def _get_latents(self, inputs, scope_name='model/enc_cla', reuse=False):
with tf.variable_scope(scope_name, reuse=reuse):
mlp = MLP(name='inputs_to_latents',
shapes=[self.xdim] + self.hidden_layer_specs['enc'] +
[self.zdim],
activ=ACTIV)
return mlp.forward(inputs)
def _get_class_logits(self, scope_name='model/preds'):
with tf.variable_scope(scope_name):
mlp = MLP(name='data_to_class_preds',
shapes=[self.xdim] +
self.hidden_layer_specs['layer_sizes'] + [self.ydim],
activ=self.hidden_layer_specs['activ'])
logits = mlp.forward(self.X)
return logits
def _get_recon_inputs(self, latents, scope_name='model/enc_cla'):
with tf.variable_scope(scope_name):
mlp = MLP(name='latents_to_reconstructed_inputs',
shapes=[self.zdim + 1] + self.hidden_layer_specs['rec'] + [self.xdim],
activ=ACTIV)
Z_and_A = tf.concat([self.Z, self.A], axis=1)
final_reps = mlp.forward(Z_and_A)
return final_reps
def _get_sensitive_logits(self,
inputs,
scope_name='model/aud',
reuse=False):
with tf.variable_scope(scope_name, reuse=reuse):
mlp = MLP(name='latents_to_sensitive_logits',
shapes=[self.zdim + 1 * self.ydim] +
self.hidden_layer_specs['aud'] + [self.adim],
activ=ACTIV)
return mlp.forward(inputs)
def _get_idks(self, scope_name='model/preds'):
with tf.variable_scope(scope_name):
mlp = MLP(name='data_to_idks',
shapes=[self.xdim + self.ydim] +
self.hidden_layer_specs['layer_sizes'] + [self.ydim],
activ=self.hidden_layer_specs['activ'])
Y_hat_no_grad = tf.stop_gradient(self.Y_hat)
self.idk_logits = mlp.forward(
tf.concat([self.X, Y_hat_no_grad], axis=1))
idks = tf.sigmoid(self.idk_logits)
return idks