from keras.engine import  Model
from keras.layers import Input
from keras_vggface.vggface import VGGFace

# Convolution Features
vgg_model_conv = VGGFace(include_top=False, input_shape=(224, 224, 3), pooling='avg') # pooling: None, avg or max

# FC7 Features
vgg_model = VGGFace() # pooling: None, avg or max
out = vgg_model.get_layer('fc7').output
vgg_model_fc7 = Model(vgg_model.input, out)

# After this point you can use your models as usual for both.
# ...

from keras.engine import  Model
from keras.layers import Flatten, Dense, Input
from keras_vggface.vggface import VGGFace

#custom parameters
nb_class = 2
hidden_dim = 512

vgg_model = VGGFace(include_top=False, input_shape=(224, 224, 3))
last_layer = vgg_model.get_layer('pool5').output
x = Flatten(name='flatten')(last_layer)
x = Dense(hidden_dim, activation='relu', name='fc6')(x)
x = Dense(hidden_dim, activation='relu', name='fc7')(x)
out = Dense(nb_class, activation='softmax', name='fc8')(x)
custom_vgg_model = Model(vgg_model.input, out)

# Train your model as usual.
# ...

import numpy as np
from keras.preprocessing import image
from keras_vggface.vggface import VGGFace

# tensorflow
model = VGGFace()

# Change the image path with yours.
img = image.load_img('../image/ak.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
# TF order aka 'channel-last'
x = x[:, :, :, ::-1]
# TH order aka 'channel-first'
# x = x[:, ::-1, :, :]
# Zero-center by mean pixel
x[:, 0, :, :] -= 93.5940
x[:, 1, :, :] -= 104.7624
x[:, 2, :, :] -= 129.1863

preds = model.predict(x)
print('Predicted:', np.argmax(preds[0]))