Increase the number of classes in image classifier - python-3.x

Question

I have done a programm on image classification of two objects namely dogs and cats using CNN in keras. Now how can I increase the number of classes,i.e, dogs, cats, and frog?
Here's the code:
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.callbacks import ModelCheckpoint
classifier = Sequential()
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
filepath="weights-improvment-{epoch:02d}-{val_acc:.2f}.hdf5"
checpoint=ModelCheckpoint(filepath,monitor='val_acc',verbose=1,save_best_only=True,mode='max')
callback_list=[checpoint]
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 10,
validation_data = test_set,
validation_steps = 2000)
classifier.save('model_after_trained.h5')

Answer 1

In order to classify more than two classes, the number of neurons(units) in the last layer must be changed to the number of classes to be predicted.
Suppose if you want to predict 3 objects, the last layer must be changed as:
classifier.add(Dense(units = 3, activation = 'sigmoid'))
Please find the below link which will help you to do multi-class classification using CNN: https://www.codesofinterest.com/2017/08/bottleneck-features-multi-class-classification-keras.html
Hope this helps!!!