Introduction to TensorFlow Keras Model
Keras is the library available in deep learning which is subtopic of machine learning and consists of many other sub-libraries such as TensorFlow and Theano. This are used in Artificial intelligence and robotics as this technology follows the use of algorithms that are developed by using the algorithms which are based on the patterns in which human brain works and are capable of self-learning. Tensorflow Keras is one of the most popular and highly progressing field in technology right now as it possesses the potential to change the future of technology. In this article, we will have a look at the model or Keras TensorFlow and also look at the methods followed in that model.
Overview of TensorFlow Keras Model
Keras is the library of python used for deep learning. TensorFlow is a technology that is used in machine learning and is the open-source platform available on GitHub provided by google for end-to-end communication in that incredibly changes the way to build models of machine learning for experts as well as beginners.
In order to understand actually what is TensorFlow Keras, firstly, you need to understand what is deep learning in python. It helps in preprocessing the data and then evaluate, model and optimization of the neural network. While machine learning basically focuses on designing and creating the algorithms capable of learning on their own while deep learning is the sub-branch of machine learning where the algorithms designed have their inspiration from the way the human brain works and its structure. They are also referred as Artificial Neural Networks. Keras is the learning model and the python library available for machine learning which is very easy to use and powerful at the same time. It consists of libraries such as Tensorflow and Theano that help in numerical computations.
Let us understand how Artificial Neural Networks work by comparing the basic neuron of biology and Keras model. The simple neural network is a single neuron present in human body is also referred as perceptron which consists of dendrites and axons for collecting and passing the data respectively. Similarly, the neuron in artificial network has multiple inputs and a single output node. The below figure shows the comparison between biological and artificial neuron –
In order to recognize or find out the solution for complex real-life scenarios, we will require multi-layer perceptrons. We can make use of the Keras sequential model which is in fact a linear stack of such perceptron layers. We can create an instance of such a model by simply supplying the list of the layers when calling the construction of Sequential class. We can do the set up for the same by using the below statement –
nameOfModel = Sequential()
Talking about multi-layer perceptron. It’s made up of different layers out of which the first one is the input layer which is the source of getting and collecting the data, then some hidden layers for manipulation and calculations, and finally the output layer for passing the information. While creating our model in Keras we need to mention the input shape in clarity that means what kind and structure of data you are supposed to pass as an input to your model. For this, you can make use of input_shape, batch_size, input_length, and input_dim parameters in the layers.
In complex cases, you will make the use of Dense layer as there is a requirement to have a completely connected layer. The operations that are carried out by the dense layer is as shown below –
Output (result) = activation (dot (input, kernel) +bias)
The dense layer contains two operations of dot product and addition if we do not use the activation function.
TensorFlow Keras model and method
As you have understood that TensorFlow Keras model is used for deep learning and it involves various other thing than just collecting the data, manipulating the same, and displaying the output. Let us understand the method by observing the steps that we need to carry out while developing a model in TensorFlow and Keras libraries.
- Understand the available data – You can give a check to all the variables that are included in the data description folder.
- Loading the data in to the system – There are various python libraries such as pandas that make this task really easy. You can use this package by simply using import statements. Along with that, you can also make the use of read_csv() method along with sep argument to specify the separator.
- Explore the data – You can check all your data frames and also cross-check about the variables and their data types present inside the data description file. Keep a track of the data and make sure you know well about the presence of null values and their structure, so that you can plan to handle the data accordingly while cleaning up or manipulations. Pandas function of info(), tail(), sample(), head(), min(), max(), describe(), isnull() and may others can prove helpful to you.
- Data Visualization – You can create the scatter plots of the variables of data set to check all possible correlations present in it. You can make the use of histogram() function that is available in numpy package or can also look at the matplotlib libraries out there that can help you in doing this.
- Preprocess your data – After having the correct data with you can go for this step where you can set the parameter value of ignore_index to true if you don’t want the labels of the index to be kept while appending your data frames.
- Training of the model and testing – In this step, you can make the use of train_test_split function from sklearn.model_selection library.
- Data standardization – you can import StandardScalar module from the package of sklearn. preprocessing.
- Model the data – You will create your own neural network by using multi-layer percepton for complex applications.
- Compilation of the model and fitting – You can make the use of compile() method and fit() function for your model. You have to fit the model to the available data.
- Predictions – predict by using the predict() function and checks if the rest is pass or fail.
- Evaluation of model – evaluate() function can be used for this.
Tensorflow Keras is the library available in python used for deep learning and creating models based on artificial neural networks.
This is a guide to TensorFlow Keras Model. Here we discuss TensorFlow Keras Model and Method, examples with code implementation. You may also have a look at the following articles to learn more –