The shape attribute for numpy arrays returns the dimensions of the array. If Y has n rows and m columns, then Y.shape is (n,m). So Y.shape[0] is n.

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 …

Nov 30, 2017 · 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(). …

Jan 7, 2018 · 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 …

Jul 21, 2018 · In python shape[0] returns the dimension but in this code it is returning total number of set. Please can someone tell me work of shape[0] and shape[1]? Code: m_train = …

Oct 22, 2018 · 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 …

Feb 14, 2022 · Currently, shape type information is reflected in ndarray.shape. However, most numpy functions that change the dimension or size of an array, however, don't necessarily know how to …

Jan 23, 2020 · In R graphics and ggplot2 we can specify the shape of the points. I am wondering what is the main difference between shape = 19, shape = 20 and shape = 16? Is it the size? This post might …

Mar 5, 2021 · input_shape = first_parameter.size () this is for the weight size, if you save the model and open it in neuron, you would see that the weight size is the same as the input shape.

Oct 6, 2017 · np.arange(x.shape[0]) you are giving the arange function the first scalar in the tuple provided by x.shape and in my example producing an array like this [0,1] because the first index in …