shape is a tuple that gives you an indication of the number of dimensions in the array. So in your case, since the index value of Y.shape[0] is 0, your are working along the first dimension of your array.

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 shapes, Parentheses around a tuple force the evaluation order and prevent it to be read as a list of values (e.g. in function calls). This tuple ...

2018年1月7日 · x is a 2D array, which can also be looked upon as an array of 1D arrays, having 10 rows and 1024 columns.x[0] is the first 1D sub-array which has 1024 elements (there are 10 such 1D sub-arrays in x), and x[0].shape gives the shape of that sub-array, which happens to be a 1-tuple - (1024, ).

2013年9月9日 · When reshaping an array, the new shape must contain the same number of elements as the old shape, meaning the products of the two shapes' dimensions must be equal. When using a -1, the dimension corresponding to the -1 will be the product of the dimensions of the original array divided by the product of the dimensions given to reshape so as to ...

2017年11月30日 · 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 ndarrays))

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 ...

2018年7月21日 · In this case your training set will be of the shape (n,x,y). The fourth dimension in your data is the number of channels (3, or RGB in this case). In your dataset the height and the width of each image is the same and thus the size of the image can be retrieved merely by the third line: num_px = train_set_x_orig.shape[1]

2017年10月19日 · P.S.: Note that tensor.shape is an alias to tensor.size(), though tensor.shape is an attribute of the tensor in question whereas tensor.size() is a function. Share Improve this answer

2018年10月22日 · So, when you call shape, you get the N dimension shape of the array, so you can see exactly how your array looks like. In essence, size is equal to the product of the elements of shape. EDIT: The difference in name can be attributed to 2 parts: firstly, you can initialise your array with a size. However, you do not know the shape of it.

2014年1月9日 · shape only gives the output only if the variable is attribute of numpy library. In other words it must be a np.array or any other data structure of numpy. In other words it must be a np.array or any other data structure of numpy.