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The shape attribute for numpy arrays returns the dimensions of the array 'none' elements represent dimensions where the shape is not known. If y has n rows and m columns, then y.shape is (n,m)
(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 Elements of this tuple can be none 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()
X.shape[0] gives the first element in that tuple, which is 10 Here's a demo with some smaller numbers, which should hopefully be easier to understand. 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.
From onnx import shape_inference inferred_model = shape_inference.infer_shapes(original_model) and find the shape info in inferred_model.graph.value_info You can also use netron or from github to have a visual representation of that information. So in line with the previous answers, df.shape is good if you need both dimensions, for a single dimension, len() seems more appropriate conceptually Looking at property vs method answers, it all points to usability and readability of code.
I'm creating a plot in ggplot from a 2 x 2 study design and would like to use 2 colors and 2 symbols to classify my 4 different treatment combinations
Currently i have 2 legends, one for the colo. For any keras layer (layer class), can someone explain how to understand the difference between input_shape, units, dim, etc. For example the doc says units specify the output shape of a layer. A shape tuple (integers), not including the batch size
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