While training a CNN model, you receive the OOM (out of memory) error message. What can you do to resolve the issue? Choose all that apply.4 pointsReduce the batch size Reduce the number of epochsReduce the number of filters in conv layersReduce the stride of conv layersReduce the number of layers
Question
While training a CNN model, you receive the OOM (out of memory) error message. What can you do to resolve the issue? Choose all that apply.4 pointsReduce the batch size Reduce the number of epochsReduce the number of filters in conv layersReduce the stride of conv layersReduce the number of layers
Solution
To resolve the OOM (out of memory) error message while training a CNN model, you can:
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Reduce the batch size: The batch size is the number of training examples used in one iteration. By reducing the batch size, you can decrease the amount of memory needed for each iteration, thus potentially resolving the OOM error.
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Reduce the number of filters in conv layers: Each filter in a convolutional layer requires its own memory. Therefore, reducing the number of filters can decrease the memory usage.
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Reduce the number of layers: Each layer in a neural network requires memory for its weights and biases. By reducing the number of layers, you can decrease the overall memory usage.
Reducing the number of epochs or the stride of conv layers would not typically help resolve an OOM error. The number of epochs is related to how long the training process runs, not how much memory it uses at any given moment. The stride of conv layers affects the size of the output, but not the amount of memory used.
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The memory space reserved when calling malloc is on:
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