def apply_google_net(x):
mean_values = np.array([104, 117, 123]).reshape((3, 1, 1))
# Convert RGB to BGR
xx = x[:, ::-1, :, :] * 255.0
xx = xx - mean_values[np.newaxis, :, :, :].astype('float32')
net = create_theano_expressions(inputs=('data', xx))
pre_softmax = net[0]['loss3/classifier']
return pre_softmax.flatten(2)
def apply_vgg(x):
from sklearn_theano.feature_extraction.caffe.vgg import create_theano_expressions
mean_values = np.array([104, 117, 123]).reshape((3, 1, 1))
# Convert RGB to BGR
xx = x[:, ::-1, :, :] * 255.0
xx = xx - mean_values[np.newaxis, :, :, :].astype('float32')
net = create_theano_expressions(inputs=('data', xx))
pre_softmax = net[0]['prob']
return pre_softmax.flatten(2)
