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Advanced Convolutional Neural Networks
def gram_matrix(input_tensor):
# image channels first
channels = int(input_tensor.shape[-1])
a = tf.reshape(input_tensor, [-1, channels])
n = tf.shape(a)[0]
gram = tf.matmul(a, a, transpose_a=True)
return gram / tf.cast(n, tf.float32)
def get_style_loss(base_style, gram_target):
# height, width, num filters of each layer
height, width, channels = base_style.get_shape().as_list()
gram_style = gram_matrix(base_style)
return tf.reduce_mean(tf.square(gram_style - gram_target))
In short, the concepts behind style transfer are simple: first, we use VGG19 as a
feature extractor and then we define two suitable function distances, one for style
and the other one for contents, which are appropriately minimized. If you want to
try this out for yourself, then TensorFlow tutorials are available online. If you are
interested in a demo of this technique, you can go to deepart.io free site where they
do style transfer.
A tutorial is available at https://colab.research.google.
com/github/tensorflow/models/blob/master/research/
nst_blogpost/4_Neural_Style_Transfer_with_Eager_
Execution.ipynb.
Next, we'll check out another interesting image-related application of CNNs.
Creating a DeepDream network
Another interesting application of CNNs is DeepDream, a computer vision program
created by Google [8], which uses a CNN to find and enhance patterns in images.
The result is a dream-like hallucinogenic effect. Similarly to the previous example,
we are going to use a pretrained network to extract features. However, in this case
we want to "enhance" patterns in images, meaning that we need to maximize some
functions. This tells us that we need to use a gradient ascent and not a descent. First,
let's see an example from Google gallery (https://photos.google.com/share/AF1
QipPX0SCl7OzWilt9LnuQliattX4OUCj_8EP65_cTVnBmS1jnYgsGQAieQUc1VQWdgQ?k
ey=aVBxWjhwSzg2RjJWLWRuVFBBZEN1d205bUdEMnhB) where the classic Seattle
landscape is "inceptioned" with hallucinogenic dreams such as birds, cards, and
strange flying objects.
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