Keras is a deep-learning framework and open source library for Python. It provides a Python interface for artificial neural networks and a suitable way to define and train nearly any kind of deep-learning model. Keras was originally developed for scholars and researchers. Its main aim was to enable rapid experimentation. Keras version 2.3 supported multiple backs ends like;
- Microsoft Cognitive Toolkit
- Keras permits the same code to run flawlessly on CPU or GPU.
- Its user-friendly API makes it easy to fast prototype deep-learning models.
- This was built-in support for computer vision& sequence processing or any combination of both.
- Random network architectures are being supported by Keras. These include multi-input/output models and layer or model sharing, and so on. Keras is suitable for building fundamentally any deep-learning model. It may be from a reproductive confrontational network to a neural Turing machine.
- Keras may be freely used in profitable projects. It’s well-matched with any version of Python from 2.7 to 3.6.
- There have been over 200,000 users of Keras. They are ranging from academic researchers and engineers to graduate students and hobbyists.
- At Google, CERN, Netflix, Uber, Square, Yelp, and working on a wide range of problems, Keras is being used.
- This is also a widespread framework on Kaggle. That is a machine-learning competition website. Nearly all recent deep-learning competition has been gained using Keras models.
- Keras emphasizes being user-friendly, extensible, and modular due to its design to allow fast research with deep neural networks.
- The code models use Keras (https://keras.io). The code is presented on GitHub and public support forums.
- Keras cares about other joint utility layers like batch normalization, dropout, and pooling.
- It allows operators to productize deep models on smartphones, the web, and on the Java Virtual Machine.
- It also lets users of distributed training of deep-learning models on clusters of GPU and TPU.
Keras & TensorFlow 2
Tensor Flow 2 is a machine learning open-source platform. This is an end-to-end platform. We can think of it as an infrastructure layer for differential programming. It joined four key abilities:
- Executing low-level tensor operations on CPU, GPU, or TPU efficiently.
- Arbitrary differentiable expressions computing the gradient.
- Computation scaling to many devices.
- Exporting programs to external run times for example browsers, servers, mobile, and embedded devices.
Keras is the high-level API of TensorFlow 2. This is an approachable, highly productive interface for solving machine learning problems. Its main focus is on modern deep learning. It offers essential abstractions and building blocks for developing and shipping machine learning solutions with high iteration velocity.
Keras empowers engineers and researchers to take full advantage of the scalability and cross-platform capabilities of TensorFlow 2. We can run Keras on TPU or on large clusters of GPUs. We can export our Keras models to run in the browser or on a mobile device.
Keras was created to work with Python. The API was not designed for machines as it was designed for human beings. That follows best practices for reducing cognitive load.
We can combine to create new models that all standalone modules such that neural layers, cost functions, optimizer, initialization schemes, activation functions, and regularization schemes. New modules are easy to add like new classes and functions. Models are explained in Python code, not separate model configuration files.
Keras features a big choice of predefined layer types. It also supports writing our own layers.
Core layers contain;
- Dense (dot product plus bias)
- Activation (transfer function or neuron shape)
- Dropout (randomly set a fraction of input units to 0 at each training update to avoid overfitting)
- Lambda (it wrap an arbitrary expression as a Layer object)
Convolution layers (the use of a filter to make a feature map) run from 1D to 3D. These include the foremost common variants, like cropping and transposed convolution layers for every dimensionality. For image recognition, we use 2D convolution that was inspired by the functionality of the visual area.
Downscaling or pooling layers run from 1D to 3D. These are included in the foremost common variants, like max and average pooling. Connected layers locally act like convolution layers, except that the weights are unshared. Recurrent layers are useful for language processing, among other applications. Noise layers help to avoid overfitting.
Keras provides seven of the common deep learning sample datasets. These datasets are supplied through the keras.datasets class. That has cifar10 and cifar100 small color images, IMDB movie reviews, Reuter’s newswire topics, MNIST handwritten digits, MNIST fashion images, and Boston housing prices.
Keras applications and examples
Keras also provide ten well-known models, called Keras Applications, pretrained against ImageNet: Xception, VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, MobileNet, DenseNet, NASNet, MobileNetV2TK. you’ll use these to predict the classification of images, extract features from them, and fine-tune the models on a special set of classes.
By the way, fine-tuning existing models may be a great way to hurry up training. for instance, you’ll add layers as you would like, freeze the bottom layers to coach the new layers, then unfreeze a number of the bottom layers to fine-tune the training. We’ll freeze a layer by setting layer.trainable = False.