What is a drop out rate in keras?

What is a drop out rate in keras?

What is a drop out rate in keras?

This recipe explains what is a drop out rate in keras

Recipe Objective

Drop out rate in keras

Drop out is a powerful regularization technique for neural networks and deep learning models.

In the dropout technique, randomly selected neurons are ignored during training. Their contribution to the activation of downstream neurons is temporally removed on the forward pass then any weight updates are not applied to the neuron on the backward pass.

Dropout can be implemented by randomly selecting any nodes to be dropped with a given probability (10% or 0.1) each weight update cycle. Dropout is only used during the training of a model is not used when evaluating the skill of the model.

Step 1- Import Libraries

#importing Libraries import pandas as pd import numpy as np from keras.datasets import mnist from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from tensorflow.keras import layers

Step 2- Load the dataset

#Loading Dataset (X_train, y_train), (X_test, y_test) = mnist.load_data()

Step 3- Defining the model and then define the layers, kernel initializer, and its input nodes shape.

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

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

Step 4- We will define the activation function as relu


Step 5- Adding Layers.

We will add layers by using 'add', we will specify the dropout rate as 0.2 and 0.1 for both the layers

#Adding Layers model.add(Dense(512)) model.add(Dropout(0.2)) model.add(Dense(256, activation='relu')) model.add(Dropout(0.1))

Step 6- Printing the model

We will Print the model


Relevant Projects

Locality Sensitive Hashing Python Code for Look-Alike Modelling
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.

Predict Churn for a Telecom company using Logistic Regression
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

Human Activity Recognition Using Multiclass Classification in Python
In this human activity recognition project, we use multiclass classification machine learning techniques to analyse fitness dataset from a smartphone tracker.

German Credit Dataset Analysis to Classify Loan Applications
In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.

Data Science Project in Python on BigMart Sales Prediction
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Machine learning for Retail Price Recommendation with Python
Use the Mercari Dataset with dynamic pricing to build a price recommendation algorithm using machine learning in Python to automatically suggest the right product prices.

Abstractive Text Summarization using Transformers-BART Model
Deep Learning Project to implement an Abstractive Text Summarizer using Google's Transformers-BART Model to generate news article headlines.

Data Science Project-TalkingData AdTracking Fraud Detection
Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.

Time Series Analysis Project in R on Stock Market forecasting
In this time series project, you will build a model to predict the stock prices and identify the best time series forecasting model that gives reliable and authentic results for decision making.

Expedia Hotel Recommendations Data Science Project
In this data science project, you will contextualize customer data and predict the likelihood a customer will stay at 100 different hotel groups.