tensorflow2.0

来源： https://stackoverflow.com/questions/61458419/solved-tensorflow-federated-tff-learning-from-keras-model-with-a-model-wit

来源： https://stackoverflow.com/questions/60059410/tensorflow-2-tf-function-decorator

来源： https://stackoverflow.com/questions/61980124/full-batch-gradient-descent-with-tf-gradienttape-oom-error

来源： https://stackoverflow.com/questions/56889038/tensorflow-2-0-how-make-share-parameters-among-convolutional-layers

问题 I'm trying to get the rows of a 3D tensor in a specific order of indices. Here are the inputs: import tensorflow as tf matrix = tf.constant([ [[0, 1], [2, 3], [4, 5], [6, 7]], [[8, 9], [10, 11], [12, 13], [14, 15]], [[16, 17], [18, 19], [20, 21], [22, 23]], [[24, 25], [26, 27], [28, 29], [30, 31]], [[32, 33], [34, 35], [36, 37], [38, 39]] ]) indx = tf.constant([[3,2,1,0], [0,1,2,3], [1,0,3,2], [0,3,1,2], [1,2,3,0]]) # required output tensor: [[[6, 7], [4, 5], [2, 3], [0, 1]], [[8, 9], [10, 11

问题 I'm trying to get the rows of a 3D tensor in a specific order of indices. Here are the inputs: import tensorflow as tf matrix = tf.constant([ [[0, 1], [2, 3], [4, 5], [6, 7]], [[8, 9], [10, 11], [12, 13], [14, 15]], [[16, 17], [18, 19], [20, 21], [22, 23]], [[24, 25], [26, 27], [28, 29], [30, 31]], [[32, 33], [34, 35], [36, 37], [38, 39]] ]) indx = tf.constant([[3,2,1,0], [0,1,2,3], [1,0,3,2], [0,3,1,2], [1,2,3,0]]) # required output tensor: [[[6, 7], [4, 5], [2, 3], [0, 1]], [[8, 9], [10, 11

问题 I'm trying to get the rows of a 3D tensor in a specific order of indices. Here are the inputs: import tensorflow as tf matrix = tf.constant([ [[0, 1], [2, 3], [4, 5], [6, 7]], [[8, 9], [10, 11], [12, 13], [14, 15]], [[16, 17], [18, 19], [20, 21], [22, 23]], [[24, 25], [26, 27], [28, 29], [30, 31]], [[32, 33], [34, 35], [36, 37], [38, 39]] ]) indx = tf.constant([[3,2,1,0], [0,1,2,3], [1,0,3,2], [0,3,1,2], [1,2,3,0]]) # required output tensor: [[[6, 7], [4, 5], [2, 3], [0, 1]], [[8, 9], [10, 11

问题 I'm trying to get the rows of a 3D tensor in a specific order of indices. Here are the inputs: import tensorflow as tf matrix = tf.constant([ [[0, 1], [2, 3], [4, 5], [6, 7]], [[8, 9], [10, 11], [12, 13], [14, 15]], [[16, 17], [18, 19], [20, 21], [22, 23]], [[24, 25], [26, 27], [28, 29], [30, 31]], [[32, 33], [34, 35], [36, 37], [38, 39]] ]) indx = tf.constant([[3,2,1,0], [0,1,2,3], [1,0,3,2], [0,3,1,2], [1,2,3,0]]) # required output tensor: [[[6, 7], [4, 5], [2, 3], [0, 1]], [[8, 9], [10, 11

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:

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: