daylen

import pandas as pd
from collections import Counter

months = ['JANUARY', 'FEBRUARY', 'MARCH', 'APRIL', 'MAY', 'JUNE', 'JULY', 'AUGUST', 'SEPTEMBER', 'OCTOBER', 'NOVEMBER', 'DECEMBER']
timelines = []
for month in months:
  timelines.append(pd.read_json('2021_' + month + '.json')['timelineObjects'])

dist_by_travel_method = Counter()

daylen

%YAML 1.2
---
name: '256x20-t40'                  # ideally no spaces
gpu: 0                                 # gpu id to process on

dataset: 
  num_chunks: 1000000                   # newest nof chunks to parse
  train_ratio: 0.90                    # trainingset ratio
  # For separated test and train data.
  input_train: '/ramdisk/dev/train/' # supports glob

daylen

#!/usr/bin/env python
# coding: utf-8
# In[27]:
import pandas as pd
import datetime
# In[2]:
df = pd.read_csv('~/Downloads/Health Metrics - Sleep.csv')
# In[78]:
# In[87]:
def find_latest_end():

daylen

name: "deep"
layer {
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {

daylen

name: "deep"
layer {
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {