tensorflow.python.framework.errors_impl.resourceexhaustederror: oom when all

available memory is already in use

This error occurs when TensorFlow is unable to allocate enough memory to complete a computation. This can happen when the input data or the model parameters are too large to fit into the available memory. It can also happen if the computation requires more memory than the system has available.

To resolve this issue, you can try the following steps:

1. Reduce the batch size: If you are using batch processing, try reducing the batch size to a smaller value. This will reduce the amount of memory required to process each batch.

2. Reduce the size of the input data: If the input data is too large, you can try reducing its size by downsampling or cropping the data.

3. Use a smaller model: If the model is too large, try using a smaller model. You can also try reducing the number of layers or the number of neurons in each layer.

4. Use a GPU with more memory: If you are using a GPU, try upgrading to a GPU with more memory.

5. Use distributed training: If you have multiple GPUs or machines available, you can try using distributed training to spread the computation across multiple devices.

6. Clear memory: You can also try clearing the memory by restarting the kernel or using the TensorFlow function `tf.reset_default_graph()`.