{"title":"MNIST\u306e\u624b\u66f8\u304d\u6570\u5b57\u3092\u8a8d\u8b58\u3055\u305b\u308b\u30b3\u30fc\u30c9\u306e\u6bd4\u8f03","provider_name":"Hatena Blog","type":"rich","image_url":null,"blog_title":"\u3089\u3093\u3060\u3080\u306a\u8a18\u61b6","categories":["machine_learning"],"author_url":"https://blog.hatena.ne.jp/derwind/","height":"190","provider_url":"https://hatena.blog","author_name":"derwind","description":"Keras\u3092\u4f7f\u3063\u305f\u5834\u5408\u3067\u306e\u30d6\u30e9\u30c3\u30af\u30dc\u30c3\u30af\u30b9\u611f\u304c\u51c4\u3044\u30fb\u30fb\u30fb\u3002 [TensorFlow] import tensorflow as tf mnist = tf.keras.datasets.mnist (x_train, y_train),(x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.la\u2026","published":"2019-09-29 06:34:34","version":"1.0","width":"100%","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Frandommemory.hatenablog.com%2Fentry%2F2019%2F09%2F29%2F063434\" title=\"MNIST\u306e\u624b\u66f8\u304d\u6570\u5b57\u3092\u8a8d\u8b58\u3055\u305b\u308b\u30b3\u30fc\u30c9\u306e\u6bd4\u8f03 - \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/","url":"https://randommemory.hatenablog.com/entry/2019/09/29/063434"}