Accompanying code examples of the book "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python" by Sebastian Raschka. All code examples are released under the MIT license. If you find this content useful, please consider supporting the work by buying a copy of the book.

Other code examples and content are available on GitHub. The PDF and ebook versions of the book are available through Leanpub.

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In [1]:

%watermark -a 'Sebastian Raschka' -v -p tensorflow

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Sebastian Raschka

CPython 3.6.1
IPython 6.0.0

tensorflow 1.2.0

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# Model Zoo -- Logistic Regression

Implementation of classic logistic regression for binary class labels.

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In [2]:

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from io import BytesIO

##########################
### DATASET
##########################

ds = np.lib.DataSource()
fp = ds.open('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data')

x = np.genfromtxt(BytesIO(fp.read().encode()), delimiter=',', usecols=range(2), max_rows=100)
y = np.zeros(100)
y[50:] = 1

np.random.seed(1)
idx = np.arange(y.shape[0])
np.random.shuffle(idx)
x_test, y_test = x[idx[:25]], y[idx[:25]]
x_train, y_train = x[idx[25:]], y[idx[25:]]
mu, std = np.mean(x_train, axis=0), np.std(x_train, axis=0)
x_train, x_test = (x_train - mu) / std, (x_test - mu) / std

fig, ax = plt.subplots(1, 2, figsize=(7, 2.5))
ax[0].scatter(x_train[y_train == 1, 0], x_train[y_train == 1, 1])
ax[0].scatter(x_train[y_train == 0, 0], x_train[y_train == 0, 1])
ax[1].scatter(x_test[y_test == 1, 0], x_test[y_test == 1, 1])
ax[1].scatter(x_test[y_test == 0, 0], x_test[y_test == 0, 1])
plt.show()

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In [3]:

##########################
### HELPER FUNCTIONS
##########################

def iterate_minibatches(arrays, batch_size, shuffle=False, seed=None):
rgen = np.random.RandomState(seed)
indices = np.arange(arrays[0].shape[0])

if shuffle:
rgen.shuffle(indices)

for start_idx in range(0, indices.shape[0] - batch_size + 1, batch_size):
index_slice = indices[start_idx:start_idx + batch_size]

yield (ary[index_slice] for ary in arrays)

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In [4]:

import tensorflow as tf

##########################
### SETTINGS
##########################

n_features = x.shape[1]
n_samples = x.shape[0]
learning_rate = 0.05
training_epochs = 15
batch_size = 10

##########################
### GRAPH DEFINITION
##########################

g = tf.Graph()
with g.as_default() as g:

# Input data
tf_x = tf.placeholder(dtype=tf.float32,
shape=[None, n_features], name='inputs')
tf_y = tf.placeholder(dtype=tf.float32,
shape=[None], name='targets')

# Model parameters
params = {
'weights': tf.Variable(tf.zeros(shape=[n_features, 1],
dtype=tf.float32), name='weights'),
'bias': tf.Variable([[0.]], dtype=tf.float32, name='bias')}

# Logistic Regression
linear = tf.matmul(tf_x, params['weights']) + params['bias']
pred_proba = tf.sigmoid(linear, name='predict_probas')

# Loss and optimizer
r = tf.reshape(pred_proba, [-1])
cost = tf.reduce_mean(tf.reduce_sum((-tf_y * tf.log(r)) -
((1. - tf_y) * tf.log(1. - r))), name='cost')
train = optimizer.minimize(cost, name='train')

# Class prediction
pred_labels = tf.round(tf.reshape(pred_proba, [-1]), name='predict_labels')
correct_prediction = tf.equal(tf_y, pred_labels)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')

##########################
### TRAINING & EVALUATION
##########################

with tf.Session(graph=g) as sess:
sess.run(tf.global_variables_initializer())

avg_cost = np.nan
count = 1

for epoch in range(training_epochs):

train_acc = sess.run('accuracy:0', feed_dict={tf_x: x_train,
tf_y: y_train})
valid_acc = sess.run('accuracy:0', feed_dict={tf_x: x_test,
tf_y: y_test})

print("Epoch: %03d | AvgCost: %.3f" % (epoch, avg_cost / count), end="")
print(" | Train/Valid ACC: %.2f/%.2f" % (train_acc, valid_acc))

avg_cost = 0.
for x_batch, y_batch in iterate_minibatches(arrays=[x_train, y_train],
batch_size=batch_size,
shuffle=True, seed=123):

feed_dict = {'inputs:0': x_batch,
'targets:0': y_batch}
_, c = sess.run(['train', 'cost:0'], feed_dict=feed_dict)

avg_cost += c
count += 1

weights, bias = sess.run(['weights:0', 'bias:0'])
print('\nWeights:\n', weights)
print('\nBias:\n', bias)

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Epoch: 000 | AvgCost: nan | Train/Valid ACC: 0.53/0.40
Epoch: 001 | AvgCost: 4.221 | Train/Valid ACC: 1.00/1.00
Epoch: 002 | AvgCost: 1.225 | Train/Valid ACC: 1.00/1.00
Epoch: 003 | AvgCost: 0.610 | Train/Valid ACC: 1.00/1.00
Epoch: 004 | AvgCost: 0.376 | Train/Valid ACC: 1.00/1.00
Epoch: 005 | AvgCost: 0.259 | Train/Valid ACC: 1.00/1.00
Epoch: 006 | AvgCost: 0.191 | Train/Valid ACC: 1.00/1.00
Epoch: 007 | AvgCost: 0.148 | Train/Valid ACC: 1.00/1.00
Epoch: 008 | AvgCost: 0.119 | Train/Valid ACC: 1.00/1.00
Epoch: 009 | AvgCost: 0.098 | Train/Valid ACC: 1.00/1.00
Epoch: 010 | AvgCost: 0.082 | Train/Valid ACC: 1.00/1.00
Epoch: 011 | AvgCost: 0.070 | Train/Valid ACC: 1.00/1.00
Epoch: 012 | AvgCost: 0.061 | Train/Valid ACC: 1.00/1.00
Epoch: 013 | AvgCost: 0.053 | Train/Valid ACC: 1.00/1.00
Epoch: 014 | AvgCost: 0.047 | Train/Valid ACC: 1.00/1.00

Weights:
[[ 3.31176686]
[-2.40808702]]

Bias:
[[-0.01001291]]

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