def __init__(self, n_epochs, tr_batch_size, optimizer_params):
self._learning_rate = tf.placeholder(tf.float32,
shape=[],
name="learn_rate")
self._n_epochs = n_epochs
self._batch_size = tr_batch_size
self._dataset = DatasetMNIST(val_size=10000)
self._model = Model()
self._optimizer = tf.train.AdamOptimizer(
learning_rate=self._learning_rate,
beta1=0.9,
beta2=0.999,
epsilon=1e-08,
use_locking=False,
name='Adam')
self._writer = tf.summary.FileWriter('./summary')
self.out_real = self._model.get_reconstruction()[0]
self.out_image = self._model.get_reconstruction()[1]
#self.output = tf.concat([self.out_real, self.out_image], axis=1)
self.Trans_real = self._model.get_Transmit()[0]
self.Trans_image = self._model.get_Transmit()[1]
self.loss_real = tf.math.subtract(self.Trans_real, self.out_real)
self.loss_image = tf.math.subtract(self.Trans_image, self.out_image)
def __init__(self, n_epochs, tr_batch_size, dataset_params, encoder_params, channel_params, decoder_params, decision_params, optimizer_params):
self._n_epochs = n_epochs
self._batch_size = tr_batch_size
self._dataset = DatasetMNIST(val_size=10000)
self._model = Model(dataset_params=dataset_params, encoder_params=encoder_params, channel_params=channel_params, decoder_params=decoder_params, decision_params=decision_params)
self._optimizer = tf.train.AdamOptimizer(learning_rate=optimizer_params['lr']) ## Tried GradientDescentOptimizer, but it seems hard to converge, but the paper mentioned SGD with Adam optimizer, so I thing the way is SGD and the optimizer is Adam
self._writer = tf.summary.FileWriter('./summary')
def __init__(self, n_epochs, tr_batch_size, optimizer_params):
self._n_epochs = n_epochs
self._batch_size = tr_batch_size
self._dataset = DatasetMNIST(val_size=10000)
self._model = Model()
self._optimizer = tf.train.AdamOptimizer(learning_rate=optimizer_params['lr'])
self._writer = tf.summary.FileWriter('./summary')
def __init__(self, n_epochs, tr_batch_size, dataset_params, encoder_params,
decoder_params, optimizer_params):
self._n_epochs = n_epochs
self._batch_size = tr_batch_size
self._dataset = DatasetMNIST(val_size=10000)
self._model = Model(dataset_params=dataset_params,
encoder_params=encoder_params,
decoder_params=decoder_params)
self._optimizer = tf.train.GradientDescentOptimizer(
learning_rate=optimizer_params['lr'])
self._writer = tf.summary.FileWriter('./summary')
def __init__(self, n_epochs, tr_batch_size, optimizer_params):
self._n_epochs = n_epochs
self._batch_size = tr_batch_size
self._dataset = DatasetMNIST(val_size=10000)
self._model = Model()
self._optimizer = tf.train.AdamOptimizer(
learning_rate=optimizer_params['lr'])
self._writer = tf.summary.FileWriter('./summary')
self.out_real = self._model.get_reconstruction()[0]
self.out_image = self._model.get_reconstruction()[1]
self.loss_real = tf.math.subtract(self._model.y_real, self.out_real)
self.loss_image = tf.math.subtract(self._model.y_image, self.out_image)
def __init__(self, n_epochs, tr_batch_size, optimizer_params):
self._learning_rate = tf.placeholder(tf.float32,
shape=[],
name="learn_rate")
self._n_epochs = n_epochs
self._batch_size = tr_batch_size
self._dataset = DatasetMNIST(val_size=10000)
self._model = Model()
self._optimizer = tf.train.AdamOptimizer(
learning_rate=self._learning_rate)
#AdaBoundOptimizer(learning_rate=0.01, final_lr=0.01, beta1=0.9, beta2=0.999, amsbound=False)
self._writer = tf.summary.FileWriter('./summary')
self.out_real = self._model.get_reconstruction()[0]
self.out_image = self._model.get_reconstruction()[1]
self.loss_real = tf.math.subtract(self._model.y_real, self.out_real)
self.loss_image = tf.math.subtract(self._model.y_image, self.out_image)
print("self._model.y_real", self._model.y_real.shape)
print("self.out_real", self.out_real.shape)
def __init__(self, n_epochs, tr_batch_size, optimizer_params):
self._n_epochs = n_epochs
self._batch_size = tr_batch_size
self._dataset = DatasetMNIST(val_size=10000)
self._model = Model()