Add single element to array in numpy
append() creates a new array which can be the old array with the appended element. I think it’s more normal to use the proper method for adding an element:
numpy
Can’t broadcast input array from shape (3,1) into shape (3,)
V[k:m,k] = v; v has shape (3,1), but the target is (3,). k:m is a 3 term slice; k is a scalar. Try using v.ravel(). Or V[k:m,[k]]. But also understand why v has its shape.
How to remove specific elements in a numpy array
Use numpy.delete() – returns a new array with sub-arrays along an axis deleted For your specific question: Note that numpy.delete() returns a new array since array scalars are immutable, similar to strings in Python, so each time a change is made to it, a new object is created. I.e., to quote the delete() docs: “A copy of arr with the elements specified by obj removed. Note that delete … Read more
Most efficient way to reverse a numpy array
When you create reversed_arr you are creating a view into the original array. You can then change the original array, and the view will update to reflect the changes. Are you re-creating the view more often than you need to? You should be able to do something like this: I’m not a numpy expert, but this seems … Read more
Pytorch reshape tensor dimension
Use torch.unsqueeze(input, dim, out=None) >>> a = a.unsqueeze(0) >>> a 1 2 3 4 5 [torch.FloatTensor of size 1×5]
How to implement the Softmax function in Python
They’re both correct, but yours is preferred from the point of view of numerical stability. You start with By using the fact that a^(b – c) = (a^b)/(a^c) we have Which is what the other answer says. You could replace max(x) with any variable and it would cancel out.
index 1 is out of bounds for axis 0 with size 1
I don’t know why I am getting an error of index. I am quite new to python and therefore am not able to figure out what to do. I think I am initialzing some wrong dimensions but I am not able to break it.
Python – TypeError: Object of type ‘int64’ is not JSON serializable
json does not recognize NumPy data types. Convert the number to a Python int before serializing the object:
Python reshape list to ndim array
You can think of reshaping that the new shape is filled row by row (last dimension varies fastest) from the flattened original list/array. An easy solution is to shape the list into a (100, 28) array and then transpose it: Update regarding the updated example: