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.
Table of Contents
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
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