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 numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
##########################
### SETTINGS
##########################
# General settings
random_seed = 0
# Hyperparameters
learning_rate = 0.001
training_epochs = 5
batch_size = 100
margin = 1.0
# Architecture
n_hidden_1 = 256
n_hidden_2 = 256
n_input = 784
n_classes = 1 # for 'true' and 'false' matches
def fully_connected(inputs, output_nodes, activation=None, seed=None):
input_nodes = inputs.get_shape().as_list()[1]
weights = tf.get_variable(name='weights',
shape=(input_nodes, output_nodes),
initializer=tf.truncated_normal_initializer(
mean=0.0,
stddev=0.001,
dtype=tf.float32,
seed=seed))
biases = tf.get_variable(name='biases',
shape=(output_nodes,),
initializer=tf.constant_initializer(
value=0.0,
dtype=tf.float32))
act = tf.matmul(inputs, weights) + biases
if activation is not None:
act = activation(act)
return act
def euclidean_distance(x_1, x_2):
return tf.sqrt(tf.maximum(tf.sum(
tf.square(x - y), axis=1, keepdims=True), 1e-06))
def contrastive_loss(x_1, x_2, margin=1.0):
return (x_1 * tf.square(x_2) +
(1.0 - x_1) * tf.square(tf.maximum(margin - x_2, 0.)))
##########################
### GRAPH DEFINITION
##########################
g = tf.Graph()
with g.as_default():
tf.set_random_seed(random_seed)
# Input data
tf_x_1 = tf.placeholder(tf.float32, [None, n_input], name='inputs_1')
tf_x_2 = tf.placeholder(tf.float32, [None, n_input], name='inputs_2')
tf_y = tf.placeholder(tf.float32, [None],
name='targets') # here: 'true' or 'false' valuess
# Siamese Network
def build_mlp(inputs):
with tf.variable_scope('fc_1'):
layer_1 = fully_connected(inputs, n_hidden_1,
activation=tf.nn.relu)
with tf.variable_scope('fc_2'):
layer_2 = fully_connected(layer_1, n_hidden_2,
activation=tf.nn.relu)
with tf.variable_scope('fc_3'):
out_layer = fully_connected(layer_2, n_classes,
activation=tf.nn.relu)
return out_layer
with tf.variable_scope('siamese_net', reuse=False):
pred_left = build_mlp(tf_x_1)
with tf.variable_scope('siamese_net', reuse=True):
pred_right = build_mlp(tf_x_2)
# Loss and optimizer
loss = contrastive_loss(pred_left, pred_right)
cost = tf.reduce_mean(loss, name='cost')
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
train = optimizer.minimize(cost, name='train')
##########################
### TRAINING & EVALUATION
##########################
np.random.seed(random_seed) # set seed for mnist shuffling
mnist = input_data.read_data_sets("./", one_hot=False)
with tf.Session(graph=g) as sess:
print('Initializing variables:')
sess.run(tf.global_variables_initializer())
for i in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='siamese_net'):
print(i)
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = mnist.train.num_examples // batch_size // 2
for i in range(total_batch):
batch_x_1, batch_y_1 = mnist.train.next_batch(batch_size)
batch_x_2, batch_y_2 = mnist.train.next_batch(batch_size)
batch_y = (batch_y_1 == batch_y_2).astype('float32')
_, c = sess.run(['train', 'cost:0'], feed_dict={'inputs_1:0': batch_x_1,
'inputs_2:0': batch_x_2,
'targets:0': batch_y})
avg_cost += c
print("Epoch: %03d | AvgCost: %.3f" % (epoch + 1, avg_cost / (i + 1)))