This problem appeared as an assignment in the online coursera course * Convolution Neural Networks* by

**Prof Andrew Ng**, (

**deeplearing.ai**). The description of the problem is taken straightway from the assignment.

* Neural Style Transfer* algorithm was created by Gatys et al. (2015) , the paper can be found here .

In this assignment, we shall:

- Implement the neural style transfer algorithm
- Generate novel artistic images using our algorithm

Most of the algorithms we’ve studied * optimize* a

*cost function*to get a set of parameter values. In Neural Style Transfer, we shall optimize a cost function to get pixel values!

## Problem Statement

**Neural Style Transfer** (NST) is one of the most fun techniques in deep learning. As seen below, it ** merges** two images, namely,

- a “
**content**” image (**C**) and - a “
**style**” image (**S**),

to create a “**generated**” image (**G**). The** generated image G** combines the “**content**” of the **image C** with the “**style**” of **image S**.

In this example, we are going to generate an image of the Louvre museum in Paris (**content** image C), mixed with a painting by Claude Monet, a leader of the impressionist movement (**style** image S).

Let’s see how we can do this.

## Transfer Learning

**Neural Style Transfer** (NST) uses a previously trained **convolutional** network, and builds on top of that. The idea of using a network trained on a different task and applying it to a new task is called **transfer learning**.

Following the original NST paper, we shall use the **VGG network**. Specifically, we’ll use **VGG-19**, a 19-layer version of the VGG network. This model has already been trained on the very large **ImageNet** database, and thus has learned to recognize a variety of **low level features** (at the **earlier** layers) and **high level features** (at the **deeper** layers). The following figure (taken from the google image search results) shows how a VGG-19 convolution neural net looks like, without the last fully-connected (FC) layers.

We run the following code to load parameters from the **pre-trained VGG-19 model** serialized in a matlab file. This takes a few seconds.

model = load_vgg_model(“imagenet-vgg-verydeep-19.mat”)

import pprint

pprint.pprint(model){‘avgpool1’: <tf.Tensor ‘AvgPool_5:0’ shape=(1, 150, 200, 64) dtype=float32>,

‘avgpool2’: <tf.Tensor ‘AvgPool_6:0’ shape=(1, 75, 100, 128) dtype=float32>,

‘avgpool3’: <tf.Tensor ‘AvgPool_7:0’ shape=(1, 38, 50, 256) dtype=float32>,

‘avgpool4’: <tf.Tensor ‘AvgPool_8:0’ shape=(1, 19, 25, 512) dtype=float32>,

‘avgpool5’: <tf.Tensor ‘AvgPool_9:0’ shape=(1, 10, 13, 512) dtype=float32>,

‘conv1_1’: <tf.Tensor ‘Relu_16:0’ shape=(1, 300, 400, 64) dtype=float32>,

‘conv1_2’: <tf.Tensor ‘Relu_17:0’ shape=(1, 300, 400, 64) dtype=float32>,

‘conv2_1’: <tf.Tensor ‘Relu_18:0’ shape=(1, 150, 200, 128) dtype=float32>,

‘conv2_2’: <tf.Tensor ‘Relu_19:0’ shape=(1, 150, 200, 128) dtype=float32>,

‘conv3_1’: <tf.Tensor ‘Relu_20:0’ shape=(1, 75, 100, 256) dtype=float32>,

‘conv3_2’: <tf.Tensor ‘Relu_21:0’ shape=(1, 75, 100, 256) dtype=float32>,

‘conv3_3’: <tf.Tensor ‘Relu_22:0’ shape=(1, 75, 100, 256) dtype=float32>,

‘conv3_4’: <tf.Tensor ‘Relu_23:0’ shape=(1, 75, 100, 256) dtype=float32>,

‘conv4_1’: <tf.Tensor ‘Relu_24:0’ shape=(1, 38, 50, 512) dtype=float32>,

‘conv4_2’: <tf.Tensor ‘Relu_25:0’ shape=(1, 38, 50, 512) dtype=float32>,

‘conv4_3’: <tf.Tensor ‘Relu_26:0’ shape=(1, 38, 50, 512) dtype=float32>,

‘conv4_4’: <tf.Tensor ‘Relu_27:0’ shape=(1, 38, 50, 512) dtype=float32>,

‘conv5_1’: <tf.Tensor ‘Relu_28:0’ shape=(1, 19, 25, 512) dtype=float32>,

‘conv5_2’: <tf.Tensor ‘Relu_29:0’ shape=(1, 19, 25, 512) dtype=float32>,

‘conv5_3’: <tf.Tensor ‘Relu_30:0’ shape=(1, 19, 25, 512) dtype=float32>,

‘conv5_4’: <tf.Tensor ‘Relu_31:0’ shape=(1, 19, 25, 512) dtype=float32>,

‘input’: <tensorflow.python.ops.variables.Variable object at 0x7f7a5bf8f7f0>}

The next figure shows the **content** image (C) – the Louvre museum’s pyramid surrounded by old Paris buildings, against a sunny sky with a few clouds.

For the above **content** image, the activation outputs from the convolution layers are visualized in the next few figures.

## How to ensure that the generated image G matches the content of the image C?

As we know, the earlier (*shallower*) layers of a *ConvNet* tend to detect *lower-level features* such as *edges* and *simple textures*, and the later (*deeper*) layers tend to detect *higher-level features* such as more *complex textures* as well as *object* classes.

We would like the “generated” image G to have similar content as the input image C. Suppose we have chosen some layer’s activations to represent the content of an image. In practice, we shall get the most visually pleasing results if we choose a layer in the middle of the network – neither too shallow nor too deep.

First we need to compute the “**content cost**” using *TensorFlow*.

- The
*content cost*takes a*hidden layer activation*of the neural network, and measures how different**a(C)**and**a(G)**are. - When we
*minimize*the*content cost*later, this will help make sure**G**

has*similar content*as**C**.

def

compute_content_cost(a_C, a_G):

“””

Computes the content costArguments:

a_C — tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image C

a_G — tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image GReturns:

J_content — scalar that we need to compute using equation 1 above.

“””# Retrieve dimensions from a_G

m, n_H, n_W, n_C = a_G.get_shape().as_list()# Reshape a_C and a_G

a_C_unrolled = tf.reshape(tf.transpose(a_C), (m, n_H * n_W, n_C))

a_G_unrolled = tf.reshape(tf.transpose(a_G), (m, n_H * n_W, n_C))# compute the cost with tensorflow

J_content = tf.reduce_sum((a_C_unrolled – a_G_unrolled)**2 / (4.* n_H * n_W * \

n_C))return J_content

## Computing the style cost

For our running example, we will use the following **style image** (S). This painting was painted in the style of impressionism, by Claude Monet .

def

gram_matrix(A):

“””

Argument:

A — matrix of shape (n_C, n_H*n_W)Returns:

GA — Gram matrix of A, of shape (n_C, n_C)

“””GA = tf.matmul(A, tf.transpose(A))

return GA

def

compute_layer_style_cost(a_S, a_G):

“””

Arguments:

a_S — tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image S

a_G — tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image GReturns:

J_style_layer — tensor representing a scalar value, style cost defined above by equation (2)

“””# Retrieve dimensions from a_G

m, n_H, n_W, n_C = a_G.get_shape().as_list()# Reshape the images to have them of shape (n_C, n_H*n_W)

a_S = tf.reshape(tf.transpose(a_S), (n_C, n_H * n_W))

a_G = tf.reshape(tf.transpose(a_G), (n_C, n_H * n_W))# Computing gram_matrices for both images S and G (≈2 lines)

GS = gram_matrix(a_S)

GG = gram_matrix(a_G)# Computing the loss

J_style_layer = tf.reduce_sum((GS – GG)**2 / (4.* (n_H * n_W * n_C)**2))return J_style_layer

- The
**style**of an image can be represented using the**Gram matrix**of a**hidden**layer’s activations. However, we get even better results combining this representation from multiple different layers. This is in contrast to the content representation, where usually using just a single hidden layer is sufficient. **Minimizing**the**style cost**will cause the**image G**to follow the**style**of the**image S**.

## Defining the total cost to optimize

Finally, let’s create and implement a **cost function** that **minimizes** both the **style** and the **content cost**. The formula is:

def

total_cost(J_content, J_style, alpha = 10, beta = 40):

“””

Computes the total cost functionArguments:

J_content — content cost coded above

J_style — style cost coded above

alpha — hyperparameter weighting the importance of the content cost

beta — hyperparameter weighting the importance of the style costReturns:

J — total cost as defined by the formula above.

“””J = alpha * J_content + beta * J_style

return J

- The
**total cost**is a**linear combination**of the content cost**J_content(C,G)**and the style cost**J_style(S,G)**. - α and
**β**are**hyperparameters**that control the**relative weighting**between**content**and**style**.

## Solving the optimization problem

Finally, let’s put everything together to implement **Neural Style Transfer**!

Here’s what the program will have to do:

- Create an Interactive Session
- Load the content image
- Load the style image
- Randomly initialize the image to be generated
- Load the VGG19 model
- Build the TensorFlow graph:
- Run the content image through the VGG19 model and compute the content cost.
- Run the style image through the VGG19 model and compute the style cost

Compute the total cost. - Define the optimizer and the learning rate.

- Initialize the TensorFlow graph and run it for a large number of iterations, updating the generated image at every step.

Let’s first load, reshape, and normalize our “**content**” image (the Louvre museum picture) and “**style**” image (Claude Monet’s painting).

Now, we **initialize** the “**generated**” image as a **noisy** image created from the **content_image**. By initializing the pixels of the generated image to be mostly noise but still slightly correlated with the content image, this will help the content of the “**generated**” image more rapidly match the content of the “**content**” image. The following figure shows the noisy image:

Next, let’s load the **pre-trained VGG-19 model**.

To get the program to compute the content cost, we will now assign *a_C* and *a_G* to be the appropriate hidden layer activations. We will use layer ** conv4_2 **to compute the

*content cost*. We need to do the following:

- Assign the content image to be the input to the VGG model.
- Set a_C to be the tensor giving the hidden layer activation for layer “conv4_2”.
- Set a_G to be the tensor giving the hidden layer activation for the same layer.
- Compute the content cost using a_C and a_G.

Next, we need to compute the *style cost *and compute the total cost J by taking a linear combination of the two. Use alpha = 10 and beta = 40.

Then we are going to set up the *Adam optimizer* in TensorFlow, using a learning rate of 2.0.

Finally, we need to initialize the variables of the tensorflow graph, assign the input image (initial generated image) as the input of the VGG19 model and runs the model to minimize the total cost **J** for a large number of iterations.

## Results

The following figures / animations show the **generated images (G)** with different **content (C)** and **style** images (**S**) at different iterations in the optimization process.

**Content**

**Style (Claud Monet’s The Poppy Field near Argenteuil)**

**Generated**

**Content**

**Style**

**Generated**

**Content**

**Style**

**Generated**

**Content**

**Style (Van Gogh’s The Starry Night)**

**Generated**

**Content**

**Style**

**Generated**

**Content (Victoria Memorial Hall)**

**Style (Van Gogh’s The Starry Night)**

**Generated**

**Content (Taj Mahal)**

**Style (Van Gogh’s Starry Night Over the Rhone)**

**Generated**

**Content**

**Style (Claud Monet’s Sunset in Venice)**

**Generated**

**Content (Visva Bharati)**

**Style (Abanindranath Tagore’s Rabindranath in the role of blind singer )**

**Generated**

**Content (Howrah Bridge)**

**Style (Van Gogh’s The Starry Night)**

**Generated**

**Content (Leonardo Da Vinci’s Mona Lisa)**

**Style (Van Gogh’s The Starry Night)**

**Generated**

**Content (My sketch: Rabindranath Tagore)**

**Style (Abanindranath Tagore’s Rabindranath in the role of blind singer )**

**Generated
**

**Content (me)**

**Style (Van Gogh’s Irises)**

**Generated**

**Content**

**Style**

**Generated**

**Content**

**Style (Publo Picaso’s Factory at Horto de Ebro)**

**Generated**

The following animations show how the generated image changes with the change in VGG-19 convolution layer used for computing content cost.

**Content**

**Style (Van Gogh’s The Starry Night)**

**Generated**

**convolution layer 3_2 used**

**convolution layer 4_2 used**

**convolution layer 5_2 used**

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