{"published":"2019-09-16 15:32:11","author_name":"derwind","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Frandommemory.hatenablog.com%2Fentry%2F2019%2F09%2F16%2F153211\" title=\"\u5c64\u306e\u91cd\u307f - \u3089\u3093\u3060\u3080\u306a\u8a18\u61b6\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","blog_url":"https://randommemory.hatenablog.com/","version":"1.0","image_url":null,"provider_name":"Hatena Blog","author_url":"https://blog.hatena.ne.jp/derwind/","description":"Udacity\u306eud187\u306e\u8ab2\u984c\u300cCelsius to Fahrenheit\u300d\u3092\u518d\u3073\u5f15\u304d\u5408\u3044\u306b\u51fa\u3059\u3002\u25cf\u30e2\u30c7\u30eb\u306e\u5b9f\u88c5 model = tf.keras.Sequential([ tf.keras.layers.Dense(units=1, input_shape=[1]) ]) model.summary(): Model: \"sequential\" _________________________________________________________________ Layer (type) Output Shape Param # ========================\u2026","title":"\u5c64\u306e\u91cd\u307f","url":"https://randommemory.hatenablog.com/entry/2019/09/16/153211","provider_url":"https://hatena.blog","width":"100%","type":"rich","categories":["machine_learning"],"height":"190","blog_title":"\u3089\u3093\u3060\u3080\u306a\u8a18\u61b6"}