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Shape is a tuple that gives you an indication of the number of dimensions in the array 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. So in your case, since the index value of y.shape[0] is 0, your are working along the first dimension of your array.
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(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 Shape (in the numpy context) seems to me the better option for an argument name 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
And i want to make this black area a bit
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.
