text_plain_output_1.png
from keras.layers import Dense, LeakyReLU

from keras.models import Sequential

classifier = Sequential()

classifier.add(Dense(units=6, kernel_initializer='uniform'))
classifier.add(LeakyReLU(alpha=0.1))

classifier.add(Dense(units=6, kernel_initializer='uniform'))
classifier.add(LeakyReLU(alpha=0.1))

classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
'as it is a binary classification problem thats why using 1 as number of neuron\nand using sigmoid as we have only 2 categories. If we would have more than 2 categories\nthen we might have used softmax activation function'

classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
' Here optimizer is used as Adam a special type of Stochastic gradient descent(SGD), \nbinary_crossentropy loss function is used and accuracy metrics is considered'