嗨，我是 DL 和 tensorflow 的初學者，
我創建了一個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'])
我嘗試用 cpu 構建和訓練它，它成功完成（但速度很慢），所以我決定安裝 tensorflow-gpu。按照https://www.tensorflow.org/install/gpu 中的說明安裝所有內容）。
但是現在當我嘗試構建模型時出現此錯誤：
> 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問題？
uj5u.com熱心網友回復：
該錯誤告訴您它無法分配與您使用的一樣多的 VRAM。解決此類問題的最簡單方法是將批量大小減少到適合 GPU VRAM 的數字。
uj5u.com熱心網友回復：
您收到的錯誤訊息tensorflow.python.framework.errors_impl.ResourceExhaustedError: failed to allocate memory [Op:AddV2]可能表明您的 GPU 沒有足夠的記憶體用于您要運行的訓練作業。您使用的是什么 GPU，它有多少 vRAM？
當涉及到訓練時的“記憶體不足”（OOM）錯誤時，最直接的做法就是減少batch_size超引數。
batch_size除了反復試驗之外，沒有直接的方法來確定訓練時可以使用的最大容量適合 GPU 的可用 vRAM。然而，一般規則是使用 2 的冪（例如8, 16, 32）。
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