Question

我正在使用张量流构建一个非常简单的Keras模型。当我启动它时，它因OOM异常而失败，因为它试图分配与整个数据集大小成比例的张量。这里会发生什么？

相关形状：

数据集形状：[60000、28、28、1]
批量大小（自动）：10，
steps_per_epoch：6000
错误消息：分配具有shape [60000,256,28,28]并键入float的张量时出现OOM

注意：我不使用顺序模型，因为以后需要非顺序层。

Tensorflow：1.12.0；凯拉斯：2.1.6-tf

最小工作示例：

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)
               )

这里是例外：

---------------------------------------------------------------------------
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.

当我将模型定义为keras.Sequential（）时，问题消失了。

Answer 1

对我来说同样的问题。我只是查看一些示例并发现：

dummy_x = tf.zeros((1, 224, 224, 1))

model._set_inputs(dummy_x)

如果此代码适合您，则不会显示oom。

Answer 2

要分批训练，应使用fit_generator方法。为此，您需要首先创建数据生成器。您需要使用flow_from_directory所遵循的ImageDataGenerator（例如）。这样，keras可以分批提供数据。您应该调整批处理大小，以确保GPU的内存足够。通常，批量大小在32-64之间。通常，批量越大越好。

Keras文档： https://keras.io/preprocessing/image/

您可以在此处查看用法示例： https://www.kaggle.com/vbookshelf/skin-lesion-analyzer-tensorflow-js-web-app

Answer 3

嗯，我想您忘记定义要在网络中填充的batch_size了！

尝试使用类似的东西：

autoencoder.fit(x_train, x_train,
                epochs=50,
                batch_size = 32,
                steps_per_epoch=int(int(x_train.shape[0])/10),
                shuffle=True,
                verbose=1,
                validation_data=(x_test, x_test)
               )

为什么Keras model.fit（）将整个数据集作为批处理使用，并且内存不足？

3 个答案: