MSWon

import bentoml
import tensorflow as tf
from tensorflow import keras

from bentoml.artifact import TensorflowSavedModelArtifact
from bentoml.adapters import JsonInput



REVIEW_CLASSES = ['negative', 'positive']

MSWon

$ paste -d : file1 file2 | shuf
five:5
two:2
one:1
four:4
three:3
$ paste -d ':' file1 file2 | shuf | awk -v FS=":" '{ print $1 > "out1" ; print $2 > "out2" }'

MSWon

import tensorflow as tf
import numpy as np

def my_func(input_x):
    result = []
    for idx in input_x:
        if idx % 2 == 1:
            result.append(3)
    return np.array(result)
  

MSWon

import tensorflow as tf

X_1 = tf.placeholder(shape=None, dtype=tf.float32)
Y_1 = tf.placeholder(shape=None, dtype=tf.float32)
Y_2 = tf.placeholder(shape=None, dtype=tf.float32)

W1 = tf.get_variable("W1", [1,10])
W2 = tf.get_variable("W2", [1,10])
result = tf.multiply(X_1, W1)

MSWon

# -*- coding: utf-8 -*-
"""
Created on Fri Dec  6 18:13:34 2019

@author: jbk48
"""

import pandas as pd
import sentencepiece as spm

MSWon

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

def bar_plot(col, data, hue=None): ## 정수형을 위한 
    f, ax = plt.subplots(figsize=(10, 5))
    sns.countplot(x=col, hue=hue, data=data, alpha=0.5)
    plt.show()

MSWon

library(MASS)
library(car)

install.packages("cars")

a = read.csv("남한산성_train.csv", sep=",")

train_data = a[, -1]

linear_model <- lm(eightdays_aud ~. , data = train_data)

MSWon

import random
import numpy as np

class shuffle():
    
    def __init__(self):
        np.random.seed(1234)
        
    def shuffle_string(self, string):
        chars = list(string)

MSWon

import tensorflow as tf
import numpy as np
import sys

def _bytes_feature(value):
  """Returns a bytes_list from a string / byte."""
  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

def _float_feature(value):
  """Returns a float_list from a float / double."""

MSWon

import tensorflow as tf
import numpy as np

features, labels = (np.random.sample((100,2)), np.random.randint(low=0, high=10 , size=(100,1)))
dataset = tf.data.Dataset.from_tensor_slices((features,labels))

dataset = tf.data.Dataset.from_tensor_slices((features,labels))
dataset = dataset.shuffle(len(features))
dataset = dataset.batch(16)
dataset = dataset.repeat()