age_classifier.py

num_classes = len(AGE_CLASS)
input_shape = (227,227,3)

model = Sequential()

model.add(Conv2D(32, kernel_size=(3, 3), activation='relu',input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(3,3),strides=2))
model.add(BatchNormalization())

model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(BatchNormalization())

model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(BatchNormalization())

model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(BatchNormalization())

model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(BatchNormalization())

model.add(Dense(256, activation='relu'))
model.add(Dropout(0.25))

model.add(Dense(512, activation='relu'))
model.add(Dropout(0.25))
model.add(Flatten())

model.add(Dense(num_classes, activation='softmax'))

model.summary()
model.load_weights('age.h5')