What are optimizers in keras models?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

What are optimizers in keras models?

What are optimizers in keras models?

This recipe explains what are optimizers in keras models

0

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.

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)

Relevant Projects

Predict Credit Default | Give Me Some Credit Kaggle
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

Sequence Classification with LSTM RNN in Python with Keras
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset​ using Keras in Python.

Mercari Price Suggestion Challenge Data Science Project
Data Science Project in Python- Build a machine learning algorithm that automatically suggests the right product prices.

Natural language processing Chatbot application using NLTK for text classification
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

Choosing the right Time Series Forecasting Methods
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.

Solving Multiple Classification use cases Using H2O
In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.

Predict Employee Computer Access Needs in Python
Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

Identifying Product Bundles from Sales Data Using R Language
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.

Build an Image Classifier for Plant Species Identification
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

Ensemble Machine Learning Project - All State Insurance Claims Severity Prediction
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.