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from subprocess import Popen, PIPE


def image_markdown_conversion():
    try:
        while True:
            text = input("Input:").strip()
            brackets, split_point = 1, 0
            description = ""
            assert len(text) > 5, "wrong format!"

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class TemporalBlock(tf.layers.Layer):
    def __init__(self, n_outputs, kernel_size, strides, dilation_rate, dropout=0.2, 
                 trainable=True, name=None, dtype=None, 
                 activity_regularizer=None, **kwargs):
        super(TemporalBlock, self).__init__(
            trainable=trainable, dtype=dtype,
            activity_regularizer=activity_regularizer,
            name=name, **kwargs
        )        
        self.dropout = dropout

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from zipfile import ZipFile
import zipfile
from pathlib import Path
import base64
import sys
import io


def write_folder(zfile: ZipFile, dir_path: Path, prefix: str = ""):
    assert dir_path.is_dir()

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# WARNING: this script is out-dated since the last update of the Tourism Bureau website.
from pathlib import Path

import pandas as pd

SCHEMAS = [
    (201201, "schema/residence-2012-01.csv"),
    (201101, "schema/residence-2011-01.csv")
]
DATA_FILE_PATTERN = "raw_data/{year}-{month}.xls"

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Data Analysis Pieces

• Taiwan Tourism Dataset
  • An Analysis of the Impact of Chinese Tourism Ban to Taiwan
  • Country-level Analysis of Numbers of Visitors to Taiwan

Competition Summary / Notes

• Learning Notes from Planet Competition
• Kaggle Instacart Competition

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library(ggplot2)
library(ggthemes)

dat <- data.frame(
    name = c("CPU", "GPU", "TPU"), 
    time = c(3 * 3600 + 6 * 60 + 4, 3 * 60 + 16, 1 * 60 + 42)
)
dat$log_time = log(dat$time)

ggplot(data=dat, aes(x=name, y=log_time)) + 

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import tensorflow as tf

class CausalConv1D(tf.layers.Conv1D):
    def __init__(self, filters,
               kernel_size,
               strides=1,
               dilation_rate=1,
               activation=None,
               use_bias=True,
               kernel_initializer=None,