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Modelingの中にあるDeep LearningとDeep Learning(Tensor)の違いがわかりません。オペレータも同じでどういった違いがあるのか
"What is a "single dataset"? Is it a scalar? Is it a matrix?"
データセットは、行列として捉えることができます。それは、行と列に整理された、2次元の値の集合です。
"You can think of a dataset as a matrix. It is a two dimensional set of values, organized in rows and columns."
"I do not understand the difference between "Deep Learning" and "Deep Learning (Tensor)" within the Modeling section. Since the operators appear to be identical, what exactly distinguishes the two?"
@shinshu_shun さん、こんにちは。お使いのオペレーターをプロセスに組み込み、そのプロセスを .rmp ファイルとしてエクスポートした上で、こちらに添付していただけますでしょうか。そうしていただければ、どのオペレーターについてのお話なのかを正確に把握することができます。よろしくお願いいたします。
Hi @shinshu_shun , please add your operators to a process and then export the process into a .rmp file and attach it here, so we can see exactly which operators you are referring to, thank you.
オペレータの中にあります。
Extensions → Modeling
@shinshu_shun さん、こんにちは。スクリーンショットをありがとうございます。これら2つのオペレーターの主な違いは、Deep Learningオペレーターが単一のデータセットを入力として受け取るのに対し、Deep Learning (Tensor) オペレーターはTensorを入力として受け取る点にあります。ここで言うTensorとは、データセットの集合体を指します。
Hi @shinshu_shun , Thank you for the screenshot. The difference between the two operators is that the Deep Learning operator takes a single dataset as input, whereas the Deep Learning (Tensor) operators takes a Tensor input, where a Tensor is a collection of datasets.
”single dataset”とはなんですか？スカラーですか？行列ですか？
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