def __init__(self, image_size=24, num_classes=10, batch_size=50, channels=3):
self._image_size = image_size
self._num_classes = num_classes
self._batch_size = batch_size
self._channels = channels
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
self._session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
self._images = tf.placeholder(tf.float32, shape=[None, self._image_size, self._image_size, self._channels])
self._labels = tf.placeholder(tf.int64, shape=[None])
self._keep_prob = tf.placeholder(tf.float32)
self._global_step = tf.Variable(0, tf.int64, name="global_step")
self._logits = self._inference(self._images, self._keep_prob)
self._avg_loss = self._loss(self._labels, self._logits)
self._train_op = self._train(self._avg_loss)
self._accuracy = F.accuracy_score(self._labels, self._logits)
self._saver = tf.train.Saver(tf.all_variables())
self._session.run(tf.initialize_all_variables())
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