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8月22日 · :param lst: the nested list :param shape: the shape up to the current recursion depth :return: the shape including the current depth (finally this will be the full depth) """ if not isinstance(lst, Sequence): # base case return shape # peek ahead and assure all lists in the next depth # have the same length if isinstance(lst[0], Sequence): l ...

2011年6月22日 · Since PIL is a bit cryptic, you can also use numpy: numpy.array(im).shape – Alex Kreimer. Commented Jun ...

2017年6月25日 · Given the input shape, all other shapes are results of layers calculations. The "units" of each layer will define the output shape (the shape of the tensor that is produced by the layer and that will be the input of the next layer). Each type of layer works in a particular way.

2016年5月24日 · So maybe df.shape[0] + df.shape[1] is slower than len(df.index) + len(df.columns). 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.

In the first case (a), numpy couldn't coerce both arrays to have the same shape. However, in the second case (b), since one is an 1D array (shape=(3,)) and the other is a 2D array (shape=(2,1)), both can be broadcast into an array of shape=(2,3).

I have some problems finding the documentation of the definitions of shapes in XML for Android. I would like to define a simple circle filled with a solid color in an XML File to include it into my