What is a drop out rate in keras?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

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

0

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

model.add(layers.Activation('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

print(model)

Relevant Projects

Walmart Sales Forecasting Data Science Project
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

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.

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.

Credit Card Fraud Detection as a Classification Problem
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

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.

Zillow’s Home Value Prediction (Zestimate)
Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.

Customer Churn Prediction Analysis using Ensemble Techniques
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.

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.

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.

Perform Time series modelling using Facebook Prophet
In this project, we are going to talk about Time Series Forecasting to predict the electricity requirement for a particular house using Prophet.