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
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 = 0.1
training_epochs = 10
batch_size = 64
# Architecture
n_hidden_1 = 128
n_hidden_2 = 256
n_input = 784
n_classes = 10
##########################
### GRAPH DEFINITION
##########################
g = tf.Graph()
with g.as_default():
# Input data
tf_x = tf.placeholder(tf.float32, [None, n_input], name='features')
tf_y = tf.placeholder(tf.float32, [None, n_classes], name='targets')
# Model parameters
weights = {
'h1': tf.Variable(tf.truncated_normal([n_input, n_hidden_1], stddev=0.1)),
'h2': tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2], stddev=0.1)),
'out': tf.Variable(tf.truncated_normal([n_hidden_2, n_classes], stddev=0.1))
}
biases = {
'b1': tf.Variable(tf.zeros([n_hidden_1])),
'b2': tf.Variable(tf.zeros([n_hidden_2])),
'out': tf.Variable(tf.zeros([n_classes]))
}
# Multilayer perceptron
layer_1 = tf.add(tf.matmul(tf_x, weights['h1']), biases['b1'])
layer_1 = tf.nn.relu(layer_1)
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
layer_2 = tf.nn.relu(layer_2)
out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
# Loss and optimizer
loss = tf.nn.softmax_cross_entropy_with_logits(logits=out_layer, labels=tf_y)
cost = tf.reduce_mean(loss, name='cost')
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
train = optimizer.minimize(cost, name='train')
# Prediction
correct_prediction = tf.equal(tf.argmax(tf_y, 1), tf.argmax(out_layer, 1))
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={'features:0': batch_x,
'targets:0': batch_y})
avg_cost += c
train_acc = sess.run('accuracy:0', feed_dict={'features:0': mnist.train.images,
'targets:0': mnist.train.labels})
valid_acc = sess.run('accuracy:0', feed_dict={'features:0': mnist.validation.images,
'targets:0': 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={'features:0': mnist.test.images,
'targets:0': mnist.test.labels})
print('Test ACC: %.3f' % test_acc)
In [3]:
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 = 0.1
training_epochs = 10
batch_size = 64
# Architecture
n_hidden_1 = 128
n_hidden_2 = 256
n_input = 784
n_classes = 10
##########################
### GRAPH DEFINITION
##########################
g = tf.Graph()
with g.as_default():
# Input data
tf_x = tf.placeholder(tf.float32, [None, n_input], name='features')
tf_y = tf.placeholder(tf.float32, [None, n_classes], name='targets')
# Multilayer perceptron
layer_1 = tf.layers.dense(tf_x, n_hidden_1, activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.1))
layer_2 = tf.layers.dense(layer_1, n_hidden_2, activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.1))
out_layer = tf.layers.dense(layer_2, n_classes, activation=None)
# Loss and optimizer
loss = tf.nn.softmax_cross_entropy_with_logits(logits=out_layer, labels=tf_y)
cost = tf.reduce_mean(loss, name='cost')
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
train = optimizer.minimize(cost, name='train')
# Prediction
correct_prediction = tf.equal(tf.argmax(tf_y, 1), tf.argmax(out_layer, 1))
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={'features:0': batch_x,
'targets:0': batch_y})
avg_cost += c
train_acc = sess.run('accuracy:0', feed_dict={'features:0': mnist.train.images,
'targets:0': mnist.train.labels})
valid_acc = sess.run('accuracy:0', feed_dict={'features:0': mnist.validation.images,
'targets:0': 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:0', feed_dict={'features:0': mnist.test.images,
'targets:0': mnist.test.labels})
print('Test ACC: %.3f' % test_acc)