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)
