What are optimizers in keras models?

This recipe explains what are optimizers in keras models

Recipe Objective

What are optimization in keras models?

Whenever a neural network finishes processing a batch through the ANN model and generates prediction results, it calculates the difference between the true value and predicted value and then decide how to use the difference between them, then adjust the weights on the nodes so that the network steps towards the required solution. The algorithm that determines that step is known as the optimization algorithm.

There are many types of optimizers like SGD, SGD with [Nesterov] momentum, Adagrad, Adadelta, RMSprop, Adam, AdaMax, Nadam, AMSgrad We will take the example of the ADAM optimizer as it is more common.

Learn About the Application of ARCH and GARCH models in Real-World

Step 1- Importing Libraries

from tensorflow import keras from tensorflow.keras import layers

Step 2- Loading the Sequential model.

We will define the layers, kernel initializer, and its input nodes shape.

model = keras.Sequential() model.add(layers.Dense(64, kernel_initializer='uniform', input_shape=(10,)))

Step 3- Defining the activation function.

We will define Relu as the activation function.

model.add(layers.Activation('relu'))

Step 4- Initializing the optimizer.

we will use Adam optimizer with the learning rate = 0.001 and loss function as 'categorical_crossentropy'.

optimizer = keras.optimizers.Adam(learning_rate=0.001) model.compile(loss='categorical_crossentropy', optimizer=optimizer)

What Users are saying..

profile image

Gautam Vermani

Data Consultant at Confidential
linkedin profile url

Having worked in the field of Data Science, I wanted to explore how I can implement projects in other domains, So I thought of connecting with ProjectPro. A project that helped me absorb this topic... Read More

Relevant Projects

MLOps Project to Deploy Resume Parser Model on Paperspace
In this MLOps project, you will learn how to deploy a Resume Parser Streamlit Application on Paperspace Private Cloud.

ML Model Deployment on AWS for Customer Churn Prediction
MLOps Project-Deploy Machine Learning Model to Production Python on AWS for Customer Churn Prediction

Image Classification Model using Transfer Learning in PyTorch
In this PyTorch Project, you will build an image classification model in PyTorch using the ResNet pre-trained model.

Deep Learning Project- Real-Time Fruit Detection using YOLOv4
In this deep learning project, you will learn to build an accurate, fast, and reliable real-time fruit detection system using the YOLOv4 object detection model for robotic harvesting platforms.

Linear Regression Model Project in Python for Beginners Part 2
Machine Learning Linear Regression Project for Beginners in Python to Build a Multiple Linear Regression Model on Soccer Player Dataset.

Forecasting Business KPI's with Tensorflow and Python
In this machine learning project, you will use the video clip of an IPL match played between CSK and RCB to forecast key performance indicators like the number of appearances of a brand logo, the frames, and the shortest and longest area percentage in the video.

Loan Eligibility Prediction Project using Machine learning on GCP
Loan Eligibility Prediction Project - Use SQL and Python to build a predictive model on GCP to determine whether an application requesting loan is eligible or not.

Multi-Class Text Classification with Deep Learning using BERT
In this deep learning project, you will implement one of the most popular state of the art Transformer models, BERT for Multi-Class Text Classification

Time Series Project to Build a Multiple Linear Regression Model
Learn to build a Multiple linear regression model in Python on Time Series Data

Recommender System Machine Learning Project for Beginners-4
Collaborative Filtering Recommender System Project - Comparison of different model based and memory based methods to build recommendation system using collaborative filtering.