasicLSTMCell(rnn_unit)修改成cell=rnn.BasicLSTMCell(rnn_unit, reuse=True)或者rnn.BasicLSTMCell(rnn_unit,reuse = tf.get_variable_scope().reuse)还是报错：
ValueError: Variable rnn/basic_lstm_cell/weights already exists, disallowed. Did you mean to set reuse=True in VarScope?
请问是怎么运行该代码啊？

感谢您的代码，能不能说明一下所使用的包的版本？tensorflow是什么版本的呀？

你好，请问time_step的取值是依据什么？我看你time_step取的是20

我只想说小姐姐那篇adaboost的文章写的太棒了，顺带看了你的博客和游记，我从来没有见过这么聪明漂亮还会玩的小姑娘，为了评论，博客园刚注册没法评论，你的github博客也没法评论，只能来这了，希望可以改掉文中wm,i˜=exp(−yi(Fm−1(xi)+αm−1Gm−1(xi)))=wm−1,i˜exp(−yiαm−1Gm−1(xi))这个小错误，方便我这样的小白更好的崇拜你

——————————————————定义神经网络变量——————————————————
X=tf.placeholder(tf.float32, [None,time_step,input_size]) #每批次输入网络的tensor
Y=tf.placeholder(tf.float32, [None,time_step,output_size]) #每批次tensor对应的标签

运行到这里报错：
AttributeError: module 'tensorflow' has no attribute 'placeholder'

用的tensorflow 1.2.1, 对stock_predict.py进行了一些修改后运行正常。

然而在学习您的stock_predict_2.py时遇到问题了，问题出在第44行。

以下是详细错误信息：
/usr/local/lib/python3.5/dist-packages/numpy/core/fromnumeric.py:2909: RuntimeWarning: Mean of empty slice.
out=out, **kwargs)
/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:73: RuntimeWarning: invalid value encountered in true_divide
ret, rcount, out=ret, casting='unsafe', subok=False)
/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:135: RuntimeWarning: Degrees of freedom <= 0 for slice
keepdims=keepdims)
/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:105: RuntimeWarning: invalid value encountered in true_divide
arrmean, rcount, out=arrmean, casting='unsafe', subok=False)
/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:125: RuntimeWarning: invalid value encountered in true_divide
ret, rcount, out=ret, casting='unsafe', subok=False)
/usr/local/lib/python3.5/dist-packages/numpy/lib/function_base.py:1110: RuntimeWarning: Mean of empty slice.
avg = a.mean(axis)
/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars
ret = ret.dtype.type(ret / rcount)

在网上搜到一些解决方式，尝试修改为
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
mean=np.mean(data_test,axis=0)

然而并无法解决问题。
想请教这个问题如何处理？

开放一下数据集下载 谢谢啊

你的数据集链接不能用了啊 小姐姐！求更新

module_file = tf.train.latest_checkpoint()

TypeError: latest_checkpoint() takes at least 1 argument (0 given)

请问po主用的python3.几以及相应的tensorflow版本是多少？我这里会报错
AttributeError: module 'tensorflow.python.ops.nn' has no attribute 'rnn_cell'
stackoverflow上说是因为我的tensorflow版本太新，改动比较大

'gbk' codec can't decode byte 0xb4 in position 35: illegal multibyte sequence

强制换成utf-8 能跑，但是训练到几千次的时候会报类似的错误，也是can't decode之类的