What is the meaning of loss functions in keras?
This recipe explains what is the meaning of loss functions in keras
Recipe Objective
To understand the meaning of loss functions in keras.
The loss is calculated to get the gradients(please refer to gradient descent graph to understand) concerning model weights and update those weights accordingly via backpropagation. Loss is calculated then network weights are updated after every iteration until model updates don't get close or make any improvement in the desired evaluation metric.
Step 1- Importing Libraries.
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
Step 2- Loading the Sequential model.
We will define the layers, kernel initializer, and its input nodes shape in the model.
model = keras.Sequential()
model.add(layers.Dense(64, kernel_initializer='uniform', input_shape=(10,)))
Step 3- Defining the activation function.
We will define the activation function as relu.
model.add(layers.Activation('relu'))
Step 4- Initialize the Loss function.
We will initialize the loss function as 'Binary_Cross_entropy' with reduction as 'sum_over_batch_size'.
BC = keras.losses.BinaryCrossentropy(reduction='sum_over_batch_size')
model.compile(loss=loss_fn, optimizer='adamax')
Step 5- Taking a sample dataset
Let's take a sample dataset of predicted and true values then calculate the loss.
y_true = [[1, 2], [4, 6],[0.5, 0.7],[0.4, 0.6]]
y_pred = [[1.5, 1.4], [5, 7],[0.6, 0.5],[0.7, 0.7]]
BC(y_true, y_pred).numpy()
-16.878973
As we can see minimum loss with this model is -16.878973 for the sample dataset. We can improve it by choosing another type of loss function or optimizer.
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