def contrastive_loss_old(labels, dists):
label_first = labels[0:1, :]
other_labels = labels[1:, :]
labels_shifted = K.concatenate(
[labels, other_labels, label_first],
axis=0) # [ l1 ........ ln | l2 ... ln-1 ln ]
labels_orig = K.concatenate(
[labels, labels], axis=0) # [ l1 ........ ln | l1 ... ln-2 ln ]
zeros = K.zeros_like(labels_orig) # [ 0 ........ 0 | 0 ... 0 0 ]
h = K.cast(K.equal(labels_orig - labels_shifted, zeros),
dtype='float32') # [ 1 1 ...... 1 | 0 ... 1 0 ]
# h: ALL ONES | MOST ZEROS
# h[i] = 1 where labels_orig[i] == labels_shifted[i] (i-th image correlated with i+1-th image, i.e. same artwork)
# h[i] = 0 where labels_orig[i] != labels_shifted[i]
first_dist = dists[0:1]
other_dists = dists[1:]
shifted_dists = K.concatenate(
[dists, other_dists, first_dist],
axis=0) # [ d1 ........ dn | d1 ... dn-2 dn ]
# equation: Lcon = (1/2N) SUM[ h(i) d(i)^2 + (1-h(i)) max(1-d(i), 0)^2
Z = K.zeros_like(shifted_dists)
max_z_sd = K.max(K.stack([1 - shifted_dists, Z]), axis=0, keepdims=False)
#max_z_sd = K.sqrt(K.cast(K.shape(shifted_dists)[0], dtype='float32')) - shifted_dists
first_operand = h * K.square(shifted_dists)
second_operand = (1 - h) * K.square(max_z_sd)
tensor_sum = first_operand + second_operand
sum = K.sum(tensor_sum, axis=0) / K.cast(K.shape(shifted_dists)[0],
dtype='float32')
return K.mean(sum)
def bias_initializer(shape, *args, **kwargs):
return K.concatenate([
self.bias_initializer((self.units, ), *args, **kwargs),
initializers.Ones()((self.units, ), *args, **kwargs),
self.bias_initializer((self.units * 2, ), *args,
**kwargs),
])
def avg_batch_mse_loss(y_true, y_pred):
# batch_size = K.int_shape(y_pred)[-1]
# loss = 0
# for i in range(0, batch_size):
# loss += mean_squared_error(y_pred[i], y_pred[i-1])
# loss/=batch_size
# max distance in the euclidean spaces with domain [0, 1] for each dimensionality, is the square root of the number
# of dimensions (1 for 1D, 1.414.. 2D, 1.73 3D, 2 4D....)
y_pred_first_row = y_pred[0:1, :]
y_pred_other_rows = y_pred[1:, :]
y_pred_shifted = K.concatenate([y_pred_other_rows, y_pred_first_row],
axis=0)
return -K.mean(K.square(y_pred - y_pred_shifted))
def contrastive_loss_2(labels, im_outputs):
distances = K.sqrt(K.sum(K.square(im_outputs - text_outputs), axis=-1))
first_text = text_outputs[0:1, :]
last_texts = text_outputs[1:, :]
shifted_texts = K.concatenate([last_texts, first_text], axis=0)
shifted_distances = K.sqrt(
K.sum(K.square(im_outputs - shifted_texts), axis=-1))
#loss = K.mean((distances + K.maximum(margin-shifted_distances, 0)))
loss = K.mean((K.square(distances) +
K.square(K.maximum(margin - shifted_distances, 0))))
#loss = K.mean(distances - shifted_distances)
return loss
def my_distance_shifted(tensors):
first_row = tensors[0][0:1, :]
last_rows = tensors[0][1:, :]
shifted_a = K.concatenate([first_row, last_rows], axis=0)
return my_distance([shifted_a, tensors[1]])
