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# from https://towardsdatascience.com/train-gpt-2-in-your-own-language-fc6ad4d60171
import tensorflow as tf
from gensim.corpora import WikiCorpus
import os
import argparse


def store(corpus, lang):
    base_path = os.getcwd()
    store_path = os.path.join(base_path, '{}_corpus'.format(lang))

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How to download cityscapes dataset via terminal

To download the dataset the server check the authentication information of the cookie and confirm that the server is an authenticated user

Step 1. User authentication

Since it is authentication using cookie, it is necessary to acquire cookie first. This can also be done with wget! The command is as follows.

wget --keep-session-cookies --save-cookies=cookies.txt --post-data 'username=YOUR_EMAIL&password=YOUR_PASSWORD&submit=Login' https://www.cityscapes-dataset.com/login/; history -d $((HISTCMD-1))

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package org.example;

import org.tensorflow.SavedModelBundle;
import org.tensorflow.Tensor;
import org.tensorflow.ndarray.IntNdArray;
import org.tensorflow.ndarray.NdArrays;
import org.tensorflow.ndarray.Shape;
import org.tensorflow.proto.framework.SignatureDef;
import org.tensorflow.types.TInt32;

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#!/bin/bash
pip install autopep8
for filename in ./dags/*.py; do
   echo $filename
   autopep8 -i $filename
done

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    plt.figure(figsize=(20, 20))
    num_classes = 10

    for c in range(num_classes):
        # Select samples_per_class random keys of the labels == current class
        keys = np.random.choice(np.where(label == c)[0], examples_each_row)
        images = data[keys]
        labels = label[keys]
        for i in range(examples_each_row):
            f = plt.subplot(examples_each_row, num_classes, i * num_classes + c + 1)

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How to install virtualenv:

Install pip first

sudo apt-get install python3-pip

Then install virtualenv using pip3

sudo pip3 install virtualenv 

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remove Docker container

docker rm tf

stop the container

docker stop tf

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export JAVA_7_HOME=$(/usr/libexec/java_home -v1.7)
export JAVA_8_HOME=$(/usr/libexec/java_home -v1.8)
export JAVA_9_HOME=$(/usr/libexec/java_home -v9)

alias java7='export JAVA_HOME=$JAVA_7_HOME'
alias java8='export JAVA_HOME=$JAVA_8_HOME'
alias java9='export JAVA_HOME=$JAVA_9_HOME'

#default java8
export JAVA_HOME=$JAVA_8_HOME

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import matplotlib.pyplot as plt
#   plot 0     plot 1    plot 2   plot 3
x=[[1,2,3,4],[1,4,3,4],[1,2,3,4],[9,8,7,4]]
y=[[3,2,3,4],[3,6,3,4],[6,7,8,9],[3,2,2,4]]
plots = zip(x,y)

figs={}
axs={}
for idx,plot in enumerate(plots):
    figs[idx]=plt.figure()

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import tensorflow as tf

def get_weight(shape, name, trainable=True):
    #initial = tf.random_uniform(shape, minval=-0.1, maxval = 0.1)
    initial = tf.contrib.layers.xavier_initializer()(shape)
    return tf.Variable(initial, trainable=trainable, name=name+'_W', dtype=tf.float32)

def get_bias(shape, name, trainable=True):
    """
    filter_height, filter_width, in_channels, out_channels]