TensorFlow.jsで遊ぶ / nds59

第59回 長岡IT開発者勉強会 (NDS#59) で発表した資料。
テーマ「学習・AI・入学」

Transcript

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7. 5FOTPS'MPXKTͰֶश // Define a model for linear regression. const model = tf.sequential(); model.add(tf.layers.dense({units: 1, inputShape: [1]})); // Prepare the model for training: Specify the loss and the optimizer. model.compile({loss: 'meanSquaredError', optimizer: 'sgd'}); // Generate some synthetic data for training. const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]); const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]); // Train the model using the data. model.fit(xs, ys).then(() => { // Use the model to do inference on a data point the model hasn't seen before: // Open the browser devtools to see the output model.predict(tf.tensor2d([5], [1, 1])).print(); }); Ҿ༻
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13. ,FSBTϞσϧͷίϯόʔτ // install $ pip install tensorflowjs // convert $ tensorflowjs_converter --input_format keras model.h5 ./model // modelͷத਎ $ ls model/ group1-shard10of17 group1-shard13of17 group1-shard16of17 group1- shard2of17 group1-shard5of17 group1-shard8of17 group1-shard11of17 group1-shard14of17 group1-shard17of17 group1- shard3of17 group1-shard6of17 group1-shard9of17 group1-shard12of17 group1-shard15of17 group1-shard1of17 group1- shard4of17 group1-shard7of17 model.json IܗࣜͷϞσϧϑΝΠϧΛίϯόʔτ͠ɺUBSHFU@EJS ͜͜Ͱ͸NPEFM ʹอଘ
14. ,FSBTϞσϧͷίϯόʔτ import keras import tensorflowjs as tfjs model = keras.models.load_model(”./model.h5”) tfjs.converters.save_keras_model(model, ’./model’) 1ZUIPOεΫϦϓτ্Ͱ΋ɺ,FSBTϞσϧϑΝΠϧอଘ࣌ʹม׵Մೳ
15. +4ͰͷϞσϧͷϩʔυ const model = await tf.loadModel(”./model/model.json”); NPEFMKTPOΛಡΈࠐΈ࣌ʹࢦఆ͢Ε͹ྑ͍
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