0ba6d95e768eb7a0d74a6fda3ea893e7fb2d7a67,examples/variational_autoencoder_deconv.py,,sampling,#Any#,62

Before Change

def sampling(args):
    z_mean, z_log_var = args
    epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim),
                              mean=0., stddev=epsilon_std)
    return z_mean + K.exp(z_log_var) * epsilon

After Change

    z_mean, z_log_var = args
    batch = K.shape(z_mean)[0]
    dim = K.int_shape(z_mean)[1]
    // by default, random_normal has mean=0 and std=1.0
    epsilon = K.random_normal(shape=(batch, dim))
    return z_mean + K.exp(0.5 * z_log_var) * epsilon

Instances