added doc for allow_smaller_final_batch
@@ -250,6 +250,7 @@ function parameters. See [Signals and event handlers](extending_event_handler.ht
[name](#name) | `string` | `name=niftynet.network.toynet.ToyNet` | `''`
[activation_function](#activation-function) | `string` | `activation_function=prelu` | `relu`
[batch_size](#batch-size) | `integer` | `batch_size=10` | `2`
+[allow_smaller_final_batch](#allow-smaller-final-batch) | `boolean` | | `-1`
[decay](#decay) | `non-negative float` | `decay=1e-5` | `0.0`
[reg_type](#reg-type) | `string` | `reg_type=L1` | `L2`
[volume_padding_size](#volume-padding-size) | `integer array` | `volume_padding_size=4, 4, 4` | `0,0,0`
@@ -278,6 +279,12 @@ Sets number of image windows to be processed at each iteration.
When `num_gpus` is greater than 1, `batch_size` is used for each computing device.
That is, the effective inputs at each iteration become `batch_size` x `num_gpus`.
+###### `allow_smaller_final_batch`
+If True, allow the final batch to be smaller if there are insufficient items left
+in the queue, and the batch size will be undetermined during graph construction.
+The default value is `-1`, which means `allow_smaller_final_batch = False`
+for training, and `True` for inference.
+
###### `reg_type`
Type of trainable parameter regularisation; currently the available choices are "L1" and "L2".
The loss will be added to `tf.GraphKeys.REGULARIZATION_LOSSES` collection.
