Keras is an open-source deep learning library written in Python. It is suitable for building and evaluating powerful learning models. As a rule, the libraries Theano and TensorFlow effectively integrated. Deep learning is a new trend that uses artificial intelligence to build machines and neural networks. Software developers can effectively build neural networks without having to worry about mathematical aspects such as tensor algebra, optimisation methods and other numerical techniques. However, they should know that Keras is only applicable to human learning and not to machine learning.
Key features of Keras
- The system has a high-level interface for user
- It runs smoothly on GPU and CPU
- The system supports most neural network models
- It is flexible
- With its Python-oriented framework, errors can be fixed very easily
Steps to create your first CNN with Keras
1. set up your environment
Start by hanging up your motivational poster. Then you can install Python 2.7- on your computer. You can also use Python 3. However, 2.7 is more popular for data technology. Others might prefer to use SciPY with NumPY or Matplotlib.
2. install the Keras system
When using Anaconda, you can use the "pip" management system. In most cases, it can be accessed quite simply by typing "pip" in the command line.
Import libraries and modules
Start by importing NumPy and setting up Seed as a pseudo in your number generator.
4. load the image from MNIST
5. pre-processing of the input data
Depending on the system you want to use, you may need different depths for the photo. Full colour images always work best with more than 3 RGB channels.
6. pre-processing of different classes
With more than 9 different classes, you can select the dimensional array you want. Check the y_train and y_test data as they are not split into these different classes.
7. define the model architecture
Most experts prefer to use the Cs231n class. Not only does it provide space to learn more, but it also replicates on other infrastructures. You can compile the model and prepare to work with training data.
8. evaluate the model against test data
When all preparations have been made, all that remains is to evaluate the model using the test data.