orionpax00

{"lastUpload":"2020-04-09T18:36:14.879Z","extensionVersion":"v3.4.3"}

orionpax00

Attention

The Attention mechanism in Deep Learning is based off the concept of directing model's focus, and it pays greater attention to certain factors when processing the data.[1]

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orionpax00

from awake.visualise import Plotting
plot1 = Plotting()
plot1.plot(data={x:[],y:[]},type="line",interactivity="low",...)



from awake.visualise import Plotting
plot = Plotting()
plot.plot(data, type, style_info, interactivity, summary, summary_out, ...)

orionpax00

import os
import glob
import pandas as pd
import xml.etree.ElementTree as ET


def xml_to_csv(path):
    xml_list = []
    for xml_file in glob.glob(path + '/*.xml'):
        tree = ET.parse(xml_file)

orionpax00

import '@tensorflow/tf' as tf
async function trainModel(model, inputs, labels) {
  // Prepare the model for training.  
  model.compile({
    optimizer: tf.train.adam(),
    loss: tf.losses.meanSquaredError,
    metrics: ['mse'],
  });
  
  const batchSize = 28;