Gender Classification(VGG16 Model).py

optimizer = RMSprop(lr=1e-4)
objective = 'binary_crossentropy'


def malefemale():
    
    model = Sequential()

    model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=(3, ROWS, COLS), activation='relu'))
    model.add(Convolution2D(32, 3, 3, border_mode='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))

    model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu'))
    model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))
    
    model.add(Convolution2D(128, 3, 3, border_mode='same', activation='relu'))
    model.add(Convolution2D(128, 3, 3, border_mode='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))
    
    model.add(Convolution2D(256, 3, 3, border_mode='same', activation='relu'))
    model.add(Convolution2D(256, 3, 3, border_mode='same', activation='relu'))
#     model.add(Convolution2D(256, 3, 3, border_mode='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))



    model.add(Flatten())
    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.5))
    
    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.5))

    model.add(Dense(1))
    model.add(Activation('sigmoid'))

    model.compile(loss=objective, optimizer=optimizer, metrics=['accuracy'])
    return model


model = malefemale()