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What is Keras?

Keras is  High-Level Deep learning Python library extensively used by Data-scientists when it comes to architect the neural networks for complex problems. Higher  level API means that Keras can act as front end while you can ask Tensor-flow or Theano to work as back end.

Other Deep learning libraries

There are many development options that you can get with TensorFlow and its installation is also quick.

  1. Caffe - Caffe which is really good at speed, implementing the matrix multiplication and ease of use.

  2. Torch - Torch is another Deep learning library written in Lua and C programming. A most sought skill in Data science is ability to work with Torch, besides others. It is lighting fast in implementing the matrix multiplications using numpy as its base data arrays

  3. Tensor-flow - Tensor-flow is the number one populous deep learning library across the industry till date and it is developed by Google. It uses tensors as the basic operations ( e.g Matrix multiplication )

Implementing Simple Neural Network Using Keras

# Import all the necessary functions to build the neural network

import keras

import keras.layers import Conv1D

from keras.optimizers import Adam

fromkeras.models import sequential


# Lets start building 3 layered convolutional network

def create_model():

  model = sequential()

  # First layer

  model.add(Conv1D ( filters = 10, kernel_size = 10, input_shape, activation = 'relu')

  # Second layer

  model.add(Conv1D ( filters = 10, Kernel_size = 10, activation = 'relu' )

  # third layer

  model.add(Conv1D ( filters = 10, kernel_size = 10, activation = 'relu' )

  # flatten



# compile the model

  model.compile( loss = 'binary_corssentropy', optimizers = Adam(1e-4), metrics= ['accuracy'])


  return model

Architecture of Keras

Keras API can be divided into three main categories −

  • Model

  • Layer

  • Core Modules

Keras Models

Keras Models divided into two categories


Sequential Model

The sequential model is basically a linear composition of Keras Layers. Sequential model is easy, minimal as well as has the ability to represent nearly all available neural networks.


A simple sequential model is as follows −

#Import Libraries

from keras.models import Sequential
from keras.layers import Dense, Activation

#Use the Model

model = Sequential()  
model.add(Dense(512, activation = 'relu', input_shape = (784,)))

Core Model

Keras also provides a lot of built-in neural network related functions to properly create the Keras model and Keras layers.