yourarray.shape or np.shape() or np.ma.shape() returns the shape of your ndarray as a tuple; And you can get the (number of) dimensions of your array using yourarray.ndim or np.ndim(). (i.e. it gives the n of the ndarray since all arrays in NumPy are just n-dimensional arrays (shortly called as ndarray s)) For a 1D array, the shape would be (n,) where n is the number of elements in your array ...
Shape n, expresses the shape of a 1D array with n items, and n, 1 the shape of a n-row x 1-column array. (R,) and (R,1) just add (useless) parentheses but still express respectively 1D and 2D array shapes, Parentheses around a tuple force the evaluation order and prevent it to be read as a list of values (e.g. in function calls).
I already know how to set the opacity of the background image but I need to set the opacity of my shape object. In my Android app, I have it like this: and I want to make this black area a bit
Shape (in the numpy context) seems to me the better option for an argument name. The actual relation between the two is size = np.prod(shape) so the distinction should indeed be a bit more obvious in the arguments names.
The whole shape is combined using 4 gradients: 2 gradients to create the top part and 2 for the bottom parts. each gradient is taking 1/4 of size and placed at a corner.
I'm new to python and numpy in general. I read several tutorials and still so confused between the differences in dim, ranks, shape, aixes and dimensions. My mind seems to be stuck at the matrix
You can think of a placeholder in TensorFlow as an operation specifying the shape and type of data that will be fed into the graph.placeholder X defines that an unspecified number of rows of shape (128, 128, 3) of type float32 will be fed into the graph. a Placeholder does not hold state and merely defines the type and shape of the data to flow ...
ValueError: shape mismatch: objects cannot be broadcast to a single shape It computes the first two (I am running several thousand of these tests in a loop) and then dies.
