![]() Manufacturer pedigree aside, the ASUS Tinker Board S is a powerful SBC that doesn’t shy away from trying to mimic the Raspberry Pi in form factor and versatility. paddings = specifies the amount to zero-padįrom input dimension i + 1, which corresponds to spatial dimension i.You’ll find a generous spread of ports – four USB 2.0 ports, Gigabit Ethernet, 3.5 mm audio, HDMI port, micro USB port, SD Card slot – and the same 40-pin GPIO for HAT expansion modules. Must have shape, all values must be >=Ġ. The dataFormat attr specifies the layout of the input and output tensors The depth of the input tensor must be divisible by blockSize * blockSize The Y, X coordinates within each block of the output image are determinedīy the high order component of the input channel index The width the output tensor is inputWidth * blockSize, whereas the Into non-overlapping blocks of size blockSize x blockSize The attr blockSize indicates the input block size and how the data isĬhunks of data of size blockSize * blockSize from depth are rearranged This op outputs a copy of the input tensor where values from the depthĭimension are moved in spatial blocks to the height and width dimensions. Rearranges data from depth into blocks of spatial data. , x.shape * blockShape - crops - crops,x.shape. , x.shape]Ĭrop the start and end of dimensions of reshapedPermutedĪccording to crops to produce the output of shape: * blockShape - crops - crops. Reshape permuted to produce reshapedPermuted of shape * blockShape. Permute dimensions of reshaped to produce permuted of shape, blockShape. , blockShape, batch / prod(blockShape), x.shape. This operation is equivalent to the following steps: That cropStart + cropEnd <= blockShape * inputShape Must have shape, all values must be >= 0.Ĭrops = specifies the amount to crop from inputĭimension i + 1, which corresponds to spatial dimension i. N-D with x.shape = + spatialShape + remainingShape, where spatialShape has M dimensions. x ( tf.Tensor| TypedArray|Array) A tf.Tensor.shape (number) The shape of the tensor.ZeroCopy is true, this GPUBuffer is bound directly by the tensor, so do notĭestroy this GPUBuffer until all access is done. This passing GPUBuffer can be destroyed after tensor is created. When zeroCopy is false or undefined(default), buffer.size should notīe smaller than the byte size of tensor shape. GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC 3. Object, the dtype could only be 'float32' or 'int32 and the object has to Size, zeros will be padded at the rear.). (If the values passed from texture is less than the tensor Tensor, such as 'R' or 'BR' (The order of the channels affect the order of Of 'RGBA', indicating the values of which channels will be passed to the Texture's canvas) and the internal texture format for the input texture mustīe floating point or normalized integer 2. TFJS's WebGL backend (you could create a custom WebGL backend from your WebGLTexture, the texture must share the same WebGLRenderingContext with If the values is a WebGLData object, the dtypeĬould only be 'float32' or 'int32' and the object has to have: 1. If the values are strings, they will be encoded as utf-8Īnd kept as Uint8Array. Or a flat array, or a TypedArray, or a WebGLData object, or a values ( TypedArray|Array|WebGLData|WebGPUData) The values of the tensor.findBackend( 'custom-webgl') = null), shape, dtype) Ĭonst b = tf. For example, if your application includes a preprocessing step on the GPU, // you could upload the GPU output directly to TF.js, rather than first // downloading the values. This makes it possible for TF.js applications to avoid GPU / CPU sync. Pass a `WebGLData` object and specify a shape yourself. Pass a flat array and specify a shape yourself. ![]() Pass a nested array of values to make a matrix or a higher // dimensional tensor. Pass an array of values to create a vector. Creates a tf.Tensor with the provided values, shape and dtype. ![]()
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