deep-learning

问题 I have a laptop that has an RTX 2060 GPU and I am using Keras and TF 2 to train an LSTM on it. I am also monitoring the gpu use by nvidia-smi and I noticed that the jupyter notebook and TF are using maximum 35% and usually the gpu is being used between 10-25%. With current conditions, it took more than 7 hours to train this model, I want to know if I am doing something wrong or it is a limitation of Keras and TF? My nvidia-smi output: Sun Nov 3 00:07:37 2019 +---------------------------------

问题 I have a laptop that has an RTX 2060 GPU and I am using Keras and TF 2 to train an LSTM on it. I am also monitoring the gpu use by nvidia-smi and I noticed that the jupyter notebook and TF are using maximum 35% and usually the gpu is being used between 10-25%. With current conditions, it took more than 7 hours to train this model, I want to know if I am doing something wrong or it is a limitation of Keras and TF? My nvidia-smi output: Sun Nov 3 00:07:37 2019 +---------------------------------

问题 What will happen when I use batch normalization but set batch_size = 1 ? Because I am using 3D medical images as training dataset, the batch size can only be set to 1 because of GPU limitation. Normally, I know, when batch_size = 1 , variance will be 0. And (x-mean)/variance will lead to error because of division by 0. But why did errors not occur when I set batch_size = 1 ? Why my network was trained as good as I expected? Could anyone explain it? Some people argued that: The

问题 What will happen when I use batch normalization but set batch_size = 1 ? Because I am using 3D medical images as training dataset, the batch size can only be set to 1 because of GPU limitation. Normally, I know, when batch_size = 1 , variance will be 0. And (x-mean)/variance will lead to error because of division by 0. But why did errors not occur when I set batch_size = 1 ? Why my network was trained as good as I expected? Could anyone explain it? Some people argued that: The

问题 What will happen when I use batch normalization but set batch_size = 1 ? Because I am using 3D medical images as training dataset, the batch size can only be set to 1 because of GPU limitation. Normally, I know, when batch_size = 1 , variance will be 0. And (x-mean)/variance will lead to error because of division by 0. But why did errors not occur when I set batch_size = 1 ? Why my network was trained as good as I expected? Could anyone explain it? Some people argued that: The

问题 According to the keras documentation: predict_on_batch(self, x) Returns predictions for a single batch of samples. However, there does not seem to be any difference with the standard predict method when called on a batch, whether it being with one or multiple elements. model.predict_on_batch(np.zeros((n, d_in))) is the same as model.predict(np.zeros((n, d_in))) (a numpy.ndarray of shape (n, d_out ) 回答1: The difference lies in when you pass as x data that is larger than one batch. predict will

问题 According to the keras documentation: predict_on_batch(self, x) Returns predictions for a single batch of samples. However, there does not seem to be any difference with the standard predict method when called on a batch, whether it being with one or multiple elements. model.predict_on_batch(np.zeros((n, d_in))) is the same as model.predict(np.zeros((n, d_in))) (a numpy.ndarray of shape (n, d_out ) 回答1: The difference lies in when you pass as x data that is larger than one batch. predict will

问题 According to the keras documentation: predict_on_batch(self, x) Returns predictions for a single batch of samples. However, there does not seem to be any difference with the standard predict method when called on a batch, whether it being with one or multiple elements. model.predict_on_batch(np.zeros((n, d_in))) is the same as model.predict(np.zeros((n, d_in))) (a numpy.ndarray of shape (n, d_out ) 回答1: The difference lies in when you pass as x data that is larger than one batch. predict will

问题 It is not yet clear for me what metrics are (as given in the code below). What exactly are they evaluating? Why do we need to define them in the model ? Why we can have multiple metrics in one model? And more importantly what is the mechanics behind all this? Any scientific reference is also appreciated. model.compile(loss='mean_squared_error', optimizer='sgd', metrics=['mae', 'acc']) 回答1: So in order to understand what metrics are, it's good to start by understanding what a loss function is.

问题 I am currently reading a source code for slim library that is based on Tensorflow and they use values argument for variable_scope method alot, like here. From the API page I can see: This context manager validates that the (optional) values are from the same graph, ensures that graph is the default graph, and pushes a name scope and a variable scope. My question is: variables from values are only being checked if they are from the same graph? What are the use cases for this and why someone