Neural networks have seen widespread use in recent years, with applications ranging from image classification to natural language processing (NLP). With the explosion of smartphone apps that require some level of computational power, there is an ever-growing need for more powerful computer systems. Applications such as voice recognition and automated chatbots depend heavily on advanced neural network algorithms.
Since most of these mobile apps are web services, they also rely on the HTTP protocol and port numbers to function properly. This brings us to our next topic: how to deploy deep learning models using Flask.
There are many ways to do this, but one of the most popular methods is called transfer learning. In this article, we will be looking at how to implement a pre-trained state-of-the-art convolutional neural network (CNN) architecture for object detection using this technique. Object detection refers to finding all instances of specific categories such as “car” or “cat”. Once trained, the model can perform this task quickly without having to learn new concepts like shapes and features again!
We will take a closer look at what each layer of the CNN does, how it functions, and then apply it to detecting cars and cats. When everything has been completed, your app will be able to identify both car and cat images efficiently.
Install Python and pip
First, you will need to install python and then your machine learning toolkit such as TensorFlow or PyTorch should be installed using its own package manager called Pip.
You can use either of these two as your deployment engine, but we will show both here so that you are fully familiar with them!
The software development platform that is most commonly used for creating web applications is PHP. This article will talk about how to deploy a pre-trained neural network model using this language and framework.
If you are looking to deploy your model as a web application, then Theano is one of the most popular deep learning frameworks available. It is very common to use Python for developing applications, so choosing Theano as a framework makes sense.
The installation process for Theano is pretty straightforward. You can find it here.
After installing Theano, you will need to install some dependencies such as NumPy and PyTorch. These two libraries play an integral part in the development of neural networks. They also make sure that Theano works properly by taking care of all mathematical functions used in neural network training.
Once these pre-requisites have been installed, you can begin configuring Theano! To do this, open up the Theano folder which has been extracted during setup. Inside this folder, there will be three files; gpu_theaneo.py, cpu_theaneo.py, __init__.yaml.
Open each file using notepad or your favorite text editor. Find where it says “THEANO_FLAGS=” and add -D USE_CUDA = False at the end. This way, when you run python TheanaFile.py, it will skip any configurations related to GPU usage and just use CPU only.
Now, let us get into coding! Create a new directory called myapp. In this directory, create another directory named models.
One of the most important steps in deploying any deep learning model is installing the keras library. Keras is a high level API that allows you to build your models quickly by taking care of some of the tedious work for you. There are two main types of keras: sequential and parallel. Sequential keras works best when you have a small number of layers in your network, whereas parallelized networks can be much faster due to their efficient structure.
In this article we will use sequential keras since our example networks do not contain too many parameters! Before you start building your neural net, you must pick a backend for keras. The backends available include Google’s Tensorflow or Microsoft’s CNTK. Both of these have free community versions, but the paid professional version also has great tools such as GPU acceleration and automatic memory allocation. For this reason, I would recommend using the paid version at least for now- it does not cost very much and gives you more features.
I will assume that you have already installed tensorflow and cntk before moving onto configuring flask and keras. If not, go through those installation instructions first! Also make sure that your python path contains both environments so that they can find each other.
Create a new Python file that will contain your model
Now, let’s create our deploy script! We will need to add some code to execute certain commands such as starting HTTP servers or copying files. Once this is done, we can run these scripts using a program called Python.
First, open up your favorite text editor (such as Notepad++). Then, enter the following into the first line:
Press Save and then Run to test it out! You should now be able to access your website via http://localhost/. Make sure you have MySQL running and accessible before moving onto the next step.
Now, go back to your terminal window and navigate to where your models are stored. For the purposes of this tutorial, we will use Google Cloud Storage (GCS) which is free for one terabyte. This way you do not have to worry about paying monthly fees!
Open GCP and click “Explore services” at the top left corner. Search under Datastore and pick the option for JSON Files. Find your trained model and right-click it to copy the URL address.
Next, go back to your local computer and Navigate to the same folder location. Inside this folder, create a new directory named gcloud. Within the gcloud directory, create another subdirectory called ml. Copy the URL from Step 5 above and paste it into the appropriate field within the ml directory.
Create a new Flask file
In this article, you will learn how to deploy your own neural network model using Flask as a backend server-side framework. You will also explore some of the key concepts of deploying AI models online. If you have ever wanted to try out your skills by building your own chatbot or intelligent assistant that uses artificial intelligence (AI) then this is for you!
In this tutorial, we will be creating an example of such a tool — Ask Azaar. An intelligent bot that helps users search Amazon products through their web interface. It was first published back in March 2018 here at TechRepublic.
Define your model
A deep neural network (DNN) is an algorithm that uses artificial neurons to learn internal representations of data. Neural networks are popular because they can achieve very good performance on classification tasks, especially when there is a lot of available training data.
In this article we will be deploying a DNN to perform image recognition using the VGG-16 architecture. This model was first proposed by Alex Krizhevsky and Soumith Chintala at the University of Toronto and later improved upon by Vijay Rajan and Andrew Ng at Stanford University.
The main reason why people use VGG models for image recognition is due to how well it generalizes. By incorporating more parameters into the model, it becomes able to capture finer details about the input images. These fine details may not directly relate to what category the image belongs to, but they help determine overall class identity.
We will assume that you have already downloaded the trained weights for VGG-16 from somewhere such as here or here.
Deploy your model
Now that you have tuned your models, it is time to deploy them! Depending on what type of task you set as your project’s goal, there are several ways to do this. For example, if your project’s goal was predicting whether or not someone will go into debt, then creating an image classification model and hosting it online using Amazon’s Cloud Machine Learning (CML) service is appropriate.
If your project’s goal is building a chatbot that interacts with users, then developing your own chat platform is necessary. If your project’s goal is finding new recipes to make food, then launching a web application collecting recipe information is perfect.
There are many different types of applications that can be built using AI technology. The best way to choose which one fits your needs depends mostly on your budget and how much development experience you have.
Test your model
The most important thing before deploying any deep learning algorithm is testing it!
You should test different configurations of your neural network at least once, using the datasets that have been discussed so far. You can also experiment with different architectures, activations, and optimizers.
By doing this, you will avoid overfitting the problem which would result in poor accuracy and loss of precision in solving similar problems in the future.
Because underfitting is much more common than overfitting, many algorithms include risk functions to measure how well-trained the model is. These risk functions increase as the model gets less confident in its answers and therefore may be used for weighting the models’ predictions.
Using such a model to make decisions may not be reliable because the model does not believe in itself or the field it operates in. Risk minimization is an integral part of creating good AI.