text_plain_output_1.png
"""
#developing the model using Dropout
#here we will initialize dropout in each layer except the output layer as it might result into a unstable network
classifier=Sequential() 
#1st hidden layer
classifier.add(Dense(units=6, kernel_initializer="uniform")) 
classifier.add(LeakyReLU(alpha=0.1))
classifier.add(Dropout(p=0.1)) #adding dropout rate 10%. Its better to start with a small number
#2nd hidden layer 
classifier.add(Dense(units=6, kernel_initializer="uniform")) 
classifier.add(LeakyReLU(alpha=0.1)) 
classifier.add(Dropout(p=0.1))
#output layer
classifier.add(Dense(units=1, kernel_initializer="uniform",activation="sigmoid")) 
#compiling the network  
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
"""