small updates, dataset, low dim cvae

losses.py

@@ -258,6 +258,19 @@ def bend_loss(x, F, L, mask):
 #     return loss
 
 
+def log_normal_pdf(sample, mean, logvar, raxis=1):
+    log2pi = tf.math.log(2. * np.pi)
+    res = tf.reduce_sum(-.5 * ((sample - mean) ** 2. * tf.exp(-logvar) + logvar + log2pi), axis=raxis)
+    return res
+
+
+def kl_loss(mean, logvar):
+    kl_loss = 1 + logvar - tf.square(mean) - tf.exp(logvar)
+    kl_loss = tf.reduce_mean(kl_loss)
+    kl_loss *= -0.5
+    return kl_loss
+
+
 if __name__ == '__main__':
     V = tf.ones((4, 14475, 3))
     B = tf.zeros((4, 14475, 3))