$ docker pull kaizenjapan/anaconda-chris

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

(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

(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

OSError: No such file or directory: '/Users/f00/nltk_data/corpora/stopwords/english'

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_

(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'
 - ''
**********************************************************************

import nltk
nltk.download('punkt')

(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.

/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

# 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")

(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 

$ 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

(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

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

# 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
 ---------------------------|-----------------
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 patchelf-0.9 | hf484d3e_2 68 KB
 cryptography-2.3.1 | py36hc365091_0 585 KB
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 pygments-2.2.0 | py36_0 1.3 MB
 mkl-2019.0 | 118 204.4 MB
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 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
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 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
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 sympy-1.2 | py36_0 8.8 MB
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 alabaster-0.7.11 | py36_0 17 KB
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 blosc-1.14.4 | hdbcaa40_0 601 KB
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 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
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 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

(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 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