deep-learning

来源： https://stackoverflow.com/questions/61969959/training-mnist-data-set-in-google-colab-issue

来源： https://stackoverflow.com/questions/55045523/how-to-get-only-last-output-of-sequence-model-in-keras

来源： https://stackoverflow.com/questions/55045523/how-to-get-only-last-output-of-sequence-model-in-keras

来源： https://stackoverflow.com/questions/56430566/transfer-learning-val-loss-strange-behaviour

来源： https://stackoverflow.com/questions/56430566/transfer-learning-val-loss-strange-behaviour

来源： https://stackoverflow.com/questions/63092118/valueerror-input-0-of-layer-sequential-9-is-incompatible-with-the-layer-expect

来源： https://stackoverflow.com/questions/63202871/model-performs-differently-for-model-fit-and-custom-training-loop

来源： https://stackoverflow.com/questions/63202871/model-performs-differently-for-model-fit-and-custom-training-loop

来源： https://stackoverflow.com/questions/63131273/how-to-get-input-tensor-shape-of-an-unknown-pytorch-model

问题 I installed TensorFlow 2.2.0 and TensorFlow-gpu 2.2.0 in windows 10 . Also, I installed CUDA Toolkit v10.1 and copy cuDNN 7.6.5 files in CUDA directories . My GPU is NVIDIA GeForce 940 MX . In addition, I set CUDA Path on windows. When I test devices through the below code, both CPU and GPU are recognized: from tensorflow.python.client import device_lib device_lib.list_local_devices() The output is: [name: "/device:CPU:0" device_type: "CPU" memory_limit: 268435456 locality { } incarnation: