I can never remember how to do this this.
How can go
(n1)
) to a Column Matrix (size (n1,1)
)? (n1,n2)
) to a Array{T,3} (size (n1,n2,1)
)? (n1,n2,n3)
) to a Array{T,4} (size (n1,n2,n3, 1)
)? I want to know to take Array and use it to define a new Array with an extra singleton trailing dimension.
I.e. the opposite of squeeze
The numpy.expand_dims () function adds a new dimension to a NumPy array. It takes the array to be expanded and the new axis as arguments. It returns a new array with extra dimensions. We can specify the axis to be expanded in the axis parameter. myArray = np.append (myArray, [ ["R","Go","Kotlin"]], axis=0)
squeeze () is also provided as a method of numpy.ndarray. Usage is the same as numpy.squeeze (). The first argument is axis. squeeze () method also returns a view like numpy.squeeze (). The original object remains the same.
The initial shape of the array is (4,0). After passing .shape () as (2,2), the shape of the array is changed accordingly. The reshape function takes a parameter order. This allows us to reshape either row or column-wise.
An error will occur if you specify a dimension whose size is not 1 or a dimension that does not exist. axis can also be specified as a negative value. -1 corresponds to the last dimension and can be specified by the position from the back. You can specify multiple dimensions with tuples.
Try this
function extend_dims(A,which_dim)
s = [size(A)...]
insert!(s,which_dim,1)
return reshape(A, s...)
end
the variable extend_dim
specifies which dimension to extend
Thus
extend_dims(randn(3,3),1)
will produce a 1 x 3 x 3
array and so on.
I find this utility helpful when passing data into convolutional neural networks.
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