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 reweight(y_true,
y_pred,
tp_weight=.3,
tn_weight=.3,
fp_weight=4,
fn_weight=0.7):
# Get predictions
y_pred_classes = K.greater_equal(y_pred, 0.5)
y_pred_classes_float = K.cast(y_pred_classes, K.floatx())
# Get misclassified examples
wrongly_classified = K.not_equal(y_true, y_pred_classes_float)
wrongly_classified_float = K.cast(wrongly_classified, K.floatx())
# Get correctly classified examples
correctly_classified = K.equal(y_true, y_pred_classes_float)
correctly_classified_float = K.cast(correctly_classified, K.floatx())
# Get tp, fp, tn, fn
tp = correctly_classified_float * y_true
tn = correctly_classified_float * (1 - y_true)
fp = wrongly_classified_float * y_true
fn = wrongly_classified_float * (1 - y_true)
# Get weights
weight_tensor = tp_weight * tp + fp_weight * fp + tn_weight * tn + fn_weight * fn
loss = K.binary_crossentropy(y_true, y_pred)
weighted_loss = loss * weight_tensor
return weighted_loss
def acc(y_true, y_pred):
y_true, payoffs = splitter(y_true)
return K.mean(K.equal(y_true, K.round(y_pred)), axis=-1)
def seq_accuracy(y_true, y_pred):
return K.cast(K.all(K.equal(y_true, K.round(y_pred)), axis=-2), 'float32')
def binary_accuracy(y_true, y_pred):
return K.mean(K.equal(y_true, K.round(y_pred)), axis=-1)
def accuracy(y_true, y_pred): # Tensor上的操作
'''Compute classification accuracy with a fixed threshold on distances.
'''
return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))
def binary_accuracy(y_true, y_pred):
return K.mean(K.equal(y_true, K.round(y_pred)), axis=-1)
