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Nach einigen Iterationen der ersten Epoche stoppt der Trainingsprozess ohne Ausgabe oder Fehlermeldung. SSD Implementierung in Keras von https://github.com/rykov8/ssd_keras verwendet wurdeTraining in SSD-Implementierung in Keras hält nach ein paar Iterationen ohne Ausgabe oder Fehler
base_lr = 3e-4
#optim = keras.optimizers.Adam(lr=base_lr)
optim = keras.optimizers.RMSprop(lr=base_lr)
#optim = keras.optimizers.SGD(lr=base_lr, momentum=0.9, decay=decay, nesterov=True)
model.compile(optimizer=optim,
loss=MultiboxLoss(NUM_CLASSES+1, neg_pos_ratio=2.0).compute_loss)
nb_epoch = 10
history = model.fit_generator(gen.generate(True), gen.train_batches,
nb_epoch, verbose=1,
callbacks=None,
validation_data=gen.generate(False),
nb_val_samples=gen.val_batches,
nb_worker=1
)
Die Ausgabe des Programms ist wie folgt:
Epoch 1/10
/home/deepesh/Documents/ssd_traffic/ssd_utils.py:119: RuntimeWarning: divide by zero encountered in log
assigned_priors_wh)
2017-10-15 18:00:53.763886: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.54GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-15 18:01:02.602807: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.14GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-15 18:01:03.831092: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.17GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-15 18:01:03.831138: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.10GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-15 18:01:04.774444: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.26GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-15 18:01:05.897872: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.46GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-15 18:01:05.897923: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.94GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-15 18:01:09.133494: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.27GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-15 18:01:09.133541: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.15GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-15 18:01:11.266114: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
13/14 [==========================>...] - ETA: 9s - loss: 2.9617
Es erfolgt keine Ausgabe bzw. Fehlermeldung danach.
Ich habe das Modell auf AMS g2.8xlarge Instanz trainieren, aber das Problem ist nicht gelöst. Wenn ich die Stapelgröße auf nur 2 reduziere, ist das Problem gelöst. –
gut zu hören :) – Paddy