def split2d_prior(z, hps): n_z2 = int(z.get_shape()[3]) n_z1 = n_z2 h = conv2d_zeros(z, 2 * n_z1, name="conv") mean, logsd = tf.split(h, 2, axis=-1) rescale = tf.get_variable("rescale", [], initializer=tf.constant_initializer(1.)) scale_shift = tf.get_variable("scale_shift", [], initializer=tf.constant_initializer(0.)) logsd = tf.tanh(logsd) * rescale + scale_shift return gaussian_diag(mean, logsd)
def split2d_prior(z, hps): n_z2 = int(z.get_shape()[3]) n_z1 = n_z2 h = conv2d_zeros(z, 2 * n_z1, name="conv") mean, logsd = tf.split(h, 2, axis=-1) rescale = tf.get_variable("rescale", [], initializer=tf.constant_initializer(1.)) scale_shift = tf.get_variable("scale_shift", [], initializer=tf.constant_initializer(0.)) logsd = tf.tanh(logsd) * rescale + scale_shift return gaussian_diag(mean, logsd)
def prior(y_onehot, hps, name=None): n_z = hps.top_shape[-1] h = tf.zeros([tf.shape(y_onehot)[0]]+hps.top_shape[:2]+[2*n_z]) h = conv2d_zeros(h, 2*n_z, name="p") if hps.ycond: h += tf.reshape(linear_zeros(y_onehot, 2*n_z, name="y_emb"), [-1, 1, 1, 2 * n_z]) mean, logsd = tf.split(h, 2, axis=-1) rescale = tf.get_variable("rescale", [], initializer=tf.constant_initializer(1.)) scale_shift = tf.get_variable("scale_shift", [], initializer=tf.constant_initializer(0.)) logsd = tf.tanh(logsd) * rescale + scale_shift pz = gaussian_diag(mean, logsd) logp = lambda z1: pz.logp(z1) eps = lambda z1: pz.get_eps(z1) sample = lambda eps: pz.sample(eps) return logp, sample, eps
def prior(name, y_onehot, cfg): with tf.variable_scope(name): cfg.top_shape = [1, 1, 768] n_z = cfg.top_shape[-1] h = tf.zeros([tf.shape(y_onehot)[0]]+cfg.top_shape[:2]+[2*n_z]) if cfg.learntop: assert(False) h = ops._conv2d('p', h, 2*n_z, 3, 1, True) if cfg.ycond: assert(False) h += tf.reshape(ops.dense("y_emb", y_onehot, 2*n_z, True, init_zero=True), [-1, 1, 1, 2 * n_z]) pz = ops.gaussian_diag(h[:, :, :, :n_z], h[:, :, :, n_z:]) def logp(z1): objective = pz.logp(z1) return objective def sample(eps=None, eps_std=None, temp=1.0): if eps is not None: # Already sampled eps. Don't use eps_std z = pz.sample_eps(eps) elif eps_std is not None: # Sample with given eps_std z = pz.sample_eps(pz.eps * tf.reshape(eps_std, [-1, 1, 1, 1])) else: # Sample normally z = pz.sample(temp) return z def eps(z1): return pz.get_eps(z1) return logp, sample, eps
def prior(y_onehot, hps, name=None): n_z = hps.top_shape[-1] h = tf.zeros([tf.shape(y_onehot)[0]] + hps.top_shape[:2] + [2 * n_z]) h = conv2d_zeros(h, 2 * n_z, name="p") if hps.ycond: h += tf.reshape(linear_zeros(y_onehot, 2 * n_z, name="y_emb"), [-1, 1, 1, 2 * n_z]) mean, logsd = tf.split(h, 2, axis=-1) rescale = tf.get_variable("rescale", [], initializer=tf.constant_initializer(1.)) scale_shift = tf.get_variable("scale_shift", [], initializer=tf.constant_initializer(0.)) logsd = tf.tanh(logsd) * rescale + scale_shift pz = gaussian_diag(mean, logsd) logp = lambda z1: pz.logp(z1) eps = lambda z1: pz.get_eps(z1) sample = lambda eps: pz.sample(eps) return logp, sample, eps
def split2d_prior(z, cfg): n_z = int(z.get_shape()[3]) h = f_('split2d_prior', z, cfg, n_out=n_z * 2) mean = h[:, :, :, 0::2] logs = h[:, :, :, 1::2] return ops.gaussian_diag(mean, logs)