如何将数据馈送到 TensorFlow 中的 LSTM 单元以进行多类分类？

Question

我有一个单行句子数据集，每个句子根据上下文属于一个 class。我创建了一个重要单词词典，并将我的输入数据转换为一个特征列表，其中每个特征都是词典长度的向量。我想将此数据输入动态 LSTM 单元，但无法弄清楚如何重塑它。考虑我的 batch_size = 100，length_lexicon = 64，nRows_Input = 1000

Answer 1

为什么不用numpy.reshape？查看此文档：https://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html

例如： >>> a = np.arange(6).reshape((3, 2)) >>> a array([[0, 1], [2, 3], [4, 5]])

numpy.reshape¶

numpy.reshape(a, newshape, order='C')
Gives a new shape to an array without changing its data.
Parameters:   

a : array_like

    Array to be reshaped.

newshape : int or tuple of ints

    The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D array of that length. One
shape dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions.
order : {‘C’, ‘F’, ‘A’}, optional

    Read the elements of a using this index order, and place the elements into the reshaped array using this index order. ‘C’ means to
read / write the elements using C-like index order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to read / write the elements using Fortran-like index order, with the first index changing fastest, and the last index changing slowest. Note that the ‘C’ and ‘F’ options take no account of the memory layout of the underlying array, and only refer to the order of indexing. ‘A’ means to read / write the elements in Fortran-like index order if a is Fortran contiguous in memory, C-like order otherwise.
Returns:  

reshaped_array : ndarray

    This will be a new view object if possible; otherwise, it will be a copy. Note there is no guarantee of the memory layout (C- or
Fortran- contiguous) of the returned array.

如何将数据馈送到 TensorFlow 中的 LSTM 单元以进行多类分类？

How to feed data to LSTM cells in TensorFlow for multiclass classification?

python

lstm

tensorflow

rnn