How to deal with triplet loss when at time of inpu

I am implementing a siamese network in which i know how to calculate triplet loss by picking anchor, positive and negative by dividing input in three parts(which is a handcrafted feature vector) and then calculating it at time of training.

anchor_output = ...  # shape [None, 128]
positive_output = ...  # shape [None, 128]
negative_output = ...  # shape [None, 128]

d_pos = tf.reduce_sum(tf.square(anchor_output - positive_output), 1)
d_neg = tf.reduce_sum(tf.square(anchor_output - negative_output), 1)

loss = tf.maximum(0., margin + d_pos - d_neg)
loss = tf.reduce_mean(loss)

But the problem is when at time of testing i would be having only two files positive and negative then how i would deal with(triplets, as i need one more anchor file but my app only take one picture and compare with in database so only two files in this case), I searched a lot but nobody provided code to deal with this problem only there was code to implement triplet loss but not for whole scenario. AND I DONT WANT TO USE CONTRASTIVE LOSS

Colab notebook with test code on CIFAR 10: https://colab.research.google.com/drive/1VgOTzr_VZNHkXh2z9IiTAcEgg5qr19y0

The general idea:

from tensorflow import keras
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
from tensorflow.keras import backend as K

img_width = 128
img_height = 128
img_colors = 3

margin = 1.0

VECTOR_SIZE = 32

def triplet_loss(y_true, y_pred):
  """ y_true is a dummy value that should be ignored

      Uses the inverse of the cosine similarity as a loss.
  """
  anchor_vec = y_pred[:, :VECTOR_SIZE]
  positive_vec = y_pred[:, VECTOR_SIZE:2*VECTOR_SIZE]
  negative_vec = y_pred[:, 2*VECTOR_SIZE:]
  d1 = keras.losses.cosine_proximity(anchor_vec, positive_vec)
  d2 = keras.losses.cosine_proximity(anchor_vec, negative_vec)
  return K.clip(d2 - d1 + margin, 0, None)


def make_image_model():
  """ Build a convolutional model that generates a vector
  """
  inp = Input(shape=(img_width, img_height, img_colors))
  l1 = Conv2D(8, (2, 2))(inp)
  l1 = MaxPooling2D()(l1)
  l2 = Conv2D(16, (2, 2))(l1)
  l2 = MaxPooling2D()(l2)
  l3 = Conv2D(16, (2, 2))(l2)
  l3 = MaxPooling2D()(l3)
  conv_out = Flatten()(l3)
  out = Dense(VECTOR_SIZE)(conv_out)
  model = Model(inp, out)
  return model

def make_siamese_model(img_model):
  """ Siamese model input are 3 images base, positive, negative
      output is a dummy variable that is ignored for the purposes of loss
      calculation.
  """
  anchor = Input(shape=(img_width, img_height, img_colors))
  positive = Input(shape=(img_width, img_height, img_colors))
  negative = Input(shape=(img_width, img_height, img_colors))
  anchor_vec = img_model(anchor)
  positive_vec = img_model(positive)
  negative_vec = img_model(negative)
  vecs = Concatenate(axis=1)([anchor_vec, positive_vec, negative_vec])
  model = Model([anchor, positive, negative], vecs)
  model.compile('adam', triplet_loss)
  return model

img_model = make_image_model()
train_model = make_siamese_model(img_model)
img_model.summary()
train_model.summary()

###
train_model.fit(X, dummy_y, ...)

img_model.save('image_model.h5')

###
# In order to use the model
vec_base = img_model.predict(base_image)
vec_test = img_model.predict(test_image)

compare cosine similarity of vec_base and vec_test in order to determine whether base and test are within the acceptable criteria.