谷歌正式開源Model Search!自動優化並識別AI模型,最佳模版唾手可得
{"type":"doc","content":[{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"日前,谷歌開源Model Search,幫助研發人員開發最佳機器學習學習模型。"}]}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"Model Search:查找最佳機器學習模型的開源平臺"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"2月19日,谷歌"},{"type":"link","attrs":{"href":"https:\/\/ai.googleblog.com\/2021\/02\/introducing-model-search-open-source.html","title":"","type":null},"content":[{"type":"text","text":"宣佈"}]},{"type":"text","text":"發佈了"},{"type":"link","attrs":{"href":"http:\/\/github.com\/google\/model_search","title":"","type":null},"content":[{"type":"text","text":"Model Search"}]},{"type":"text","text":",這是一個開源平臺,旨在幫助研究人員高效、自動地開發和創建機器學習模型。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"神經網絡(NN)技術是否成功,往往取決於此類模型能否在多種任務中實現良好泛化。但這類高泛化能力模型的設計往往極爲困難,學術界甚至還沒有就神經網絡的泛化思路達成統一:"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"對於特定問題,合適的神經網絡應該是個什麼樣子?"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"最好選擇怎樣的深度?"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"應該使用哪些層類型?"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"LSTM就夠了,還是說Transformer層會更好?或者說應該二者相結合?"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"裝配或蒸餾等方法會提高模型性能嗎?"}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"正是這種種棘手問題的存在,才導致機器學習成爲一個嚴重依賴於工程師個人理解與直覺判斷的領域。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"近年來,AutoML算法開始快速興起,旨在幫助研究人員以無需手動實驗的方式快速找到合適的神經網絡。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"神經架構搜索(NAS)等技術能夠使用強化學習(RL)、進化算法與組合搜索等方法在給定的搜索空間之內構建起神經網絡。只要得到正確設置,這些技術已經可以帶來超越手動設計的性能表現。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"但這類算法通常涉及巨大的運算量,而且在實際收斂之前往往需要藉助成千上萬套模型進行訓練。此外,這類算法只能探索特定領域的搜索空間,而且需要藉助大量難以跨域傳播的先驗性知識。以圖像分類爲例,傳統的NAS會搜索出兩個良好的構建塊(卷積與下采樣塊),再根據以往慣例對構建塊進行排列以創建完整網絡。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"爲了克服這些不足,並將AutoML解決方案的適用範圍推向更廣泛的研究社區,我們在這裏正式公佈Model Search的開源版本。這套平臺能夠幫助研究人員高效自動開發出最佳機器學習模型。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Model Search不受特定領域限制,擁有良好的領域中立性,且能夠靈活找到最適合給定數據集與待解決問題的理想架構,同時儘可能降低編碼時長、工作量與算力需求。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Model Search以Tensorflow爲基礎構建而成,能夠在單設備或分佈式設備集羣當中運行。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"概述"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Model Search系統由多個訓練器、一項搜索算法、一項遷移母算法以及一套擁有大量已評估模型的數據庫。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Model Search能夠以自適應、異步方式運行多種機器學習模型(採用不同架構與訓練方法)的指令與評估實驗,確保所有訓練器都能從實驗中共享專業知識,並據此獨立完成更多實驗。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在每個輪次開始時,搜索算法都會介入全部已經完成的實驗,並使用beam search決定接下來該做出哪些嘗試。之後,搜索算法會通過“變異”識別出多種最佳架構,並將結果模型返回至訓練器處。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"embedcomp","attrs":{"type":"table","data":{"content":"
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