In your example you are treating output [0, 0, 0, 1] as probabilities as required by the mathematical definition of cross entropy. But PyTorch treats them as outputs, that don’t need to sum to 1, and need to be first converted into probabilities for which it uses the softmax function. So H(p, q) becomes: Translating the output [0, 0, 0, 1] into … Read more
stack Concatenates sequence of tensors along a new dimension. cat Concatenates the given sequence of seq tensors in the given dimension. So if A and B are of shape (3, 4), torch.cat([A, B], dim=0) will be of shape (6, 4) and torch.stack([A, B], dim=0) will be of shape (2, 3, 4).
For this question, it’s a simple answer from the primary author: With fit_generator, you can use a generator for the validation data as well. In general, I would recommend using fit_generator, but using train_on_batch works fine too. These methods only exist for the sake of convenience in different use cases, there is no “correct” method. train_on_batch allows you to expressly update … Read more
You are just supposed to provide the predict method with the same 2D array, but with one value that you want to process (or more). In short, you can just replace With And it should work. EDIT: This answer became popular so I thought I’d add a little more explanation about ML. The short version: we can … Read more
You are just supposed to provide the predict method with the same 2D array, but with one value that you want to process (or more). In short, you can just replace With And it should work. EDIT: This answer became popular so I thought I’d add a little more explanation about ML. The short version: we can … Read more
I have a machine learning classification problem with 80% categorical variables. Must I use one hot encoding if I want to use some classifier for the classification? Can i pass the data to a classifier without the encoding? I am trying to do the following for feature selection: I read the train file:num_rows_to_read = 10000 … Read more
Seems, expected dimension is wrong. Could you try:
In the following TensorFlow function, we must feed the activation of artificial neurons in the final layer. That I understand. But I don’t understand why it is called logits? Isn’t that a mathematical function?