cntk Embedding layer

“Embedding” refers to representing words or other discrete items by dense continuous vectors. This layer assumes that the input is in one-hot form. E.g., for a vocabulary size of 10,000, each input vector is expected to have dimension 10,000 and consist of zeroes except for one position that contains a 1. The index of that location is the index of the word or item it represents.
通過上面一段話，Embedding layer的工作就是把 one_hot類型的向量轉換為dense continuous vector。該轉換過程是通過矩陣相乘實現的，但是當 vocabulary size 很大時， 為了提高矩陣乘法的效率，在進行相乘之前，需要將one_hot類型向量變換為稀疏形式的寫法，這種轉換是通過參數 is_sparse=True來實現的：
input = C.input_variable(shape=(784, ), is_sparse=True)

轉載于:https://www.cnblogs.com/huizhu135/p/6044860.html

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