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lstmemory这个layer的输出是什么样子? #451
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您的理解是不对的,公式里的输入、输出都是向量表示,并不是单个数,此类写法在deep learning相关论文中是比较普遍的。相应的,lstmemory在每个时间步t的输出是一个向量,而非一个数。 |
@pengli09 谢谢您的回答,我刚刚看了一下lstm的公式http://www.paddlepaddle.org/doc/ui/api/trainer_config_helpers/layers.html#lstmemory |
公式里的x_t是向量,不是一个数,W_*都是矩阵。这种向量化的符号表示参见http://ufldl.stanford.edu/wiki/index.php/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C 中的例子 |
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* Add avx info 1.1 (PaddlePaddle#279) * refine avx info representation * refine avx info representation * refine avx info representation * updatetoavx * fix_AVX
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* add_api_cn (PaddlePaddle#437) * add gpu benchmark page (PaddlePaddle#436) * add gpu benchmark * fix comments * add chinese link in gen_doc.py (PaddlePaddle#439) * modified gen_doc.py * Update gen_doc.py * update gen_doc.py * Update mobile doc (PaddlePaddle#440) * update_en_structure * update_Paddle_commitid * delete_DS * update_mobile_doc * Update book commit (PaddlePaddle#438) * update_en_structure * update_Paddle_commitid * delete_DS * update_book_commit_id * update_api_rst * Adjust structure of advanced usage (PaddlePaddle#442) * adjust_structure_of_advanced_usage * Update paddle_gpu_benchmark.md * adjust_toctree_hidden * fix_api_guide * Update index.rst * fix deadlinkes (PaddlePaddle#443) * add_toctree (PaddlePaddle#445) * adjust_beginners_structure (PaddlePaddle#446) * adjust_beginners_structure * Update index.rst * update install_doc for python3 (PaddlePaddle#418) * update install_doc for python3 * update python3.5+ info,test=develop * fix review, test=develop * fix_pic (PaddlePaddle#447) * delete en2zn link * update lod_tensor * 1207 1.english api reference links removed 2. formatted * update transpiler * update profilse_cn * Update docker-image instroduce, test=develop (PaddlePaddle#459) * fix_bug (PaddlePaddle#460) * [cp2dev]Add avx info 1.1 (PaddlePaddle#279) (PaddlePaddle#451) * Add avx info 1.1 (PaddlePaddle#279) * refine avx info representation * refine avx info representation * refine avx info representation * updatetoavx * fix_AVX * update_en_api (PaddlePaddle#461) * update api guides for cn (PaddlePaddle#462) * Update commit_id of Paddle (PaddlePaddle#463) * update_en_api * update_again * Fix bug (PaddlePaddle#465) * update_en_api * update_again * bug_fix * fix_2nd
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* add_api_cn (PaddlePaddle#437) * add gpu benchmark page (PaddlePaddle#436) * add gpu benchmark * fix comments * add chinese link in gen_doc.py (PaddlePaddle#439) * modified gen_doc.py * Update gen_doc.py * update gen_doc.py * Update mobile doc (PaddlePaddle#440) * update_en_structure * update_Paddle_commitid * delete_DS * update_mobile_doc * Update book commit (PaddlePaddle#438) * update_en_structure * update_Paddle_commitid * delete_DS * update_book_commit_id * update_api_rst * Adjust structure of advanced usage (PaddlePaddle#442) * adjust_structure_of_advanced_usage * Update paddle_gpu_benchmark.md * adjust_toctree_hidden * fix_api_guide * Update index.rst * fix deadlinkes (PaddlePaddle#443) * add_toctree (PaddlePaddle#445) * adjust_beginners_structure (PaddlePaddle#446) * adjust_beginners_structure * Update index.rst * update install_doc for python3 (PaddlePaddle#418) * update install_doc for python3 * update python3.5+ info,test=develop * fix review, test=develop * fix_pic (PaddlePaddle#447) * delete en2zn link * update lod_tensor * 1207 1.english api reference links removed 2. formatted * update transpiler * update profilse_cn * Update docker-image instroduce, test=develop (PaddlePaddle#459) * fix_bug (PaddlePaddle#460) * [cp2dev]Add avx info 1.1 (PaddlePaddle#279) (PaddlePaddle#451) * Add avx info 1.1 (PaddlePaddle#279) * refine avx info representation * refine avx info representation * refine avx info representation * updatetoavx * fix_AVX * update_en_api (PaddlePaddle#461) * update api guides for cn (PaddlePaddle#462) * Update commit_id of Paddle (PaddlePaddle#463) * update_en_api * update_again * Fix bug (PaddlePaddle#465) * update_en_api * update_again * bug_fix * fix_2nd * add_release_note
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看了代码没有看的太懂,所以想问问lstmemory这个layer的输出是什么?我理解的是,根据公式,在某个时间t,lstm的输出就是一个浮点数,其维度为1没错吧?然后输入一个时间序列x的话,输出的就是一个1维的时间序列y,通过maxpooling之后得到的就是得到的就是max(y),也只有1维,就是一个浮点数,请问有理解错吗?
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