pranjalg2308

<?xml version="1.0" encoding="utf-8"?>
<layer-list xmlns:android="http://schemas.android.com/apk/res/android">

    <item
        android:drawable="@color/white"/>

    <item>
        <bitmap
            android:gravity="center"
            android:src="@drawable/ic_icon"/>

pranjalg2308

# Cosine Similarity
def triplet_loss(alpha, emb_dim):
    def loss(y_true, y_pred):
        anc, pos, neg = y_pred[:,:emb_size], y_pred[:,emb_size:2*emb_size], y_pred[:,2*emb_size:]
        distance1 = tf.keras.losses.cosine_similarity(anc, pos)
        distance2 = tf.keras.losses.cosine_similarity(anc, neg)
        return tf.keras.backend.clip(distance1 - distance2 + alpha, 0., None)
    return loss

pranjalg2308

# L2 Distance
def triplet_loss(alpha, emb_dim):
    def loss(y_true, y_pred):
        anc, pos, neg = y_pred[:,:emb_size], y_pred[:,emb_size:2*emb_size], y_pred[:,2*emb_size:]
        distance1 = tf.sqrt(tf.reduce_sum(tf.pow(anc - pos, 2), 1, keepdims=True))
        distance2 = tf.sqrt(tf.reduce_sum(tf.pow(anc - neg, 2), 1, keepdims=True))
        return tf.reduce_mean(tf.maximum(distance1 - distance2 + alpha, 0.))
    return loss