mindspore.ops
Operators can be used in the construct function of Cell.
Examples
>>> import mindspore.ops as ops
Compared with the previous version, the added, deleted and supported platforms change information of mindspore.ops operators in MindSpore, please refer to the link https://gitee.com/mindspore/docs/blob/r1.6/resource/api_updates/ops_api_updates.md.
operations
The Primitive operators in operations need to be instantiated before being used.
Neural Network Operators
API Name |
Description |
Supported Platforms |
Computes inverse hyperbolic cosine of the inputs element-wise. |
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Updates gradients by the Adaptive Moment Estimation (Adam) algorithm. |
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Updates gradients by the Adaptive Moment Estimation (Adam) algorithm. |
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Updates gradients by the Adaptive Moment Estimation algorithm with weight decay (AdamWeightDecay). |
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AdaptiveAvgPool2D operation. |
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Updates relevant entries according to the adadelta scheme. |
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Updates relevant entries according to the adagrad scheme. |
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Update var according to the proximal adagrad scheme. |
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Updates relevant entries according to the adagradv2 scheme. |
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Updates relevant entries according to the adamax scheme. |
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Updates relevant entries according to the AddSign algorithm. |
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Optimizer that implements the centered RMSProp algorithm. |
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Updates var by subtracting alpha * delta from it. |
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Optimizer that implements the Momentum algorithm. |
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Updates relevant entries according to the AddSign algorithm. |
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Updates relevant entries according to the proximal adagrad algorithm. |
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Updates relevant entries according to the FOBOS(Forward Backward Splitting) algorithm. |
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Optimizer that implements the Root Mean Square prop(RMSProp) algorithm. |
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Average pooling operation. |
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3D Average pooling operation. |
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It's similar to operator |
Deprecated |
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Batch Normalization for input data and updated parameters. |
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Adds sigmoid activation function to input logits, and uses the given logits to compute binary cross entropy between the logits and the label. |
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Returns sum of input and bias tensor. |
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Computes the binary cross entropy between the logits and the labels. |
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Compute accidental hits of sampled classes which match target classes. |
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2D convolution layer. |
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The Conv2DBackpropInput interface is deprecated, please refer to |
Deprecated |
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Compute a 2D transposed convolution, which is also known as a deconvolution (although it is not an actual deconvolution). |
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3D convolution layer. |
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Computes a 3D transposed convolution, which is also known as a deconvolution (although it is not an actual deconvolution). |
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Performs greedy decoding on the logits given in inputs. |
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Calculates the CTC (Connectionist Temporal Classification) loss and the gradient. |
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Returns the dimension index in the destination data format given in the source data format. |
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DepthwiseConv2dNative will be deprecated in the future. |
Deprecated |
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During training, randomly zeroes some of the elements of the input tensor with probability 1-keep_prob from a Bernoulli distribution. |
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During training, randomly zeroes some of the channels of the input tensor with probability 1-keep_prob from a Bernoulli distribution(For a 4-dimensional tensor with a shape of NCHW, the channel feature map refers to a 2-dimensional feature map with the shape of HW). |
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During training, randomly zeroes some of the channels of the input tensor with probability 1-keep_prob from a Bernoulli distribution(For a 5-dimensional tensor with a shape of NCDHW, the channel feature map refers to a 3-dimensional feature map with a shape of DHW). |
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The DropoutDoMask interface is deprecated, please use the |
Deprecated |
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The DropoutGenMask interface is deprecated, please use the |
Deprecated |
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Applies a single-layer gated recurrent unit (GRU) to an input sequence. |
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Applies a recurrent neural network to the input. |
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Computes exponential linear: |
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Fast Gaussian Error Linear Units activation function. |
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Flattens a tensor without changing its batch size on the 0-th axis. |
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Computes the remainder of division element-wise. |
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Merges the duplicate value of the gradient and then updates parameters by the Adaptive Moment Estimation (Adam) algorithm. |
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Merges the duplicate value of the gradient and then updates parameters by the Adaptive Moment Estimation (Adam) algorithm. |
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Merges the duplicate value of the gradient and then updates relevant entries according to the proximal adagrad algorithm. |
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Gaussian Error Linear Units activation function. |
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Returns the next element in the dataset queue. |
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Applies the hard shrinkage function element-wise, each element complies with the following function: |
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Hard sigmoid activation function. |
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Hard swish activation function. |
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Computes the Kullback-Leibler divergence between the logits and the labels. |
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Calculates half of the L2 norm of a tensor without sqrt. |
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L2 Normalization Operator. |
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Conducts LARS (layer-wise adaptive rate scaling) update on the sum of squares of gradient. |
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Applies the Layer Normalization to the input tensor. |
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Log Softmax activation function. |
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Local Response Normalization. |
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Performs the Long Short-Term Memory (LSTM) on the input. |
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Max pooling operation. |
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3D max pooling operation. |
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Performs max pooling on the input Tensor and returns both max values and indices. |
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Pads the input tensor according to the paddings and mode. |
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Computes MISH(A Self Regularized Non-Monotonic Neural Activation Function) of input tensors element-wise. |
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Gets the negative log likelihood loss between logits and labels. |
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Computes a one-hot tensor. |
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Pads the input tensor according to the paddings. |
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Parametric Rectified Linear Unit activation function. |
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Computes ReLU (Rectified Linear Unit activation function) of input tensors element-wise. |
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Computes ReLU (Rectified Linear Unit) upper bounded by 6 of input tensors element-wise. |
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Rectified Linear Unit activation function. |
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Resizes an image to a certain size using the bilinear interpolation. |
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Computes the RNNTLoss and its gradient with respect to the softmax outputs. |
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Computes the Region of Interest (RoI) Align operator. |
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Computes SeLU (scaled exponential Linear Unit) of input tensors element-wise. |
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Computes the stochastic gradient descent. |
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Sigmoid activation function. |
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Uses the given logits to compute sigmoid cross entropy between the logits and the label. |
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Computes smooth L1 loss, a robust L1 loss. |
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SoftMarginLoss operation. |
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Softmax operation. |
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Gets the softmax cross-entropy value between logits and labels with one-hot encoding. |
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Softplus activation function. |
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Applies the SoftShrink function element-wise. |
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Softsign activation function. |
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Updates relevant entries according to the adagrad scheme. |
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Updates relevant entries according to the adagrad scheme, one more epsilon attribute than SparseApplyAdagrad. |
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Updates relevant entries according to the proximal adagrad algorithm. |
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Computes the softmax cross-entropy value between logits and sparse encoding labels. |
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Stacks a list of tensors in specified axis. |
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Tanh activation function. |
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Finds values and indices of the k largest entries along the last dimension. |
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Unstacks tensor in specified axis. |
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Math Operators
API Name |
Description |
Supported Platforms |
Returns absolute value of a tensor element-wise. |
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Computes accumulation of all input tensors element-wise. |
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Computes arccosine of input tensors element-wise. |
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Adds two input tensors element-wise. |
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Computes addition of all input tensors element-wise. |
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Returns True if abs(x-y) is smaller than tolerance element-wise, otherwise False. |
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Computes arcsine of input tensors element-wise. |
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Computes inverse hyperbolic sine of the input element-wise. |
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Updates a Parameter by adding a value to it. |
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Updates a Parameter by subtracting a value from it. |
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Computes the trigonometric inverse tangent of the input element-wise. |
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Returns arctangent of x/y element-wise. |
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Computes inverse hyperbolic tangent of the input element-wise. |
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Computes matrix multiplication between two tensors by batch. |
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Computes BesselI0e of input element-wise. |
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Computes BesselI1e of input element-wise. |
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Returns bitwise and of two tensors element-wise. |
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Returns bitwise or of two tensors element-wise. |
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Returns bitwise xor of two tensors element-wise. |
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Computes batched the p-norm distance between each pair of the two collections of row vectors. |
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Rounds a tensor up to the closest integer element-wise. |
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Computes cosine of input element-wise. |
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Returns a tensor of complex numbers that are the complex conjugate of each element in input. |
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Computes hyperbolic cosine of input element-wise. |
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Computes the cumulative product of the tensor x along axis. |
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Computes the cumulative sum of input tensor along axis. |
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Computes the quotient of dividing the first input tensor by the second input tensor element-wise. |
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Computes a safe divide and returns 0 if the y is zero. |
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Creates a tensor filled with minimum value in x dtype. |
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Computes the equivalence between two tensors element-wise. |
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Computes the number of the same elements of two tensors. |
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Computes the Gauss error function of x element-wise. |
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Computes the complementary error function of x element-wise. |
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Computes the inverse error function of input. |
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Returns exponential of a tensor element-wise. |
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Returns exponential then minus 1 of a tensor element-wise. |
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Determines if the elements contain Not a Number(NaN), infinite or negative infinite. |
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Rounds a tensor down to the closest integer element-wise. |
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Divides the first input tensor by the second input tensor element-wise and round down to the closest integer. |
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Ger product of x1 and x2. |
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Computes the boolean value of \(x > y\) element-wise. |
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Computes the boolean value of \(x >= y\) element-wise. |
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Returns a rank 1 histogram counting the number of entries in values that fall into every bin. |
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Returns a new tensor containing imaginary value of the input. |
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Adds tensor y to specified axis and indices of tensor x. |
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Adds v into specified rows of x. |
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Subtracts v into specified rows of x. |
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Computes Reciprocal of input tensor element-wise. |
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Flips all bits of input tensor element-wise. |
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Determines which elements are inf or -inf for each position |
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Determines which elements are NaN for each position. |
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Does a linear interpolation of two tensors start and end based on a float or tensor weight. |
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Computes the boolean value of \(x < y\) element-wise. |
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Computes the boolean value of \(x <= y\) element-wise. |
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Returns a Tensor whose value is num evenly spaced in the interval start and stop (including start and stop), and the length of the output Tensor is num. |
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Returns the natural logarithm of a tensor element-wise. |
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Returns the natural logarithm of one plus the input tensor element-wise. |
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Computes the "logical AND" of two tensors element-wise. |
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Computes the "logical NOT" of a tensor element-wise. |
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Computes the "logical OR" of two tensors element-wise. |
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Returns the matrix norm or vector norm of a given tensor. |
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Multiplies matrix a and matrix b. |
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Returns the inverse of the input matrix. |
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Computes the maximum of input tensors element-wise. |
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Computes the minimum of input tensors element-wise. |
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Computes the remainder of dividing the first input tensor by the second input tensor element-wise. |
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Multiplies two tensors element-wise. |
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Computes x * y element-wise. |
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Returns a tensor with negative values of the input tensor element-wise. |
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When object detection problem is performed in the computer vision field, object detection algorithm generates a plurality of bounding boxes. |
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Computes the non-equivalence of two tensors element-wise. |
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Allocates a flag to store the overflow status. |
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Clears the flag which stores the overflow status. |
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Updates the flag which is the output tensor of NPUAllocFloatStatus with the latest overflow status. |
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Computes a tensor to the power of the second input. |
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Returns a Tensor that is the real part of the input. |
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Divides the first input tensor by the second input tensor in floating-point type element-wise. |
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Returns reciprocal of a tensor element-wise. |
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Reduces a dimension of a tensor by the "logicalAND" of all elements in the dimension, by default. |
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Reduces a dimension of a tensor by the "logical OR" of all elements in the dimension, by default. |
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Reduces a dimension of a tensor by the maximum value in this dimension, by default. |
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Reduces a dimension of a tensor by averaging all elements in the dimension, by default. |
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Reduces a dimension of a tensor by the minimum value in the dimension, by default. |
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Reduces a dimension of a tensor by multiplying all elements in the dimension, by default. |
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Reduces a dimension of a tensor by summing all elements in the dimension, by default. |
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Returns half to even of a tensor element-wise. |
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Computes reciprocal of square root of input tensor element-wise. |
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Performs sign on the tensor element-wise. |
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Computes sine of the input element-wise. |
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Computes hyperbolic sine of the input element-wise. |
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Returns square root of a tensor element-wise. |
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Returns square of a tensor element-wise. |
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Subtracts the second input tensor from the first input tensor element-wise and returns square of it. |
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Returns the square sum of a tensor element-wise |
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Subtracts the second input tensor from the first input tensor element-wise. |
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Computes tangent of x element-wise. |
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Divides the first input tensor by the second input tensor element-wise for integer types, negative numbers will round fractional quantities towards zero. |
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Returns the remainder of division element-wise. |
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Divides the first input tensor by the second input tensor element-wise. |
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Computes the first input tensor multiplied by the logarithm of second input tensor element-wise. |
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Array Operators
API Name |
Description |
Supported Platforms |
Updates relevant entries according to the FTRL scheme. |
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Returns the indices of the maximum value of a tensor across the axis. |
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Calculates the maximum value with the corresponding index. |
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Returns the indices of the minimum value of a tensor across the axis. |
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Calculates the minimum value with corresponding index, and returns indices and values. |
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Divides batch dimension with blocks and interleaves these blocks back into spatial dimensions. |
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Divides batch dimension with blocks and interleaves these blocks back into spatial dimensions. |
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Broadcasts input tensor to a given shape. |
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Returns a tensor with the new specified data type. |
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Connect tensor in the specified axis. |
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Rearrange blocks of depth data into spatial dimensions. |
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Returns the data type of the input tensor as mindspore.dtype. |
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Returns the shape of the input tensor. |
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Computes the Levenshtein Edit Distance. |
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Returns a slice of input tensor based on the specified indices. |
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Adds an additional dimension to input_x at the given axis. |
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Extract patches from input and put them in the "depth" output dimension. |
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Creates a tensor with ones on the diagonal and zeros in the rest. |
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Creates a tensor filled with a scalar value. |
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Merges the duplicate value of the gradient and then updates relevant entries according to the FTRL-proximal scheme. |
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Returns a slice of the input tensor based on the specified indices and axis. |
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Gathers values along an axis specified by dim. |
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Gathers slices from a tensor by indices. |
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Returns a Tensor with the same shape and contents as input. |
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Updates specified rows with values in v. |
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Computes the inverse of an index permutation. |
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Determines which elements are finite for each position. |
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Checks whether an object is an instance of a target type. |
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Checks whether this type is a sub-class of another type. |
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Fills elements of self tensor with value where mask is True. |
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Returns a new 1-D Tensor which indexes the input tensor according to the boolean mask. |
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Generates coordinate matrices from given coordinate tensors. |
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Creates a tensor filled with value ones. |
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Creates a new tensor. |
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Extends the last dimension of the input tensor from 1 to pad_dim_size, by filling with 0. |
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Concats tensor in the first dimension. |
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Generates n random samples from 0 to n-1 without repeating. |
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Returns the rank of a tensor. |
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Reshapes the input tensor with the same values based on a given shape tuple. |
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Resizes the input tensor by using the nearest neighbor algorithm. |
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Reverses variable length slices. |
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Reverses specific dimensions of a tensor. |
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Returns an integer that is closest to x element-wise. |
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Checks whether the data type and shape of two tensors are the same. |
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Casts the input scalar to another type. |
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Converts a scalar to a Tensor. |
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Converts a scalar to a Tensor, and converts the data type to the specified type. |
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Updates the value of the input tensor through the addition operation. |
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Updates the value of the input tensor through the divide operation. |
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Updates the value of the input tensor through the maximum operation. |
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Updates the value of the input tensor through the minimum operation. |
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Updates the value of the input tensor through the multiply operation. |
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Scatters a tensor into a new tensor depending on the specified indices. |
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Applies sparse addition to individual values or slices in a tensor. |
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Applies sparse subtraction to individual values or slices in a tensor. |
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Updates tensor values by using input indices and value. |
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Applies sparse addition to the input using individual values or slices. |
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Updates the value of the input tensor through the subtraction operation. |
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Updates tensor values by using input indices and value. |
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Returns the selected elements, either from input \(x\) or input \(y\), depending on the condition. |
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Returns the shape of the input tensor. |
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Returns the size of a Tensor. |
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Slices a tensor in the specified shape. |
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Sorts the elements of the input tensor along a given dimension in ascending order by value. |
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SpaceToBatch is deprecated. |
Deprecated |
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Divides spatial dimensions into blocks and combines the block size with the original batch. |
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Rearrange blocks of spatial data into depth. |
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Updates relevant entries according to the FTRL-proximal scheme. |
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Updates relevant entries according to the FTRL-proximal scheme. |
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Returns a slice of input tensor based on the specified indices and axis. |
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Splits the input tensor into output_num of tensors along the given axis and output numbers. |
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Splits the input tensor into num_split tensors along the given dimension. |
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Returns a tensor with the same data type but dimensions of 1 are removed based on axis. |
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Extracts a strided slice of a tensor. |
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Creates a new tensor by adding the values from the positions in input_x indicated by indices, with values from updates. |
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By comparing the value at the position indicated by the index in input_x with the value in the update, the value at the index will eventually be equal to the largest one to create a new tensor. |
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By comparing the value at the position indicated by the index in input_x with the value in the updates, the value at the index will eventually be equal to the smallest one to create a new tensor. |
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Creates a new tensor by subtracting the values from the positions in input_x indicated by indices, with values from updates. |
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Creates a new tensor by updating the positions in input_x indicated by indices, with values from update. |
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Replicates a tensor with given multiples times. |
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Permutes the dimensions of the input tensor according to input permutation. |
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Converts a tuple to a tensor. |
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Returns the unique elements of input tensor and also return a tensor containing the index of each value of input tensor corresponding to the output unique tensor. |
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Returns unique elements and relative indexes in 1-D tensor, filled with padding num. |
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Computes the maximum along segments of a tensor. |
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Computes the minimum of a tensor along segments. |
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Computes the product of a tensor along segments. |
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Computes the sum of a tensor along segments. |
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Creates a tensor filled with value zeros. |
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Creates a new tensor. |
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Communication Operators
Note that the APIs in the following list need to preset communication environment variables. For the Ascend devices, users need to prepare the rank table, set rank_id and device_id. Please see the Ascend tutorial for more details. For the GPU device, users need to prepare the host file and mpi, please see the GPU tutorial .
API Name |
Description |
Supported Platforms |
Gathers tensors from the specified communication group. |
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Reduces the tensor data across all devices in such a way that all devices will get the same final result. |
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AlltoAll is a collective operation. |
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Broadcasts the tensor to the whole group. |
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NeighborExchange is a collective operation. |
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NeighborExchangeV2 is a collective operation. |
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Operation options for reducing tensors. |
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Reduces and scatters tensors from the specified communication group. |
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Debug Operators
API Name |
Description |
Supported Platforms |
Outputs the tensor to protocol buffer through histogram summary operator. |
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This operation is used as a tag to hook gradient in intermediate variables. |
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Outputs the image tensor to protocol buffer through image summary operator. |
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Attaches callback to the graph node that will be invoked on the node's gradient. |
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Outputs the tensor or string to stdout. |
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Outputs a scalar to a protocol buffer through a scalar summary operator. |
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Outputs a tensor to a protocol buffer through a tensor summary operator. |
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Random Operators
API Name |
Description |
Supported Platforms |
Produces random positive floating-point values x, distributed according to probability density function: |
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Generates random labels with a log-uniform distribution for sampled_candidates. |
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Returns a tensor sampled from the multinomial probability distribution located in the corresponding row of tensor input. |
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Produces random non-negative integer values i, distributed according to discrete probability function: |
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Generates random samples from a given categorical distribution tensor. |
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Generates a random sample as index tensor with a mask tensor from a given tensor. |
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Generates random numbers according to the Laplace random number distribution (mean=0, lambda=1). |
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Generates random numbers according to the standard Normal (or Gaussian) random number distribution. |
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Uniform candidate sampler. |
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Produces random integer values i, uniformly distributed on the closed interval [minval, maxval), that is, distributed according to the discrete probability function: |
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Produces random floating-point values i, uniformly distributed to the interval [0, 1). |
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Image Operators
API Name |
Description |
Supported Platforms |
Extracts crops from the input image tensor and resizes them. |
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Sparse Operators
API Name |
Description |
Supported Platforms |
Converts a sparse representation into a dense tensor. |
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Multiplies sparse matrix A by dense matrix B. |
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Custom Operators
API Name |
Description |
Supported Platforms |
Custom primitive is used for user defined operators and is to enhance the expressive ability of built-in primitives. |
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Other Operators
API Name |
Description |
Supported Platforms |
Assigns Parameter with a value. |
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Decodes bounding boxes locations. |
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Encodes bounding boxes locations. |
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Checks whether the data type and the shape of corresponding elements from tuples x and y are the same. |
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Checks bounding box. |
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Depend is used for processing dependency operations. |
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Determines whether the targets are in the top k predictions. |
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Calculates intersection over union for boxes. |
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Updates log_probs with repeat n-grams. |
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Makes a partial function instance. |
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Calculates population count. |
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composite
The composite operators are the pre-defined combination of operators.
API Name |
Description |
Supported Platforms |
Computation of batch dot product between samples in two tensors containing batch dims. |
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Clips tensor values by the ratio of the sum of their norms. |
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Clips tensor values to a specified min and max. |
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A decorator that adds a flag to the function. |
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Count number of nonzero elements across axis of input tensor |
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Computation of the cumulative minimum of elements of 'x' in the dimension axis, and the index location of each maximum value found in the dimension 'axis'. |
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Computation a dot product between samples in two tensors. |
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Generates random numbers according to the Gamma random number distribution. |
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A higher-order function which is used to generate the gradient function for the input function. |
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Hypermap will apply the set operation to input sequences. |
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Generates random numbers according to the Laplace random number distribution. |
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Map will apply the set operation on input sequences. |
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Returns the matrix product of two arrays. |
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Returns a tensor sampled from the multinomial probability distribution located in the corresponding row of the input tensor. |
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Generates overloaded functions. |
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Generates random numbers according to the Normal (or Gaussian) random number distribution. |
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Generates random numbers according to the Poisson random number distribution. |
|
|
Repeat elements of a tensor along an axis, like np.repeat. |
|
|
Returns a mask tensor representing the first N positions of each cell. |
|
|
Computation of Tensor contraction on arbitrary axes between tensors a and b. |
|
|
Generates random numbers according to the Uniform random number distribution. |
|
functional
The functional operators are the pre-instantiated Primitive operators, which can be used directly as a function. The use cases of some functional operators are as follows:
from mindspore import Tensor, ops
from mindspore import dtype as mstype
input_x = Tensor(-1, mstype.int32)
input_dict = {'x':1, 'y':2}
result_abs = ops.absolute(input_x)
print(result_abs)
result_in_dict = ops.in_dict('x', input_dict)
print(result_in_dict)
result_not_in_dict = ops.not_in_dict('x', input_dict)
print(result_not_in_dict)
result_isconstant = ops.isconstant(input_x)
print(result_isconstant)
result_typeof = ops.typeof(input_x)
print(result_typeof)
# outputs:
# 1
# True
# False
# True
# Tensor[Int32]
functional |
Description |
|---|---|
mindspore.ops.absolute |
Refer to |
mindspore.ops.acos |
Refer to |
mindspore.ops.acosh |
Refer to |
mindspore.ops.add |
Refer to |
mindspore.ops.addn |
Refer to |
mindspore.ops.asin |
Refer to |
mindspore.ops.asinh |
Refer to |
mindspore.ops.assign |
Refer to |
mindspore.ops.assign_add |
Refer to |
mindspore.ops.assign_sub |
Refer to |
mindspore.ops.atan |
Refer to |
mindspore.ops.atan2 |
Refer to |
mindspore.ops.atanh |
Refer to |
mindspore.ops.bitwise_and |
Refer to |
mindspore.ops.bitwise_or |
Refer to |
mindspore.ops.bitwise_xor |
Refer to |
mindspore.ops.bool_and |
Calculate the result of logical AND operation. (Usage is the same as “and” in Python) |
mindspore.ops.bool_eq |
Determine whether the Boolean values are equal. (Usage is the same as “==” in Python) |
mindspore.ops.bool_not |
Calculate the result of logical NOT operation. (Usage is the same as “not” in Python) |
mindspore.ops.bool_or |
Calculate the result of logical OR operation. (Usage is the same as “or” in Python) |
mindspore.ops.cast |
Refer to |
mindspore.ops.cos |
Refer to |
mindspore.ops.cosh |
Refer to |
mindspore.ops.cumprod |
Refer to |
mindspore.ops.cumsum |
Refer to |
mindspore.ops.div |
Refer to |
mindspore.ops.depend |
Refer to |
mindspore.ops.dtype |
Refer to |
mindspore.ops.erf |
Refer to |
mindspore.ops.erfc |
Refer to |
mindspore.ops.eye |
Refer to |
mindspore.ops.equal |
Refer to |
mindspore.ops.expand_dims |
Refer to |
mindspore.ops.exp |
Refer to |
mindspore.ops.fill |
Refer to |
mindspore.ops.floor |
Refer to |
mindspore.ops.floordiv |
Refer to |
mindspore.ops.floormod |
Refer to |
mindspore.ops.gather |
Refer to |
mindspore.ops.gather_d |
Refer to |
mindspore.ops.gather_nd |
Refer to |
mindspore.ops.ge |
Refer to |
mindspore.ops.gt |
Refer to |
mindspore.ops.invert |
Refer to |
mindspore.ops.in_dict |
Determine if a str in dict. |
mindspore.ops.is_not |
Determine whether the input is not the same as the other one. (Usage is the same as “is not” in Python) |
mindspore.ops.is_ |
Determine whether the input is the same as the other one. (Usage is the same as “is” in Python) |
mindspore.ops.isconstant |
Determine whether the object is constant. |
mindspore.ops.isfinite |
Refer to |
mindspore.ops.isinstance_ |
Refer to |
mindspore.ops.isnan |
Refer to |
mindspore.ops.issubclass_ |
Refer to |
mindspore.ops.log |
Refer to |
mindspore.ops.logical_and |
Refer to |
mindspore.ops.le |
Refer to |
mindspore.ops.less |
Refer to |
mindspore.ops.logical_and |
Refer to |
mindspore.ops.logical_not |
Refer to |
mindspore.ops.logical_or |
Refer to |
mindspore.ops.maximum |
Refer to |
mindspore.ops.minimum |
Refer to |
mindspore.ops.mul |
Refer to |
mindspore.ops.neg_tensor |
Refer to |
mindspore.ops.not_equal |
Refer to |
mindspore.ops.not_in_dict |
Determine whether the object is not in the dict. |
mindspore.ops.ones_like |
Refer to |
mindspore.ops.partial |
Refer to |
mindspore.ops.pows |
Refer to |
mindspore.ops.print_ |
Refer to |
mindspore.ops.rank |
Refer to |
mindspore.ops.reduce_max |
Refer to |
mindspore.ops.reduce_mean |
Refer to |
mindspore.ops.reduce_min |
Refer to |
mindspore.ops.reduce_prod |
Refer to |
mindspore.ops.reduce_sum |
Refer to |
mindspore.ops.reshape |
Refer to |
mindspore.ops.same_type_shape |
Refer to |
mindspore.ops.scalar_add |
Get the sum of two numbers. (Usage is the same as “+” in Python) |
mindspore.ops.scalar_cast |
Refer to |
mindspore.ops.check_bprop |
Refer to |
mindspore.ops.scalar_div |
Get the quotient of dividing the first input number by the second input number. (Usage is the same as “/” in Python) |
mindspore.ops.scalar_eq |
Determine whether two numbers are equal. (Usage is the same as “==” in Python) |
mindspore.ops.scalar_floordiv |
Divide the first input number by the second input number and round down to the closest integer. (Usage is the same as “//” in Python) |
mindspore.ops.scalar_ge |
Determine whether the number is greater than or equal to another number. (Usage is the same as “>=” in Python) |
mindspore.ops.scalar_gt |
Determine whether the number is greater than another number. (Usage is the same as “>” in Python) |
mindspore.ops.scalar_le |
Determine whether the number is less than or equal to another number. (Usage is the same as “<=” in Python) |
mindspore.ops.scalar_log |
Get the natural logarithm of the input number. |
mindspore.ops.scalar_lt |
Determine whether the number is less than another number. (Usage is the same as “<” in Python) |
mindspore.ops.scalar_mod |
Get the remainder of dividing the first input number by the second input number. (Usage is the same as “%” in Python) |
mindspore.ops.scalar_mul |
Get the product of the input two numbers. (Usage is the same as “*” in Python) |
mindspore.ops.scalar_ne |
Determine whether two numbers are not equal. (Usage is the same as “!=” in Python) |
mindspore.ops.scalar_pow |
Compute a number to the power of the second input number. |
mindspore.ops.scalar_sub |
Subtract the second input number from the first input number. (Usage is the same as “-” in Python) |
mindspore.ops.scalar_to_array |
Refer to |
mindspore.ops.scalar_to_tensor |
Refer to |
mindspore.ops.scalar_uadd |
Get the positive value of the input number. |
mindspore.ops.scalar_usub |
Get the negative value of the input number. |
mindspore.ops.scatter_nd |
Refer to |
mindspore.ops.scatter_nd_update |
Refer to |
mindspore.ops.scatter_update |
Refer to |
mindspore.ops.shape |
Refer to |
mindspore.ops.shape_mul |
The input of shape_mul must be shape multiply elements in tuple(shape). |
mindspore.ops.sin |
Refer to |
mindspore.ops.sinh |
Refer to |
mindspore.ops.size |
Refer to |
mindspore.ops.sort |
Refer to |
mindspore.ops.sqrt |
Refer to |
mindspore.ops.square |
Refer to |
mindspore.ops.squeeze |
Refer to |
mindspore.ops.stack |
Refer to |
mindspore.ops.stop_gradient |
Disable update during back propagation. (stop_gradient) |
mindspore.ops.strided_slice |
Refer to |
mindspore.ops.string_concat |
Concatenate two strings. |
mindspore.ops.string_eq |
Determine if two strings are equal. |
mindspore.ops.sub |
Refer to |
mindspore.ops.tan |
Refer to |
mindspore.ops.tanh |
Refer to |
mindspore.ops.tensor_add |
Refer to |
mindspore.ops.tensor_div |
Refer to |
mindspore.ops.tensor_exp |
Refer to |
mindspore.ops.tensor_expm1 |
Refer to |
mindspore.ops.tensor_floordiv |
Refer to |
mindspore.ops.tensor_ge |
Refer to |
mindspore.ops.tensor_gt |
Refer to |
mindspore.ops.tensor_le |
Refer to |
mindspore.ops.tensor_lt |
Refer to |
mindspore.ops.tensor_mod |
Refer to |
mindspore.ops.tensor_mul |
Refer to |
mindspore.ops.tensor_pow |
Refer to |
mindspore.ops.tensor_scatter_add |
Refer to |
mindspore.ops.tensor_scatter_update |
Refer to |
mindspore.ops.tensor_slice |
Refer to |
mindspore.ops.tensor_sub |
Refer to |
mindspore.ops.tile |
Refer to |
mindspore.ops.transpose |
Refer to |
mindspore.ops.tuple_to_array |
Refer to |
mindspore.ops.typeof |
Get type of object. |
mindspore.ops.zeros_like |
Refer to |
API Name |
Description |
Supported Platforms |
A wrapper function to generate the gradient function for the input function. |
|
|
Compute the jacobian-vector-product of the given network. |
|
|
Returns a narrowed tensor from input tensor. |
|
|
Returns the selected elements, either from input \(x\) or input \(y\), depending on the condition cond. |
|
|
Compute the vector-jacobian-product of the given network. |
|
primitive
Creates a PrimitiveWithInfer operator that can infer the value at compile time. |
|
Primitive attributes register. |
|
Primitive is the base class of operator primitives in python. |
|
PrimitiveWithCheck is the base class of primitives in python defines functions for checking operator input arguments but used the infer method registered in c++ source codes. |
|
PrimitiveWithInfer is the base class of primitives in python and defines functions for tracking inference in python. |
vm_impl_registry
Gets the virtual implementation function by a primitive object or primitive name. |
op_info_register
Class for AiCPU operator information register. |
|
A decorator which is used to bind the registration information to the func parameter of |
|
Class used for generating the registration information for the func parameter of |
|
Various combinations of dtype and format of Ascend ops. |
|
A decorator which is used to register an operator. |
|
Class for TBE operator information register. |