Phil 5.22.18

8:00 – 5:00 ASRC MKT

  • EAMS meeting
    • Rational
    • Sensitivity knn. Marching cubes, or write into space. Pos lat/lon altitude speed lat lon (4 dimensions)
    • Do they have flight path?
    • Memory
    • Retraining (batch)
    • inference real time
    • How will time be used
    • Much discussion of simulation
  • End-to-end Machine Learning with Tensorflow on GCP
    • In this workshop, we walk through the process of building a complete machine learning pipeline covering ingest, exploration, training, evaluation, deployment, and prediction. Along the way, we will discuss how to explore and split large data sets correctly using BigQuery and Cloud Datalab. The machine learning model in TensorFlow will be developed on a small sample locally. The preprocessing operations will be implemented in Cloud Dataflow, so that the same preprocessing can be applied in streaming mode as well. The training of the model will then be distributed and scaled out on Cloud ML Engine. The trained model will be deployed as a microservice and predictions invoked from a web application. This lab consists of 7 parts and will take you about 3 hours. It goes along with this slide deck
    • Slides
    • Codelab
  • Added in JuryRoom Text rough. Next is Research Browser
  • Worked with Aaron on LSTM some more. More ndarray slicing experience:
    import numpy as np
    dimension = 3
    size = 10
    dataset1 = np.ndarray(shape=(size, dimension))
    dataset2 = np.ndarray(shape=(size, dimension))
    for x in range(size):
        for y in range(dimension):
            val = (y+1) * 10 + x +1
            dataset1[x,y] = val
            val = (y+1) * 100 + x +1
            dataset2[x,y] = val
    
    
    dataset1[:, 0:1] = dataset2[:, -1:]
    print(dataset1)
    print(dataset2)
  • Results in:
    [[301.  21.  31.]
     [302.  22.  32.]
     [303.  23.  33.]
     [304.  24.  34.]
     [305.  25.  35.]
     [306.  26.  36.]
     [307.  27.  37.]
     [308.  28.  38.]
     [309.  29.  39.]
     [310.  30.  40.]]
    [[101. 201. 301.]
     [102. 202. 302.]
     [103. 203. 303.]
     [104. 204. 304.]
     [105. 205. 305.]
     [106. 206. 306.]
     [107. 207. 307.]
     [108. 208. 308.]
     [109. 209. 309.]
     [110. 210. 310.]]

     

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