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What is a Keras ?
Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It is an open source software library that provides a python interface for artificial neural networks.
Simple -- but not simplistic. Keras reduces developer cognitive load to free you to focus on the parts of the problem that really matter.
Flexible -- Keras adopts the principle of progressive disclosure of complexity: simple workflows should be quick and easy, while arbitrarily advanced workflows should be possible via a clear path that builds upon what you've already learned.
Powerful -- Keras provides industry-strength performance and scalability: it is used by organizations and companies including NASA, YouTube, or Waymo.
It can be very easy to code with keras by keeping on adding layers which you can invoke with functions and keep on building neural networks. This is easy to work with keras. This keras is broadly adopted in the industry and also among the research community. So that's why most data scientists most widely preferred deep learning frameworks in keras. It is very easy to convert all the keras models into end to end products so that we can easily launch it on some platform.
Keras is a high level API which is written in python and this high level API can run on a lot of low level api such as Tensorflow, cntk, theano etc. keras has multi GPU support, it means it basically divides the data or we can train the data on multiple GPUs. For example, if you have input data which consists of 100 records, we can divide it into five mini batches. Now We can train each individual mini batch on separate GPUs. Each of that GPU would generate an individual result which is aggregate and will get the final result. So this process can speed up the model.
There are basically two type of model in keras model
sequential model
functional model
Sequential model
The sequential model is a linear stack of layers. One layer is on top of that one another layer, we can add another layer on top of the second layer. We can create a sequential model by passing a list of layer instances to the constructor. stacking convolutional layers one above the other can be an example of a sequential model. Input layer is the first layer. On top of the input layer would be the first hidden layer, and second hidden layer top on the first hidden layer. After that would be the output layer.
How to build the sequential model using keras
from keras.models import Sequential
from keras.layers import Dense, Activation
model = Sequential() ## create a model
model.add(Dense(32,input_dim=784))
model.add(Activation('relu'))In the above code first create a model using sequential(). After that whatever layer we want to add in the model we can add using the add method.
Functional model
Functional models help us to create complex models. In the Sequential model we can give input at the beginning stage. If I want to add input at the second layer it is not possible in the sequential model. The functional model is different from the sequential model. In functional models connected any layer to any other layer. Following these three steps are to create a functional model.
Defining the input
Connecting Layers
Creating the model
Defining the input : In a functional model, we must define the standalone input layer that specifies the shape of the input data. Input layer takes a shape argument which is tuple that indicates the dimensionality of the input data.
# creating input layer
from keras.layers import Input
visible = Input(shape(2,1))Connecting Layers : Once created the input layer we can add other layers. Layers in the model are connected pairwise. This is achieved by specifying where the input comes from while defining each new layer. A bracket notation is used to specify the layer from which the input is received to the current layer, after the layer is created. For example build the input layer as above and then create a hidden layer as Dense layer that receives input only from the input layer.
# adding other layers
from keras.layers import Input
from keras.layers import Dense
visible = Input(shape(2,1))
hidden =Dense(2)(visible) # this layer is connected with visible layer Creating the model : After creating all of your model layers and connecting them together, you must define the model. Keras provides a model class that you can use to create a model from your created layer. It requires you to only specify the input and output layers
# adding other layers
from keras.layers import Input
from keras.layers import Dense
visible = Input(shape(2,1))
hidden =Dense(2)(visible) # this layer is connected with visible layer
model = Model(input =visible, output=hidden)Predefined Neural network Layer
Keras has a number of predefined layers:
Core layers
Normaliation layers
Noise layer
Convolutional Layers
Pooling layer
Embedding Layer
Locally Connected Layer
Merged Layers
Recurrent Layers
Advance Activation Layers
Features
Keras includes a lot of implementations of standard neural-network building blocks like layers, objectives, activation functions, optimizers, and other tools to make working with image and text data easier while also simplifying coding required to write deep neural network code.
Keras also support convolutional and recurrent neural networks. It supports other layers also batch normalization, dropout, pooling.
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