矩阵做 transpose

import torch tensor_Matrix_A = torch.tensor([[1,2],[4,5],[7,8]
], dtype=torch.float32)
print(tensor_Matrix_A.T)# 结果
tensor([[1., 4., 7.],[2., 5., 8.]])

---

torch.nn.Linear() 模块也被称为 “feed-forward layer"或者"fully connected layer”。公式如下：
$$y=x\cdot A^T+b$$

解释上面的公式：

• X: It is the input to the layer (deep learning is a stack of layers like torch.nn.Linear() and others on top of each other).
• A: It is the weights matrix created by the layer, this starts out as random numbers that get adjusted as a neural network learns to better represent patterns in the data (notice the “T”, that’s because the weights matrix gets transposed).
• b: It is the bias term used to slightly offset the weights and inputs
• y: It is the output (a manipulation of the input in the hopes to discover patterns in it).

使用代码来举例

import torchtensor_Matrix_A = torch.tensor([[1,2],[4,5],[7,8]
], dtype=torch.float32)torch.manual_seed(42)
linear = torch.nn.Linear(in_features=2,out_features=6)
print(linear)
x = tensor_Matrix_B
output = linear(x)
print(f"linear weight: {linear.weight}")  # 得将获得结果进行 transposed, 再做 linear 预算
print(f"Input shape: {x.shape}\n")
print(f"Out:\n{output}\n\nOutput shape: {output.shape}")# 结果如下：
Linear(in_features=2, out_features=6, bias=True)
linear weight: Parameter containing:
tensor([[ 0.5406,  0.5869],[-0.1657,  0.6496],[-0.1549,  0.1427],[-0.3443,  0.4153],[ 0.6233, -0.5188],[ 0.6146,  0.1323]], requires_grad=True)
Input shape: torch.Size([3, 2])Out:
tensor([[ 1.1093,  0.7453,  0.4836,  0.3154,  0.0263,  0.2369],[ 3.9513,  2.3627,  0.6018,  0.8727, -0.2832,  1.8631],[ 5.6657,  3.4961,  0.7323,  1.3590, -0.6974,  2.7424]],grad_fn=<AddmmBackward0>)Output shape: torch.Size([3, 6])

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