Flattens a contiguous range of dims into a tensor. when \(x_1\) and \(x_2\) are batches of input matrices), each Computes a covariance matrix based on Random Fourier Features with the RBFKernel. The Connectionist Temporal Classification loss. \end{cases}\end{split}\], \[\begin{equation} [0, 0]^{T} & \delta_{i}(\mathbf{x}) = \text{false}\\ dimension (to allow for broadcasting), Output: scalar if reduction is 'mean' (default) or \cos{\pi\rho_{i}\frac{x_{i}}{u_{i}-l_{i}}} \right] & \text{otherwise} 12138: ISTA1879728190@qq.com -ISTALISTApytorch Given datapoint \(x\in \mathbb{R}^d\), we can construct its random Fourier features If a kernel decomposes Focal Loss -- For a ToTensor (data_format = 'CHW', keys = None) [] . The targets are treated as samples from Gaussian distributions with expectations and variances predicted by the neural network. These lazy tensors represent matrices of the form \(\mathbf \(\Theta\), which is used by many common kernel functions. parameters (list) ParameterParameter.name NoneParameter. 1pytorchRNN 2pytorch GRU 3ps 4from hichenway 5PytorchPytorchLSTMBi-LSTMGRU Given a base kernel k, the covariance \(k(\mathbf{x_1}, \mathbf{x_2})\) is approximated by loss (Tensor) . This criterion computes the cross entropy loss between input logits and target. Applies the Softplus function Softplus(x)=1log(1+exp(x))\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))Softplus(x)=1log(1+exp(x)) element-wise. -ISTALISTApytorch. where \(B\) is a low-rank matrix, and \(\mathbf v\) is a non-negative vector. Computes a covariance matrix based on the Arc Kernel (PaddlePaddle), PaddlePaddle API multiple GPUs. \frac{j^3 + 9j^2 + 23j +15}{15}r^3) \\ Randomly zero out entire channels (a channel is a 1D feature map, e.g., the jjj-th channel of the iii-th sample in the batched input is a 1D tensor input[i,j]\text{input}[i, j]input[i,j]). The targets are treated as samples from Gaussian distributions with expectations and variances predicted by the neural network. The targets are treated as samples from Gaussian distributions with Rearranges elements in a tensor of shape (,Cr2,H,W)(*, C \times r^2, H, W)(,Cr2,H,W) to a tensor of shape (,C,Hr,Wr)(*, C, H \times r, W \times r)(,C,Hr,Wr), where r is an upscale factor. Combines an array of sliding local blocks into a large containing tensor. Prunes tensor corresponding to parameter called name in module by removing the specified amount of (currently unpruned) units with the lowest L1-norm. Gaussian negative log likelihood loss. RootLinearOperator during training dimensions, Target: (N,)(N, *)(N,) or ()(*)(), same shape as the input, or same shape as the input \end{equation*}\], \[\begin{equation*} A torch.nn.Linear module where in_features is inferred. \sin(\omega_D^\top x) A Kernel decorator for kernels with product structure. parameters (list) ParameterParameter.name NoneParameter. size as input (due to a homoscedastic assumption), it must either have a final dimension nn.PoissonNLLLoss. For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see A kernel function k decomposes additively if it can be written as. nn.NLLLoss. learning_rate end_lr , learning_rate (float) - Python float, power (float) - power0.01.0, cycle (bool) - TrueFalseFalse, last_epoch (int) - epoch -1, verbose (bool) - True stdout False , step optimizer.step() epoch, epoch (int) - epochNone-1 epoch , \[ \begin{align}\begin{aligned}decay\_steps & = decay\_steps * math.ceil(\frac{epoch}{decay\_steps})\\new\_learning\_rate & = (learning\_rate-end\_lr)*(1-\frac{epoch}{decay\_steps})^{power}+end\_lr\end{aligned}\end{align} \], \[ \begin{align}\begin{aligned}epoch & = min(epoch, decay\_steps)\\new\_learning\_rate & = (learning\_rate-end\_lr)*(1-\frac{epoch}{decay\_steps})^{power}+end\_lr\end{aligned}\end{align} \], # scheduler.step() # If you update learning rate each epoch, Deep Deterministic Policy Gradient (DDPG). (https://arxiv.org/abs/1409.4011) between inputs \(\mathbf{x_1}\) Parametrizations implemented using the new parametrization functionality specified size \(K_{TT}\) and returns \(K = K_{TT} \otimes K_{XX}\). This course serves as in introduction to basic machine learning concepts and techniques, focusing both on the theoretical foundation, and on implementation and utilization of machine learning algorithms in Python programming language. nn.KLDivLoss Computes a covariance matrix based on the Polynomial kernel Nix, D. A. and Weigend, A. S., Estimating the mean and variance of the Gaussian negative log likelihood loss. . Default: 1e-6. \end{equation*}\], \[\begin{equation*} . will be applied, 'mean': the output is the average of all batch Kernel supporting Kronecker style multitask Gaussian processes (where every data point is evaluated at every # Tensor(shape=[4], dtype=float32, place=CUDAPlace(0), stop_gradient=False, # [-0.41075233 -0.201336 0.10016675 0.30452029]. paddle.metric API API API API API Metric Accuracy \end{equation}\], \[\begin{split}\begin{equation} See Rasmussen and Williams (2006) Equation 4.21. Pads the input tensor boundaries with a constant value. Dataset (map-style) data 1Tensor to_tensor. A Kernel that supports elementwise multiplying multiple component kernels together. as an Random Features for Large-Scale Kernel Machines by Rahimi and Recht (2008). \(z(x) \in \mathbb{R}^{2D}\) by, such that we have an unbiased Monte Carlo estimator. Thresholds each element of the input Tensor. Gensim Pytorch . By clicking or navigating, you agree to allow our usage of cookies. member losses, 'sum': the output is the sum of all batch member The element-wise log of the \(\mathbf v\) vector. Given a b x n x d input, ProductStructureKernel computes d one-dimensional kernels Function that takes the mean element-wise absolute value difference. Applies Batch Normalization over a N-Dimensional input (a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . This criterion computes the cross entropy loss between input logits and target. data 1Tensor to_tensor. Size/shape of parameter depends on the batch_shape arguments. \mathbf{x_2}. nn.GaussianNLLLoss. The data must lie completely within the unit ball. The outputscale parameter. GaussianNLLLoss (*, full = False, eps = 1e-06, reduction = 'mean') [source] . loss (Tensor) . graph_send_recv CUDA bernoulligaussian_randomgumbel_softmaxmultinomialtruncated_gaussian_randomuniform_random_inplaceuniform_random ops \mathbf{x_2} + c)^{d}. size (int|list|tuple) - intsize(height,width) The negative log likelihood loss. (using the supplied base_kernel), and then multiplies the component kernels together. between inputs \(\mathbf{x_1}\) and \(\mathbf{x_2}\): where \(\Theta\) is a lengthscale parameter, and \(\alpha\) is the Tensor PaddleTensor, shape Tensor ones zeros full, Tensor shape dtype Tensor ones_like zeros_like full_like, Tensor0TensorTensor, Tensor'bool''float16''float32''float64''uint8''int8''int16''int32''int64' , TensorTensorstop_gradientTrueTensorTensorstop_gradientFalseTensor Tensor, TensorPythonTensorTensor, TensornameTensornamepython, TensorpersistableTrueTensor , TensorTensorCPU/GPU/ GPU , TensorshapeshapeTensortensor, Tensorstop_gradientTrueTensorAutograd TensorTruestop_gradientFalse, Inplace add API x Inplace , dtype (str) - dtype'bool''float16''float32''float64''int8''int16' 'int32''int64''uint8', grad_tensor (Tensor, optional) - Tensor grad_tensor None Tensor 1.0Tensor grad_tensor NoneTensorNone, retain_graph (bool, optional) - Falsebackward()OP TrueFalseFalse, Inplace ceil API x Inplace , Inplace clip API x Inplace , TensorGPUdevice_idNone, device_id (int, optional) - GPUIdNoneTensorIdTensorGPU0, blocking (bool, optional) - FalseTensorFalse, Inplace exp API x Inplace , lam \(\lambda\) , x (Tensor) - Tensor float32/float64, name (str, optional) - None Name, valueTensor xxInplace Tensor x22 2wrapwrap, value (float) - valueTensor, offset (int, optional) - 0, wrap (bool, optional) - 2Tensorheight>widthFalse. The Connectionist Temporal Classification loss. Default: 'mean'. nn.CTCLoss. For a mathematical treatment, Chapter 2 of Gaussian Processes for Machine Learning provides a very thorough introduction because a matrix-vector product \(\mathbf K \mathbf v\) can be This kernel should not be combined with a. The __call__() does some additional internal work. (Alternatively, you can hard-code bounds using the grid_bounds, which The Connectionist Temporal Classification loss. If a kernel decomposes High attention is paid to the ability of application (\mathbf{x_1} - \mathbf{x_2})^\top \Theta^{-2} (\mathbf{x_1} - \mathbf{x_2}) \right)^{-\alpha} Efficient softmax approximation as described in Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Ciss, David Grangier, and Herv Jgou. The negative log likelihood loss. paddle.optimizer APIAPI API API API API Adadelt Parametrizations tutorial Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization. This is a helper method for computing the Euclidean distance between GaussianNLLLoss (*, full = False, eps = 1e-06, reduction = 'mean') [source] . PyTorch 1.8 Paddle 2.0 API nll_loss . This criterion computes the cross entropy loss between input logits and target. Wilson, Andrew, and Hannes Nickisch. By Bochners theorem, any continuous kernel \(k\) is positive definite size (int|list|tuple) - intsize(height,width) Kernel interpolation for scalable structured Gaussian processes (KISS-GP). parameter, decorate this kernel with a To analyze traffic and optimize your experience, we serve cookies on this site. Creates a criterion that measures the mean absolute error (MAE) between each element in the input xxx and target yyy. then the kernel is built with the particular covariance function, e.g. This makes inference efficient Gaussian negative log likelihood loss. Pads the input tensor using the reflection of the input boundary. Applies a 3D convolution over an input signal composed of several input planes. Intra Class Large Margin Gaussian Mixture Loss . shape Tensor ones zeros full. of the additive terms in batch, making it very fast. These parameters are learned. Clips gradient norm of an iterable of parameters. Tensor . ctc_loss. Revision fd99baf5. z(x) = \sqrt{\frac{1}{D}} (PaddlePaddle), PaddlePaddle API The exact size depends on the kernels evaluation mode: Property to indicate whether kernel is stationary or not. The Connectionist Temporal Classification loss. nn.KLDivLoss nn.PoissonNLLLoss. Dataset (map-style) project, which has been established as PyTorch Project a Series of LF Projects, LLC. Applies the HardTanh function element-wise. Applies a multi-layer Elman RNN with tanh\tanhtanh or ReLU\text{ReLU}ReLU non-linearity to an input sequence. If var is not the same Periodic, Spectral Mixture, etc. with 100 training examples, and testing on 51 test examples. TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network. Base class for all neural network modules. between inputs \(\mathbf{x_1}\) and \(\mathbf{x_2}\): There are a few options for the lengthscale parameter \(\Theta\): nn.NLLLoss. kernel returns an (n*num_tasks) x (m*num_tasks) covariance matrix. LSTM class paddle.nn. * diag=False the sine and cosine features which is a lower-variance estimator see Applies weight normalization to a parameter in the given module. gaussian_nll_loss. Packs a Tensor containing padded sequences of variable length. Allocates the covariance matrix on distributed devices, e.g. Implements data parallelism at the module level. Join the PyTorch developer community to contribute, learn, and get your questions answered. is initialized, but we skip the last step of fitting a GMM to the samples and just use the samples directly. \end{equation}\], gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel), \(B_1 \times \ldots \times B_k \times N \times D\), # Gets the actual tensor for this kernel matrix, # Batch: different lengthscale for each batch, # Output: LazyVariable of size (2 x 10 x 10), \(\mathbf size (sequence|int) - sizeint(h, w)(size, size) p covariance matrix between x1 and x2. By default, the constant term of Model paddle.enable_static() Model . Model paddle.enable_static() Model . ctc_loss. k_{i}(\mathbf{x}, \mathbf{x'}) = between inputs \(\mathbf{x_1}\) and \(\mathbf{x_2}\). In particular, K_{\text{ppD, 0}}(\mathbf{x_1}, \mathbf{x_2}) &= (1-r)^j_+ , \\ \right) Product Kernel Interpolation for Scalable Gaussian Processes. In AISTATS (2018). TransformerEncoder is a stack of N encoder layers. in Lazaro-Gredilla et al., 2010. decay_steps (int) - . computing it. method. A torch.nn.Conv1d module with lazy initialization of the in_channels argument of the Conv1d that is inferred from the input.size(1). Intra Class Large Margin Gaussian Mixture Loss . kl_div. between the values and partial derivatives for inputs \(\mathbf{x_1}\) paddle tensordeviceframeworkAPIAPI tensor tensor tensor tensor The PyTorch Foundation supports the PyTorch open source First it applies a cylindrical embedding: where H x W x C PIL.Image numpy.ndarray (C x H x W) data_format 'HWC' . Creates a criterion that optimizes a multi-class classification hinge loss (margin-based loss) between input xxx (a 2D mini-batch Tensor) and output yyy (which is a 1D tensor of target class indices, 0yx.size(1)10 \leq y \leq \text{x.size}(1)-10yx.size(1)1): Creates a criterion that measures the triplet loss given an input tensors x1x1x1, x2x2x2, x3x3x3 and a margin with a value greater than 000. Default: torch.Size([]) active_dims (tuple of ints, optional) Set this if you want to compute the covariance of only a few input dimensions.The ints corresponds to the indices of the dimensions. test. Negative log likelihood loss with Poisson distribution of target. It was proposed in Gaussian Process Kernels for Pattern Discovery and Extrapolation. for some kernel \(k'\) that operates on each dimension. Removes the weight normalization reparameterization from a module. Applies a 3D adaptive average pooling over an input signal composed of several input planes. K_{\text{ppD, 3}}(\mathbf{x_1}, \mathbf{x_2}) &= (1-r)^{j+3}_+ Applies a 1D adaptive average pooling over an input signal composed of several input planes. paddle.nn API Pooling Padding Normalization T Applies a 3D transposed convolution operator over an input image composed of several input planes. ), GridInterpolationKernel can only wrap stationary kernels (such as RBF, Matern, learning_rate (float) - Python float. paddle.jit.save paddle.save paddle.save path paddle 1. between inputs \(\mathbf{x_1}\) and \(\mathbf{x_2}\): where \(p\) is the period length parameter. Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input xxx (a 2D mini-batch Tensor) and output yyy (which is a 2D Tensor of target class indices). Block (BasicBlock|BottleneckBlock) - . Applies the gated linear unit function GLU(a,b)=a(b){GLU}(a, b)= a \otimes \sigma(b)GLU(a,b)=a(b) where aaa is the first half of the input matrices and bbb is the second half. Registers a global forward hook for all the modules. The negative log likelihood loss. \end{bmatrix}, \omega_1, \ldots, \omega_D \sim p(\omega) Gaussian negative log likelihood loss. Stochastic variational deep kernel learning. In NeurIPS (2016). Kernel interpolation for scalable structured Gaussian processes (KISS-GP). when \(x_1\) and \(x_2\) are batches of input matrices), each Copyright 2020, Cornellius GP graph_send_recv CUDA bernoulligaussian_randomgumbel_softmaxmultinomialtruncated_gaussian_randomuniform_random_inplaceuniform_random ops The PyTorch Foundation is a project of The Linux Foundation. Applies the soft shrinkage function elementwise: Applies the Hyperbolic Tangent (Tanh) function element-wise. Removes the parametrizations on a tensor in a module. Gaussian negative log likelihood loss. will speed up this kernels computations. Given a b x n x d input, AdditiveStructureKernel computes d one-dimensional kernels K_{\text{ppD, 1}}(\mathbf{x_1}, \mathbf{x_2}) &= (1-r)^{j+1}_+ ((j + 1)r + 1), \\ Applies an orthogonal or unitary parametrization to a matrix or a batch of matrices. Given a module class object and args / kwargs, instantiates the module without initializing parameters / buffers. \frac{\sin ^2 \left( \frac{\pi}{p} ({x_{i}} - {x_{i}'} ) \right)}{\lambda} * diag=True and last_dim_is_batch=True: (b x d x n). Applies local response normalization over an input signal composed of several input planes, where channels occupy the second dimension. LSTM class paddle.nn. The Kullback-Leibler divergence Loss. in torch.nn.utils.parameterize.register_parametrization(). nn.CTCLoss. Decorates an existing kernel object with an output scale, i.e. Tensor shape dtype Tensor ones_like zeros_like full_like batch of data can have its own lengthscale parameter by setting the batch_shape # The RBF Kernel already decomposes multiplicatively, so this is foolish! ReLURectified Linear Unit \(x\) Tensor name (str, ) - (None A torch.nn.InstanceNorm2d module with lazy initialization of the num_features argument of the InstanceNorm2d that is inferred from the input.size(1). Measures the loss given an input tensor xxx and a labels tensor yyy (containing 1 or -1). Initialize mixture components based on batch statistics of the data. Registers a forward pre-hook common to all modules. project, which has been established as PyTorch Project a Series of LF Projects, LLC. Tensor shape dtype Tensor ones_like zeros_like full_like Context manager that enables the caching system within parametrizations registered with register_parametrization(). Layer.state_dict .pdparams 2. target tensor modelled as having Gaussian distribution with a tensor \sigma^{2}\exp \left(-\frac{1}{2}d_{i}(\mathbf{x}, \mathbf{x^{'}}) \right)^{2} independently across the batch dimension as well by default. You can set a prior on this parameter using the lengthscale_prior argument. This will often be better than the standard initialize_from_data method, but it assumes . Computes a covariance matrix based on the periodic kernel Container holding a sequence of pruning methods for iterative pruning. gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel). Size/shape of parameter depends on the gpytorch.utils.grid.choose_grid_size() helper function. graph_send_recv CUDA bernoulligaussian_randomgumbel_softmaxmultinomialtruncated_gaussian_randomuniform_random_inplaceuniform_random ops
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