def loadData(): images_dir = os.path.join(current_dir, 'image_data') images =  for each in os.listdir(images_dir): images.append(os.path.join(images_dir,each)) all_images = tf.convert_to_tensor(images, dtype = tf.string) images_batch = tf.train.shuffle_batch( [all_images], batch_size = BATCH_SIZE) return images_batch
ValueError: Cannot create a tensor proto whose content is larger than 2GB.
I’m trying to load about 11GB of images. How can I overcome those limitation?
Edit: Possbile duplicate: You can split the output classes into multiple operations and concatenate them at the end is suggest, but I do not have multiple classes I can split.
Edit2: Solutions to this problem suggest using placeholders. So now I’m not sure who to use placeholders in that case and where I can feed the array of images to tensorflow.
Here’s a minimal version of my train function to show how I initialize the session.
def train(): images_batch = loadData() sess = tf.Session() saver = tf.train.Saver() sess.run(tf.global_variables_initializer()) sess.run(tf.local_variables_initializer()) for i in range(EPOCH): train_image = sess.run(image_batch)
convert_to_tensor has the unexpected effect of adding your images to the computational graph, which has a hard limit of 2GB. If you hit this limit, you should reconsider how to feed images for the training process.
We already have a simple solution in TensorFlow, just use placeholders (
session.run. The only disadvantage in this case is that you have to produce batches of your data manually.