Function test_generator

cnn_class2/siamese.py:196–229

()

# same thing as the train generator except no shuffling and it uses the test set
def test_generator():
  n_batches = int(np.ceil(len(test_positives) / batch_size))

  while True:
    n_samples = batch_size * 2
    shape = [n_samples] + list(img.shape)
    x_batch_1 = np.zeros(shape)
    x_batch_2 = np.zeros(shape)
    y_batch = np.zeros(n_samples)

    for i in range(n_batches):
      pos_batch_indices = test_positives[i * batch_size: (i + 1) * batch_size]

      # fill up x_batch and y_batch
      j = 0
      for idx1, idx2 in pos_batch_indices:
        x_batch_1[j] = test_images[idx1]
        x_batch_2[j] = test_images[idx2]
        y_batch[j] = 1 # match
        j += 1

      # get negative samples
      neg_indices = np.random.choice(len(test_negatives), size=len(pos_batch_indices), replace=False)
      for neg in neg_indices:
        idx1, idx2 = test_negatives[neg]
        x_batch_1[j] = test_images[idx1]
        x_batch_2[j] = test_images[idx2]
        y_batch[j] = 0 # non-match
        j += 1

      x1 = x_batch_1[:j]
      x2 = x_batch_2[:j]
      y = y_batch[:j]
      yield [x1, x2], y