def Loss(embedding, label, _lossType="Softmax", loss_l2_reg=FLAGS.loss_l2_reg):
embedding_split = tf.split(embedding, 2, axis=0)
label_split = tf.split(label, 2, axis=0)
embedding_anchor = embedding_split[0]
embedding_positive = embedding_split[1]
label_positive = label_split[1]
_Loss = 0
if _lossType == "Softmax":
print("Use Softmax")
W_fc2 = nn_Ops.weight_variable([FLAGS.gap_dim, 10])
b_fc2 = nn_Ops.bias_variable([10])
y_conv = tf.matmul(embedding, W_fc2) + b_fc2
_Loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=label,
logits=y_conv))
elif _lossType == "Contrastive_Loss":
print("Use Contrastive_Loss_v2")
embedding_anchor = tf.split(embedding_anchor, 2, axis=0)
embedding_positive = tf.split(embedding_positive, 2, axis=0)
_Loss = contrastive_loss_v2(embedding_anchor[0],
embedding_anchor[1],
embedding_positive[0],
embedding_positive[1],
alpha=1.0)
elif _lossType == "Triplet_Semihard":
print("Use Triplet_semihard")
_Loss = triplet_semihard_loss(label, embedding)
elif _lossType == "LiftedStructLoss":
print("Use LiftedStructLoss")
_Loss = lifted_struct_loss(label, embedding)
elif _lossType == "NpairLoss":
print("Use NpairLoss")
_Loss = npairs_loss(label_positive,
embedding_anchor,
embedding_positive,
reg_lambda=loss_l2_reg)
elif _lossType == "Triplet":
print("Use Triplet Loss")
embedding3 = tf.split(embedding, 3, axis=0)
anchor = embedding3[0]
positive = embedding3[1]
negative = embedding3[2]
_Loss = triplet_loss(anchor, positive, negative)
elif _lossType == "New_npairLoss":
print("Use new NpairLoss")
_Loss = new_npair_loss(labels=label,
embedding_anchor=embedding_anchor,
embedding_positive=embedding_positive,
reg_lambda=loss_l2_reg,
equal_shape=True,
half_batch_size=int(FLAGS.batch_size / 2))
return _Loss
def cross_entropy(embedding, label, size=1024):
with tf.variable_scope("Softmax_classifier"):
W_fc = weight_variable([size, FLAGS.num_class], "softmax_w", wd=False)
b_fc = bias_variable([FLAGS.num_class], "softmax_b")
Logits = tf.matmul(embedding, W_fc) + b_fc
cross_entropy = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(labels=label,
logits=Logits))
return cross_entropy, W_fc, b_fc
def Binary_sofmax(anc_emb, pos_emb, label, cycle, weight, emb_size):
cross_entropy = tf.constant(0., tf.float32)
with tf.variable_scope("Softmax_classifier"):
W_fc = nn_Ops.weight_variable([2048, 2], "softmax_w", wd=False)
b_fc = nn_Ops.bias_variable([2], "softmax_b")
for i in range(cycle):
if i >= 64:
break
pos_f = tf.slice(input_=pos_emb, begin=[0, 0], size=[i, emb_size])
label_f = tf.slice(input_=label, begin=[0], size=[i])
pos_b = tf.slice(input_=pos_emb, begin=[i, 0], size=[64 - i, emb_size])
label_b = tf.slice(input_=label, begin=[i], size=[64 - i])
pos_temp = tf.concat([pos_b, pos_f], axis=0)
label_temp = tf.concat([label_b, label_f], axis=0)
logits = tf.matmul(tf.concat([anc_emb, pos_temp], axis=1), W_fc) + b_fc
label_binary = tf.cast(tf.equal(label, label_temp), tf.int32)
weight_m = tf.cast(tf.logical_not(tf.equal(label, label_temp)), tf.float32) \
* weight + tf.cast(label_binary, tf.float32)
cross_entropy += tf.reduce_mean(
tf.multiply(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=label_binary),
weight_m)) / np.float32(cycle)
return cross_entropy, W_fc, b_fc
