what will the equivalent code be for
z_loss = 0.5 * tf.reduce_sum(tf.square(z_mean) + tf.exp(z_logvar) - z_logvar - 1, axis = [1,2,3])
What are the pytorch equivalent for reduce_mean and reduce_sum
Thanks
what will the equivalent code be for
z_loss = 0.5 * tf.reduce_sum(tf.square(z_mean) + tf.exp(z_logvar) - z_logvar - 1, axis = [1,2,3])
What are the pytorch equivalent for reduce_mean and reduce_sum
Thanks
torch.mean
and torch.sum
would be the replacements (or call .mean()
or .sum()
on a tensor directly).
cosim = tf.maximum(cosine_similarity(tensor_a, tensor_b), 1e-1) ** cossim_pow
can we use torch.clap_min instead of tf.maximum ??for above tensor flow implementation in pytorch
cosim = torch.pow(torch.clamp_min(cosine_similarity(tensor_a, tensor_b), 1e-1), cossim_pow)