ImageNet Data Set

class deepobs.tensorflow.datasets.imagenet.imagenet(batch_size, data_augmentation=True, train_eval_size=50000)

DeepOBS data set class for the ImageNet data set.

Note

We use 1001 classes which includes an additional background class, as it is used for example by the inception net.

Parameters:

• batch_size (int) -- The mini-batch size to use. Note that, if batch_size is not a divider of the dataset size the remainder is dropped in each epoch (after shuffling).
• data_augmentation (bool) -- If True some data augmentation operations (random crop window, horizontal flipping, lighting augmentation) are applied to the training data (but not the test data).
• train_eval_size (int) -- Size of the train eval dataset. Defaults to 10 000.

batch

A tuple (x, y) of tensors, yielding batches of ImageNet images (x with shape (batch_size, 224, 224, 3)) and corresponding one-hot label vectors (y with shape (batch_size, 1001)). Executing these tensors raises a tf.errors.OutOfRangeError after one epoch.

train_init_op

A tensorflow operation initializing the dataset for the training phase.

train_eval_init_op

A tensorflow operation initializing the testproblem for evaluating on training data.

valid_init_op

A tensorflow operation initializing the testproblem for evaluating on validation data.

test_init_op

A tensorflow operation initializing the testproblem for evaluating on test data.

phase

A string-value tf.Variable that is set to train, train_eval, valid, or test, depending on the current phase. This can be used by testproblems to adapt their behavior to this phase.