def Face(train=False):
base_model = VGGFace(input_shape=(200, 200, 3),
include_top=False,
model='resnet50',
weights=None,
pooling='avg')
# load pre-trained weights if used for fine-tuning
if train:
base_model.load_weights(BASE_WEIGHTS_PATH)
for layer in base_model.layers[:len(base_model.layers) - 50]:
layer.trainable = False
base_output = base_model.output
# age 1~93, treat age as classifications task
output_a = Dense(93, activation='softmax',
name='predications_age')(base_output)
# gender 0 or 1
output_g = Dense(2, activation='softmax',
name='predications_gender')(base_output)
# race 0~4
output_r = Dense(5, activation='softmax',
name='predications_race')(base_output)
new_model = Model(inputs=base_model.input,
outputs=[output_a, output_g, output_r],
name='network_based_vggface')
return new_model
from vggface import VGGFace
from scipy import misc
import copy
import numpy as np
if __name__ == '__main__':
model = VGGFace(weights=None)
model.load_weights(
'../temp/weight/rcmalli_vggface_tf_weights_tf_ordering.h5')
print 'model loaded.'
im = misc.imread('../image/ak2.jpg')
im = misc.imresize(im, (224, 224)).astype(np.float32)
aux = copy.copy(im)
im[:, :, 0] = aux[:, :, 2]
im[:, :, 2] = aux[:, :, 0]
# Remove image mean
im[:, :, 0] -= 93.5940
im[:, :, 1] -= 104.7624
im[:, :, 2] -= 129.1863
im = np.expand_dims(im, axis=0)
res = model.predict(im)
print np.argmax(res[0])