9/3/2023 0 Comments Arrsync run as root![]() ![]() This is the basic building block of tensor-based computation. ![]() Since C/C++ use row-major ordering for arrays while Julia follows a column-major ordering. It offers a large number of options that control every aspect of its behavior and permit very flexible specification of the set of files to be copied. It can copy locally, to/from another host over any remote shell, or to/from a remote rsync daemon. To keep things consistent, we keep the underlying data in their original layout, but use language-native convention when we talk about shapes. Rsync is a fast and extraordinarily versatile file copying tool. tanh.(x::NDArray)Defined in src/operator/tensor/elemwise_unary_op_:L234 tan.(x::NDArray)Defined in src/operator/tensor/elemwise_unary_op_:L83 sinh.(x::NDArray)Defined in src/operator/tensor/elemwise_unary_op_:L201 sin.(x::NDArray)Defined in src/operator/tensor/elemwise_unary_op_:L46 reshape(arr::NDArray, dim reverse=false)ĭefined in src/operator/tensor/matrix_op.cc:L165Ĭosh.(x::NDArray)Defined in src/operator/tensor/elemwise_unary_op_:L216 cos.(x::NDArray)Defined in src/operator/tensor/elemwise_unary_op_:L63 For example, a mini-batch of 100 MNIST images is a tensor of C/C++/Python shape (100,1,28,28), while in Julia, the same piece of memory have shape (28,28,1,100). broadcast_axis(x::NDArray, dim, size)īroadcasts the input array over particular axis(axes). julia> xĭefined in src/operator/tensor/broadcast_reduce_op_:L207 Parameter dim and size could be a scalar, a Tuple or an Array.īroadcast_axes is just an alias. broadcast_to(x::NDArray, dims)īroadcasts the input array to a new shape. Julia> x = mx.ones(2, 3, 4) ĭefined in src/operator/tensor/broadcast_reduce_op_:L231 In the case of broacasting doesn't work out of box, you can expand the NDArray first. ![]()
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