def decode(self, latent_input):
return apply_tf_op(inputs=latent_input,
session=self.sess,
input_gate=self.latent_ph,
output_gate=self.dec_output_ph,
batch_size=self.args.nbatch,
train_gate=self.istrain)
def custom_apply_tf_op(inputs, output_gate):
return apply_tf_op(inputs=inputs,
session=self.sess,
input_gate=self.img,
output_gate=output_gate,
batch_size=self.args.nbatch,
dim=4,
train_gate=self.istrain)
def custom_apply_tf_op(inputs, output_gate): return apply_tf_op(inputs=inputs, session=self.sess, input_gate=self.img, output_gate=output_gate, batch_size=self.args.nbatch, dim=4, train_gate=self.istrain) self.val_embed = custom_apply_tf_op(inputs=self.val_image, output_gate=self.embed_l2_norm)