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.
In [1]:
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p tensorflow
Implementation of softmax regression (multinomial logistic regression).
In [2]:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
##########################
### DATASET
##########################
mnist = input_data.read_data_sets("./", one_hot=True)
##########################
### SETTINGS
##########################
# Hyperparameters
learning_rate = 0.5
training_epochs = 30
batch_size = 256
# Architecture
n_features = 784
n_classes = 10
##########################
### GRAPH DEFINITION
##########################
g = tf.Graph()
with g.as_default():
# Input data
tf_x = tf.placeholder(tf.float32, [None, n_features])
tf_y = tf.placeholder(tf.float32, [None, n_classes])
# Model parameters
params = {
'weights': tf.Variable(tf.zeros(shape=[n_features, n_classes],
dtype=tf.float32), name='weights'),
'bias': tf.Variable([[n_classes]], dtype=tf.float32, name='bias')}
# Softmax regression
linear = tf.matmul(tf_x, params['weights']) + params['bias']
pred_proba = tf.nn.softmax(linear, name='predict_probas')
# Loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits=linear, labels=tf_y), name='cost')
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
train = optimizer.minimize(cost, name='train')
# Class prediction
pred_labels = tf.argmax(pred_proba, 1, name='predict_labels')
correct_prediction = tf.equal(tf.argmax(tf_y, 1), 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())
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = mnist.train.num_examples // batch_size
for i in range(total_batch):
batch_x, batch_y = mnist.train.next_batch(batch_size)
_, c = sess.run(['train', 'cost:0'], feed_dict={tf_x: batch_x,
tf_y: batch_y})
avg_cost += c
train_acc = sess.run('accuracy:0', feed_dict={tf_x: mnist.train.images,
tf_y: mnist.train.labels})
valid_acc = sess.run('accuracy:0', feed_dict={tf_x: mnist.validation.images,
tf_y: mnist.validation.labels})
print("Epoch: %03d | AvgCost: %.3f" % (epoch + 1, avg_cost / (i + 1)), end="")
print(" | Train/Valid ACC: %.3f/%.3f" % (train_acc, valid_acc))
test_acc = sess.run(accuracy, feed_dict={tf_x: mnist.test.images,
tf_y: mnist.test.labels})
print('Test ACC: %.3f' % test_acc)