def get_bias_mod(self, name, shape, trainable):
   b = caffe_bias(name)
   indices, known = input_data.get_indices()
   B = np.array(np.zeros((len(indices),)), dtype=np.float32)
   B[known] = b[indices[known]]
   t = tf.Variable(B, name="bias")
   return t
 def get_fc_weight_mod(self, name, shape, trainable, decay=None):
   cw = caffe_weights(name).transpose((1,0))
   indices, known = input_data.get_indices()
   W = np.array(np.random.randn(cw.shape[0], len(indices)), dtype=np.float32) * 1e-2
   for i in known:
     W[:, i] = cw[:, indices[i]]
   t = tf.Variable(W, name="weight")
   return t