- Student: Souvik Sen
- Github: @invokesus
- Organisation: The Vega Visualization Tools by the UW Interactive Data Lab
This summer I worked on Vega-Lite which is a part of the Vega Stack.
This summer I worked on Vega-Lite which is a part of the Vega Stack.
Step | Time | Epoch | Loss | Accuracy | Validation_Loss | Validation_Accuracy | |
---|---|---|---|---|---|---|---|
200 | 55.955 | 010 | 1.91523 | 0.3506 | 1.74671 | 0.4779 | |
400 | 55.965 | 020 | 1.49748 | 0.5097 | 1.31340 | 0.5882 | |
600 | 55.085 | 030 | 1.07349 | 0.6599 | 1.06813 | 0.5882 | |
800 | 56.277 | 040 | 0.98565 | 0.6910 | 0.89610 | 0.6912 | |
1000 | 56.289 | 050 | 1.05948 | 0.7457 | 0.77642 | 0.7574 | |
1200 | 56.707 | 060 | 0.57501 | 0.8147 | 0.75292 | 0.7279 | |
1400 | 57.118 | 070 | 0.41966 | 0.8882 | 0.68322 | 0.7794 | |
1600 | 57.114 | 080 | 0.44839 | 0.9001 | 0.72408 | 0.7721 |
Step | Time | Epoch | Loss | Accuracy | Validation_Loss | Validation_Accuracy | |
---|---|---|---|---|---|---|---|
200 | 18.144 | 010 | 2.18292 | 0.2485 | 2.04492 | 0.2868 | |
400 | 18.193 | 020 | 1.67891 | 0.4755 | 1.69865 | 0.3750 | |
600 | 18.246 | 030 | 1.62197 | 0.5297 | 1.40128 | 0.5221 | |
800 | 18.443 | 040 | 1.14746 | 0.6103 | 1.30761 | 0.5882 | |
1000 | 18.256 | 050 | 0.96806 | 0.6662 | 1.13990 | 0.6397 | |
1200 | 18.281 | 060 | 0.77709 | 0.7608 | 1.09431 | 0.5809 | |
1400 | 18.808 | 070 | 0.69050 | 0.7918 | 0.99878 | 0.6397 |
Step | Time | Epoch | Loss | Accuracy | Validation_Loss | Validation_Accuracy | |
---|---|---|---|---|---|---|---|
200 | 18.180 | 010 | 2.35994 | 0.1949 | 2.23020 | 0.2941 | |
400 | 17.945 | 020 | 1.82016 | 0.3878 | 1.79125 | 0.3897 | |
600 | 18.158 | 030 | 1.58055 | 0.4531 | 1.41728 | 0.6103 | |
800 | 18.191 | 040 | 1.33468 | 0.5897 | 1.25512 | 0.6324 | |
1000 | 18.109 | 050 | 1.09587 | 0.6299 | 1.11825 | 0.6471 | |
1200 | 18.344 | 060 | 1.00513 | 0.6910 | 1.00183 | 0.6765 | |
1400 | 18.393 | 070 | 0.75215 | 0.7444 | 0.95590 | 0.6250 | |
1600 | 18.466 | 080 | 0.67934 | 0.7572 | 0.88305 | 0.7132 | |
1800 | 18.574 | 090 | 0.53124 | 0.8205 | 0.82189 | 0.7353 |
Step | Time | Epoch | Loss | Accuracy | Validation_Loss | Validation_Accuracy | |
---|---|---|---|---|---|---|---|
200 | 24.382 | 010 | 2.30355 | 0.2032 | 2.10794 | 0.2941 | |
400 | 24.266 | 020 | 1.81161 | 0.3971 | 1.86578 | 0.3162 | |
600 | 24.366 | 030 | 1.41484 | 0.5317 | 1.27272 | 0.5662 | |
800 | 24.365 | 040 | 1.20015 | 0.5952 | 1.14674 | 0.6544 | |
1000 | 24.563 | 050 | 1.03396 | 0.6639 | 1.08285 | 0.6471 | |
1200 | 24.644 | 060 | 0.83834 | 0.7371 | 1.03589 | 0.6397 | |
1400 | 24.551 | 070 | 0.70238 | 0.7784 | 0.87082 | 0.7353 | |
1600 | 24.811 | 080 | 0.68126 | 0.7802 | 0.97635 | 0.6838 | |
1800 | 24.742 | 090 | 0.50884 | 0.8427 | 0.78722 | 0.7647 |
Step | Time | Epoch | Loss | Accuracy | Validation_Loss | Validation_Accuracy | |
---|---|---|---|---|---|---|---|
200 | 18.180 | 010 | 2.3127412 | 0.198798 | 2.185596 | 0.29998199999999997 | |
400 | 17.945 | 020 | 1.7837568 | 0.39555599999999996 | 1.7554249999999998 | 0.397494 | |
600 | 18.158 | 030 | 1.548939 | 0.462162 | 1.3889344 | 0.622506 | |
800 | 18.191 | 040 | 1.3079864 | 0.601494 | 1.2300176 | 0.6450480000000001 | |
1000 | 18.109 | 050 | 1.0739526 | 0.6424980000000001 | 1.095885 | 0.660042 | |
1200 | 18.344 | 060 | 0.9850274000000001 | 0.70482 | 0.9817933999999999 | 0.69003 | |
1400 | 18.393 | 070 | 0.737107 | 0.759288 | 0.936782 | 0.6375 | |
1600 | 18.466 | 080 | 0.6657532 | 0.7723439999999999 | 0.865389 | 0.7274639999999999 | |
1800 | 18.574 | 090 | 0.5206152000000001 | 0.83691 | 0.8054522 | 0.750006 |
[{"name":"area","nodes":[{"_children":[1],"_data":{"url":"data/unemployment-across-industries.json","format":{"type":"json"}},"id":0,"nodetype":"SourceNode"},{"_children":[2],"_parent":0,"_parse":{"date":"date","count":"number"},"id":1,"nodetype":"ParseNode"},{"_children":[3],"_parent":1,"formula":{"yearmonth_date":{"as":"yearmonth_date","timeUnit":"yearmonth","field":"date"}},"id":2,"nodetype":"TimeUnitNode"},{"_children":[4],"_parent":2,"dimensions":{"yearmonth_date":true},"measures":{"count":{"sum":"sum_count"}},"id":3,"nodetype":"AggregateNode"},{"debugName":"main","_children":[],"_parent":3,"type":"main","refCounts":{"main":3,"raw":0},"_name":"main","_source":"main","id":4,"nodetype":"OutputNode"}],"links":[{"id":"0_1","source":0,"target":1},{"id":"1_2","source":1,"target":2},{"id":"2_3","source":2,"target":3},{"id":"3_4","source":3,"target":4}]},{"name":"area_cumulative_freq","nodes":[{"_children":[1],"_data":{"url":"data/movies.json","format":{"type":"json"}},"id":0,"nodetype":"SourceNode"},{"_children |
{ | |
"data": [{ | |
"t": 1539415804029, | |
"x": -0.586273193359375, | |
"y": -0.260101318359375, | |
"z": 9.895584106445312 | |
}, { | |
"t": 1539415804229, | |
"x": -0.710693359375, | |
"y": -0.41802978515625, |
id | gender | age_group | designation | education | |
---|---|---|---|---|---|
1 | male | <30 | junior | high school | |
2 | female | >50 | leader | mba | |
3 | female | 31-40 | leader | mba | |
4 | female | 31-40 | leader | mba | |
5 | female | 41-50 | senior | undergrad | |
6 | female | >50 | executive | postgrad | |
7 | male | 31-40 | senior | mba | |
8 | female | 31-40 | senior | undergrad | |
9 | male | <30 | senior | undergrad |
import os.path | |
import time | |
from json import loads | |
from darkflow.net.build import TFNet | |
import cv2 | |
options = {"model": "cfg/yolo.cfg", "load": "bin/yolo.weights", "threshold": 0.5, "gpu": 1.0} | |
tfnet = TFNet(options) | |
initial_file_name = 0 |