python - tensorflow.python.framework.errors_impl.ResourceExhaustedError：分配内存失败 [Op:AddV2]

Question

嗨，我是 DL 和 tensorflow 的初学者，

我创建了一个 CNN（你可以看到下面的模型）

model = tf.keras.Sequential()

model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=7, activation="relu", input_shape=[512, 640, 3]))
model.add(tf.keras.layers.MaxPooling2D(2))
model.add(tf.keras.layers.Conv2D(filters=128, kernel_size=3, activation="relu"))
model.add(tf.keras.layers.Conv2D(filters=128, kernel_size=3, activation="relu"))
model.add(tf.keras.layers.MaxPooling2D(2))
model.add(tf.keras.layers.Conv2D(filters=256, kernel_size=3, activation="relu"))
model.add(tf.keras.layers.Conv2D(filters=256, kernel_size=3, activation="relu"))
model.add(tf.keras.layers.MaxPooling2D(2))

model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(2, activation='softmax'))

optimizer = tf.keras.optimizers.SGD(learning_rate=0.2) #, momentum=0.9, decay=0.1)
model.compile(optimizer=optimizer, loss='mse', metrics=['accuracy'])

我尝试使用 cpu 构建和训练它，它成功完成（但非常缓慢）所以我决定安装 tensorflow-gpu。按照https://www.tensorflow.org/install/gpu中的说明安装所有内容）。

但是现在当我尝试构建模型时，会出现此错误：

> Traceback (most recent call last):   File
> "C:/Users/thano/Documents/Py_workspace/AI_tensorflow/fire_detection/main.py",
> line 63, in <module>
>     model = create_models.model1()   File "C:\Users\thano\Documents\Py_workspace\AI_tensorflow\fire_detection\create_models.py",
> line 20, in model1
>     model.add(tf.keras.layers.Dense(128, activation='relu'))   File "C:\Python37\lib\site-packages\tensorflow\python\training\tracking\base.py",
> line 530, in _method_wrapper
>     result = method(self, *args, **kwargs)   File "C:\Python37\lib\site-packages\keras\engine\sequential.py", line 217,
> in add
>     output_tensor = layer(self.outputs[0])   File "C:\Python37\lib\site-packages\keras\engine\base_layer.py", line 977,
> in __call__
>     input_list)   File "C:\Python37\lib\site-packages\keras\engine\base_layer.py", line 1115,
> in _functional_construction_call
>     inputs, input_masks, args, kwargs)   File "C:\Python37\lib\site-packages\keras\engine\base_layer.py", line 848,
> in _keras_tensor_symbolic_call
>     return self._infer_output_signature(inputs, args, kwargs, input_masks)   File
> "C:\Python37\lib\site-packages\keras\engine\base_layer.py", line 886,
> in _infer_output_signature
>     self._maybe_build(inputs)   File "C:\Python37\lib\site-packages\keras\engine\base_layer.py", line 2659,
> in _maybe_build
>     self.build(input_shapes)  # pylint:disable=not-callable   File "C:\Python37\lib\site-packages\keras\layers\core.py", line 1185, in
> build
>     trainable=True)   File "C:\Python37\lib\site-packages\keras\engine\base_layer.py", line 663,
> in add_weight
>     caching_device=caching_device)   File "C:\Python37\lib\site-packages\tensorflow\python\training\tracking\base.py",
> line 818, in _add_variable_with_custom_getter
>     **kwargs_for_getter)   File "C:\Python37\lib\site-packages\keras\engine\base_layer_utils.py", line
> 129, in make_variable
>     shape=variable_shape if variable_shape else None)   File "C:\Python37\lib\site-packages\tensorflow\python\ops\variables.py",
> line 266, in __call__
>     return cls._variable_v1_call(*args, **kwargs)   File "C:\Python37\lib\site-packages\tensorflow\python\ops\variables.py",
> line 227, in _variable_v1_call
>     shape=shape)   File "C:\Python37\lib\site-packages\tensorflow\python\ops\variables.py",
> line 205, in <lambda>
>     previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)   File "C:\Python37\lib\site-packages\tensorflow\python\ops\variable_scope.py",
> line 2626, in default_variable_creator
>     shape=shape)   File "C:\Python37\lib\site-packages\tensorflow\python\ops\variables.py",
> line 270, in __call__
>     return super(VariableMetaclass, cls).__call__(*args, **kwargs)   File
> "C:\Python37\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py",
> line 1613, in __init__
>     distribute_strategy=distribute_strategy)   File "C:\Python37\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py",
> line 1740, in _init_from_args
>     initial_value = initial_value()   File "C:\Python37\lib\site-packages\keras\initializers\initializers_v2.py",
> line 517, in __call__
>     return self._random_generator.random_uniform(shape, -limit, limit, dtype)   File
> "C:\Python37\lib\site-packages\keras\initializers\initializers_v2.py",
> line 973, in random_uniform
>     shape=shape, minval=minval, maxval=maxval, dtype=dtype, seed=self.seed)   File
> "C:\Python37\lib\site-packages\tensorflow\python\util\dispatch.py",
> line 206, in wrapper
>     return target(*args, **kwargs)   File "C:\Python37\lib\site-packages\tensorflow\python\ops\random_ops.py",
> line 315, in random_uniform
>     result = math_ops.add(result * (maxval - minval), minval, name=name)   File
> "C:\Python37\lib\site-packages\tensorflow\python\util\dispatch.py",
> line 206, in wrapper
>     return target(*args, **kwargs)   File "C:\Python37\lib\site-packages\tensorflow\python\ops\math_ops.py",
> line 3943, in add
>     return gen_math_ops.add_v2(x, y, name=name)   File "C:\Python37\lib\site-packages\tensorflow\python\ops\gen_math_ops.py",
> line 454, in add_v2
>     _ops.raise_from_not_ok_status(e, name)   File "C:\Python37\lib\site-packages\tensorflow\python\framework\ops.py",
> line 6941, in raise_from_not_ok_status
>     six.raise_from(core._status_to_exception(e.code, message), None)   File "<string>", line 3, in raise_from
> tensorflow.python.framework.errors_impl.ResourceExhaustedError: failed
> to allocate memory [Op:AddV2]

任何想法可能是什么问题？

标签： pythontensorflowdeep-learningconv-neural-networkgpu

Answer 1

该错误告诉您它无法分配与您使用的一样多的 VRAM。克服此类问题的最简单方法是将批量大小减少到适合您 GPU 的 VRAM 的数字。