How to create a custom cost function to evaluate keras model?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

How to create a custom cost function to evaluate keras model?

How to create a custom cost function to evaluate keras model?

This recipe helps you create a custom cost function to evaluate keras model

Recipe Objective

How to create a custom cost function to evaluate keras model

The formula for every loss function is predefined, but if we want to create a loss function(cost function) specifically for our model then we can create. So we can define our own loss function and call it a custom cost function.

Step 1- Importing Libraries

import keras as k from keras.models import Sequential from keras.layers import Dense import numpy as np

Step 2- Defining two sample arrays.

We will define two sample arrays as predicted and actual to calculate the loss. y_pred=np.array([2,3,5,7,9]) y_actual=np.array([4,2,8,5,2])

Step 3- Define your new custom loss function.

we are considering the formula for MSE here.

def custom_loss(y_true,y_pred): return K.mean(K.square(y_pred - y_actual) + K.square(layer), axis=-1)

Step 4- Creating the Neural Network Model.

We will pass our own custom cost function to the model.

# create model model = Sequential() model.add(Dense(12, input_dim=8, activation='relu')) model.add(Dense(8, activation='relu')) model.add(Dense(1, activation='sigmoid')) # Compile model model.compile(loss='custom_loss', optimizer='adam', metrics=['accuracy'])

Step 5- Create the model summary.

model.summary
>

Relevant Projects

Build a Collaborative Filtering Recommender System in Python
Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.

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.

Medical Image Segmentation Deep Learning Project
In this deep learning project, you will learn to implement Unet++ models for medical image segmentation to detect and classify colorectal polyps.

Time Series Python Project using Greykite and Neural Prophet
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.

Demand prediction of driver availability using multistep time series analysis
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.

Loan Eligibility Prediction in Python using H2O.ai
In this loan prediction project you will build predictive models in Python using H2O.ai to predict if an applicant is able to repay the loan or not.

Resume parsing with Machine learning - NLP with Python OCR and Spacy
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.

Machine Learning project for Retail Price Optimization
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

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.

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.