dockerで機械学習(24) with anaconda(24)「Machine Learning with Python Cookbook」 Chris Albon著

# 1.すぐに利用したい方へ(as soon as)
「Machine Learning with Python Cookbook」 By Chris Albon
![cat24.gif](https://qiita-image-store.s3.amazonaws.com/0/51423/9705dbd4-a0d9-8575-dd16-acb7462af6ae.gif)
http://shop.oreilly.com/product/0636920085423.do

## docker
dockerを導入し、Windows, Macではdockerを起動しておいてください。
Windowsでは、BiosでIntel Virtualizationをenableにしないとdockerが起動しない場合があります。
また、セキュリティの警告などが出ることがあります。

## docker pull and run

```
$ docker pull kaizenjapan/anaconda-chris

$ docker run -it -p 8888:8888 kaizenjapan/anaconda-chris /bin/bash
```

以下のshell sessionでは
(base) root@f19e2f06eabb:/#は入力促進記号(comman prompt)です。実際には数字の部分が違うかもしれません。この行の#の右側を入力してください。
それ以外の行は出力です。出力にエラー、違いがあれば、コメント欄などでご連絡くださると幸いです。
それぞれの章のフォルダに移動します。

dockerの中と、dockerを起動したOSのシェルとが表示が似ている場合には、どちらで捜査しているか間違えることがあります。dockerの入力促進記号(comman prompt)に気をつけてください。

## ファイル共有または複写
dockerとdockerを起動したOSでは、ファイル共有をするか、ファイル複写するかして、生成したファイルをブラウザ等表示させてください。参考文献欄にやり方のURLを記載しています。

複写の場合は、dockerを起動したOS側コマンドを実行しました。お使いのdockerの番号で置き換えてください。複写したファイルをブラウザで表示し内容確認しました。


```
(base) root@19b116a46da8:/# ls
bin   deep-learning-with-keras-ja	   dev	home  lib64  mnt  proc	run   srv  tmp	var
boot  deep-learning-with-python-notebooks  etc	lib   media  opt  root	sbin  sys  usr
(base) root@19b116a46da8:/# cd deep-learning-with-python-notebooks/
(base) root@19b116a46da8:/deep-learning-with-python-notebooks# ls
2.1-a-first-look-at-a-neural-network.ipynb    5.3-using-a-pretrained-convnet.ipynb		     8.1-text-generation-with-lstm.ipynb
3.5-classifying-movie-reviews.ipynb	      5.4-visualizing-what-convnets-learn.ipynb		     8.2-deep-dream.ipynb
3.6-classifying-newswires.ipynb		      6.1-one-hot-encoding-of-words-or-characters.ipynb      8.3-neural-style-transfer.ipynb
3.7-predicting-house-prices.ipynb	      6.1-using-word-embeddings.ipynb			     8.4-generating-images-with-vaes.ipynb
4.4-overfitting-and-underfitting.ipynb	      6.2-understanding-recurrent-neural-networks.ipynb      8.5-introduction-to-gans.ipynb
5.1-introduction-to-convnets.ipynb	      6.3-advanced-usage-of-recurrent-neural-networks.ipynb  LICENSE
5.2-using-convnets-with-small-datasets.ipynb  6.4-sequence-processing-with-convnets.ipynb	     README.md

```


## jupyternotebook


```
(base) root@19b116a46da8:/deep-learning-with-python-notebooks#  jupyter notebook --ip=0.0.0.0 --allow-root
[I 04:47:52.531 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret
[I 04:47:52.776 NotebookApp] JupyterLab beta preview extension loaded from /opt/conda/lib/python3.6/site-packages/jupyterlab
[I 04:47:52.776 NotebookApp] JupyterLab application directory is /opt/conda/share/jupyter/lab
[I 04:47:52.785 NotebookApp] Serving notebooks from local directory: /deep-learning-with-python-notebooks
[I 04:47:52.786 NotebookApp] 0 active kernels
[I 04:47:52.786 NotebookApp] The Jupyter Notebook is running at:
[I 04:47:52.786 NotebookApp] http://19b116a46da8:8888/?token=5ca23859604dcac80e266f93ec2194c802e98f432729aa5d
[I 04:47:52.786 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[W 04:47:52.787 NotebookApp] No web browser found: could not locate runnable browser.
[C 04:47:52.787 NotebookApp] 
    
    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:
        http://19b116a46da8:8888/?token=5ca23859604dcac80e266f93ec2194c802e98f432729aa5d&token=5ca23859604dcac80e266f93ec2194c802e98f432729aa5d
[I 04:48:11.426 NotebookApp] 302 GET / (172.17.0.1) 0.64ms
[W 04:48:11.433 NotebookApp] Clearing invalid/expired login cookie username-localhost-8888
[W 04:48:11.434 NotebookApp] Clearing invalid/expired login cookie username-localhost-8888
[I 04:48:11.435 NotebookApp] 302 GET /tree? (172.17.0.1) 2.66ms
[I 04:48:16.289 NotebookApp] 302 POST /login?next=%2Ftree%3F (172.17.0.1) 1.77ms
[I 04:48:21.752 NotebookApp] Writing notebook-signing key to /root/.local/share/jupyter/notebook_secret
[W 04:48:21.757 NotebookApp] Notebook 2.1-a-first-look-at-a-neural-network.ipynb is not trusted
[I 04:48:22.837 NotebookApp] Kernel started: fe0e9fe5-2acc-488a-b574-315edf559da0
[I 04:48:23.453 NotebookApp] Adapting to protocol v5.1 for kernel fe0e9fe5-2acc-488a-b574-315edf559da0
[I 04:50:22.814 NotebookApp] Saving file at /2.1-a-first-look-at-a-neural-network.ipynb
[W 04:50:22.818 NotebookApp] Notebook 2.1-a-first-look-at-a-neural-network.ipynb is not trusted
[W 04:50:27.598 NotebookApp] Notebook 2.1-a-first-look-at-a-neural-network.ipynb is not trusted
[I 04:50:28.635 NotebookApp] Adapting to protocol v5.1 for kernel fe0e9fe5-2acc-488a-b574-315edf559da0
```
ブラウザで
localhost:8888
を開く

<img width="453" alt="68747470733a2f2f71696974612d696d6167652d73746f72652e73332e616d617a6f6e6177732e636f6d2f302f35313432332f34643333383165622d653832302d336437372d316635352d6665303161396231353731342e706e67.png" src="https://qiita-image-store.s3.amazonaws.com/0/51423/7c4bd7ae-2a80-cd52-e897-483f0c20267f.png">


上記の場合は、token に
5ca23859604dcac80e266f93ec2194c802e98f432729aa5d
を入れる。

![py17-1.png](https://qiita-image-store.s3.amazonaws.com/0/51423/c7396ba8-3bc0-5130-5f0e-66c835e7e451.png)


![py17-2.png](https://qiita-image-store.s3.amazonaws.com/0/51423/55f7c40b-234a-2b9e-d15a-5a4265421a32.png)


##6.1 Cleaning Text

```
OSError: No such file or directory: '/Users/f00/nltk_data/corpora/stopwords/english'
```
###ju24-6.py

ソースを切りはりして一つのファイルにして実行してみた。

```python:ju24-6.py
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer

text_data = np.array(['I love Brazil. Brazil!', 'Sweden is best', 'Germany beats both'])

# create the tf-idf feature matrix
tfidf = TfidfVectorizer()
feature_matrix = tfidf.fit_transform(text_data)

feature_matrix

text_data = ["  Interrobang. By aishwarya Henriette    ",
            "Parking And Going. By Karl Gautier",
            "    Today Is The night. By Jarek Prakash"]

# strip whitespaces
strip_whitespace = [string.strip() for string in text_data]
strip_whitespace

remove_periods = [string.replace(".", "") for string in strip_whitespace]
remove_periods

def capitalizer(string: str) -> str:
    return string.upper()

[capitalizer(string) for string in remove_periods]

import re

def replace_letters_with_X(string: str) -> str:
    return re.sub(r"[a-zA-Z]", "X", string)

[replace_letters_with_X(string) for string in remove_periods]

from bs4 import BeautifulSoup

html = """
    <div class='full_name'><span style='font-weight:bold'>Yan</span> Chin</div>
"""

soup = BeautifulSoup(html)

soup.find("div", {"class": "full_name"}).text

import unicodedata
import sys

text_data = ['Hi!!! I. Love. This. Song.....', '10000% Agree!!!! #LoveIT', 'Right?!?!']

# create a dictionary of punctuation characters
punctuation = dict.fromkeys(i for i in range(sys.maxunicode) if unicodedata.category(chr(i)).startswith('P'))

# for each string, remove any punctuation characters
[string.translate(punctuation) for string in text_data]

from nltk.tokenize import word_tokenize
string = "The science of today is the technology of tommorrow"

# tokenize words
word_tokenize(string)

from nltk.tokenize import sent_tokenize
string = "The science of today is the technology of tommorw. Tommorrow is today"

# tokenize sentences
sent_tokenize(string)

from nltk.corpus import stopwords
import nltk
nltk.download('stopwords')

tokenized_words = ['i', 'am', 'going', 'to', 'go', 'to', 'the', 'store', 'and', 'park']

stop_words = stopwords.words('english')

# remove stop words
[word for word in tokenized_words if word not in stop_words]

from nltk.stem.porter import PorterStemmer

tokenized_words = ['i', 'am', 'humbled', 'by', 'this', 'traditional', 'meeting']

# create stemmer
porter = PorterStemmer()

# apply stemmer
[porter.stem(word) for word in tokenized_words]

from nltk import pos_tag
from nltk import word_tokenize
import nltk
nltk.download('averaged_perceptron_tagger')

text_data = "Chris loved outdoor running"

text_tagged = pos_tag(word_tokenize(text_data))

text_tagged

[word for word, tag in text_tagged if tag in ['NN', 'NNS', 'NNP', 'NNPS']]

from sklearn.preprocessing import MultiLabelBinarizer

tweets = ["I am eating a burrito for breakfast",
         "Political science is an amazing field",
         "San Francisco is an awesome city"]

tagged_tweets = []

# tag each word and each tweet
for tweet in tweets:
    tweet_tag = nltk.pos_tag(word_tokenize(tweet))
    tagged_tweets.append([tag for word, tag in tweet_tag])

# use one hot encoding to convert the tags into features
one_hot_multi = MultiLabelBinarizer()
one_hot_multi.fit_transform(tagged_tweets)

# show feature names
one_hot_multi.classes_

from nltk.corpus import brown
from nltk.tag import UnigramTagger
from nltk.tag import BigramTagger
from nltk.tag import TrigramTagger
import nltk
nltk.download('brown')
    
# get some text from the Brown
sentences = brown.tagged_sents(categories='news')

# split into 4000 stences for training and 623 for testing
train = sentences[:4000]
test = sentences[4000:]

# create backoff tagger
unigram = UnigramTagger(train)
bigram = BigramTagger(train, backoff=unigram)
trigram = TrigramTagger(train, backoff=bigram)

trigram.evaluate(test)

import numpy as np
from sklearn.feature_extraction.text import CountVectorizer

text_data = np.array(['I love Brazil. Brazil!', 'Sweden is best', 'Germany beats both'])

count = CountVectorizer()
bag_of_words = count.fit_transform(text_data)

bag_of_words

bag_of_words.toarray()

count.get_feature_names()
count_2gram = CountVectorizer(ngram_range=(1,2), stop_words='english', vocabulary=['brazil'])
bag = count_2gram.fit_transform(text_data)
bag.toarray()

count_2gram.vocabulary_

feature_matrix.toarray()

tfidf.vocabulary_
```
###実行

```shell-session

(base) root@b350954ba6b4:/machine-learning-with-python-cookbook-notes# python ju24-6.py
ju24-6.py:41: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system ("lxml"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.

The code that caused this warning is on line 41 of the file ju24-6.py. To get rid of this warning, pass the additional argument 'features="lxml"' to the BeautifulSoup constructor.

  soup = BeautifulSoup(html)
Traceback (most recent call last):
  File "ju24-6.py", line 60, in <module>
    word_tokenize(string)
  File "/opt/conda/lib/python3.6/site-packages/nltk/tokenize/__init__.py", line 128, in word_tokenize
    sentences = [text] if preserve_line else sent_tokenize(text, language)
  File "/opt/conda/lib/python3.6/site-packages/nltk/tokenize/__init__.py", line 94, in sent_tokenize
    tokenizer = load('tokenizers/punkt/{0}.pickle'.format(language))
  File "/opt/conda/lib/python3.6/site-packages/nltk/data.py", line 836, in load
    opened_resource = _open(resource_url)
  File "/opt/conda/lib/python3.6/site-packages/nltk/data.py", line 954, in _open
    return find(path_, path + ['']).open()
  File "/opt/conda/lib/python3.6/site-packages/nltk/data.py", line 675, in find
    raise LookupError(resource_not_found)
LookupError: 
**********************************************************************
  Resource punkt not found.
  Please use the NLTK Downloader to obtain the resource:

  >>> import nltk
  >>> nltk.download('punkt')
  
  Searched in:
    - '/root/nltk_data'
    - '/usr/share/nltk_data'
    - '/usr/local/share/nltk_data'
    - '/usr/lib/nltk_data'
    - '/usr/local/lib/nltk_data'
    - '/opt/conda/nltk_data'
    - '/opt/conda/share/nltk_data'
    - '/opt/conda/lib/nltk_data'
    - ''
**********************************************************************
```

2行追加

```
import nltk
nltk.download('punkt')
```
###再実行ju24-6.py

```shell_session

(base) root@b350954ba6b4:/machine-learning-with-python-cookbook-notes# python ju24-6.py
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Unzipping tokenizers/punkt.zip.
ju24-6.py:44: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system ("lxml"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.

The code that caused this warning is on line 44 of the file ju24-6.py. To get rid of this warning, pass the additional argument 'features="lxml"' to the BeautifulSoup constructor.

  soup = BeautifulSoup(html)
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Unzipping corpora/stopwords.zip.
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data]     /root/nltk_data...
[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.
[nltk_data] Downloading package brown to /root/nltk_data...
[nltk_data]   Unzipping corpora/brown.zip.
```

##Chapter 21 - Saving and Loading Trained Models

```
/Users/f00/anaconda/envs/machine_learning_cookbook/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
  from numpy.core.umath_tests import inner1d
```
###ファイル
切りはりして１つのファイルに

```python:ju24-21.py
# load libraries
import numpy as np
from keras.datasets import imdb
from keras.preprocessing.text import Tokenizer
from keras import models
from keras import layers
from keras.models import load_model

# set random seed
np.random.seed(0)

# set the number of features we want
number_of_features = 1000

# load data and target vector from movie review data
(train_Data, train_target), (test_data, test_target) = imdb.load_data(num_words=number_of_features)

# convert movie review data to a one-hot encoded feature matrix
tokenizer = Tokenizer(num_words=number_of_features)
train_features = tokenizer.sequences_to_matrix(train_data, mode="binary")
test_features = tokenizer.sequences_to_matrix(test_data, mode="binary")

# start neural network
network = models.Sequential()

# add fully connected layer with ReLU activation function
network.add(layers.Dense(units=16, activation="relu", input_shape=(number_of_features,)))

# add fully connected layer with a sigmoid activation function
network.add(layers.Dense(units=1, activation="sigmoid"))

# compile neural network
network.compile(loss="binary_crossentropy", optimizer="rmsprop", metrics=["accuracy"])

# train neural network
history = network.fit(train_features, train_target, epochs=3, verbose=0, batch_size=100, validation_data=(test_features, test_target))

# save neural network
network.save("model.h5")

# load neural network
network = load_model("model.h5")


```
###実行

```
(base) root@b350954ba6b4:/machine-learning-with-python-cookbook-notes# python ju24-21.py
Using TensorFlow backend.
Downloading data from https://s3.amazonaws.com/text-datasets/imdb.npz
 9371648/17464789 [===============>..............] - ETA: 4:16 
```
## 資料がない章
8, 9, 10, 20


#2. dockerを自力で構築する方へ

ここから下は、上記のpullしていただいたdockerをどういう方針で、どういう手順で作ったかを記録します。
上記のdockerを利用する上での参考資料です。本の続きを実行する上では必要ありません。
自力でdocker/anacondaを構築する場合の手順になります。
dockerfileを作る方法ではありません。ごめんなさい。
##docker

ubuntu, debianなどのLinuxを、linux, windows, mac osから共通に利用できる仕組み。
利用するOSの設定を変更せずに利用できるのがよい。
同じ仕様で、大量の人が利用することができる。
ソフトウェアの開発元が公式に対応しているものと、利用者が便利に仕立てたものの両方が利用可能である。今回は、公式に配布しているものを、自分で仕立てて、他の人にも利用できるようにする。
##python

DeepLearningの実習をPhthonで行って来た。
pythonを使う理由は、多くの機械学習の仕組みがpythonで利用できることと、Rなどの統計解析の仕組みもpythonから容易に利用できることがある。
###anaconda

pythonには、2と３という版の違いと、配布方法の違いなどがある。
Anacondaでpython3をこの１年半利用してきた。
Anacondaを利用した理由は、統計解析のライブラリと、JupyterNotebookが初めから入っているからである。
##docker公式配布

ubuntu, debianなどのOSの公式配布,gcc, anacondaなどの言語の公式配布などがある。
これらを利用し、docker-hubに登録することにより、公式配布の質の確認と、変更権を含む幅広い情報の共有ができる。dockerが公式配布するものではなく、それぞれのソフト提供者の公式配布という意味。
###docker pull

docker公式配布の利用は、URLからpullすることで実現する。
###docker Anaconda

anacondaが公式配布しているものを利用。

```
$  docker pull kaizenjapan/anaconda-keras
Using default tag: latest
latest: Pulling from continuumio/anaconda3
Digest: sha256:e07b9ca98ac1eeb1179dbf0e0bbcebd87701f8654878d6d8ce164d71746964d1
Status: Image is up to date for continuumio/anaconda3:latest

$ docker run -it continuumio/anaconda3 /bin/bash
````
実際にはkeras, tensorflow を利用していた他のpushをpull

##apt-get

```shell-session
(base) root@d8857ae56e69:/# apt-get update

(base) root@d8857ae56e69:/# apt-get install -y procps

(base) root@d8857ae56e69:/# apt-get install -y vim

(base) root@d8857ae56e69:/# apt-get install -y apt-utils

(base) root@d8857ae56e69:/# apt-get install sudo
```

##ソース git

```
(base) root@f19e2f06eabb:/# git clone https://github.com/f00-/machine-learning-with-python-cookbook-notes/
```

## conda

```
# conda update --prefix /opt/conda anaconda
Solving environment: done

## Package Plan ##

  environment location: /opt/conda

  added / updated specs: 
    - anaconda


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    qtawesome-0.4.4            |           py36_0         159 KB
    patchelf-0.9               |       hf484d3e_2          68 KB
    cryptography-2.3.1         |   py36hc365091_0         585 KB
    attrs-18.2.0               |   py36h28b3542_0          50 KB
    pygments-2.2.0             |           py36_0         1.3 MB
    mkl-2019.0                 |              118       204.4 MB
    singledispatch-3.4.0.3     |           py36_0          15 KB
    imagesize-1.1.0            |           py36_0           9 KB
    mkl_fft-1.0.4              |   py36h4414c95_1         150 KB
    blaze-0.11.3               |           py36_0         603 KB
    qt-5.9.6                   |       h8703b6f_2        87.1 MB
    pyparsing-2.2.0            |           py36_1          96 KB
    html5lib-1.0.1             |           py36_0         184 KB
    llvmlite-0.24.0            |   py36hdbcaa40_0        15.3 MB
    gevent-1.3.6               |   py36h7b6447c_0         1.9 MB
    prompt_toolkit-1.0.15      |           py36_0         339 KB
    sphinxcontrib-1.0          |           py36_1           3 KB
    typed-ast-1.1.0            |   py36h14c3975_0         196 KB
    fontconfig-2.13.0          |       h9420a91_0         291 KB
    pytest-3.8.0               |           py36_0         317 KB
    pycparser-2.18             |           py36_1         169 KB
    urllib3-1.23               |           py36_0         152 KB
    prometheus_client-0.3.1    |   py36h28b3542_0          52 KB
    rope-0.11.0                |           py36_0         282 KB
    locket-0.2.0               |           py36_1           8 KB
    pillow-5.2.0               |   py36heded4f4_0         586 KB
    dask-core-0.19.1           |           py36_0         1.1 MB
    babel-2.6.0                |           py36_0         5.7 MB
    cytoolz-0.9.0.1            |   py36h14c3975_1         419 KB
    sphinx-1.7.9               |           py36_0         1.6 MB
    cloudpickle-0.5.5          |           py36_0          26 KB
    sympy-1.2                  |           py36_0         8.8 MB
    pango-1.42.4               |       h049681c_0         528 KB
    pytest-remotedata-0.3.0    |           py36_0          12 KB
    pytz-2018.5                |           py36_0         232 KB
    ptyprocess-0.6.0           |           py36_0          23 KB
    scikit-learn-0.19.2        |   py36h4989274_0         5.2 MB
    parso-0.3.1                |           py36_0         114 KB
    xlrd-1.1.0                 |           py36_1         194 KB
    nbformat-4.4.0             |           py36_0         141 KB
    pandocfilters-1.4.2        |           py36_1          13 KB
    nbconvert-5.4.0            |           py36_1         416 KB
    pyodbc-4.0.24              |   py36he6710b0_0          66 KB
    spyder-3.3.1               |           py36_1         2.6 MB
    tqdm-4.26.0                |   py36h28b3542_0          59 KB
    wrapt-1.10.11              |   py36h14c3975_2          45 KB
    greenlet-0.4.15            |   py36h7b6447c_0          20 KB
    zeromq-4.2.5               |       hf484d3e_1         567 KB
    fribidi-1.0.5              |       h7b6447c_0         112 KB
    cffi-1.11.5                |   py36he75722e_1         212 KB
    zict-0.1.3                 |           py36_0          18 KB
    twisted-18.7.0             |   py36h14c3975_1         4.9 MB
    bottleneck-1.2.1           |   py36h035aef0_1         127 KB
    mpfr-4.0.1                 |       hdf1c602_3         575 KB
    appdirs-1.4.3              |   py36h28b3542_0          16 KB
    entrypoints-0.2.3          |           py36_2           9 KB
    jeepney-0.3.1              |           py36_0          36 KB
    tornado-5.1                |   py36h14c3975_0         666 KB
    qtconsole-4.4.1            |           py36_0         156 KB
    sqlalchemy-1.2.11          |   py36h7b6447c_0         1.6 MB
    alabaster-0.7.11           |           py36_0          17 KB
    click-6.7                  |           py36_0         105 KB
    constantly-15.1.0          |   py36h28b3542_0          13 KB
    xlwt-1.3.0                 |           py36_0         163 KB
    automat-0.7.0              |           py36_0          52 KB
    pexpect-4.6.0              |           py36_0          77 KB
    pytest-astropy-0.4.0       |           py36_0           5 KB
    olefile-0.46               |           py36_0          48 KB
    blosc-1.14.4               |       hdbcaa40_0         601 KB
    setuptools-40.2.0          |           py36_0         556 KB
    zope.interface-4.5.0       |   py36h14c3975_0         201 KB
    jupyter_console-5.2.0      |           py36_1          36 KB
    notebook-5.6.0             |           py36_0         7.4 MB
    boto-2.49.0                |           py36_0         1.5 MB
    ruamel_yaml-0.15.46        |   py36h14c3975_0         245 KB
    mccabe-0.6.1               |           py36_1          14 KB
    cython-0.28.5              |   py36hf484d3e_0         3.3 MB
    numexpr-2.6.8              |   py36hd89afb7_0         190 KB
    nose-1.3.7                 |           py36_2         214 KB
    requests-2.19.1            |           py36_0          96 KB
    kiwisolver-1.0.1           |   py36hf484d3e_0          83 KB
    bitarray-0.8.3             |   py36h14c3975_0          55 KB
    libgfortran-ng-7.3.0       |       hdf63c60_0         1.3 MB
    freetype-2.9.1             |       h8a8886c_1         822 KB
    numba-0.39.0               |   py36h04863e7_0         2.4 MB
    tk-8.6.8                   |       hbc83047_0         3.1 MB
    multipledispatch-0.6.0     |           py36_0          21 KB
    ipywidgets-7.4.1           |           py36_0         148 KB
    wcwidth-0.1.7              |           py36_0          25 KB
    zope-1.0                   |           py36_1           3 KB
    bkcharts-0.2               |           py36_0         127 KB
    jedi-0.12.1                |           py36_0         225 KB
    docutils-0.14              |           py36_0         689 KB
    pycrypto-2.6.1             |   py36h14c3975_9         465 KB
    jupyterlab_launcher-0.13.1 |           py36_0          36 KB
    bleach-2.1.4               |           py36_0          33 KB
    ipython_genutils-0.2.0     |           py36_0          39 KB
    service_identity-17.0.0    |   py36h28b3542_0          18 KB
    anaconda-client-1.7.2      |           py36_0         141 KB
    backports-1.0              |           py36_1           3 KB
    libuuid-1.0.3              |       h1bed415_2          16 KB
    astroid-2.0.4              |           py36_0         247 KB
    secretstorage-3.1.0        |           py36_0          23 KB
    libstdcxx-ng-8.2.0         |       hdf63c60_1         2.9 MB
    markupsafe-1.0             |   py36h14c3975_1          24 KB
    expat-2.2.6                |       he6710b0_0         187 KB
    curl-7.61.0                |       h84994c4_0         141 KB
    path.py-11.1.0             |           py36_0          53 KB
    et_xmlfile-1.0.1           |           py36_0          20 KB
    ipython-6.5.0              |           py36_0         1.0 MB
    cycler-0.10.0              |           py36_0          13 KB
    lxml-4.2.5                 |   py36hefd8a0e_0         1.6 MB
    distributed-1.23.1         |           py36_0         829 KB
    intel-openmp-2019.0        |              118         721 KB
    astropy-3.0.4              |   py36h14c3975_0         6.8 MB
    numpy-base-1.15.1          |   py36h81de0dd_0         4.2 MB
    msgpack-python-0.5.6       |   py36h6bb024c_1          99 KB
    sortedcontainers-2.0.5     |           py36_0          43 KB
    openssl-1.0.2p             |       h14c3975_0         3.5 MB
    chardet-3.0.4              |           py36_1         189 KB
    libgcc-ng-8.2.0            |       hdf63c60_1         7.6 MB
    ipykernel-4.9.0            |           py36_1         146 KB
    datashape-0.5.4            |           py36_1         100 KB
    h5py-2.8.0                 |   py36h989c5e5_3         1.1 MB
    pyzmq-17.1.2               |   py36h14c3975_0         454 KB
    pycosat-0.6.3              |   py36h14c3975_0         104 KB
    spyder-kernels-0.2.6       |           py36_0          69 KB
    six-1.11.0                 |           py36_1          21 KB
    lazy-object-proxy-1.3.1    |   py36h14c3975_2          30 KB
    imageio-2.4.1              |           py36_0         3.3 MB
    scikit-image-0.14.0        |   py36hf484d3e_1        24.1 MB
    pickleshare-0.7.4          |           py36_0          12 KB
    hyperlink-18.0.0           |           py36_0          62 KB
    snowballstemmer-1.2.1      |           py36_0          85 KB
    keyring-13.2.1             |           py36_0          46 KB
    matplotlib-2.2.3           |   py36hb69df0a_0         6.6 MB
    pyasn1-0.4.4               |   py36h28b3542_0         101 KB
    pyasn1-modules-0.2.2       |           py36_0          86 KB
    traitlets-4.3.2            |           py36_0         133 KB
    openpyxl-2.5.6             |           py36_0         330 KB
    glib-2.56.2                |       hd408876_0         5.0 MB
    beautifulsoup4-4.6.3       |           py36_0         138 KB
    colorama-0.3.9             |           py36_0          23 KB
    glob2-0.6                  |           py36_0          17 KB
    testpath-0.3.1             |           py36_0          90 KB
    contextlib2-0.5.5          |           py36_0          15 KB
    jinja2-2.10                |           py36_0         184 KB
    anaconda-5.3.0             |           py36_0          11 KB
    certifi-2018.8.24          |           py36_1         140 KB
    webencodings-0.5.1         |           py36_1          19 KB
    xlsxwriter-1.1.0           |           py36_0         210 KB
    pandas-0.23.4              |   py36h04863e7_0        10.1 MB
    incremental-17.5.0         |           py36_0          25 KB
    atomicwrites-1.2.1         |           py36_0          11 KB
    jupyterlab-0.34.9          |           py36_0        10.0 MB
    itsdangerous-0.24          |           py36_1          20 KB
    pywavelets-1.0.0           |   py36hdd07704_0         4.4 MB
    scipy-1.1.0                |   py36hfa4b5c9_1        18.0 MB
    mkl-service-1.1.2          |   py36h90e4bf4_5          11 KB
    widgetsnbextension-3.4.1   |           py36_0         1.7 MB
    defusedxml-0.5.0           |           py36_1          29 KB
    jupyter-1.0.0              |           py36_7           6 KB
    tblib-1.3.2                |           py36_0          16 KB
    graphite2-1.3.12           |       h23475e2_2         106 KB
    libcurl-7.61.0             |       h1ad7b7a_0         494 KB
    filelock-3.0.8             |           py36_0          13 KB
    pylint-2.1.1               |           py36_0         795 KB
    anaconda-project-0.8.2     |           py36_0         478 KB
    py-1.6.0                   |           py36_0         136 KB
    mkl_random-1.0.1           |   py36h4414c95_1         373 KB
    libtiff-4.0.9              |       he85c1e1_2         567 KB
    unixodbc-2.3.7             |       h14c3975_0         319 KB
    pytables-3.4.4             |   py36ha205bf6_0         1.5 MB
    more-itertools-4.3.0       |           py36_0          83 KB
    odo-0.5.1                  |           py36_0         193 KB
    cairo-1.14.12              |       h8948797_3         1.3 MB
    harfbuzz-1.8.8             |       hffaf4a1_0         863 KB
    unicodecsv-0.14.1          |           py36_0          24 KB
    sphinxcontrib-websupport-1.1.0|           py36_1          36 KB
    pycurl-7.43.0.2            |   py36hb7f436b_0          60 KB
    idna-2.7                   |           py36_0         132 KB
    bokeh-0.13.0               |           py36_0         5.0 MB
    backports.shutil_get_terminal_size-1.0.0|           py36_2           8 KB
    pyqt-5.9.2                 |   py36h05f1152_2         5.6 MB
    pytest-arraydiff-0.2       |   py36h39e3cac_0          14 KB
    pyflakes-2.0.0             |           py36_0          88 KB
    clyent-1.2.2               |           py36_1          18 KB
    numpy-1.15.1               |   py36h1d66e8a_0          37 KB
    mpc-1.1.0                  |       h10f8cd9_1          94 KB
    sqlite-3.24.0              |       h84994c4_0         1.8 MB
    mpmath-1.0.0               |           py36_2         892 KB
    qtpy-1.5.0                 |           py36_0          50 KB
    sortedcollections-1.0.1    |           py36_0          15 KB
    readline-7.0               |       h7b6447c_5         392 KB
    partd-0.3.8                |           py36_0          31 KB
    pluggy-0.7.1               |   py36h28b3542_0          25 KB
    pyyaml-3.13                |   py36h14c3975_0         178 KB
    seaborn-0.9.0              |           py36_0         379 KB
    flask-cors-3.0.6           |           py36_0          21 KB
    psutil-5.4.7               |   py36h14c3975_0         305 KB
    dask-0.19.1                |           py36_0           3 KB
    python-3.6.6               |       hc3d631a_0        29.4 MB
    gmpy2-2.0.8                |   py36h10f8cd9_2         165 KB
    jupyter_core-4.4.0         |           py36_0          63 KB
    jsonschema-2.6.0           |           py36_0          62 KB
    statsmodels-0.9.0          |   py36h035aef0_0         9.0 MB
    ------------------------------------------------------------
                                           Total:       552.9 MB

The following NEW packages will be INSTALLED:

    appdirs:                            1.4.3-py36h28b3542_0   
    atomicwrites:                       1.2.1-py36_0           
    automat:                            0.7.0-py36_0           
    constantly:                         15.1.0-py36h28b3542_0  
    defusedxml:                         0.5.0-py36_1           
    fribidi:                            1.0.5-h7b6447c_0       
    hyperlink:                          18.0.0-py36_0          
    incremental:                        17.5.0-py36_0          
    jeepney:                            0.3.1-py36_0           
    keyring:                            13.2.1-py36_0          
    libuuid:                            1.0.3-h1bed415_2       
    prometheus_client:                  0.3.1-py36h28b3542_0   
    pyasn1:                             0.4.4-py36h28b3542_0   
    pyasn1-modules:                     0.2.2-py36_0           
    secretstorage:                      3.1.0-py36_0           
    service_identity:                   17.0.0-py36h28b3542_0  
    spyder-kernels:                     0.2.6-py36_0           
    tqdm:                               4.26.0-py36h28b3542_0  
    twisted:                            18.7.0-py36h14c3975_1  
    typed-ast:                          1.1.0-py36h14c3975_0   
    zope:                               1.0-py36_1             
    zope.interface:                     4.5.0-py36h14c3975_0   

The following packages will be UPDATED:

    alabaster:                          0.7.10-py36h306e16b_0   --> 0.7.11-py36_0          
    anaconda:                           5.2.0-py36_3            --> 5.3.0-py36_0           
    anaconda-client:                    1.6.14-py36_0           --> 1.7.2-py36_0           
    anaconda-project:                   0.8.2-py36h44fb852_0    --> 0.8.2-py36_0           
    astroid:                            1.6.3-py36_0            --> 2.0.4-py36_0           
    astropy:                            3.0.2-py36h3010b51_1    --> 3.0.4-py36h14c3975_0   
    attrs:                              18.1.0-py36_0           --> 18.2.0-py36h28b3542_0  
    babel:                              2.5.3-py36_0            --> 2.6.0-py36_0           
    backports:                          1.0-py36hfa02d7e_1      --> 1.0-py36_1             
    backports.shutil_get_terminal_size: 1.0.0-py36hfea85ff_2    --> 1.0.0-py36_2           
    beautifulsoup4:                     4.6.0-py36h49b8c8c_1    --> 4.6.3-py36_0           
    bitarray:                           0.8.1-py36h14c3975_1    --> 0.8.3-py36h14c3975_0   
    bkcharts:                           0.2-py36h735825a_0      --> 0.2-py36_0             
    blaze:                              0.11.3-py36h4e06776_0   --> 0.11.3-py36_0          
    bleach:                             2.1.3-py36_0            --> 2.1.4-py36_0           
    blosc:                              1.14.3-hdbcaa40_0       --> 1.14.4-hdbcaa40_0      
    bokeh:                              0.12.16-py36_0          --> 0.13.0-py36_0          
    boto:                               2.48.0-py36h6e4cd66_1   --> 2.49.0-py36_0          
    bottleneck:                         1.2.1-py36haac1ea0_0    --> 1.2.1-py36h035aef0_1   
    cairo:                              1.14.12-h7636065_2      --> 1.14.12-h8948797_3     
    certifi:                            2018.4.16-py36_0        --> 2018.8.24-py36_1       
    cffi:                               1.11.5-py36h9745a5d_0   --> 1.11.5-py36he75722e_1  
    chardet:                            3.0.4-py36h0f667ec_1    --> 3.0.4-py36_1           
    click:                              6.7-py36h5253387_0      --> 6.7-py36_0             
    cloudpickle:                        0.5.3-py36_0            --> 0.5.5-py36_0           
    clyent:                             1.2.2-py36h7e57e65_1    --> 1.2.2-py36_1           
    colorama:                           0.3.9-py36h489cec4_0    --> 0.3.9-py36_0           
    contextlib2:                        0.5.5-py36h6c84a62_0    --> 0.5.5-py36_0           
    cryptography:                       2.2.2-py36h14c3975_0    --> 2.3.1-py36hc365091_0   
    curl:                               7.60.0-h84994c4_0       --> 7.61.0-h84994c4_0      
    cycler:                             0.10.0-py36h93f1223_0   --> 0.10.0-py36_0          
    cython:                             0.28.2-py36h14c3975_0   --> 0.28.5-py36hf484d3e_0  
    cytoolz:                            0.9.0.1-py36h14c3975_0  --> 0.9.0.1-py36h14c3975_1 
    dask:                               0.17.5-py36_0           --> 0.19.1-py36_0          
    dask-core:                          0.17.5-py36_0           --> 0.19.1-py36_0          
    datashape:                          0.5.4-py36h3ad6b5c_0    --> 0.5.4-py36_1           
    distributed:                        1.21.8-py36_0           --> 1.23.1-py36_0          
    docutils:                           0.14-py36hb0f60f5_0     --> 0.14-py36_0            
    entrypoints:                        0.2.3-py36h1aec115_2    --> 0.2.3-py36_2           
    et_xmlfile:                         1.0.1-py36hd6bccc3_0    --> 1.0.1-py36_0           
    expat:                              2.2.5-he0dffb1_0        --> 2.2.6-he6710b0_0       
    filelock:                           3.0.4-py36_0            --> 3.0.8-py36_0           
    flask-cors:                         3.0.4-py36_0            --> 3.0.6-py36_0           
    fontconfig:                         2.12.6-h49f89f6_0       --> 2.13.0-h9420a91_0      
    freetype:                           2.8-hab7d2ae_1          --> 2.9.1-h8a8886c_1       
    gevent:                             1.3.0-py36h14c3975_0    --> 1.3.6-py36h7b6447c_0   
    glib:                               2.56.1-h000015b_0       --> 2.56.2-hd408876_0      
    glob2:                              0.6-py36he249c77_0      --> 0.6-py36_0             
    gmpy2:                              2.0.8-py36hc8893dd_2    --> 2.0.8-py36h10f8cd9_2   
    graphite2:                          1.3.11-h16798f4_2       --> 1.3.12-h23475e2_2      
    greenlet:                           0.4.13-py36h14c3975_0   --> 0.4.15-py36h7b6447c_0  
    h5py:                               2.7.1-py36ha1f6525_2    --> 2.8.0-py36h989c5e5_3   
    harfbuzz:                           1.7.6-h5f0a787_1        --> 1.8.8-hffaf4a1_0       
    html5lib:                           1.0.1-py36h2f9c1c0_0    --> 1.0.1-py36_0           
    idna:                               2.6-py36h82fb2a8_1      --> 2.7-py36_0             
    imageio:                            2.3.0-py36_0            --> 2.4.1-py36_0           
    imagesize:                          1.0.0-py36_0            --> 1.1.0-py36_0           
    intel-openmp:                       2018.0.0-8              --> 2019.0-118             
    ipykernel:                          4.8.2-py36_0            --> 4.9.0-py36_1           
    ipython:                            6.4.0-py36_0            --> 6.5.0-py36_0           
    ipython_genutils:                   0.2.0-py36hb52b0d5_0    --> 0.2.0-py36_0           
    ipywidgets:                         7.2.1-py36_0            --> 7.4.1-py36_0           
    itsdangerous:                       0.24-py36h93cc618_1     --> 0.24-py36_1            
    jedi:                               0.12.0-py36_1           --> 0.12.1-py36_0          
    jinja2:                             2.10-py36ha16c418_0     --> 2.10-py36_0            
    jsonschema:                         2.6.0-py36h006f8b5_0    --> 2.6.0-py36_0           
    jupyter:                            1.0.0-py36_4            --> 1.0.0-py36_7           
    jupyter_console:                    5.2.0-py36he59e554_1    --> 5.2.0-py36_1           
    jupyter_core:                       4.4.0-py36h7c827e3_0    --> 4.4.0-py36_0           
    jupyterlab:                         0.32.1-py36_0           --> 0.34.9-py36_0          
    jupyterlab_launcher:                0.10.5-py36_0           --> 0.13.1-py36_0          
    kiwisolver:                         1.0.1-py36h764f252_0    --> 1.0.1-py36hf484d3e_0   
    lazy-object-proxy:                  1.3.1-py36h10fcdad_0    --> 1.3.1-py36h14c3975_2   
    libcurl:                            7.60.0-h1ad7b7a_0       --> 7.61.0-h1ad7b7a_0      
    libgcc-ng:                          7.2.0-hdf63c60_3        --> 8.2.0-hdf63c60_1       
    libgfortran-ng:                     7.2.0-hdf63c60_3        --> 7.3.0-hdf63c60_0       
    libstdcxx-ng:                       7.2.0-hdf63c60_3        --> 8.2.0-hdf63c60_1       
    libtiff:                            4.0.9-he85c1e1_1        --> 4.0.9-he85c1e1_2       
    llvmlite:                           0.23.1-py36hdbcaa40_0   --> 0.24.0-py36hdbcaa40_0  
    locket:                             0.2.0-py36h787c0ad_1    --> 0.2.0-py36_1           
    lxml:                               4.2.1-py36h23eabaa_0    --> 4.2.5-py36hefd8a0e_0   
    markupsafe:                         1.0-py36hd9260cd_1      --> 1.0-py36h14c3975_1     
    matplotlib:                         2.2.2-py36h0e671d2_1    --> 2.2.3-py36hb69df0a_0   
    mccabe:                             0.6.1-py36h5ad9710_1    --> 0.6.1-py36_1           
    mkl:                                2018.0.2-1              --> 2019.0-118             
    mkl-service:                        1.1.2-py36h17a0993_4    --> 1.1.2-py36h90e4bf4_5   
    mkl_fft:                            1.0.1-py36h3010b51_0    --> 1.0.4-py36h4414c95_1   
    mkl_random:                         1.0.1-py36h629b387_0    --> 1.0.1-py36h4414c95_1   
    more-itertools:                     4.1.0-py36_0            --> 4.3.0-py36_0           
    mpc:                                1.0.3-hec55b23_5        --> 1.1.0-h10f8cd9_1       
    mpfr:                               3.1.5-h11a74b3_2        --> 4.0.1-hdf1c602_3       
    mpmath:                             1.0.0-py36hfeacd6b_2    --> 1.0.0-py36_2           
    msgpack-python:                     0.5.6-py36h6bb024c_0    --> 0.5.6-py36h6bb024c_1   
    multipledispatch:                   0.5.0-py36_0            --> 0.6.0-py36_0           
    nbconvert:                          5.3.1-py36hb41ffb7_0    --> 5.4.0-py36_1           
    nbformat:                           4.4.0-py36h31c9010_0    --> 4.4.0-py36_0           
    nose:                               1.3.7-py36hcdf7029_2    --> 1.3.7-py36_2           
    notebook:                           5.5.0-py36_0            --> 5.6.0-py36_0           
    numba:                              0.38.0-py36h637b7d7_0   --> 0.39.0-py36h04863e7_0  
    numexpr:                            2.6.5-py36h7bf3b9c_0    --> 2.6.8-py36hd89afb7_0   
    numpy:                              1.14.3-py36hcd700cb_1   --> 1.15.1-py36h1d66e8a_0  
    numpy-base:                         1.14.3-py36h9be14a7_1   --> 1.15.1-py36h81de0dd_0  
    odo:                                0.5.1-py36h90ed295_0    --> 0.5.1-py36_0           
    olefile:                            0.45.1-py36_0           --> 0.46-py36_0            
    openpyxl:                           2.5.3-py36_0            --> 2.5.6-py36_0           
    openssl:                            1.0.2o-h20670df_0       --> 1.0.2p-h14c3975_0      
    pandas:                             0.23.0-py36h637b7d7_0   --> 0.23.4-py36h04863e7_0  
    pandocfilters:                      1.4.2-py36ha6701b7_1    --> 1.4.2-py36_1           
    pango:                              1.41.0-hd475d92_0       --> 1.42.4-h049681c_0      
    parso:                              0.2.0-py36_0            --> 0.3.1-py36_0           
    partd:                              0.3.8-py36h36fd896_0    --> 0.3.8-py36_0           
    patchelf:                           0.9-hf79760b_2          --> 0.9-hf484d3e_2         
    path.py:                            11.0.1-py36_0           --> 11.1.0-py36_0          
    pexpect:                            4.5.0-py36_0            --> 4.6.0-py36_0           
    pickleshare:                        0.7.4-py36h63277f8_0    --> 0.7.4-py36_0           
    pillow:                             5.1.0-py36h3deb7b8_0    --> 5.2.0-py36heded4f4_0   
    pluggy:                             0.6.0-py36hb689045_0    --> 0.7.1-py36h28b3542_0   
    prompt_toolkit:                     1.0.15-py36h17d85b1_0   --> 1.0.15-py36_0          
    psutil:                             5.4.5-py36h14c3975_0    --> 5.4.7-py36h14c3975_0   
    ptyprocess:                         0.5.2-py36h69acd42_0    --> 0.6.0-py36_0           
    py:                                 1.5.3-py36_0            --> 1.6.0-py36_0           
    pycosat:                            0.6.3-py36h0a5515d_0    --> 0.6.3-py36h14c3975_0   
    pycparser:                          2.18-py36hf9f622e_1     --> 2.18-py36_1            
    pycrypto:                           2.6.1-py36h14c3975_8    --> 2.6.1-py36h14c3975_9   
    pycurl:                             7.43.0.1-py36hb7f436b_0 --> 7.43.0.2-py36hb7f436b_0
    pyflakes:                           1.6.0-py36h7bd6a15_0    --> 2.0.0-py36_0           
    pygments:                           2.2.0-py36h0d3125c_0    --> 2.2.0-py36_0           
    pylint:                             1.8.4-py36_0            --> 2.1.1-py36_0           
    pyodbc:                             4.0.23-py36hf484d3e_0   --> 4.0.24-py36he6710b0_0  
    pyparsing:                          2.2.0-py36hee85983_1    --> 2.2.0-py36_1           
    pyqt:                               5.9.2-py36h751905a_0    --> 5.9.2-py36h05f1152_2   
    pytables:                           3.4.3-py36h02b9ad4_2    --> 3.4.4-py36ha205bf6_0   
    pytest:                             3.5.1-py36_0            --> 3.8.0-py36_0           
    pytest-arraydiff:                   0.2-py36_0              --> 0.2-py36h39e3cac_0     
    pytest-astropy:                     0.3.0-py36_0            --> 0.4.0-py36_0           
    pytest-remotedata:                  0.2.1-py36_0            --> 0.3.0-py36_0           
    python:                             3.6.5-hc3d631a_2        --> 3.6.6-hc3d631a_0       
    pytz:                               2018.4-py36_0           --> 2018.5-py36_0          
    pywavelets:                         0.5.2-py36he602eb0_0    --> 1.0.0-py36hdd07704_0   
    pyyaml:                             3.12-py36hafb9ca4_1     --> 3.13-py36h14c3975_0    
    pyzmq:                              17.0.0-py36h14c3975_0   --> 17.1.2-py36h14c3975_0  
    qt:                                 5.9.5-h7e424d6_0        --> 5.9.6-h8703b6f_2       
    qtawesome:                          0.4.4-py36h609ed8c_0    --> 0.4.4-py36_0           
    qtconsole:                          4.3.1-py36h8f73b5b_0    --> 4.4.1-py36_0           
    qtpy:                               1.4.1-py36_0            --> 1.5.0-py36_0           
    readline:                           7.0-ha6073c6_4          --> 7.0-h7b6447c_5         
    requests:                           2.18.4-py36he2e5f8d_1   --> 2.19.1-py36_0          
    rope:                               0.10.7-py36h147e2ec_0   --> 0.11.0-py36_0          
    ruamel_yaml:                        0.15.35-py36h14c3975_1  --> 0.15.46-py36h14c3975_0 
    scikit-image:                       0.13.1-py36h14c3975_1   --> 0.14.0-py36hf484d3e_1  
    scikit-learn:                       0.19.1-py36h7aa7ec6_0   --> 0.19.2-py36h4989274_0  
    scipy:                              1.1.0-py36hfc37229_0    --> 1.1.0-py36hfa4b5c9_1   
    seaborn:                            0.8.1-py36hfad7ec4_0    --> 0.9.0-py36_0           
    setuptools:                         39.1.0-py36_0           --> 40.2.0-py36_0          
    singledispatch:                     3.4.0.3-py36h7a266c3_0  --> 3.4.0.3-py36_0         
    six:                                1.11.0-py36h372c433_1   --> 1.11.0-py36_1          
    snowballstemmer:                    1.2.1-py36h6febd40_0    --> 1.2.1-py36_0           
    sortedcollections:                  0.6.1-py36_0            --> 1.0.1-py36_0           
    sortedcontainers:                   1.5.10-py36_0           --> 2.0.5-py36_0           
    sphinx:                             1.7.4-py36_0            --> 1.7.9-py36_0           
    sphinxcontrib:                      1.0-py36h6d0f590_1      --> 1.0-py36_1             
    sphinxcontrib-websupport:           1.0.1-py36hb5cb234_1    --> 1.1.0-py36_1           
    spyder:                             3.2.8-py36_0            --> 3.3.1-py36_1           
    sqlalchemy:                         1.2.7-py36h6b74fdf_0    --> 1.2.11-py36h7b6447c_0  
    sqlite:                             3.23.1-he433501_0       --> 3.24.0-h84994c4_0      
    statsmodels:                        0.9.0-py36h3010b51_0    --> 0.9.0-py36h035aef0_0   
    sympy:                              1.1.1-py36hc6d1c1c_0    --> 1.2-py36_0             
    tblib:                              1.3.2-py36h34cf8b6_0    --> 1.3.2-py36_0           
    testpath:                           0.3.1-py36h8cadb63_0    --> 0.3.1-py36_0           
    tk:                                 8.6.7-hc745277_3        --> 8.6.8-hbc83047_0       
    tornado:                            5.0.2-py36_0            --> 5.1-py36h14c3975_0     
    traitlets:                          4.3.2-py36h674d592_0    --> 4.3.2-py36_0           
    unicodecsv:                         0.14.1-py36ha668878_0   --> 0.14.1-py36_0          
    unixodbc:                           2.3.6-h1bed415_0        --> 2.3.7-h14c3975_0       
    urllib3:                            1.22-py36hbe7ace6_0     --> 1.23-py36_0            
    wcwidth:                            0.1.7-py36hdf4376a_0    --> 0.1.7-py36_0           
    webencodings:                       0.5.1-py36h800622e_1    --> 0.5.1-py36_1           
    widgetsnbextension:                 3.2.1-py36_0            --> 3.4.1-py36_0           
    wrapt:                              1.10.11-py36h28b7045_0  --> 1.10.11-py36h14c3975_2 
    xlrd:                               1.1.0-py36h1db9f0c_1    --> 1.1.0-py36_1           
    xlsxwriter:                         1.0.4-py36_0            --> 1.1.0-py36_0           
    xlwt:                               1.3.0-py36h7b00a1f_0    --> 1.3.0-py36_0           
    zeromq:                             4.2.5-h439df22_0        --> 4.2.5-hf484d3e_1       
    zict:                               0.1.3-py36h3a3bf81_0    --> 0.1.3-py36_0           

Proceed ([y]/n)? y


```

## pip

```
(base) root@f19e2f06eabb:/deep-learning-from-scratch-2/ch01# pip install --upgrade pip
Collecting pip
  Downloading https://files.pythonhosted.org/packages/5f/25/e52d3f31441505a5f3af41213346e5b6c221c9e086a166f3703d2ddaf940/pip-18.0-py2.py3-none-any.whl (1.3MB)
    100% |████████████████████████████████| 1.3MB 2.0MB/s 
distributed 1.21.8 requires msgpack, which is not installed.
Installing collected packages: pip
  Found existing installation: pip 10.0.1
    Uninstalling pip-10.0.1:
      Successfully uninstalled pip-10.0.1
Successfully installed pip-18.0
(
```
# docker hubへの登録

```
## $ docker ps
CONTAINER ID        IMAGE                   COMMAND                  CREATED             STATUS              PORTS                    NAMES
caef766a99ff        continuumio/anaconda3   "/usr/bin/tini -- /b…"   10 hours ago        Up 10 hours         0.0.0.0:8888->8888/tcp   sleepy_bassi

$ docker commit caef766a99ff kaizenjapan/anaconda-chiris

$ docker push kaizenjapan/anaconda-chiris
```


# 参考資料(reference)

dockerで機械学習(1) with anaconda(1)「ゼロから作るDeep Learning - Pythonで学ぶディープラーニングの理論と実装」斎藤 康毅 著
https://qiita.com/kaizen_nagoya/items/a7e94ef6dca128d035ab
dockerで機械学習(2)with anaconda(2)「ゼロから作るDeep Learning2自然言語処理編」斎藤 康毅 著
https://qiita.com/kaizen_nagoya/items/3b80dfc76933cea522c6
dockerで機械学習(3)with anaconda(3)「直感Deep Learning」Antonio Gulli、Sujit Pal　第1章,第2章
https://qiita.com/kaizen_nagoya/items/483ae708c71c88419c32

dockerで機械学習(71) 環境構築(1) docker どっかーら、どーやってもエラーばっかり。
https://qiita.com/kaizen_nagoya/items/690d806a4760d9b9e040
dockerで機械学習(72) 環境構築(2) Docker for Windows
https://qiita.com/kaizen_nagoya/items/c4daa5cf52e9f0c2c002
dockerで機械学習(73) 環境構築(3) docker/linux/macos bash スクリプト, ms-dos batchファイル
https://qiita.com/kaizen_nagoya/items/3f7b39110b7f303a5558
dockerで機械学習(74) 環境構築(4) R 難関いくつ？
https://qiita.com/kaizen_nagoya/items/5fb44773bc38574bcf1c
dockerで機械学習(75)環境構築(5)docker関連ファイルの管理
https://qiita.com/kaizen_nagoya/items/4f03df9a42c923087b5d

OpenCVをPythonで動かそうとしてlibGL.soが無いって言われたけど解決した。
https://qiita.com/toshitanian/items/5da24c0c0bd473d514c8

サーバサイドにおけるmatplotlibによる作図Tips
https://qiita.com/TomokIshii/items/3a26ee4453f535a69e9e

Dockerでホストとコンテナ間でのファイルコピー
https://qiita.com/gologo13/items/7e4e404af80377b48fd5

Docker for Mac でファイル共有を利用する
https://qiita.com/seijimomoto/items/1992d68de8baa7e29bb5


「名古屋のIoTは名古屋のOSで」Dockerをどっかーらどうやって使えばいいんでしょう。TOPPERS/FMP on RaspberryPi with Macintosh編 ５つの関門
https://qiita.com/kaizen_nagoya/items/9c46c6da8ceb64d2d7af

64bitCPUへの道 and/or 64歳の決意
https://qiita.com/kaizen_nagoya/items/cfb5ffa24ded23ab3f60

ゼロから作るDeepLearning２自然言語処理編　読書会の進め方（例）
https://qiita.com/kaizen_nagoya/items/025eb3f701b36209302e


Ubuntu 16.04 LTS で NVIDIA Docker を使ってみる
https://blog.amedama.jp/entry/2017/04/03/235901

Ethernet 記事一覧　Ethernet(0)
https://qiita.com/kaizen_nagoya/items/88d35e99f74aefc98794

Wireshark 一覧 wireshark(0)、Ethernet(48)
https://qiita.com/kaizen_nagoya/items/fbed841f61875c4731d0

線網（Wi-Fi）空中線(antenna)(0) 記事一覧(118/300目標)
https://qiita.com/kaizen_nagoya/items/5e5464ac2b24bd4cd001

C++ Support(0)　
https://qiita.com/kaizen_nagoya/items/8720d26f762369a80514

Coding Rules(0) C Secure , MISRA and so on
https://qiita.com/kaizen_nagoya/items/400725644a8a0e90fbb0

Autosar Guidelines C++14 example code compile list(1-169)
https://qiita.com/kaizen_nagoya/items/8ccbf6675c3494d57a76

Error一覧(C/C++, python, bash...) Error(0)
https://qiita.com/kaizen_nagoya/items/48b6cbc8d68eae2c42b8

なぜdockerで機械学習するか 書籍・ソース一覧作成中 (目標100)
https://qiita.com/kaizen_nagoya/items/ddd12477544bf5ba85e2

言語処理100本ノックをdockerで。python覚えるのに最適。:10+12
https://qiita.com/kaizen_nagoya/items/7e7eb7c543e0c18438c4

プログラムちょい替え（0）一覧:4件
https://qiita.com/kaizen_nagoya/items/296d87ef4bfd516bc394

一覧の一覧( The directory of directories of mine.) Qiita(100)
https://qiita.com/kaizen_nagoya/items/7eb0e006543886138f39

官公庁・学校・公的団体（NPOを含む）システムの課題、官（０）
https://qiita.com/kaizen_nagoya/items/04ee6eaf7ec13d3af4c3

プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945

LaTeX(0) 一覧　
https://qiita.com/kaizen_nagoya/items/e3f7dafacab58c499792

自動制御、制御工学一覧（０）
https://qiita.com/kaizen_nagoya/items/7767a4e19a6ae1479e6b

Rust(0) 一覧　
https://qiita.com/kaizen_nagoya/items/5e8bb080ba6ca0281927

小川清最終講義、最終講義（再）計画, Ethernet(100) 英語(100) 安全(100)
https://qiita.com/kaizen_nagoya/items/e2df642e3951e35e6a53
### 文書履歴(document history)
ver. 0.10 初稿 20181020
ver. 0.11 参考文献等追記 20181021
### 最後までおよみいただきありがとうございました。
いいね　💚、フォローをお願いします。
#### Thank you very much for reading to the last sentence.
Please press the like icon 💚　and follow me for your happy life.