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arrays - what does numpy ndarray shape do? - Stack Overflow 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
tensorflow placeholder - understanding `shape= [None,` 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
python - x. shape [0] vs x [0]. shape in NumPy - Stack Overflow On the other hand, x shape is a 2-tuple which represents the shape of x, which in this case is (10, 1024) 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
python - shape vs len for numpy array - Stack Overflow Still, performance-wise, the difference should be negligible except for a giant giant 2D dataframe 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
tensorflow - raise ValueError (fCannot convert {shape} to a shape . . . shape: A shape tuple (integers), not including the batch size For instance, shape= (32,) indicates that the expected input will be batches of 32-dimensional vectors Elements of this tuple can be None; 'None' elements represent dimensions where the shape is not known
numpy: size vs. shape in function arguments? - Stack Overflow 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