一个参数为-1时,那么reshape函数会根据另一个参数的维度计算出数组的另外一个shape属性值。 (C:\ProgramData\Anaconda3) C:\Users\Administrator>python Python 3.6.3 |Anaconda custom (64-bit)| (default, Oct 15 2017, 03:27:45) [MSC v. 1900 64 bit (AMD64)] on win32 Type "help", "copyright...
Parameters --- a : array_like Array to be reshaped. newshape : int or tuple of ints The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D array of that length. One shape dimension can be -1. In this case, the value is inferr...
Parameters and Syntax numpy.reshape(a, newshape, order='C') ParameterTypeDescription a array_like Array to be reshaped. newshape int or tuple of integers The new shape and the original shape should be compatible.If the new shape is an integer i, the reshaped array will be a 1-D array...
nn.ModuleList() 这是一个储存不同module,并自动将每个module的parameters添加到网络之中的容器(list)。 它并没有定义一个网络,只是将不同的模块储存在一起,这些模块之间并没有什么先后顺序可言, 没有实现forward功能,可以把添加到其中的模块和参数自动注册到网络上。 如果模块被调用多次,那么它们是使用同一组的par...
defvec(X):"""Flattens the matrix X into a vector in column-major order. Parameters --- X : Expression or numeric constant The matrix to flatten. Returns --- Expression An Expression representing the flattened matrix. """X=Expression....
If faces is None, then the parameters must arrive in the form of a flattened (n x 3 x 2) matrix where n is the number of triangles. Otherwise, the faces matrix must be either (n x 3) or (n x 3 x s); if the former, each row must list the vertex indices for the faces ...
Parameters: inputs (dragon.Tensor) – The input tensor. shape (Union[Sequence[int], dragon.Tensor]) – The output shape. copy (bool, optional, default=True) – Return a new tensor or call in-place. Returns: dragon.Tensor – The output tensor.Contents...
The documentation for the function shows three parameters:a is the original array. newshape is a tuple or an integer with the shape of the new array. When newshape is an integer, the new array will have one dimension. order enables you to determine how the data is configured in the new...
pause(1.0/60.0) # Initialize covariance parameters and hiddens. Example #2Source File: ar.py From autohmm with BSD 2-Clause "Simplified" License 6 votes def _do_optim(self, p, optim_x0, gn, data, entries='all'): optim_bounds = [self.wrt_bounds[p] for k in range(np.prod(self...
After the train() method returns, model.params will contain the parameters that performed best on the validation set over the course of training. In addition, the instance variable solver.loss_history will contain a list of all losses encountered during training and the instance variables ...