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mindspore.ops.Dense

class mindspore.ops.Dense

The dense connected fusion operator.

Applies dense connected operator for the input. The implement of the operation is as:

$$output=x@w^T+b,$$

where $x$ is the input tensor, $w$ is a weight matrix with the same data type as the $x$ , and $b$ is a bias vector with the same data type as the $x$ (only if b is not None).

Inputs:

• x (Tensor) - The shape must meet the following requirement: $len(x.shape)>0$.

• w (Tensor) - The shape must meet the following requirements: If $len(x.shape)>1$, $len(w.shape)=2$. If $len(x.shape)=1$, $len(w.shape)=1$. $w.shape[-1]=x.shape[-1]$.

• b (Union[Tensor, None]) - If b is not None, the shape must meet the following requirements: If $len(x.shape)>1$, $len(b.shape)=0$ or $len(b.shape)=1$ . If $len(b.shape)=1$, $b.shape[0]=w.shape[0]$. If $len(x.shape)=1$, $len(b.shape)=0$.

Outputs:

If $len(x.shape)>1$, Tensor of shape $(\ast x.shape[:-1],w.shape[0])$. If $len(x.shape)=1$, Tensor of shape $()$.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.random.random((4, 5, 6, 7)).astype(np.float32))
>>> weight = Tensor(np.random.random((6, 7)).astype(np.float32))
>>> bias = Tensor(np.random.random((6,)).astype(np.float32))
>>> dense = ops.Dense()
>>> output = dense(x, weight, bias)
>>> print(output.shape)
(4, 5, 6, 6)