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.
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Table of Contents
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)
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