坦索弗洛:如何插入未知张量？

Question

我想序列化用于输入LSTM模型的数据，

import numpy as np
import tensorflow as tf

input_x=np.array([[1,2,1,2,1,2],[3,4,3,4,3,4],[10,20,1,2,1,2],[30,40,3,4,3,4],[100,200,1,2,1,2],[300,400,3,4,3,4]])#shape:6-6
# x = tf.placeholder(tf.float32,[None,6])
x=input_x
x_copy=x.copy()
# x_copy=tf.identity(x)

batch_size=6
n_steps=2

count=0
for i in range(int(batch_size/n_steps)-1):#total insert
    for j in range(n_steps-1):
        for k in range(n_steps):
            x_copy=np.insert(x_copy,(i+1)*n_steps+count,x[i*n_steps+j+k+1],axis=0)
            count+=1
res=x_copy
print('input_x\n',input_x)
print('res\n',res)

产出如下：

input_x
 [[  1   2   1   2   1   2]
 [  3   4   3   4   3   4]
 [ 10  20   1   2   1   2]
 [ 30  40   3   4   3   4]
 [100 200   1   2   1   2]
 [300 400   3   4   3   4]]
res
 [[  1   2   1   2   1   2]
 [  3   4   3   4   3   4]
 [  3   4   3   4   3   4]
 [ 10  20   1   2   1   2]
 [ 10  20   1   2   1   2]
 [ 30  40   3   4   3   4]
 [ 30  40   3   4   3   4]
 [100 200   1   2   1   2]
 [100 200   1   2   1   2]
 [300 400   3   4   3   4]]

当我设置n_steps=2时，除了第一行和最后一行之外，数据将重复一次。

但是，现在我想使用张量而不是array.And来操作代码，如下所示：

import numpy as np
import tensorflow as tf

input_x=np.array([[1,2,1,2,1,2],[3,4,3,4,3,4],[10,20,1,2,1,2],[30,40,3,4,3,4],[100,200,1,2,1,2],[300,400,3,4,3,4]])#shape:6-6
x = tf.placeholder(tf.float32,[None,6])
# x=input_x

# x_copy=x.copy()
x_copy=tf.identity(x)

batch_size=6
n_steps=2

count=0
for i in range(int(batch_size/n_steps)-1):#total insert
    for j in range(n_steps-1):
        for k in range(n_steps):
            x_copy=np.insert(x_copy,(i+1)*n_steps+count,x[i*n_steps+j+k+1],axis=0)
            count+=1
res=x_copy
# print('input_x\n',input_x)
# print('res\n',res)

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    batch_x=input_x
    result=sess.run([res,],feed_dict={
        x:batch_x,
    })
    print('result\n',result)

然后我遇到一个错误，可以如下所示：

TypeError: Fetch argument array(<tf.Tensor 'strided_slice_3:0' shape=(6,) dtype=float32>,
      dtype=object) has invalid type <class 'numpy.ndarray'>, must be a string or Tensor. (Can not convert a ndarray into a Tensor or Operation.)

我认为所有变量都应该是张量，但是我得到了typeerror，它显示了一个数组类型。

有人知道吗？希望你的帮助，谢谢！

Answer 1

# for i in range(int(batch_size-n_steps)+1):#total insert
    # for k in range(n_steps):
        # hidden1_copy_tmp=tf.slice(hidden1, [k+i, 0], [1, hidden1.shape[1]])
        # if hidden1_copy== None:
            # hidden1_copy=hidden1_copy_tmp
        # else:
            # hidden1_copy=tf.concat([hidden1_copy, hidden1_copy_tmp], 0)