AI News, Turning Design Mockups Into Code With Deep Learning

Turning Design Mockups Into Code With Deep Learning

Within three years deep learning will change front-end development.

The field took off last year when Tony Beltramelli introduced the pix2code paper and Airbnb launched sketch2code.

However, we can use current deep learning algorithms, along with synthesized training data, to start exploring artificial front-end automation right now.

In this post, we’ll teach a neural network how to code a basic a HTML and CSS website based on a picture of a design mockup.

In the first version, we’ll make a bare minimum version to get a hang of the moving parts.

The second version, HTML, will focus on automating all the steps and explaining the neural network layers.

If you’re new to deep learning, I’d recommend getting a feel for Python, backpropagation, and convolutional neural networks.

My three earlier posts on FloydHub’s blog will get you started [1] [2] [3].

When it predicts the next markup tag, it receives the screenshot as well as all the correct markup tags until that point.

The neural network builds features to link the input data with the output data.

We’ll feed a neural network a screenshot with a website displaying “Hello World!”, and teach it to generate the markup.

First, the neural network maps the design mockup into a list of pixel values.

From 0 - 255 in three channels - red, blue, and green.

To represent the markup in a way that the neural network understands, I use one hot encoding.

If it’s shorter than the maximum length, you fill it up with empty words, a word with just zeros.

This allows the model to learn the sequence instead of memorizing the position of each word.

To the left are the images represented in their three color channels: red, green and blue and the previous words.

I came across them when I first started learning deep learning and I’ve used them since for training and managing my deep learning experiments.

Clone the repository Login and initiate FloydHub command-line-tool Run a Jupyter notebook on a FloydHub cloud GPU machine: All the notebooks are prepared inside the floydhub directory.

This section will focus on creating a scalable implementation and the moving pieces in the neural network.

This version will not be able to predict HTML from random websites, but it’s still a great setup to explore the dynamics of the problem.

Features are the building blocks that the network creates to connect the design mockups with the markup.

The decoder then takes the combined design and markup feature and creates a next tag feature.

This feature is run through a fully connected neural network to predict the next tag.

Since we need to insert one screenshot for each word, this becomes a bottleneck when training the network (example).

Although they are hard to understand for us, a neural network can extract the objects and position of the elements from these features.

In this version, we’ll use a word embedding for the input and keep the one-hot encoding for the output.

The dimension of this word embedding is eight but often vary between 50 - 500 depending on the size of the vocabulary.

These are run through a Time distributed dense layer - think of it as a dense layer with multiple inputs and outputs.

To mix signals and find higher-level patterns we apply a TimeDistributed dense layer to the markup features.

After sticking one image feature to each markup feature, we end up with three image-markup features.

In the below example, we use three image-markup feature pairs and output one next tag feature.

The softmax activation in the dense layer distributes a probability from 0 - 1, with the sum of all predictions equal to 1.

Then you translate the one-hot encoding [0, 0, 0, 1] into the mapped value, say “end”.

By tweaking the model in the pix2code paper, the model can predict the web components with 97% accuracy (BLEU 4-ngram greedy search, more on this later).

But after a few experiments, I realized that pix2code’s end-to-end approach works better for this problem.

In this model, we replace the pre-trained image features with a light convolutional neural network.

There are two core models that enable this: convolutional neural networks (CNN) and recurrent neural networks (RNN).

The most common recurrent neural network is long-short term memory (LSTM), so that’s what I’ll refer to.

In addition to passing through an output feature for each input it also forwards the cell states, one value for each unit in the LSTM.

To get a feel for how the components within the LSTM interacts, I recommend Colah’s tutorial, Jayasiri’s Numpy implementation, and Karphay’s lecture and write-up.

used the BLEU score, best practice in machine translating and image captioning models.

To get the final score you multiply each score with 25%, (4/5) * 0.25 + (2/4) * 0.25 + (1/3) * 0.25 + (0/2) * 0.25 = 0.2 + 0.125 + 0.083 + 0 = 0.408 .

It’s easy to generate data and the current deep learning algorithms can map most of the logic.

Attention layers can keep track of variables, enabling the network to communicate between programming languages.

But in the near feature, the biggest impact will come from building a scalable way to synthesize data.

In less then two years, we’ll be able to draw an app on paper and have the corresponding front-end in less than a second.

Thanks to Jason Brownlee for his stellar Keras tutorials, I included a few snippets from his tutorial in the core Keras implementation, and Beltramelli for providing the data.

He's worked for Oxford's business school, invested in education startups, and built an education technology business.

Last year, he enrolled at Ecole 42 to apply his knowledge of human learning to machine learning.

How you can train an AI to convert your design mockups into HTML and CSS

Currently, the largest barrier to automating front-end development is computing power.

However, we can use current deep learning algorithms, along with synthesized training data, to start exploring artificial front-end automation right now.

In this post, we’ll teach a neural network how to code a basic a HTML and CSS website based on a picture of a design mockup.

Here’s a quick overview of the process: We’ll build the neural network in three iterations.

The second version, HTML, will focus on automating all the steps and explaining the neural network layers.

If you’re new to deep learning, I’d recommend getting a feel for Python, backpropagation, and convolutional neural networks.

When it predicts the next markup tag, it receives the screenshot as well as all the correct markup tags until that point.

Say we train the network to predict the sentence “I can code.” When it receives “I,” then it predicts “can.” Next time it will receive “I can” and predict “code.” It receives all the previous words and only has to predict the next word.

The network builds features to link the input data with the output data.

When you want to use the trained model for real-world usage, it’s similar to when you train the model.

The prediction is initiated with a “start tag” and stops when it predicts an “end tag” or reaches a max limit.

We’ll feed a neural network a screenshot with a website displaying “Hello World!” and teach it to generate the markup.

First, the neural network maps the design mockup into a list of pixel values.

If it’s shorter than the maximum length, you fill it up with empty words, a word with just zeros.

To the left are the images represented in their three color channels: red, green and blue and the previous words.

I came across them when I first started learning deep learning and I’ve used them since for training and managing my deep learning experiments.

You can run your first model within 30 seconds by clicking this button: It opens a Workspace on FloydHub where you will find the same environment and dataset used for the Bootstrap version.

This section will focus on creating a scalable implementation and the moving pieces in the neural network.

This version will not be able to predict HTML from random websites, but it’s still a great setup to explore the dynamics of the problem.

The decoder then takes the combined design and markup feature and creates a next tag feature.

Although they are hard to understand for us, a neural network can extract the objects and position of the elements from these features.

In this version, we’ll use a word embedding for the input and keep the one-hot encoding for the output.

The dimension of this word embedding is eight but often varies between 50–500 depending on the size of the vocabulary.

These are run through a Time distributed dense layer — think of it as a dense layer with multiple inputs and outputs.

To mix signals and find higher-level patterns, we apply a TimeDistributed dense layer to the markup features.

After sticking one image feature to each markup feature, we end up with three image-markup features.

In the below example, we use three image-markup feature pairs and output one next tag feature.

The softmax activation in the dense layer distributes a probability from 0–1, with the sum of all predictions equal to 1.

Then you translate the one-hot encoding [0, 0, 0, 1] into the mapped value, say “end”.

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