keras

问题 It seems that k-fold cross validation in convn net is not taken seriously due to huge running time of the neural network. I have a small data-set and I am interested in doing k-fold cross validation using the example given here. Is it possible? Thanks. 回答1: If you are using images with data generators, here's one way to do 10-fold cross-validation with Keras and scikit-learn. The strategy is to copy the files to training , validation , and test subfolders according to each fold. import numpy

问题 It seems that k-fold cross validation in convn net is not taken seriously due to huge running time of the neural network. I have a small data-set and I am interested in doing k-fold cross validation using the example given here. Is it possible? Thanks. 回答1: If you are using images with data generators, here's one way to do 10-fold cross-validation with Keras and scikit-learn. The strategy is to copy the files to training , validation , and test subfolders according to each fold. import numpy

问题 It seems that k-fold cross validation in convn net is not taken seriously due to huge running time of the neural network. I have a small data-set and I am interested in doing k-fold cross validation using the example given here. Is it possible? Thanks. 回答1: If you are using images with data generators, here's one way to do 10-fold cross-validation with Keras and scikit-learn. The strategy is to copy the files to training , validation , and test subfolders according to each fold. import numpy

问题 It seems that k-fold cross validation in convn net is not taken seriously due to huge running time of the neural network. I have a small data-set and I am interested in doing k-fold cross validation using the example given here. Is it possible? Thanks. 回答1: If you are using images with data generators, here's one way to do 10-fold cross-validation with Keras and scikit-learn. The strategy is to copy the files to training , validation , and test subfolders according to each fold. import numpy

问题 It seems that k-fold cross validation in convn net is not taken seriously due to huge running time of the neural network. I have a small data-set and I am interested in doing k-fold cross validation using the example given here. Is it possible? Thanks. 回答1: If you are using images with data generators, here's one way to do 10-fold cross-validation with Keras and scikit-learn. The strategy is to copy the files to training , validation , and test subfolders according to each fold. import numpy

问题 I'm trying to train a neural net on a GPU using Keras and am getting a "Resource exhausted: OOM when allocating tensor" error. The specific tensor it's trying to allocate isn't very big, so I assume some previous tensor consumed almost all the VRAM. The error message comes with a hint that suggests this: Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. That sounds good, but how do I do it?

问题 can anyone tell me how is backpropagation done in Keras? I read that it is really easy in Torch and complex in Caffe, but I can't find anything about doing it with Keras. I am implementing my own layers in Keras (A very beginner) and would like to know how to do the backward propagation. Thank you in advance 回答1: You simply don't. (Late edit: except when you are creating custom training loops, only for advanced uses) Keras does backpropagation automatically. There's absolutely nothing you

问题 can anyone tell me how is backpropagation done in Keras? I read that it is really easy in Torch and complex in Caffe, but I can't find anything about doing it with Keras. I am implementing my own layers in Keras (A very beginner) and would like to know how to do the backward propagation. Thank you in advance 回答1: You simply don't. (Late edit: except when you are creating custom training loops, only for advanced uses) Keras does backpropagation automatically. There's absolutely nothing you

问题 At the end of each epoch, I am getting for example the following output: Epoch 1/25 2018-08-06 14:54:12.555511: 2/2 [==============================] - 86s 43s/step - loss: 6.0767 - acc: 0.0469 - val_loss: 4.1037 - val_acc: 0.2000 Epoch 2/25 2/2 [==============================] - 26s 13s/step - loss: 3.6901 - acc: 0.0938 - val_loss: 2.5610 - val_acc: 0.0000e+00 Epoch 3/25 2/2 [==============================] - 66s 33s/step - loss: 3.1491 - acc: 0.1406 - val_loss: 2.4793 - val_acc: 0.0500 Epoch

问题 At the end of each epoch, I am getting for example the following output: Epoch 1/25 2018-08-06 14:54:12.555511: 2/2 [==============================] - 86s 43s/step - loss: 6.0767 - acc: 0.0469 - val_loss: 4.1037 - val_acc: 0.2000 Epoch 2/25 2/2 [==============================] - 26s 13s/step - loss: 3.6901 - acc: 0.0938 - val_loss: 2.5610 - val_acc: 0.0000e+00 Epoch 3/25 2/2 [==============================] - 66s 33s/step - loss: 3.1491 - acc: 0.1406 - val_loss: 2.4793 - val_acc: 0.0500 Epoch