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mindspore.nn.BiDense

class mindspore.nn.BiDense(in1_channels, in2_channels, out_channels, weight_init=None, bias_init=None, has_bias=True, dtype=mstype.float32)

The bilinear dense connected layer.

Applies dense connected layer for two inputs. This layer implements the operation as:

$$y=x_1^{T}A{x}_2+b,$$

where $x_1$ is the first input tensor, $x_2$ is the second input tensor , $A$ is a weight matrix with the same data type as the $x_{\ast }$ created by the layer , and $b$ is a bias vector with the same data type as the $x_{\ast }$ created by the layer (only if has_bias is True ).

Parameters

• in1_channels (int) – The number of channels in the input1 space.

• in2_channels (int) – The number of channels in the input2 space.

• out_channels (int) – The number of channels in the output space.

• weight_init (Union[Tensor, str, Initializer, numbers.Number]) – The trainable weight_init parameter. The values of str refer to the function initializer. Default: None .

• bias_init (Union[Tensor, str, Initializer, numbers.Number]) – The trainable bias_init parameter. The values of str refer to the function initializer. Default: None .

• has_bias (bool) – Specifies whether the layer uses $\text{bias}$ vector. Default: True .

• dtype (mindspore.dtype) – Dtype of Parameters. Default: mstype.float32 .

Shape:

• input1 - $(\ast ,H_{in1})$ where $H_{in1}=\text{in1\_channels}$ and $\ast$ means any number of additional dimensions including none. All but the last dimension of the inputs should be the same.

• input2 - $(\ast ,H_{in2})$ where $H_{in2}=\text{in2\_channels}$ and $\ast$ means any number of additional dimensions including none. All but the last dimension of the inputs should be the same.

• output - $(\ast ,H_{out})$ where $H_{out}=\text{out\_channels}$ and $\ast$ means any number of additional dimensions including none. All but the last dimension are the same shape as the inputs.

Dtype:

• input1 (Tensor) - The dtype must be float16 or float32 and be same as input2 .

• input2 (Tensor) - The dtype must be float16 or float32 and be same as input1 .

• output (Tensor) - With the same dtype as the inputs.

Weights:

• weight (Parameter) - The learnable weights with shape $(\text{out\_channels},\text{in1\_channels},\text{in2\_channels})$. When weight_init is None , the values are initialized from $\mathcal{U}(-\sqrt{k},\sqrt{k})$, where $k=\frac{1}{\text{in1\_channels}}$.

• bias (Parameter) - The learnable bias of shape $(\text{out\_channels})$. If has_bias is True and bias_init is None , the values are initialized from $\mathcal{U}(-\sqrt{k},\sqrt{k})$, where $k=\frac{1}{\text{in1\_channels}}$.

Raises

• TypeError – If in1_channels, in2_channels or out_channels is not an int.

• TypeError – If has_bias is not a bool.

• ValueError – If length of shape of weight_init is not equal to 3 or shape[0] of weight_init is not equal to out_channels or shape[1] of weight_init is not equal to in1_channels or shape[2] of weight_init is not equal to in2_channels.

• ValueError – If length of shape of bias_init is not equal to 1 or shape[0] of bias_init is not equal to out_channels.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, nn
>>> import numpy as np
>>> x1 = Tensor(np.random.randn(128, 20), mindspore.float32)
>>> x2 = Tensor(np.random.randn(128, 30), mindspore.float32)
>>> net = nn.BiDense(20, 30, 40)
>>> output = net(x1, x2)
>>> print(output.shape)
(128, 40)