triosage.blogg.se - Tensorflow permute dimensions

Reverse.forward_log_det_jacobian(any_value) Permutes the rightmost dimension of a Tensor. false means that both extra weightsĪnd missing weights will be silently ignored.Defined in tensorflow/contrib/distributions/python/ops/bijectors/permute.py. If true, require that the provided weights exactly match those

strict (boolean) Strict loading model: whether extraneous weights or missing.

fetchFunc (Function) A function used to override the window.fetch function.

onProgress (OnProgressCallback) Progress callback.

requestInit (RequestInit) RequestInit (options) for HTTP requests.įor detailed information on the supported fields, see.

options (Object) Options for the HTTP request, which allows to send.

modelUrl (string|io.IOHandler) The url or an io.IOHandler that loads the model.

For example, the following line runs prediction with the model on // some fake data. The model can be used for training, evaluation and prediction. First layer must have an input shape defined. Tf.model() is more generic and supports an arbitrary graph (without

Tf.sequential() is less generic, supporting only a linear stack of layers. The key difference between tf.model() and tf.sequential() is that (recurrent, Dense.) an inputDim argument. InputShape or batchInputShape argument, or for some type of layers What that means is that it should have received an This means that the first layer passed to a tf.Sequential model should haveĪ defined input shape. Topology is a simple 'stack' of layers, with no branching or skipping. Outputs of one layer are the inputs to the next layer, i.e. A sequential model is any model where the E.g., equationĬreates a tf.Sequential model. Does not support duplicate axes for any given input tensor.This implementation of einsum has the following limitations: Matrix transpose: const x = tf.tensor2d(, ]) īatch matrix transpose: const x = tf.tensor3d(, ],, ]]) Matrix multiplication: const x = tf.tensor2d(, ]) Ĭonst y = tf.tensor2d(,, ]) ĭot product: const x = tf.tensor1d() īatch dot product: const x = tf.tensor2d(, ]) Ĭonst y = tf.tensor2d(, ]) Tensor contraction over specified indices and outer product.Įinsum allows defining Tensors by defining their element-wise computation. , paddedShape / blockShape] + remainingShape Reshape permutedReshapedPadded to flatten blockShape into theīatch dimension, producing an output tensor of shape: Permute dimensions of reshapedPadded to produce permutedReshapedPadded

, paddedShape / blockShape, blockShape] + remainingShape Reshape padded to reshapedPadded of shape: Zero-pad the start and end of dimensions of the inputĪccording to paddings to produce padded of shape paddedShape. (inputShape + padStart + padEnd) % blockShape = 0 paddings = specifies the amount to zero-padįrom input dimension i + 1, which corresponds to spatial dimension i. Must have shape, all values must be >=Ġ. Tf.depthToSpace(x, blockSize, dataFormat).print() 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 reshapedto 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.