文章目录

• 遇到问题
• 解决
• 参考

遇到问题

平台google colab
使用GPU
训练dl模型时，遇到报错

--------------------------------------------------------------------------- InvalidArgumentError Traceback (most recent call last) <ipython-input-56-09c5afe5a363> in <module>() ----> 1 text_vectorization.adapt(text_only_train_ds)2 3 tfidf_2gram_train_ds = train_ds.map(4 lambda x, y: (text_vectorization(x), y),5 num_parallel_calls=4)3 frames /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)53 ctx.ensure_initialized()54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, ---> 55 inputs, attrs, num_outputs)56 except core._NotOkStatusException as e:57 if name is not None:InvalidArgumentError: Graph execution error:2 root error(s) found.(0) INVALID_ARGUMENT: During Variant Host->Device Copy: non-DMA-copy attempted of tensor type: string[[{{node map/TensorArrayUnstack/TensorListFromTensor/_42}}]][[Func/map/while/body/_1/input/_48/_72]](1) INVALID_ARGUMENT: During Variant Host->Device Copy: non-DMA-copy attempted of tensor type: string[[{{node map/TensorArrayUnstack/TensorListFromTensor/_42}}]] 0 successful operations. 0 derived errors ignored. [Op:__inference_adapt_step_194806]

上述报错由以下代码产生

text_vectorization.adapt(text_only_train_ds)tfidf_2gram_train_ds = train_ds.map(lambda x, y: (text_vectorization(x), y),num_parallel_calls=4) tfidf_2gram_val_ds = val_ds.map(lambda x, y: (text_vectorization(x), y),num_parallel_calls=4) tfidf_2gram_test_ds = test_ds.map(lambda x, y: (text_vectorization(x), y),num_parallel_calls=4)model = get_model() model.summary() callbacks = [keras.callbacks.ModelCheckpoint("tfidf_2gram.keras",save_best_only=True) ] model.fit(tfidf_2gram_train_ds.cache(),validation_data=tfidf_2gram_val_ds.cache(),epochs=10,callbacks=callbacks) model = keras.models.load_model("tfidf_2gram.keras") print(f"Test acc: {model.evaluate(tfidf_2gram_test_ds)[1]:.3f}")

解决

目前猜测应该用CPU训练，而不是GPU。果然，将runtime type改为None即可

因为这里数据预处理使用CPU进行，而具体计算使用GPU，所以数据集应用CPU来做。

参考

https://github.com/tensorflow/tensorflow/issues/28007

总结

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