"
]
},
"metadata": {},
@@ -1550,36 +1476,35 @@
},
{
"cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 32,
+ "metadata": {},
"outputs": [],
"source": [
"pipe_lr = pipe_lr.fit(X_train2, y_train)\n",
- "y_pred2 = pipe_lr.predict(X_test[:, [4, 14]])"
+ "y_labels = pipe_lr.predict(X_test[:, [4, 14]])\n",
+ "y_probas = pipe_lr.predict_proba(X_test[:, [4, 14]])[:, 1]\n",
+ "# note that we use probabilities for roc_auc\n",
+ "# the `[:, 1]` selects the positive class label only"
]
},
{
"cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 33,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "ROC AUC: 0.662\n",
+ "ROC AUC: 0.752\n",
"Accuracy: 0.711\n"
]
}
],
"source": [
"from sklearn.metrics import roc_auc_score, accuracy_score\n",
- "print('ROC AUC: %.3f' % roc_auc_score(y_true=y_test, y_score=y_pred2))\n",
- "print('Accuracy: %.3f' % accuracy_score(y_true=y_test, y_pred=y_pred2))"
+ "print('ROC AUC: %.3f' % roc_auc_score(y_true=y_test, y_score=y_probas))\n",
+ "print('Accuracy: %.3f' % accuracy_score(y_true=y_test, y_pred=y_labels))"
]
},
{
@@ -1599,10 +1524,8 @@
},
{
"cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 34,
+ "metadata": {},
"outputs": [],
"source": [
"pre_scorer = make_scorer(score_func=precision_score, \n",
@@ -1635,8 +1558,9 @@
}
],
"metadata": {
+ "anaconda-cloud": {},
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python [default]",
"language": "python",
"name": "python3"
},
@@ -1654,5 +1578,5 @@
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/code/ch08/ch08.ipynb b/code/ch08/ch08.ipynb
index bfc049c2..0be3d989 100644
--- a/code/ch08/ch08.ipynb
+++ b/code/ch08/ch08.ipynb
@@ -35,9 +35,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -178,9 +176,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stderr",
@@ -259,9 +255,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -368,9 +362,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -394,9 +386,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -427,9 +417,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -491,9 +479,7 @@
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -559,9 +545,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -607,9 +591,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -631,9 +613,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -668,9 +648,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -690,9 +668,7 @@
{
"cell_type": "code",
"execution_count": 17,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import re\n",
@@ -707,9 +683,7 @@
{
"cell_type": "code",
"execution_count": 18,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -729,9 +703,7 @@
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -751,9 +723,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"df['review'] = df['review'].apply(preprocessor)"
@@ -796,9 +766,7 @@
{
"cell_type": "code",
"execution_count": 22,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -818,9 +786,7 @@
{
"cell_type": "code",
"execution_count": 23,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -840,9 +806,7 @@
{
"cell_type": "code",
"execution_count": 24,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -873,9 +837,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -921,9 +883,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"X_train = df.loc[:25000, 'review'].values\n",
@@ -935,9 +895,7 @@
{
"cell_type": "code",
"execution_count": 29,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"from sklearn.pipeline import Pipeline\n",
@@ -976,12 +934,17 @@
" n_jobs=-1)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Note:** Some readers [encountered problems](https://github.com/rasbt/python-machine-learning-book/issues/50) running the following code on Windows. Unfortunately, problems with multiprocessing on Windows are not uncommon. So, if the following code cell should result in issues on your machine, try setting `n_jobs=1` (instead of `n_jobs=-1` in the previous code cell)."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 30,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1027,9 +990,7 @@
{
"cell_type": "code",
"execution_count": 31,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1048,9 +1009,7 @@
{
"cell_type": "code",
"execution_count": 32,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1085,9 +1044,7 @@
{
"cell_type": "code",
"execution_count": 36,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1136,9 +1093,7 @@
{
"cell_type": "code",
"execution_count": 38,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1193,9 +1148,7 @@
{
"cell_type": "code",
"execution_count": 39,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1222,9 +1175,7 @@
{
"cell_type": "code",
"execution_count": 40,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1300,9 +1251,7 @@
{
"cell_type": "code",
"execution_count": 49,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1323,9 +1272,7 @@
{
"cell_type": "code",
"execution_count": 50,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"def get_minibatch(doc_stream, size):\n",
@@ -1343,9 +1290,7 @@
{
"cell_type": "code",
"execution_count": 51,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import HashingVectorizer\n",
@@ -1363,9 +1308,7 @@
{
"cell_type": "code",
"execution_count": 52,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stderr",
@@ -1394,9 +1337,7 @@
{
"cell_type": "code",
"execution_count": 53,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1415,9 +1356,7 @@
{
"cell_type": "code",
"execution_count": 54,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"clf = clf.partial_fit(X_test, y_test)"
@@ -1455,9 +1394,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.6.1"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/code/ch09/ch09.ipynb b/code/ch09/ch09.ipynb
index 1778becf..2b9cb875 100644
--- a/code/ch09/ch09.ipynb
+++ b/code/ch09/ch09.ipynb
@@ -35,9 +35,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -177,9 +175,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -208,9 +204,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -270,9 +264,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"def get_minibatch(doc_stream, size):\n",
@@ -290,9 +282,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import HashingVectorizer\n",
@@ -310,9 +300,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stderr",
@@ -341,9 +329,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -362,9 +348,7 @@
{
"cell_type": "code",
"execution_count": 9,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"clf = clf.partial_fit(X_test, y_test)"
@@ -397,9 +381,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import pickle\n",
@@ -423,9 +405,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -492,9 +472,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import pickle\n",
@@ -508,9 +486,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -556,9 +532,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import sqlite3\n",
@@ -584,9 +558,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"conn = sqlite3.connect('reviews.sqlite')\n",
@@ -601,9 +573,7 @@
{
"cell_type": "code",
"execution_count": 17,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -620,9 +590,7 @@
{
"cell_type": "code",
"execution_count": 18,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -676,7 +644,67 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "..."
+ "Directory structure:\n",
+ "\n",
+ " 1st_flask_app_1/\n",
+ " app.py\n",
+ " templates/\n",
+ " first_app.html\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "from flask import Flask, render_template\r\n",
+ "\r\n",
+ "app = Flask(__name__)\r\n",
+ "\r\n",
+ "@app.route('/')\r\n",
+ "def index():\r\n",
+ " return render_template('first_app.html')\r\n",
+ "\r\n",
+ "if __name__ == '__main__':\r\n",
+ " app.run(debug=True)"
+ ]
+ }
+ ],
+ "source": [
+ "!cat 1st_flask_app_1/app.py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r\n",
+ "\r\n",
+ " \r\n",
+ " First app \r\n",
+ " \r\n",
+ " \r\n",
+ "\r\n",
+ " \r\n",
+ "\tHi, this is my first Flask web app!\r\n",
+ "
\r\n",
+ "\r\n",
+ " \r\n",
+ ""
+ ]
+ }
+ ],
+ "source": [
+ "!cat 1st_flask_app_1/templates/first_app.html"
]
},
{
@@ -689,9 +717,7 @@
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -716,9 +742,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -740,6 +764,90 @@
"Image(filename='../images/09_03.png', width=400) "
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Directory structure:\n",
+ " \n",
+ " 1st_flask_app_2/\n",
+ " app.py\n",
+ " static/\n",
+ " style.css\n",
+ " templates/\n",
+ " _formhelpers.html\n",
+ " first_app.html\n",
+ " hello.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "from flask import Flask, render_template, request\r\n",
+ "from wtforms import Form, TextAreaField, validators\r\n",
+ "\r\n",
+ "app = Flask(__name__)\r\n",
+ "\r\n",
+ "class HelloForm(Form):\r\n",
+ " sayhello = TextAreaField('',[validators.DataRequired()])\r\n",
+ "\r\n",
+ "@app.route('/')\r\n",
+ "def index():\r\n",
+ " form = HelloForm(request.form)\r\n",
+ " return render_template('first_app.html', form=form)\r\n",
+ "\r\n",
+ "@app.route('/hello', methods=['POST'])\r\n",
+ "def hello():\r\n",
+ " form = HelloForm(request.form)\r\n",
+ " if request.method == 'POST' and form.validate():\r\n",
+ " name = request.form['sayhello']\r\n",
+ " return render_template('hello.html', name=name)\r\n",
+ " return render_template('first_app.html', form=form)\r\n",
+ "\r\n",
+ "if __name__ == '__main__':\r\n",
+ " app.run(debug=True)"
+ ]
+ }
+ ],
+ "source": [
+ "!cat 1st_flask_app_2/app.py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{% macro render_field(field) %}\r\n",
+ " {{ field.label }}\r\n",
+ " {{ field(**kwargs)|safe }}\r\n",
+ " {% if field.errors %}\r\n",
+ " \r\n",
+ " {% for error in field.errors %}\r\n",
+ " {{ error }} \r\n",
+ " {% endfor %}\r\n",
+ " \r\n",
+ " {% endif %}\r\n",
+ " \r\n",
+ " \r\n",
+ "{% endmacro %}\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cat 1st_flask_app_2/templates/_formhelpers.html"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -758,9 +866,7 @@
{
"cell_type": "code",
"execution_count": 21,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -785,9 +891,7 @@
{
"cell_type": "code",
"execution_count": 22,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -812,9 +916,7 @@
{
"cell_type": "code",
"execution_count": 23,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -839,9 +941,7 @@
{
"cell_type": "code",
"execution_count": 24,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -863,6 +963,243 @@
"Image(filename='../images/09_07.png', width=200) "
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "from flask import Flask, render_template, request\r\n",
+ "from wtforms import Form, TextAreaField, validators\r\n",
+ "import pickle\r\n",
+ "import sqlite3\r\n",
+ "import os\r\n",
+ "import numpy as np\r\n",
+ "\r\n",
+ "# import HashingVectorizer from local dir\r\n",
+ "from vectorizer import vect\r\n",
+ "\r\n",
+ "app = Flask(__name__)\r\n",
+ "\r\n",
+ "######## Preparing the Classifier\r\n",
+ "cur_dir = os.path.dirname(__file__)\r\n",
+ "clf = pickle.load(open(os.path.join(cur_dir,\r\n",
+ " 'pkl_objects',\r\n",
+ " 'classifier.pkl'), 'rb'))\r\n",
+ "db = os.path.join(cur_dir, 'reviews.sqlite')\r\n",
+ "\r\n",
+ "def classify(document):\r\n",
+ " label = {0: 'negative', 1: 'positive'}\r\n",
+ " X = vect.transform([document])\r\n",
+ " y = clf.predict(X)[0]\r\n",
+ " proba = np.max(clf.predict_proba(X))\r\n",
+ " return label[y], proba\r\n",
+ "\r\n",
+ "def train(document, y):\r\n",
+ " X = vect.transform([document])\r\n",
+ " clf.partial_fit(X, [y])\r\n",
+ "\r\n",
+ "def sqlite_entry(path, document, y):\r\n",
+ " conn = sqlite3.connect(path)\r\n",
+ " c = conn.cursor()\r\n",
+ " c.execute(\"INSERT INTO review_db (review, sentiment, date)\"\\\r\n",
+ " \" VALUES (?, ?, DATETIME('now'))\", (document, y))\r\n",
+ " conn.commit()\r\n",
+ " conn.close()\r\n",
+ "\r\n",
+ "######## Flask\r\n",
+ "class ReviewForm(Form):\r\n",
+ " moviereview = TextAreaField('',\r\n",
+ " [validators.DataRequired(),\r\n",
+ " validators.length(min=15)])\r\n",
+ "\r\n",
+ "@app.route('/')\r\n",
+ "def index():\r\n",
+ " form = ReviewForm(request.form)\r\n",
+ " return render_template('reviewform.html', form=form)\r\n",
+ "\r\n",
+ "@app.route('/results', methods=['POST'])\r\n",
+ "def results():\r\n",
+ " form = ReviewForm(request.form)\r\n",
+ " if request.method == 'POST' and form.validate():\r\n",
+ " review = request.form['moviereview']\r\n",
+ " y, proba = classify(review)\r\n",
+ " return render_template('results.html',\r\n",
+ " content=review,\r\n",
+ " prediction=y,\r\n",
+ " probability=round(proba*100, 2))\r\n",
+ " return render_template('reviewform.html', form=form)\r\n",
+ "\r\n",
+ "@app.route('/thanks', methods=['POST'])\r\n",
+ "def feedback():\r\n",
+ " feedback = request.form['feedback_button']\r\n",
+ " review = request.form['review']\r\n",
+ " prediction = request.form['prediction']\r\n",
+ "\r\n",
+ " inv_label = {'negative': 0, 'positive': 1}\r\n",
+ " y = inv_label[prediction]\r\n",
+ " if feedback == 'Incorrect':\r\n",
+ " y = int(not(y))\r\n",
+ " train(review, y)\r\n",
+ " sqlite_entry(db, review, y)\r\n",
+ " return render_template('thanks.html')\r\n",
+ "\r\n",
+ "if __name__ == '__main__':\r\n",
+ " app.run(debug=True)\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cat ./movieclassifier/app.py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r\n",
+ "\r\n",
+ " \r\n",
+ " Movie Classification \r\n",
+ "\tPlease enter your movie review: \r\n",
+ "\r\n",
+ "{% from \"_formhelpers.html\" import render_field %}\r\n",
+ "\r\n",
+ "\r\n",
+ "\r\n",
+ " \r\n",
+ ""
+ ]
+ }
+ ],
+ "source": [
+ "!cat ./movieclassifier/templates/reviewform.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r\n",
+ "\r\n",
+ " \r\n",
+ " Movie Classification \r\n",
+ "\tYour movie review: \r\n",
+ "{{ content }}
\r\n",
+ "\r\n",
+ "Prediction: \r\n",
+ "This movie review is {{ prediction }} \r\n",
+ "\t (probability: {{ probability }}%).
\r\n",
+ "\r\n",
+ "\r\n",
+ "\t \r\n",
+ "
\r\n",
+ "\r\n",
+ "\r\n",
+ "\t \r\n",
+ "
\r\n",
+ "\r\n",
+ " \r\n",
+ "\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cat ./movieclassifier/templates/results.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "body{\r\n",
+ "\twidth:600px;\r\n",
+ "}\r\n",
+ "\r\n",
+ ".button{\r\n",
+ "\tpadding-top: 20px;\r\n",
+ "}\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cat ./movieclassifier/static/style.css"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r\n",
+ "\r\n",
+ " \r\n",
+ " Movie Classification \r\n",
+ "\tThank you for your feedback! \r\n",
+ "\r\n",
+ "\r\n",
+ "\t \r\n",
+ "
\r\n",
+ "\r\n",
+ " \r\n",
+ ""
+ ]
+ }
+ ],
+ "source": [
+ "!cat ./movieclassifier/templates/thanks.html"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -881,9 +1218,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -937,9 +1272,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import pickle\n",
@@ -980,9 +1313,7 @@
{
"cell_type": "code",
"execution_count": 27,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"cur_dir = '.'\n",
@@ -1007,6 +1338,66 @@
"# , protocol=4)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "import pickle\r\n",
+ "import sqlite3\r\n",
+ "import numpy as np\r\n",
+ "import os\r\n",
+ "\r\n",
+ "# import HashingVectorizer from local dir\r\n",
+ "from vectorizer import vect\r\n",
+ "\r\n",
+ "def update_model(db_path, model, batch_size=10000):\r\n",
+ "\r\n",
+ " conn = sqlite3.connect(db_path)\r\n",
+ " c = conn.cursor()\r\n",
+ " c.execute('SELECT * from review_db')\r\n",
+ "\r\n",
+ " results = c.fetchmany(batch_size)\r\n",
+ " while results:\r\n",
+ " data = np.array(results)\r\n",
+ " X = data[:, 0]\r\n",
+ " y = data[:, 1].astype(int)\r\n",
+ "\r\n",
+ " classes = np.array([0, 1])\r\n",
+ " X_train = vect.transform(X)\r\n",
+ " model.partial_fit(X_train, y, classes=classes)\r\n",
+ " results = c.fetchmany(batch_size)\r\n",
+ "\r\n",
+ " conn.close()\r\n",
+ " return model\r\n",
+ "\r\n",
+ "cur_dir = os.path.dirname(__file__)\r\n",
+ "\r\n",
+ "clf = pickle.load(open(os.path.join(cur_dir,\r\n",
+ " 'pkl_objects',\r\n",
+ " 'classifier.pkl'), 'rb'))\r\n",
+ "db = os.path.join(cur_dir, 'reviews.sqlite')\r\n",
+ "\r\n",
+ "clf = update_model(db_path=db, model=clf, batch_size=10000)\r\n",
+ "\r\n",
+ "# Uncomment the following lines if you are sure that\r\n",
+ "# you want to update your classifier.pkl file\r\n",
+ "# permanently.\r\n",
+ "\r\n",
+ "# pickle.dump(clf, open(os.path.join(cur_dir,\r\n",
+ "# 'pkl_objects', 'classifier.pkl'), 'wb')\r\n",
+ "# , protocol=4)\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cat ./movieclassifier_with_update/update.py"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {
@@ -1050,9 +1441,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.6.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/code/ch11/ch11.ipynb b/code/ch11/ch11.ipynb
index ba9305e7..39b62c9c 100644
--- a/code/ch11/ch11.ipynb
+++ b/code/ch11/ch11.ipynb
@@ -35,9 +35,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -151,9 +149,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -179,9 +175,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -281,9 +275,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -300,9 +292,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -350,9 +340,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -416,9 +404,7 @@
{
"cell_type": "code",
"execution_count": 9,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -465,9 +451,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -527,9 +511,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -554,9 +536,7 @@
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -651,9 +631,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -748,9 +726,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -829,7 +805,6 @@
"cell_type": "code",
"execution_count": 15,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -907,9 +882,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -985,9 +958,7 @@
{
"cell_type": "code",
"execution_count": 17,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1036,9 +1007,7 @@
{
"cell_type": "code",
"execution_count": 18,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1060,7 +1029,7 @@
"row_dendr = dendrogram(row_clusters, orientation='left')\n",
"\n",
"# reorder data with respect to clustering\n",
- "df_rowclust = df.ix[row_dendr['leaves'][::-1]]\n",
+ "df_rowclust = df.iloc[row_dendr['leaves'][::-1]]\n",
"\n",
"axd.set_xticks([])\n",
"axd.set_yticks([])\n",
@@ -1097,9 +1066,7 @@
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1137,9 +1104,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1164,9 +1129,7 @@
{
"cell_type": "code",
"execution_count": 21,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1199,9 +1162,7 @@
{
"cell_type": "code",
"execution_count": 22,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1251,9 +1212,7 @@
{
"cell_type": "code",
"execution_count": 23,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1309,7 +1268,7 @@
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
- "display_name": "Python [default]",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -1323,9 +1282,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.6.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/code/ch12/ch12.ipynb b/code/ch12/ch12.ipynb
index 37df9885..979ba574 100644
--- a/code/ch12/ch12.ipynb
+++ b/code/ch12/ch12.ipynb
@@ -35,23 +35,21 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sebastian Raschka \n",
- "last updated: 2016-09-29 \n",
+ "last updated: 2017-07-29 \n",
"\n",
- "CPython 3.5.2\n",
- "IPython 5.1.0\n",
+ "CPython 3.6.1\n",
+ "IPython 6.0.0\n",
"\n",
- "numpy 1.11.1\n",
- "scipy 0.18.1\n",
- "matplotlib 1.5.1\n"
+ "numpy 1.13.1\n",
+ "scipy 0.19.1\n",
+ "matplotlib 2.0.2\n"
]
}
],
@@ -110,9 +108,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -143,10 +141,8 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 3,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -155,7 +151,7 @@
""
]
},
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {
"image/png": {
"width": 600
@@ -185,10 +181,8 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 4,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -197,7 +191,7 @@
""
]
},
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {
"image/png": {
"width": 400
@@ -212,10 +206,8 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 5,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -224,7 +216,7 @@
""
]
},
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {
"image/png": {
"width": 500
@@ -254,10 +246,8 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 6,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -266,7 +256,7 @@
""
]
},
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {
"image/png": {
"width": 500
@@ -328,7 +318,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {
"collapsed": true
},
@@ -360,12 +350,54 @@
" return images, labels"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Important Note**\n",
+ "\n",
+ "Some readers experienced issues with the `load_mnist` function above as certain decompression tools renamed the files from *-labels-idx1-ubyte* to *-labels.idx1-ubyte*. To avoid this problem altogether, you the modified function above will directly load the dataset from the `gz` archives using Python's `gzip` module."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import struct\n",
+ "import numpy as np\n",
+ "import gzip\n",
+ " \n",
+ "def load_mnist(path, kind='train'):\n",
+ " \"\"\"Load MNIST data from `path`\"\"\"\n",
+ " labels_path = os.path.join(path, \n",
+ " '%s-labels-idx1-ubyte.gz' % kind)\n",
+ " images_path = os.path.join(path, \n",
+ " '%s-images-idx3-ubyte.gz' % kind)\n",
+ " \n",
+ " with gzip.open(labels_path, 'rb') as lbpath:\n",
+ " lbpath.read(8)\n",
+ " buffer = lbpath.read()\n",
+ " labels = np.frombuffer(buffer, dtype=np.uint8)\n",
+ "\n",
+ " with gzip.open(images_path, 'rb') as imgpath:\n",
+ " imgpath.read(16)\n",
+ " buffer = imgpath.read()\n",
+ " images = np.frombuffer(buffer, \n",
+ " dtype=np.uint8).reshape(\n",
+ " len(labels), 784).astype(np.float64)\n",
+ " \n",
+ " return images, labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -383,9 +415,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -410,9 +440,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -451,9 +479,7 @@
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -497,7 +523,7 @@
"cell_type": "code",
"execution_count": 13,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -524,9 +550,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"source": [
"## Implementing a multi-layer perceptron"
]
@@ -535,7 +559,7 @@
"cell_type": "code",
"execution_count": 8,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -868,11 +892,87 @@
" return self"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "**Note**\n",
+ "\n",
+ "In the fit method of the MLP example above,\n",
+ "\n",
+ "```python\n",
+ "\n",
+ "for idx in mini:\n",
+ "...\n",
+ " # compute gradient via backpropagation\n",
+ " grad1, grad2 = self._get_gradient(a1=a1, a2=a2,\n",
+ " a3=a3, z2=z2,\n",
+ " y_enc=y_enc[:, idx],\n",
+ " w1=self.w1,\n",
+ " w2=self.w2)\n",
+ "\n",
+ " delta_w1, delta_w2 = self.eta * grad1, self.eta * grad2\n",
+ " self.w1 -= (delta_w1 + (self.alpha * delta_w1_prev))\n",
+ " self.w2 -= (delta_w2 + (self.alpha * delta_w2_prev))\n",
+ " delta_w1_prev, delta_w2_prev = delta_w1, delta_w2\n",
+ "```\n",
+ "\n",
+ "`delta_w1_prev` (same applies to `delta_w2_prev`) is a memory view on `delta_w1` via \n",
+ "\n",
+ "```python\n",
+ "delta_w1_prev = delta_w1\n",
+ "```\n",
+ "on the last line. This could be problematic, since updating `delta_w1 = self.eta * grad1` would change `delta_w1_prev` as well when we iterate over the for loop. Note that this is not the case here, because we assign a new array to `delta_w1` in each iteration -- the gradient array times the learning rate:\n",
+ "\n",
+ "```python\n",
+ "delta_w1 = self.eta * grad1\n",
+ "```\n",
+ "\n",
+ "The assignment shown above leaves the `delta_w1_prev` pointing to the \"old\" `delta_w1` array. To illustrates this with a simple snippet, consider the following example:\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "a & b True\n",
+ "a & b False\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "a = np.arange(5)\n",
+ "b = a\n",
+ "print('a & b', np.may_share_memory(a, b))\n",
+ "\n",
+ "\n",
+ "a = np.arange(5)\n",
+ "print('a & b', np.may_share_memory(a, b))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "(End of note.)\n",
+ "\n",
+ "---"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -893,9 +993,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stderr",
@@ -922,9 +1020,7 @@
{
"cell_type": "code",
"execution_count": 17,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -953,7 +1049,7 @@
"cell_type": "code",
"execution_count": 18,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -965,9 +1061,7 @@
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -993,9 +1087,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1020,9 +1112,7 @@
{
"cell_type": "code",
"execution_count": 21,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1047,9 +1137,7 @@
{
"cell_type": "code",
"execution_count": 22,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1084,9 +1172,7 @@
{
"cell_type": "code",
"execution_count": 23,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1152,9 +1238,7 @@
{
"cell_type": "code",
"execution_count": 24,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1194,9 +1278,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1221,9 +1303,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1279,9 +1359,7 @@
{
"cell_type": "code",
"execution_count": 27,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1306,9 +1384,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1732,7 +1808,7 @@
"cell_type": "code",
"execution_count": 29,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1753,9 +1829,7 @@
{
"cell_type": "code",
"execution_count": 30,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1806,9 +1880,7 @@
{
"cell_type": "code",
"execution_count": 31,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1862,9 +1934,7 @@
{
"cell_type": "code",
"execution_count": 32,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1889,9 +1959,7 @@
{
"cell_type": "code",
"execution_count": 33,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1931,9 +1999,7 @@
{
"cell_type": "code",
"execution_count": 34,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1999,6 +2065,7 @@
}
],
"metadata": {
+ "anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
@@ -2014,9 +2081,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.6.1"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/code/ch12/neuralnet.py b/code/ch12/neuralnet.py
new file mode 100644
index 00000000..fc2b4a25
--- /dev/null
+++ b/code/ch12/neuralnet.py
@@ -0,0 +1,331 @@
+# Python Machine Learning by Sebastian Raschka, Packt Publishing Ltd. 2015
+# Code Repository: https://github.com/rasbt/python-machine-learning-book
+# Code License: MIT License
+
+import numpy as np
+from scipy.special import expit
+import sys
+
+
+class NeuralNetMLP(object):
+ """ Feedforward neural network / Multi-layer perceptron classifier.
+
+ Parameters
+ ------------
+ n_output : int
+ Number of output units, should be equal to the
+ number of unique class labels.
+ n_features : int
+ Number of features (dimensions) in the target dataset.
+ Should be equal to the number of columns in the X array.
+ n_hidden : int (default: 30)
+ Number of hidden units.
+ l1 : float (default: 0.0)
+ Lambda value for L1-regularization.
+ No regularization if l1=0.0 (default)
+ l2 : float (default: 0.0)
+ Lambda value for L2-regularization.
+ No regularization if l2=0.0 (default)
+ epochs : int (default: 500)
+ Number of passes over the training set.
+ eta : float (default: 0.001)
+ Learning rate.
+ alpha : float (default: 0.0)
+ Momentum constant. Factor multiplied with the
+ gradient of the previous epoch t-1 to improve
+ learning speed
+ w(t) := w(t) - (grad(t) + alpha*grad(t-1))
+ decrease_const : float (default: 0.0)
+ Decrease constant. Shrinks the learning rate
+ after each epoch via eta / (1 + epoch*decrease_const)
+ shuffle : bool (default: True)
+ Shuffles training data every epoch if True to prevent circles.
+ minibatches : int (default: 1)
+ Divides training data into k minibatches for efficiency.
+ Normal gradient descent learning if k=1 (default).
+ random_state : int (default: None)
+ Set random state for shuffling and initializing the weights.
+
+ Attributes
+ -----------
+ cost_ : list
+ Sum of squared errors after each epoch.
+
+ """
+ def __init__(self, n_output, n_features, n_hidden=30,
+ l1=0.0, l2=0.0, epochs=500, eta=0.001,
+ alpha=0.0, decrease_const=0.0, shuffle=True,
+ minibatches=1, random_state=None):
+
+ np.random.seed(random_state)
+ self.n_output = n_output
+ self.n_features = n_features
+ self.n_hidden = n_hidden
+ self.w1, self.w2 = self._initialize_weights()
+ self.l1 = l1
+ self.l2 = l2
+ self.epochs = epochs
+ self.eta = eta
+ self.alpha = alpha
+ self.decrease_const = decrease_const
+ self.shuffle = shuffle
+ self.minibatches = minibatches
+
+ def _encode_labels(self, y, k):
+ """Encode labels into one-hot representation
+
+ Parameters
+ ------------
+ y : array, shape = [n_samples]
+ Target values.
+
+ Returns
+ -----------
+ onehot : array, shape = (n_labels, n_samples)
+
+ """
+ onehot = np.zeros((k, y.shape[0]))
+ for idx, val in enumerate(y):
+ onehot[val, idx] = 1.0
+ return onehot
+
+ def _initialize_weights(self):
+ """Initialize weights with small random numbers."""
+ w1 = np.random.uniform(-1.0, 1.0,
+ size=self.n_hidden*(self.n_features + 1))
+ w1 = w1.reshape(self.n_hidden, self.n_features + 1)
+ w2 = np.random.uniform(-1.0, 1.0,
+ size=self.n_output*(self.n_hidden + 1))
+ w2 = w2.reshape(self.n_output, self.n_hidden + 1)
+ return w1, w2
+
+ def _sigmoid(self, z):
+ """Compute logistic function (sigmoid)
+
+ Uses scipy.special.expit to avoid overflow
+ error for very small input values z.
+
+ """
+ # return 1.0 / (1.0 + np.exp(-z))
+ return expit(z)
+
+ def _sigmoid_gradient(self, z):
+ """Compute gradient of the logistic function"""
+ sg = self._sigmoid(z)
+ return sg * (1.0 - sg)
+
+ def _add_bias_unit(self, X, how='column'):
+ """Add bias unit (column or row of 1s) to array at index 0"""
+ if how == 'column':
+ X_new = np.ones((X.shape[0], X.shape[1] + 1))
+ X_new[:, 1:] = X
+ elif how == 'row':
+ X_new = np.ones((X.shape[0] + 1, X.shape[1]))
+ X_new[1:, :] = X
+ else:
+ raise AttributeError('`how` must be `column` or `row`')
+ return X_new
+
+ def _feedforward(self, X, w1, w2):
+ """Compute feedforward step
+
+ Parameters
+ -----------
+ X : array, shape = [n_samples, n_features]
+ Input layer with original features.
+ w1 : array, shape = [n_hidden_units, n_features]
+ Weight matrix for input layer -> hidden layer.
+ w2 : array, shape = [n_output_units, n_hidden_units]
+ Weight matrix for hidden layer -> output layer.
+
+ Returns
+ ----------
+ a1 : array, shape = [n_samples, n_features+1]
+ Input values with bias unit.
+ z2 : array, shape = [n_hidden, n_samples]
+ Net input of hidden layer.
+ a2 : array, shape = [n_hidden+1, n_samples]
+ Activation of hidden layer.
+ z3 : array, shape = [n_output_units, n_samples]
+ Net input of output layer.
+ a3 : array, shape = [n_output_units, n_samples]
+ Activation of output layer.
+
+ """
+ a1 = self._add_bias_unit(X, how='column')
+ z2 = w1.dot(a1.T)
+ a2 = self._sigmoid(z2)
+ a2 = self._add_bias_unit(a2, how='row')
+ z3 = w2.dot(a2)
+ a3 = self._sigmoid(z3)
+ return a1, z2, a2, z3, a3
+
+ def _L2_reg(self, lambda_, w1, w2):
+ """Compute L2-regularization cost"""
+ return (lambda_/2.0) * (np.sum(w1[:, 1:] ** 2) +
+ np.sum(w2[:, 1:] ** 2))
+
+ def _L1_reg(self, lambda_, w1, w2):
+ """Compute L1-regularization cost"""
+ return (lambda_/2.0) * (np.abs(w1[:, 1:]).sum() +
+ np.abs(w2[:, 1:]).sum())
+
+ def _get_cost(self, y_enc, output, w1, w2):
+ """Compute cost function.
+
+ Parameters
+ ----------
+ y_enc : array, shape = (n_labels, n_samples)
+ one-hot encoded class labels.
+ output : array, shape = [n_output_units, n_samples]
+ Activation of the output layer (feedforward)
+ w1 : array, shape = [n_hidden_units, n_features]
+ Weight matrix for input layer -> hidden layer.
+ w2 : array, shape = [n_output_units, n_hidden_units]
+ Weight matrix for hidden layer -> output layer.
+
+ Returns
+ ---------
+ cost : float
+ Regularized cost.
+
+ """
+ term1 = -y_enc * (np.log(output))
+ term2 = (1.0 - y_enc) * np.log(1.0 - output)
+ cost = np.sum(term1 - term2)
+ L1_term = self._L1_reg(self.l1, w1, w2)
+ L2_term = self._L2_reg(self.l2, w1, w2)
+ cost = cost + L1_term + L2_term
+ return cost
+
+ def _get_gradient(self, a1, a2, a3, z2, y_enc, w1, w2):
+ """ Compute gradient step using backpropagation.
+
+ Parameters
+ ------------
+ a1 : array, shape = [n_samples, n_features+1]
+ Input values with bias unit.
+ a2 : array, shape = [n_hidden+1, n_samples]
+ Activation of hidden layer.
+ a3 : array, shape = [n_output_units, n_samples]
+ Activation of output layer.
+ z2 : array, shape = [n_hidden, n_samples]
+ Net input of hidden layer.
+ y_enc : array, shape = (n_labels, n_samples)
+ one-hot encoded class labels.
+ w1 : array, shape = [n_hidden_units, n_features]
+ Weight matrix for input layer -> hidden layer.
+ w2 : array, shape = [n_output_units, n_hidden_units]
+ Weight matrix for hidden layer -> output layer.
+
+ Returns
+ ---------
+ grad1 : array, shape = [n_hidden_units, n_features]
+ Gradient of the weight matrix w1.
+ grad2 : array, shape = [n_output_units, n_hidden_units]
+ Gradient of the weight matrix w2.
+
+ """
+ # backpropagation
+ sigma3 = a3 - y_enc
+ z2 = self._add_bias_unit(z2, how='row')
+ sigma2 = w2.T.dot(sigma3) * self._sigmoid_gradient(z2)
+ sigma2 = sigma2[1:, :]
+ grad1 = sigma2.dot(a1)
+ grad2 = sigma3.dot(a2.T)
+
+ # regularize
+ grad1[:, 1:] += self.l2 * w1[:, 1:]
+ grad1[:, 1:] += self.l1 * np.sign(w1[:, 1:])
+ grad2[:, 1:] += self.l2 * w2[:, 1:]
+ grad2[:, 1:] += self.l1 * np.sign(w2[:, 1:])
+
+ return grad1, grad2
+
+ def predict(self, X):
+ """Predict class labels
+
+ Parameters
+ -----------
+ X : array, shape = [n_samples, n_features]
+ Input layer with original features.
+
+ Returns:
+ ----------
+ y_pred : array, shape = [n_samples]
+ Predicted class labels.
+
+ """
+ if len(X.shape) != 2:
+ raise AttributeError('X must be a [n_samples, n_features] array.\n'
+ 'Use X[:,None] for 1-feature classification,'
+ '\nor X[[i]] for 1-sample classification')
+
+ a1, z2, a2, z3, a3 = self._feedforward(X, self.w1, self.w2)
+ y_pred = np.argmax(z3, axis=0)
+ return y_pred
+
+ def fit(self, X, y, print_progress=False):
+ """ Learn weights from training data.
+
+ Parameters
+ -----------
+ X : array, shape = [n_samples, n_features]
+ Input layer with original features.
+ y : array, shape = [n_samples]
+ Target class labels.
+ print_progress : bool (default: False)
+ Prints progress as the number of epochs
+ to stderr.
+
+ Returns:
+ ----------
+ self
+
+ """
+ self.cost_ = []
+ X_data, y_data = X.copy(), y.copy()
+ y_enc = self._encode_labels(y, self.n_output)
+
+ delta_w1_prev = np.zeros(self.w1.shape)
+ delta_w2_prev = np.zeros(self.w2.shape)
+
+ for i in range(self.epochs):
+
+ # adaptive learning rate
+ self.eta /= (1 + self.decrease_const*i)
+
+ if print_progress:
+ sys.stderr.write('\rEpoch: %d/%d' % (i+1, self.epochs))
+ sys.stderr.flush()
+
+ if self.shuffle:
+ idx = np.random.permutation(y_data.shape[0])
+ X_data, y_enc = X_data[idx], y_enc[:, idx]
+
+ mini = np.array_split(range(y_data.shape[0]), self.minibatches)
+ for idx in mini:
+
+ # feedforward
+ a1, z2, a2, z3, a3 = self._feedforward(X_data[idx],
+ self.w1,
+ self.w2)
+ cost = self._get_cost(y_enc=y_enc[:, idx],
+ output=a3,
+ w1=self.w1,
+ w2=self.w2)
+ self.cost_.append(cost)
+
+ # compute gradient via backpropagation
+ grad1, grad2 = self._get_gradient(a1=a1, a2=a2,
+ a3=a3, z2=z2,
+ y_enc=y_enc[:, idx],
+ w1=self.w1,
+ w2=self.w2)
+
+ delta_w1, delta_w2 = self.eta * grad1, self.eta * grad2
+ self.w1 -= (delta_w1 + (self.alpha * delta_w1_prev))
+ self.w2 -= (delta_w2 + (self.alpha * delta_w2_prev))
+ delta_w1_prev, delta_w2_prev = delta_w1, delta_w2
+
+ return self
\ No newline at end of file
diff --git a/code/ch12/optional-streamlined-neuralnet.py b/code/ch12/optional-streamlined-neuralnet.py
new file mode 100644
index 00000000..4af365ca
--- /dev/null
+++ b/code/ch12/optional-streamlined-neuralnet.py
@@ -0,0 +1,256 @@
+# Python Machine Learning by Sebastian Raschka, Packt Publishing Ltd. 2015
+# Code Repository: https://github.com/rasbt/python-machine-learning-book
+# Code License: MIT License
+
+import numpy as np
+import sys
+
+
+class NeuralNetMLP(object):
+ """ Feedforward neural network / Multi-layer perceptron classifier.
+
+ Parameters
+ ------------
+ n_hidden : int (default: 30)
+ Number of hidden units.
+ l2 : float (default: 0.)
+ Lambda value for L2-regularization.
+ No regularization if l2=0. (default)
+ epochs : int (default: 100)
+ Number of passes over the training set.
+ eta : float (default: 0.001)
+ Learning rate.
+ shuffle : bool (default: True)
+ Shuffles training data every epoch if True to prevent circles.
+ minibatche_size : int (default: 1)
+ Number of training samples per minibatch.
+ seed : int (default: None)
+ Random seed for initializing weights and shuffling.
+
+ Attributes
+ -----------
+ eval_ : dict
+ Dictionary collecting the cost, training accuracy,
+ and validation accuracy for each epoch during training.
+
+ """
+ def __init__(self, n_hidden=30,
+ l2=0., epochs=100, eta=0.001,
+ shuffle=True, minibatch_size=1, seed=None):
+
+ self.random = np.random.RandomState(seed)
+ self.n_hidden = n_hidden
+ self.l2 = l2
+ self.epochs = epochs
+ self.eta = eta
+ self.shuffle = shuffle
+ self.minibatch_size = minibatch_size
+
+ def _onehot(self, y, n_classes):
+ """Encode labels into one-hot representation
+
+ Parameters
+ ------------
+ y : array, shape = [n_samples]
+ Target values.
+
+ Returns
+ -----------
+ onehot : array, shape = (n_samples, n_labels)
+
+ """
+ onehot = np.zeros((n_classes, y.shape[0].astype(int)))
+ for idx, val in enumerate(y):
+ onehot[val, idx] = 1.
+ return onehot.T
+
+ def _sigmoid(self, z):
+ """Compute logistic function (sigmoid)"""
+ return 1. / (1. + np.exp(-np.clip(z, -250, 250)))
+
+ def _forward(self, X):
+ """Compute forward propagation step"""
+
+ # step 1: net input of hidden layer
+ # [n_samples, n_features] dot [n_features, n_hidden]
+ # -> [n_samples, n_hidden]
+ z_h = np.dot(X, self.w_h) + self.b_h
+
+ # step 2: activation of hidden layer
+ a_h = self._sigmoid(z_h)
+
+ # step 3: net input of output layer
+ # [n_samples, n_hidden] dot [n_hidden, n_classlabels]
+ # -> [n_samples, n_classlabels]
+
+ z_out = np.dot(a_h, self.w_out) + self.b_out
+
+ # step 4: activation output layer
+ a_out = self._sigmoid(z_out)
+
+ return z_h, a_h, z_out, a_out
+
+ def _compute_cost(self, y_enc, output):
+ """Compute cost function.
+
+ Parameters
+ ----------
+ y_enc : array, shape = (n_samples, n_labels)
+ one-hot encoded class labels.
+ output : array, shape = [n_samples, n_output_units]
+ Activation of the output layer (forward propagation)
+
+ Returns
+ ---------
+ cost : float
+ Regularized cost
+
+ """
+ L2_term = (self.l2 *
+ (np.sum(self.w_h ** 2.) +
+ np.sum(self.w_out ** 2.)))
+
+ term1 = -y_enc * (np.log(output))
+ term2 = (1. - y_enc) * np.log(1. - output)
+ cost = np.sum(term1 - term2) + L2_term
+ return cost
+
+ def predict(self, X):
+ """Predict class labels
+
+ Parameters
+ -----------
+ X : array, shape = [n_samples, n_features]
+ Input layer with original features.
+
+ Returns:
+ ----------
+ y_pred : array, shape = [n_samples]
+ Predicted class labels.
+
+ """
+ z_h, a_h, z_out, a_out = self._forward(X)
+ y_pred = np.argmax(z_out, axis=1)
+ return y_pred
+
+ def fit(self, X_train, y_train, X_valid, y_valid):
+ """ Learn weights from training data.
+
+ Parameters
+ -----------
+ X_train : array, shape = [n_samples, n_features]
+ Input layer with original features.
+ y_train : array, shape = [n_samples]
+ Target class labels.
+ X_valid : array, shape = [n_samples, n_features]
+ Sample features for validation during training
+ y_valid : array, shape = [n_samples]
+ Sample labels for validation during training
+
+ Returns:
+ ----------
+ self
+
+ """
+ n_output = np.unique(y_train).shape[0] # number of class labels
+ n_features = X_train.shape[1]
+
+ ########################
+ # Weight initialization
+ ########################
+
+ # weights for input -> hidden
+ self.b_h = np.zeros(self.n_hidden)
+ self.w_h = self.random.normal(loc=0.0, scale=0.1,
+ size=(n_features, self.n_hidden))
+
+ # weights for hidden -> output
+ self.b_out = np.zeros(n_output)
+ self.w_out = self.random.normal(loc=0.0, scale=0.1,
+ size=(self.n_hidden, n_output))
+
+ epoch_strlen = len(str(self.epochs)) # for progress formatting
+ self.eval_ = {'cost': [], 'train_acc': [], 'valid_acc': []}
+
+ y_train_enc = self._onehot(y_train, n_output)
+
+ # iterate over training epochs
+ for i in range(self.epochs):
+
+ # iterate over minibatches
+ indices = np.arange(X_train.shape[0])
+
+ if self.shuffle:
+ self.random.shuffle(indices)
+
+ for start_idx in range(0, indices.shape[0] - self.minibatch_size +
+ 1, self.minibatch_size):
+ batch_idx = indices[start_idx:start_idx + self.minibatch_size]
+
+ # forward propagation
+ z_h, a_h, z_out, a_out = self._forward(X_train[batch_idx])
+
+ ##################
+ # Backpropagation
+ ##################
+
+ # [n_samples, n_classlabels]
+ sigma_out = a_out - y_train_enc[batch_idx]
+
+ # [n_samples, n_hidden]
+ sigmoid_derivative_h = a_h * (1. - a_h)
+
+ # [n_samples, n_classlabels] dot [n_classlabels, n_hidden]
+ # -> [n_samples, n_hidden]
+ sigma_h = (np.dot(sigma_out, self.w_out.T) *
+ sigmoid_derivative_h)
+
+ # [n_features, n_samples] dot [n_samples, n_hidden]
+ # -> [n_features, n_hidden]
+ grad_w_h = np.dot(X_train[batch_idx].T, sigma_h)
+ grad_b_h = np.sum(sigma_h, axis=0)
+
+ # [n_hidden, n_samples] dot [n_samples, n_classlabels]
+ # -> [n_hidden, n_classlabels]
+ grad_w_out = np.dot(a_h.T, sigma_out)
+ grad_b_out = np.sum(sigma_out, axis=0)
+
+ # Regularization and weight updates
+ delta_w_h = (grad_w_h + self.l2*self.w_h)
+ delta_b_h = grad_b_h # bias is not regularized
+ self.w_h -= self.eta * delta_w_h
+ self.b_h -= self.eta * delta_b_h
+
+ delta_w_out = (grad_w_out + self.l2*self.w_out)
+ delta_b_out = grad_b_out # bias is not regularized
+ self.w_out -= self.eta * delta_w_out
+ self.b_out -= self.eta * delta_b_out
+
+ #############
+ # Evaluation
+ #############
+
+ # Evaluation after each epoch during training
+ z_h, a_h, z_out, a_out = self._forward(X_train)
+ cost = self._compute_cost(y_enc=y_train_enc,
+ output=a_out)
+
+ y_train_pred = self.predict(X_train)
+ y_valid_pred = self.predict(X_valid)
+
+ train_acc = ((np.sum(y_train == y_train_pred)).astype(np.float) /
+ X_train.shape[0])
+ valid_acc = ((np.sum(y_valid == y_valid_pred)).astype(np.float) /
+ X_valid.shape[0])
+
+ sys.stderr.write('\r%0*d/%d | Cost: %.2f '
+ '| Train/Valid Acc.: %.2f%%/%.2f%% ' %
+ (epoch_strlen, i+1, self.epochs, cost,
+ train_acc*100, valid_acc*100))
+ sys.stderr.flush()
+
+ self.eval_['cost'].append(cost)
+ self.eval_['train_acc'].append(train_acc)
+ self.eval_['valid_acc'].append(valid_acc)
+
+ return self
\ No newline at end of file
diff --git a/code/ch13/ch13.ipynb b/code/ch13/ch13.ipynb
index b7e92a9b..93492a53 100644
--- a/code/ch13/ch13.ipynb
+++ b/code/ch13/ch13.ipynb
@@ -35,24 +35,29 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using Theano backend.\n"
+ ]
+ },
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sebastian Raschka \n",
- "Last updated: 08/27/2015 \n",
+ "last updated: 2017-07-04 \n",
"\n",
- "CPython 3.4.3\n",
- "IPython 4.0.0\n",
+ "CPython 3.6.1\n",
+ "IPython 6.0.0\n",
"\n",
- "numpy 1.9.2\n",
- "matplotlib 1.4.3\n",
- "theano 0.7.0\n",
- "keras 0.1.2\n"
+ "numpy 1.13.0\n",
+ "matplotlib 2.0.2\n",
+ "theano 0.9.0\n",
+ "keras 2.0.5\n"
]
}
],
@@ -105,7 +110,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {
"collapsed": true
},
@@ -116,7 +121,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {
"collapsed": true
},
@@ -145,10 +150,8 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 4,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -157,7 +160,7 @@
""
]
},
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {
"image/png": {
"width": 500
@@ -208,9 +211,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 5,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -220,18 +223,16 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 6,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array(2.5)"
+ "array(2.5, dtype=float32)"
]
},
- "execution_count": 4,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -276,16 +277,14 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 7,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "float64\n"
+ "float32\n"
]
}
],
@@ -295,7 +294,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 8,
"metadata": {
"collapsed": true
},
@@ -328,10 +327,8 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 9,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -390,10 +387,8 @@
},
{
"cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 10,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -435,10 +430,8 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 11,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -480,10 +473,8 @@
},
{
"cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 12,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -542,7 +533,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 13,
"metadata": {
"collapsed": true
},
@@ -567,9 +558,9 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 14,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -620,16 +611,14 @@
},
{
"cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 15,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": 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G9eRCRPwb8MKw4RXA6mR5NXBDWj/fAZVTkpYBVwGPZltJLv0l8D+BwawLybkL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\nQJmZWS45oMzMLJf+PxNd6XAisdvzAAAAAElFTkSuQmCC\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -660,16 +649,14 @@
},
{
"cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 16,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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fMmUKFRUVLF68WIc0EhHPUUglgY6ODqqrq3nvvfci6suXL6esrIysrCxHnYmI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\n",
+ "image/png": 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"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -746,10 +733,8 @@
},
{
"cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 17,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -788,10 +773,8 @@
},
{
"cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 18,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -831,10 +814,8 @@
},
{
"cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 19,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -879,7 +860,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 20,
"metadata": {
"collapsed": true
},
@@ -895,10 +876,8 @@
},
{
"cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 21,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -918,10 +897,8 @@
},
{
"cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 22,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -929,7 +906,7 @@
"1.0"
]
},
- "execution_count": 34,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -940,10 +917,8 @@
},
{
"cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 23,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -951,7 +926,7 @@
"array([2])"
]
},
- "execution_count": 35,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -971,10 +946,8 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 24,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -983,7 +956,7 @@
""
]
},
- "execution_count": 3,
+ "execution_count": 24,
"metadata": {
"image/png": {
"width": 800
@@ -1024,7 +997,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 25,
"metadata": {
"collapsed": true
},
@@ -1038,16 +1011,14 @@
},
{
"cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 26,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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Jb5aZLQBygbnxDkqkI3viiScoLS1l4sSJjB6dko8oirSqpnamuAJ43N1L3H1B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\nIiIp7f8BNM/UAollYmAAAAAASUVORK5CYII=\n",
+ "image/png": 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x47399tskJycze/Zspk6dWiw+d+5cEhMTmTlzJjNnziwWX7hwIVFRUbz66qvM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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1091,10 +1062,8 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 27,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1103,7 +1072,7 @@
""
]
},
- "execution_count": 6,
+ "execution_count": 27,
"metadata": {
"image/png": {
"width": 700
@@ -1131,6 +1100,24 @@
"# Training neural networks efficiently using Keras"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "--- \n",
+ "**Note about installing Keras**\n",
+ "\n",
+ "As a [kind reader pointed out](http://www.mostafaelzoghbi.com/2017/04/how-to-install-keras-on-windows-10-64.html), Keras can now be installed from conda-forge, which is a community-driven effort to make packages available to the conda manager -- if you have troubles installing Keras via `pip` (`pip install keras`). To install Keras from conda-forge, you need to specify the respective conda channel as shown below:\n",
+ "\n",
+ "```bash\n",
+ "conda install -c conda-forge keras=2.0.2\n",
+ "```\n",
+ "\n",
+ "(End of note.)\n",
+ "\n",
+ "---"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -1156,7 +1143,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 28,
"metadata": {
"collapsed": true
},
@@ -1190,10 +1177,8 @@
},
{
"cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 29,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1204,16 +1189,14 @@
}
],
"source": [
- "X_train, y_train = load_mnist('mnist', kind='train')\n",
+ "X_train, y_train = load_mnist('./mnist', kind='train')\n",
"print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))"
]
},
{
"cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 30,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1246,9 +1229,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"source": [
"In order to run the following code via GPU, you can execute the Python script that was placed in this directory via\n",
"\n",
@@ -1257,9 +1238,9 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 31,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1279,10 +1260,8 @@
},
{
"cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 32,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1308,125 +1287,123 @@
},
{
"cell_type": "code",
- "execution_count": 49,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 33,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 54000 samples, validate on 6000 samples\n",
- "Epoch 0\n",
- "54000/54000 [==============================] - 1s - loss: 2.2290 - acc: 0.3592 - val_loss: 2.1094 - val_acc: 0.5342\n",
- "Epoch 1\n",
- "54000/54000 [==============================] - 1s - loss: 1.8850 - acc: 0.5279 - val_loss: 1.6098 - val_acc: 0.5617\n",
- "Epoch 2\n",
- "54000/54000 [==============================] - 1s - loss: 1.3903 - acc: 0.5884 - val_loss: 1.1666 - val_acc: 0.6707\n",
- "Epoch 3\n",
- "54000/54000 [==============================] - 1s - loss: 1.0592 - acc: 0.6936 - val_loss: 0.8961 - val_acc: 0.7615\n",
- "Epoch 4\n",
- "54000/54000 [==============================] - 1s - loss: 0.8528 - acc: 0.7666 - val_loss: 0.7288 - val_acc: 0.8290\n",
- "Epoch 5\n",
- "54000/54000 [==============================] - 1s - loss: 0.7187 - acc: 0.8191 - val_loss: 0.6122 - val_acc: 0.8603\n",
- "Epoch 6\n",
- "54000/54000 [==============================] - 1s - loss: 0.6278 - acc: 0.8426 - val_loss: 0.5347 - val_acc: 0.8762\n",
- "Epoch 7\n",
- "54000/54000 [==============================] - 1s - loss: 0.5592 - acc: 0.8621 - val_loss: 0.4707 - val_acc: 0.8920\n",
- "Epoch 8\n",
- "54000/54000 [==============================] - 1s - loss: 0.4978 - acc: 0.8751 - val_loss: 0.4288 - val_acc: 0.9033\n",
- "Epoch 9\n",
- "54000/54000 [==============================] - 1s - loss: 0.4583 - acc: 0.8847 - val_loss: 0.3935 - val_acc: 0.9035\n",
- "Epoch 10\n",
- "54000/54000 [==============================] - 1s - loss: 0.4213 - acc: 0.8911 - val_loss: 0.3553 - val_acc: 0.9088\n",
- "Epoch 11\n",
- "54000/54000 [==============================] - 1s - loss: 0.3972 - acc: 0.8955 - val_loss: 0.3405 - val_acc: 0.9083\n",
- "Epoch 12\n",
- "54000/54000 [==============================] - 1s - loss: 0.3740 - acc: 0.9022 - val_loss: 0.3251 - val_acc: 0.9170\n",
- "Epoch 13\n",
- "54000/54000 [==============================] - 1s - loss: 0.3611 - acc: 0.9030 - val_loss: 0.3032 - val_acc: 0.9183\n",
- "Epoch 14\n",
- "54000/54000 [==============================] - 1s - loss: 0.3479 - acc: 0.9064 - val_loss: 0.2972 - val_acc: 0.9248\n",
- "Epoch 15\n",
- "54000/54000 [==============================] - 1s - loss: 0.3309 - acc: 0.9099 - val_loss: 0.2778 - val_acc: 0.9250\n",
- "Epoch 16\n",
- "54000/54000 [==============================] - 1s - loss: 0.3264 - acc: 0.9103 - val_loss: 0.2838 - val_acc: 0.9208\n",
- "Epoch 17\n",
- "54000/54000 [==============================] - 1s - loss: 0.3136 - acc: 0.9136 - val_loss: 0.2689 - val_acc: 0.9223\n",
- "Epoch 18\n",
- "54000/54000 [==============================] - 1s - loss: 0.3031 - acc: 0.9156 - val_loss: 0.2634 - val_acc: 0.9313\n",
- "Epoch 19\n",
- "54000/54000 [==============================] - 1s - loss: 0.2988 - acc: 0.9169 - val_loss: 0.2579 - val_acc: 0.9288\n",
- "Epoch 20\n",
- "54000/54000 [==============================] - 1s - loss: 0.2909 - acc: 0.9180 - val_loss: 0.2494 - val_acc: 0.9310\n",
- "Epoch 21\n",
- "54000/54000 [==============================] - 1s - loss: 0.2848 - acc: 0.9202 - val_loss: 0.2478 - val_acc: 0.9307\n",
- "Epoch 22\n",
- "54000/54000 [==============================] - 1s - loss: 0.2804 - acc: 0.9194 - val_loss: 0.2423 - val_acc: 0.9343\n",
- "Epoch 23\n",
- "54000/54000 [==============================] - 1s - loss: 0.2728 - acc: 0.9235 - val_loss: 0.2387 - val_acc: 0.9327\n",
- "Epoch 24\n",
- "54000/54000 [==============================] - 1s - loss: 0.2673 - acc: 0.9241 - val_loss: 0.2265 - val_acc: 0.9385\n",
- "Epoch 25\n",
- "54000/54000 [==============================] - 1s - loss: 0.2611 - acc: 0.9253 - val_loss: 0.2270 - val_acc: 0.9347\n",
- "Epoch 26\n",
- "54000/54000 [==============================] - 1s - loss: 0.2676 - acc: 0.9225 - val_loss: 0.2210 - val_acc: 0.9367\n",
- "Epoch 27\n",
- "54000/54000 [==============================] - 1s - loss: 0.2528 - acc: 0.9261 - val_loss: 0.2241 - val_acc: 0.9373\n",
- "Epoch 28\n",
- "54000/54000 [==============================] - 1s - loss: 0.2511 - acc: 0.9264 - val_loss: 0.2170 - val_acc: 0.9403\n",
- "Epoch 29\n",
- "54000/54000 [==============================] - 1s - loss: 0.2433 - acc: 0.9293 - val_loss: 0.2165 - val_acc: 0.9412\n",
- "Epoch 30\n",
- "54000/54000 [==============================] - 1s - loss: 0.2465 - acc: 0.9279 - val_loss: 0.2135 - val_acc: 0.9367\n",
- "Epoch 31\n",
- "54000/54000 [==============================] - 1s - loss: 0.2383 - acc: 0.9306 - val_loss: 0.2138 - val_acc: 0.9427\n",
- "Epoch 32\n",
- "54000/54000 [==============================] - 1s - loss: 0.2349 - acc: 0.9310 - val_loss: 0.2066 - val_acc: 0.9423\n",
- "Epoch 33\n",
- "54000/54000 [==============================] - 1s - loss: 0.2301 - acc: 0.9334 - val_loss: 0.2054 - val_acc: 0.9440\n",
- "Epoch 34\n",
- "54000/54000 [==============================] - 1s - loss: 0.2371 - acc: 0.9317 - val_loss: 0.1991 - val_acc: 0.9480\n",
- "Epoch 35\n",
- "54000/54000 [==============================] - 1s - loss: 0.2256 - acc: 0.9352 - val_loss: 0.1982 - val_acc: 0.9450\n",
- "Epoch 36\n",
- "54000/54000 [==============================] - 1s - loss: 0.2313 - acc: 0.9323 - val_loss: 0.2092 - val_acc: 0.9403\n",
- "Epoch 37\n",
- "54000/54000 [==============================] - 1s - loss: 0.2230 - acc: 0.9341 - val_loss: 0.1993 - val_acc: 0.9445\n",
- "Epoch 38\n",
- "54000/54000 [==============================] - 1s - loss: 0.2261 - acc: 0.9336 - val_loss: 0.1891 - val_acc: 0.9463\n",
- "Epoch 39\n",
- "54000/54000 [==============================] - 1s - loss: 0.2166 - acc: 0.9369 - val_loss: 0.1943 - val_acc: 0.9452\n",
- "Epoch 40\n",
- "54000/54000 [==============================] - 1s - loss: 0.2128 - acc: 0.9370 - val_loss: 0.1952 - val_acc: 0.9435\n",
- "Epoch 41\n",
- "54000/54000 [==============================] - 1s - loss: 0.2200 - acc: 0.9351 - val_loss: 0.1918 - val_acc: 0.9468\n",
- "Epoch 42\n",
- "54000/54000 [==============================] - 2s - loss: 0.2107 - acc: 0.9383 - val_loss: 0.1831 - val_acc: 0.9483\n",
- "Epoch 43\n",
- "54000/54000 [==============================] - 1s - loss: 0.2020 - acc: 0.9411 - val_loss: 0.1906 - val_acc: 0.9443\n",
- "Epoch 44\n",
- "54000/54000 [==============================] - 1s - loss: 0.2082 - acc: 0.9388 - val_loss: 0.1838 - val_acc: 0.9457\n",
- "Epoch 45\n",
- "54000/54000 [==============================] - 1s - loss: 0.2048 - acc: 0.9402 - val_loss: 0.1817 - val_acc: 0.9488\n",
- "Epoch 46\n",
- "54000/54000 [==============================] - 1s - loss: 0.2012 - acc: 0.9417 - val_loss: 0.1876 - val_acc: 0.9480\n",
- "Epoch 47\n",
- "54000/54000 [==============================] - 1s - loss: 0.1996 - acc: 0.9423 - val_loss: 0.1792 - val_acc: 0.9502\n",
- "Epoch 48\n",
- "54000/54000 [==============================] - 1s - loss: 0.1921 - acc: 0.9430 - val_loss: 0.1791 - val_acc: 0.9505\n",
- "Epoch 49\n",
- "54000/54000 [==============================] - 1s - loss: 0.1907 - acc: 0.9432 - val_loss: 0.1749 - val_acc: 0.9482\n"
+ "Epoch 1/50\n",
+ "54000/54000 [==============================] - 0s - loss: 2.2244 - acc: 0.4249 - val_loss: 2.1019 - val_acc: 0.5712\n",
+ "Epoch 2/50\n",
+ "54000/54000 [==============================] - 0s - loss: 1.8746 - acc: 0.5297 - val_loss: 1.5900 - val_acc: 0.5878\n",
+ "Epoch 3/50\n",
+ "54000/54000 [==============================] - 0s - loss: 1.3850 - acc: 0.6134 - val_loss: 1.1486 - val_acc: 0.7050\n",
+ "Epoch 4/50\n",
+ "54000/54000 [==============================] - 0s - loss: 1.0424 - acc: 0.7195 - val_loss: 0.8696 - val_acc: 0.7933\n",
+ "Epoch 5/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.8236 - acc: 0.7928 - val_loss: 0.6872 - val_acc: 0.8498\n",
+ "Epoch 6/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.6819 - acc: 0.8328 - val_loss: 0.5700 - val_acc: 0.8740\n",
+ "Epoch 7/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.5867 - acc: 0.8578 - val_loss: 0.5052 - val_acc: 0.8860\n",
+ "Epoch 8/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.5203 - acc: 0.8702 - val_loss: 0.4494 - val_acc: 0.8937\n",
+ "Epoch 9/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.4751 - acc: 0.8822 - val_loss: 0.4112 - val_acc: 0.9020\n",
+ "Epoch 10/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.4382 - acc: 0.8884 - val_loss: 0.3725 - val_acc: 0.9122\n",
+ "Epoch 11/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.4086 - acc: 0.8953 - val_loss: 0.3575 - val_acc: 0.9130\n",
+ "Epoch 12/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3889 - acc: 0.8981 - val_loss: 0.3393 - val_acc: 0.9118\n",
+ "Epoch 13/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3724 - acc: 0.9022 - val_loss: 0.3082 - val_acc: 0.9207\n",
+ "Epoch 14/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3490 - acc: 0.9082 - val_loss: 0.3083 - val_acc: 0.9202\n",
+ "Epoch 15/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3361 - acc: 0.9095 - val_loss: 0.2950 - val_acc: 0.9200\n",
+ "Epoch 16/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3279 - acc: 0.9108 - val_loss: 0.2829 - val_acc: 0.9262\n",
+ "Epoch 17/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3210 - acc: 0.9125 - val_loss: 0.2850 - val_acc: 0.9290\n",
+ "Epoch 18/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3067 - acc: 0.9166 - val_loss: 0.2699 - val_acc: 0.9275\n",
+ "Epoch 19/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3006 - acc: 0.9173 - val_loss: 0.2502 - val_acc: 0.9380\n",
+ "Epoch 20/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2932 - acc: 0.9198 - val_loss: 0.2603 - val_acc: 0.9313\n",
+ "Epoch 21/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2859 - acc: 0.9201 - val_loss: 0.2457 - val_acc: 0.9325\n",
+ "Epoch 22/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2804 - acc: 0.9217 - val_loss: 0.2551 - val_acc: 0.9348\n",
+ "Epoch 23/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2735 - acc: 0.9233 - val_loss: 0.2457 - val_acc: 0.9342\n",
+ "Epoch 24/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2790 - acc: 0.9220 - val_loss: 0.2364 - val_acc: 0.9380\n",
+ "Epoch 25/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2747 - acc: 0.9227 - val_loss: 0.2361 - val_acc: 0.9345\n",
+ "Epoch 26/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2648 - acc: 0.9254 - val_loss: 0.2311 - val_acc: 0.9357\n",
+ "Epoch 27/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2627 - acc: 0.9249 - val_loss: 0.2319 - val_acc: 0.9343\n",
+ "Epoch 28/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2556 - acc: 0.9280 - val_loss: 0.2322 - val_acc: 0.9352\n",
+ "Epoch 29/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2599 - acc: 0.9264 - val_loss: 0.2249 - val_acc: 0.9410\n",
+ "Epoch 30/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2500 - acc: 0.9290 - val_loss: 0.2164 - val_acc: 0.9398\n",
+ "Epoch 31/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2460 - acc: 0.9291 - val_loss: 0.2132 - val_acc: 0.9425\n",
+ "Epoch 32/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2438 - acc: 0.9301 - val_loss: 0.2085 - val_acc: 0.9440\n",
+ "Epoch 33/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2366 - acc: 0.9323 - val_loss: 0.2104 - val_acc: 0.9437\n",
+ "Epoch 34/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2348 - acc: 0.9331 - val_loss: 0.2157 - val_acc: 0.9437\n",
+ "Epoch 35/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2333 - acc: 0.9323 - val_loss: 0.2109 - val_acc: 0.9405\n",
+ "Epoch 36/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2286 - acc: 0.9341 - val_loss: 0.2056 - val_acc: 0.9422\n",
+ "Epoch 37/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2261 - acc: 0.9353 - val_loss: 0.2144 - val_acc: 0.9448\n",
+ "Epoch 38/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2226 - acc: 0.9358 - val_loss: 0.2005 - val_acc: 0.9442\n",
+ "Epoch 39/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2192 - acc: 0.9365 - val_loss: 0.1990 - val_acc: 0.9450\n",
+ "Epoch 40/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2211 - acc: 0.9356 - val_loss: 0.2066 - val_acc: 0.9413\n",
+ "Epoch 41/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2237 - acc: 0.9356 - val_loss: 0.2165 - val_acc: 0.9380\n",
+ "Epoch 42/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2166 - acc: 0.9365 - val_loss: 0.1952 - val_acc: 0.9465\n",
+ "Epoch 43/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2109 - acc: 0.9392 - val_loss: 0.1905 - val_acc: 0.9492\n",
+ "Epoch 44/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2058 - acc: 0.9413 - val_loss: 0.1939 - val_acc: 0.9448\n",
+ "Epoch 45/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2075 - acc: 0.9398 - val_loss: 0.1934 - val_acc: 0.9448\n",
+ "Epoch 46/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2110 - acc: 0.9384 - val_loss: 0.1901 - val_acc: 0.9453\n",
+ "Epoch 47/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2048 - acc: 0.9406 - val_loss: 0.1833 - val_acc: 0.9493\n",
+ "Epoch 48/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2013 - acc: 0.9411 - val_loss: 0.1846 - val_acc: 0.9493\n",
+ "Epoch 49/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2014 - acc: 0.9414 - val_loss: 0.1854 - val_acc: 0.9487\n",
+ "Epoch 50/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.1942 - acc: 0.9436 - val_loss: 0.1822 - val_acc: 0.9482\n"
]
},
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 49,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
@@ -1440,87 +1417,57 @@
"\n",
"model = Sequential()\n",
"model.add(Dense(input_dim=X_train.shape[1], \n",
- " output_dim=50, \n",
- " init='uniform', \n",
+ " units=50, # formerly output_dim=50 in Keras < 2\n",
+ " kernel_initializer='uniform', # formerly init='uniform' in Keras < 2\n",
" activation='tanh'))\n",
"\n",
"model.add(Dense(input_dim=50, \n",
- " output_dim=50, \n",
- " init='uniform', \n",
+ " units=50, \n",
+ " kernel_initializer='uniform', \n",
" activation='tanh'))\n",
"\n",
"model.add(Dense(input_dim=50, \n",
- " output_dim=y_train_ohe.shape[1], \n",
- " init='uniform', \n",
+ " units=y_train_ohe.shape[1],\n",
+ " kernel_initializer='uniform', \n",
" activation='softmax'))\n",
"\n",
"sgd = SGD(lr=0.001, decay=1e-7, momentum=.9)\n",
- "model.compile(loss='categorical_crossentropy', optimizer=sgd)\n",
+ "model.compile(loss='categorical_crossentropy', \n",
+ " optimizer=sgd, \n",
+ " metrics=['accuracy'])\n",
"\n",
"model.fit(X_train, y_train_ohe, \n",
- " nb_epoch=50, \n",
+ " epochs=50, # Keras 2: former nb_epoch has been renamed to epochs\n",
" batch_size=300, \n",
" verbose=1, \n",
- " validation_split=0.1, \n",
- " show_accuracy=True)"
+ " validation_split=0.1) \n",
+ " # removed former show_accuracy=True \n",
+ " # and added `metrics=['accuracy']` to the\n",
+ " # model.compile call for Keras >= 2"
]
},
{
"cell_type": "code",
- "execution_count": 50,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 34,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "First 3 predictions: [5 0 4]\n"
+ "First 3 predictions: [5 0 4]\n",
+ "Training accuracy: 94.43%\n",
+ "Test accuracy: 93.90%\n"
]
}
],
"source": [
"y_train_pred = model.predict_classes(X_train, verbose=0)\n",
- "print('First 3 predictions: ', y_train_pred[:3])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Training accuracy: 94.51%\n"
- ]
- }
- ],
- "source": [
+ "print('First 3 predictions: ', y_train_pred[:3])\n",
+ "\n",
"train_acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]\n",
- "print('Training accuracy: %.2f%%' % (train_acc * 100))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Test accuracy: 94.39%\n"
- ]
- }
- ],
- "source": [
+ "print('Training accuracy: %.2f%%' % (train_acc * 100))\n",
+ "\n",
"y_test_pred = model.predict_classes(X_test, verbose=0)\n",
"test_acc = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0]\n",
"print('Test accuracy: %.2f%%' % (test_acc * 100))"
@@ -1552,6 +1499,7 @@
}
],
"metadata": {
+ "anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
@@ -1567,9 +1515,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.6.1"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/code/datasets/iris/iris.data b/code/datasets/iris/iris.data
index 5c4316cd..396653cc 100644
--- a/code/datasets/iris/iris.data
+++ b/code/datasets/iris/iris.data
@@ -147,5 +147,4 @@
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
-5.9,3.0,5.1,1.8,Iris-virginica
-
+5.9,3.0,5.1,1.8,Iris-virginica
\ No newline at end of file
diff --git a/code/datasets/iris/iris.names.txt b/code/datasets/iris/iris.names.txt
deleted file mode 100644
index 062b486d..00000000
--- a/code/datasets/iris/iris.names.txt
+++ /dev/null
@@ -1,69 +0,0 @@
-1. Title: Iris Plants Database
- Updated Sept 21 by C.Blake - Added discrepency information
-
-2. Sources:
- (a) Creator: R.A. Fisher
- (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
- (c) Date: July, 1988
-
-3. Past Usage:
- - Publications: too many to mention!!! Here are a few.
- 1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
- Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
- to Mathematical Statistics" (John Wiley, NY, 1950).
- 2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
- (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
- 3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
- Structure and Classification Rule for Recognition in Partially Exposed
- Environments". IEEE Transactions on Pattern Analysis and Machine
- Intelligence, Vol. PAMI-2, No. 1, 67-71.
- -- Results:
- -- very low misclassification rates (0% for the setosa class)
- 4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE
- Transactions on Information Theory, May 1972, 431-433.
- -- Results:
- -- very low misclassification rates again
- 5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II
- conceptual clustering system finds 3 classes in the data.
-
-4. Relevant Information:
- --- This is perhaps the best known database to be found in the pattern
- recognition literature. Fisher's paper is a classic in the field
- and is referenced frequently to this day. (See Duda & Hart, for
- example.) The data set contains 3 classes of 50 instances each,
- where each class refers to a type of iris plant. One class is
- linearly separable from the other 2; the latter are NOT linearly
- separable from each other.
- --- Predicted attribute: class of iris plant.
- --- This is an exceedingly simple domain.
- --- This data differs from the data presented in Fishers article
- (identified by Steve Chadwick, spchadwick@espeedaz.net )
- The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa"
- where the error is in the fourth feature.
- The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa"
- where the errors are in the second and third features.
-
-5. Number of Instances: 150 (50 in each of three classes)
-
-6. Number of Attributes: 4 numeric, predictive attributes and the class
-
-7. Attribute Information:
- 1. sepal length in cm
- 2. sepal width in cm
- 3. petal length in cm
- 4. petal width in cm
- 5. class:
- -- Iris Setosa
- -- Iris Versicolour
- -- Iris Virginica
-
-8. Missing Attribute Values: None
-
-Summary Statistics:
- Min Max Mean SD Class Correlation
- sepal length: 4.3 7.9 5.84 0.83 0.7826
- sepal width: 2.0 4.4 3.05 0.43 -0.4194
- petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
- petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
-
-9. Class Distribution: 33.3% for each of 3 classes.
diff --git a/code/optional-py-scripts/ch03.py b/code/optional-py-scripts/ch03.py
index 360d1747..3edd740c 100644
--- a/code/optional-py-scripts/ch03.py
+++ b/code/optional-py-scripts/ch03.py
@@ -20,9 +20,10 @@
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
-from sklearn.tree import export_graphviz
+# from sklearn.tree import export_graphviz
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
+import warnings
# for sklearn 0.18's alternative syntax
from distutils.version import LooseVersion as Version
@@ -108,6 +109,7 @@ def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
marker='o',
s=55, label='test set')
+
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
@@ -130,6 +132,7 @@ def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
def sigmoid(z):
return 1.0 / (1.0 + np.exp(-z))
+
z = np.arange(-7, 7, 0.1)
phi_z = sigmoid(z)
@@ -161,6 +164,7 @@ def cost_1(z):
def cost_0(z):
return - np.log(1 - sigmoid(z))
+
z = np.arange(-10, 10, 0.1)
phi_z = sigmoid(z)
@@ -207,7 +211,7 @@ def cost_0(z):
print(50 * '-')
weights, params = [], []
-for c in np.arange(-5, 5):
+for c in np.arange(-5.0, 5.0):
lr = LogisticRegression(C=10**c, random_state=0)
lr.fit(X_train_std, y_train)
weights.append(lr.coef_[1])
@@ -331,6 +335,7 @@ def entropy(p):
def error(p):
return 1 - np.max([p, 1 - p])
+
x = np.arange(0.0, 1.0, 0.01)
ent = [entropy(p) if p != 0 else None for p in x]
diff --git a/code/optional-py-scripts/ch04.py b/code/optional-py-scripts/ch04.py
index 65ba49e9..68e75873 100644
--- a/code/optional-py-scripts/ch04.py
+++ b/code/optional-py-scripts/ch04.py
@@ -239,7 +239,7 @@
'gray', 'indigo', 'orange']
weights, params = [], []
-for c in np.arange(-4, 6):
+for c in np.arange(-4.0, 6.0):
lr = LogisticRegression(penalty='l1', C=10**c, random_state=0)
lr.fit(X_train_std, y_train)
weights.append(lr.coef_[1])
diff --git a/code/optional-py-scripts/ch05.py b/code/optional-py-scripts/ch05.py
index e1ae554b..215b0150 100644
--- a/code/optional-py-scripts/ch05.py
+++ b/code/optional-py-scripts/ch05.py
@@ -17,7 +17,7 @@
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LogisticRegression
-from sklearn.lda import LDA
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.datasets import make_moons
from sklearn.datasets import make_circles
from sklearn.decomposition import KernelPCA
@@ -175,6 +175,7 @@ def plot_decision_regions(X, y, classifier, resolution=0.02):
alpha=0.8, c=cmap(idx),
marker=markers[idx], label=cl)
+
lr = LogisticRegression()
lr = lr.fit(X_train_pca, y_train)
@@ -596,6 +597,7 @@ def project_x(x_new, X, gamma, alphas, lambdas):
k = np.exp(-gamma * pair_dist)
return k.dot(alphas / lambdas)
+
# projection of the "new" datapoint
x_reproj = project_x(x_new, X, gamma=15, alphas=alphas, lambdas=lambdas)
print('Reprojection x_reproj:', x_reproj)
diff --git a/code/optional-py-scripts/ch08.py b/code/optional-py-scripts/ch08.py
index 4f1a0c05..c50145c5 100644
--- a/code/optional-py-scripts/ch08.py
+++ b/code/optional-py-scripts/ch08.py
@@ -127,6 +127,7 @@ def preprocessor(text):
' '.join(emoticons).replace('-', '')
return text
+
print('Preprocessor on Excerpt:\n\n', preprocessor(df.loc[0, 'review'][-50:]))
res = preprocessor("This :) is :( a test :-)!")
@@ -246,6 +247,7 @@ def stream_docs(path):
text, label = line[:-3], int(line[-2])
yield text, label
+
next(stream_docs(path='./movie_data.csv'))
diff --git a/code/optional-py-scripts/ch10.py b/code/optional-py-scripts/ch10.py
index f2306118..17e8eb98 100644
--- a/code/optional-py-scripts/ch10.py
+++ b/code/optional-py-scripts/ch10.py
@@ -139,6 +139,7 @@ def lin_regplot(X, y, model):
plt.plot(X, model.predict(X), color='red', linewidth=2)
return
+
lin_regplot(X_std, y_std, lr)
plt.xlabel('Average number of rooms [RM] (standardized)')
plt.ylabel('Price in $1000\'s [MEDV] (standardized)')
@@ -196,7 +197,8 @@ def lin_regplot(X, y, model):
ransac = RANSACRegressor(LinearRegression(),
max_trials=100,
min_samples=50,
- residual_metric=lambda x: np.sum(np.abs(x), axis=1),
+ residual_metric=lambda x: np.sum(
+ np.abs(x), axis=1),
residual_threshold=5.0,
random_state=0)
else:
diff --git a/code/optional-py-scripts/ch13.py b/code/optional-py-scripts/ch13.py
index a28eebc0..57941805 100644
--- a/code/optional-py-scripts/ch13.py
+++ b/code/optional-py-scripts/ch13.py
@@ -188,6 +188,7 @@ def predict_linreg(X, w):
predict = theano.function(inputs=[Xt], givens={w: w}, outputs=net_input)
return predict(X)
+
plt.scatter(X_train, y_train, marker='s', s=50)
plt.plot(range(X_train.shape[0]),
predict_linreg(X_train, w),
@@ -229,6 +230,7 @@ def logistic_activation(X, w):
z = net_input(X, w)
return logistic(z)
+
print('P(y=1|x) = %.3f' % logistic_activation(X, w)[0])
@@ -274,6 +276,7 @@ def softmax_activation(X, w):
z = net_input(X, w)
return softmax(z)
+
y_probas = softmax(Z)
print('Probabilities:\n', y_probas)
@@ -294,6 +297,7 @@ def tanh(z):
e_m = np.exp(-z)
return (e_p - e_m) / (e_p + e_m)
+
z = np.arange(-5, 5, 0.005)
log_act = logistic(z)
tanh_act = tanh(z)
@@ -359,6 +363,7 @@ def load_mnist(path, kind='train'):
return images, labels
+
X_train, y_train = load_mnist('mnist', kind='train')
print('Training rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))
diff --git a/docs/errata.md b/docs/errata.md
index 283bde59..ff4ce1bb 100644
--- a/docs/errata.md
+++ b/docs/errata.md
@@ -18,7 +18,7 @@ I would be happy if you just write me a short [mail](mailto:mail@sebastianraschk
## Donations
-- Current amount for the next donation: $5.00
+- Current amount for the next donation: $7.00
- Amount donated to charity:
- [$39.00 2016-04-07](./2016-04-07-unicef.pdf)
- [$76.00 2016-03-03](./2016-03-03-unicef.pdf)
@@ -47,6 +47,7 @@ I would be happy if you just write me a short [mail](mailto:mail@sebastianraschk
19. F. Liu ($2.00)
20. Stefan P. ($2.00)
32. Adam S. ($2.00)
+33. Harry Hummel ($2.00)
11. Elias R. ($1.00)
12. Haitham H. Saleh ($1.00)
13. Muqueet M. ($1.00)
@@ -71,6 +72,7 @@ I would be happy if you just write me a short [mail](mailto:mail@sebastianraschk
+
...
# Errata
diff --git a/docs/references.md b/docs/references.md
index 3ddbd28e..92985d15 100644
--- a/docs/references.md
+++ b/docs/references.md
@@ -26,6 +26,8 @@ A BibTeX version for your favorite reference manager is available [here](./pymle
##### Additional Resources & Further Reading
+- [Python Tutorial](https://www.scaler.com/topics/python)
+
+
+
+
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+
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diff --git a/faq/evaluate-a-model/evaluate_overview.pdf b/faq/evaluate-a-model/evaluate_overview.pdf
new file mode 100644
index 00000000..71ba9eed
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diff --git a/faq/linear-gradient-derivative/23.png b/faq/linear-gradient-derivative/23.png
index 380588c9..25c38b72 100644
Binary files a/faq/linear-gradient-derivative/23.png and b/faq/linear-gradient-derivative/23.png differ
diff --git a/faq/naive-bayes-vartypes.md b/faq/naive-bayes-vartypes.md
index c64faab5..201eff3f 100644
--- a/faq/naive-bayes-vartypes.md
+++ b/faq/naive-bayes-vartypes.md
@@ -42,7 +42,7 @@ To come back to the original question, let us consider the multi-variate Bernoul
We use the Bernoulli distribution to compute the likelihood of a binary variable.
-For example, we could estimate P(xk =1 | ωj ) via MLE as the frequency of occurences in the training set:
+For example, we could estimate P(xk =1 | ωj ) via MLE as the frequency of occurrences in the training set:
θ = P̂(xk =1 | ωj ) = Nxk , ωj / N ωj
which reads "number of training samples in class ωj that have the property xk =1 (Nxk , ωj ) divided by by all training samples in ωj (N ωj )." In context of text classification, this is basically the set of documents in class ωj that contain a particular word divided by all documents in ωj .
Now, we can compute the likelihood of the binary feature vector **x** given class ωj as
diff --git a/faq/scale-training-test.md b/faq/scale-training-test.md
index 29b8f707..970b83e9 100644
--- a/faq/scale-training-test.md
+++ b/faq/scale-training-test.md
@@ -59,7 +59,7 @@ Now, let's say our model has learned the following hypotheses: It classifies sam
- sample5: 6 cm -> class ?
- sample6: 7 cm -> class ?
-If we look at the "unstandardized “length in cm" values in our training datast, it is intuitive to say that all of these samples are likely belonging to class 2. However, if we standardize these by re-computing the *standard deviation* and and *mean* from the new data, we would get similar values as before (i.e., properties of a standard normal distribtion) in the training set and our classifier would (probably incorrectly) assign the “class 2” label to the samples 4 and 5.
+If we look at the "unstandardized “length in cm" values in our training dataset, it is intuitive to say that all of these samples are likely belonging to class 2. However, if we standardize these by re-computing the *standard deviation* and and *mean* from the new data, we would get similar values as before (i.e., properties of a standard normal distribution) in the training set and our classifier would (probably incorrectly) assign the “class 2” label to the samples 4 and 5.
- sample5: -1.21 -> class 2
- sample6: 0 -> class 2
diff --git a/faq/tensorflow-vs-scikitlearn.md b/faq/tensorflow-vs-scikitlearn.md
index 38e2a9dd..57cae08d 100644
--- a/faq/tensorflow-vs-scikitlearn.md
+++ b/faq/tensorflow-vs-scikitlearn.md
@@ -37,7 +37,7 @@ lr.fit(X, y)
In addition, I have a little helper function to plot the 2D decision surface:
```python
-from mlxtend.evaluate import plot_decision_regions
+from mlxtend.plotting import plot_decision_regions
plot_decision_regions(X, y, clf=lr)
plt.title('Softmax Regression in scikit-learn')
@@ -131,7 +131,7 @@ plt.show()
```python
-from mlxtend.evaluate import plot_decision_regions
+from mlxtend.plotting import plot_decision_regions
plot_decision_regions(X, y, clf=lr)
plt.title('Softmax Regression via Gradient Descent in TensorFlow')
@@ -139,3 +139,7 @@ plt.show()
```
+
+**Note**
+
+I've removed the TensorFlow code from mlxtend because it became pretty inconvenient to maintain. The original code should still be available through GitHub. E.g., if you install mlxtend 0.5.1, (`pip install mlxtend=0.5.1`) or browse through the files here: https://github.com/rasbt/mlxtend/tree/86e40d5af5222d78acf219cc8188cfd28a972d9e/mlxtend/tf_classifier
diff --git a/faq/underscore-convention.md b/faq/underscore-convention.md
index 01307e04..35937b44 100644
--- a/faq/underscore-convention.md
+++ b/faq/underscore-convention.md
@@ -18,14 +18,15 @@ instead of
def sigmoid(self, z):
return 1.0 / (1.0 + np.exp(-z))
-The short answer is, the trailing underscore (`self.gamma_`) in class attributes is a scikit-learn convention to denote "estimated" or "fitted" attributes. The leading underscores are (`_sigmoid(self, z)`) denote private methods that the user shouldn't bother with.
+The short answer is, the trailing underscore (`self.gamma_`) in class attributes is a scikit-learn convention to denote "estimated" or "fitted" attributes.
+The leading underscores are (`_sigmoid(self, z)`) denote private methods that the user should not bother with.
In brief: As a reader, you can safely ignore those underscores, however, if you are curious about their intention, please read on!
## Leading underscores in class methods
-The usage of underscores for naming class methods is a common Python convention to distinguish between private and public methods. Basically, you don't want the user to worry about these private methods, which is why they don't appear in the help menu.
+The usage of underscores for naming class methods is a common Python convention to distinguish between private and public methods. Basically, you do not want the user to worry about these private methods, which is why they do not appear in the help menu.
class MyClass(object):
def __init__(self, param='some_value'):
@@ -67,7 +68,7 @@ The usage of underscores for naming class methods is a common Python convention
* Note that `__init__` is an exception, `__init__` is a special method that is required to initialize a class.
-Let’s initialize a new object and call this “public” class:
+Let us initialize a new object and call this "public" class:
>>> MyObj = MyClass()
>>> MyObj.public()
@@ -81,7 +82,7 @@ The single underscore in `_indicate_private` indicates privacy. Typically, priva
- Please keep in mind that calling private methods is at your own risk; the developers usually take no responsibilities for odd things that may happen if you call private methods as a user.
-The indication of “privacy” is a bit stronger if we use 2 preceding underscores, for example, calling the `__pseudo_private` method directly like a regular method doesn’t work anymore:
+The indication of "privacy" is a bit stronger if we use 2 preceding underscores, for example, calling the `__pseudo_private` method directly like a regular method does not work anymore:
>>> MyObj.__pseudo_private()
---------------------------------------------------------------------------
@@ -91,7 +92,7 @@ The indication of “privacy” is a bit stronger if we use 2 preceding undersco
AttributeError: 'MyClass' object has no attribute '__pseudo_private'
-To call the a private methods that is prefaced with 2 underscores, we need to adhere to the “name mangling” rules; that is, we need to add a `_classname` prefix, to call the method, for example,
+To call the a private methods that is prefaced with 2 underscores, we need to adhere to the "name mangling" rules; that is, we need to add a `_classname` prefix, to call the method, for example,
>>> MyObj._MyClass__pseudo_private()
'really private method'
@@ -100,16 +101,16 @@ To call the a private methods that is prefaced with 2 underscores, we need to ad
## Class attributes with trailing underscores
-In contrast to the leading underscore, the trailing underscores in class attributes don't any "technical" effects. In fact, this is just a convention that I adopted from scikit-learn out of habit.
+In contrast to the leading underscore, the trailing underscores in class attributes do not have any "technical" effects. In fact, this is just a convention that I adopted from scikit-learn out of habit.
Here are two excerpts from the scikit-learn [developer/contributor documentation](http://scikit-learn.org/stable/developers/):
-> Attributes that have been estimated from the data must always have a name ending with trailing underscore, for example, the coefficients of some regression estimator would be stored in a coef_ attribute after fit has been called.
+> Attributes that have been estimated from the data must always have a name ending with trailing underscore `_`, for example, the coefficients of some regression estimator would be stored in a `coef_` attribute after `fit()` has been called.
-> Also it is expected that parameters with trailing _ are not to be set inside the ``__init__`` method. All and only the public attributes set by fit have a trailing _. As a result the existence of parameters with trailing _ is used to check if the estimator has been fitted.
+> Also it is expected that parameters with trailing underscore `_` are not to be set inside the ``__init__`` method. All and only the public attributes set by `fit()` have a trailing `_`. As a result the existence of parameters with trailing `_` is used to check if the estimator has been fitted.
-To see it in action, let's create a primitive `Estimator`:
+To see it in action, let us create a primitive `Estimator`:
class MyEstimator():
def __init__(self):
@@ -118,7 +119,7 @@ To see it in action, let's create a primitive `Estimator`:
def fit(self):
self.fit_param_ = 0.1
-Intuitively, attributes that are in `__init__` are accessible after we initialized a new object
+Intuitively, attributes that are in `__init__` are accessible after we initialized a new object:
>>> est = MyEstimator()
>>> est.param
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