So in your case, since the index value of y.shape[0] is 0, your are working along the first dimension of your 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 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() 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]
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. 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. 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
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. .shape returns a tuple (number of row, number of columns) Therefore dataset.shape [1] is the number of columns I in range (dataset.shape [1]) simply iterates from 0 through the number of columns. 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.
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