Я строю очень простую модель Keras с тензорным потоком.Когда я его запускаю, происходит сбой с исключением OOM, потому что он пытается выделить тензор, пропорциональный размеру всего набора данных.Что здесь может происходить?
Соответствующие формы:
- Форма набора данных: [60000, 28, 28, 1]
- Размер пакета (автоматический): 10,
- steps_per_epoch: 6000
- Сообщение об ошибке: OOM при выделении тензора с формой [60000,256,28,28] и типом float
Примечание: я не использую последовательную модель, потому что мне потребуетсянепоследовательные слои позже.
Тензор потока: 1.12.0;Keras: 2.1.6-tf
Минимальный рабочий пример:
from tensorflow.keras import layers
import tensorflow as tf
import tensorflow.keras as keras
import numpy as np
def build_mnist_model(input_img):
conv1 = layers.Conv2D(256, (3,3), activation='relu', padding='same')(input_img)
conv2 = layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')(conv1)
return conv2
(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
x_train = np.expand_dims(x_train.astype('float32') / 255., -1)
x_test = np.expand_dims(x_test.astype('float32') / 255., -1)
print(x_train.shape)
print(x_test.shape)
input_img = keras.Input(shape = (28, 28, 1))
autoencoder = keras.Model(input_img, build_mnist_model(input_img))
autoencoder.compile(loss='mean_squared_error', optimizer = tf.train.AdamOptimizer(0.001))
autoencoder.fit(x_train, x_train,
epochs=50,
steps_per_epoch=int(int(x_train.shape[0])/10),
shuffle=True,
verbose=1,
validation_data=(x_test, x_test)
)
Вот исключение:
---------------------------------------------------------------------------
ResourceExhaustedError Traceback (most recent call last)
<ipython-input-40-be75898e307a> in <module>
24 shuffle=True,
25 verbose=1,
---> 26 validation_data=(x_test, x_test)
27 )
~/tf112/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, max_queue_size, workers, use_multiprocessing, **kwargs)
1637 initial_epoch=initial_epoch,
1638 steps_per_epoch=steps_per_epoch,
-> 1639 validation_steps=validation_steps)
1640
1641 def evaluate(self,
~/tf112/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py in fit_loop(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps)
152 callbacks.on_batch_begin(step_index, batch_logs)
153 try:
--> 154 outs = f(ins)
155 except errors.OutOfRangeError:
156 logging.warning('Your dataset iterator ran out of data; '
~/tf112/lib/python3.6/site-packages/tensorflow/python/keras/backend.py in __call__(self, inputs)
2984
2985 fetched = self._callable_fn(*array_vals,
-> 2986 run_metadata=self.run_metadata)
2987 self._call_fetch_callbacks(fetched[-len(self._fetches):])
2988 return fetched[:len(self.outputs)]
~/tf112/lib/python3.6/site-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)
1437 ret = tf_session.TF_SessionRunCallable(
1438 self._session._session, self._handle, args, status,
-> 1439 run_metadata_ptr)
1440 if run_metadata:
1441 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
~/tf112/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
526 None, None,
527 compat.as_text(c_api.TF_Message(self.status.status)),
--> 528 c_api.TF_GetCode(self.status.status))
529 # Delete the underlying status object from memory otherwise it stays alive
530 # as there is a reference to status from this from the traceback due to
ResourceExhaustedError: OOM when allocating tensor with shape[60000,256,28,28] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node conv2d_95/Conv2D}} = Conv2D[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](training_15/TFOptimizer/gradients/conv2d_95/Conv2D_grad/Conv2DBackpropFilter-0-TransposeNHWCToNCHW-LayoutOptimizer, conv2d_95/Conv2D/ReadVariableOp)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
[[{{node loss_24/mul/_1261}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_255_loss_24/mul", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
Проблема исчезает, когда я определяю модель как кера.Sequential ().