def __init__(self, params, session, name="GAN", mode="train"):
     self.generator = tf.make_template(name + '/generator', Generator)
     self.discriminator = tf.make_template(name + '/discriminator', Discriminator)
     Trainer.__init__(self, session, params, name=name, mode=mode)
     self.build_eval_graph()
     self.train_X = np.transpose(self.train_X, (0, 3, 1, 2))
     self.test_X = np.transpose(self.test_X, (0, 3, 1, 2))
 def __init__(self,
              sess,
              model,
              wgan,
              params,
              name="classifier",
              mode="train"):
     '''
     Init:
     sess: tf.Session()
     model: function that takes in input placeholder, is_training placeholder and params dictionary,
     wgan: trainer for adversarial generator.
     params: dictionary of hyperparameters for the model and training.
     '''
     self.model = model
     self.gan = wgan
     Trainer.__init__(self, sess, params, name, mode)
     self.adversarial_dir = os.path.join(self.checkpoint_dir, "adversarial")
     check_folder(self.adversarial_dir)
 def __init__(self,
              sess,
              model,
              generator,
              params,
              name="classifier",
              mode="train"):
     '''
     Init:
     sess: tf.Session()
     model: function that takes in input placeholder, is_training placeholder and params dictionary,
     adv_trainer: trainer for adversarial generator, must be not None is adversarial_mode == "gan"
     returns a tensor containing the logits.
     params: dictionary of hyperparameters for the model and training.
     '''
     self.model = model
     self.generator = generator
     Trainer.__init__(self, sess, params, name, mode)
     self.adversarial_dir = os.path.join(self.checkpoint_dir, "adversarial")
     check_folder(self.adversarial_dir)