模型参数
网络内部神经网络层具有权重参数和偏置参数（如nn.Dense），这些参数会在训练过程中不断进行优化，可通过 model.parameters_and_names() 来获取参数名及对应的参数详情。
[12]:
print(f"Model structure: {model}\n\n")
for name, param in model.parameters_and_names():
print(f"Layer: {name}\nSize: {param.shape}\nValues : {param[:2]} \n")
Model structure: Network<
(flatten): Flatten<>
(dense_relu_sequential): SequentialCell<
(0): Dense
(1): ReLU<>
(2): Dense
(3): ReLU<>
(4): Dense
>
>
Layer: dense_relu_sequential.0.weight
Size: (512, 784)
Values : [[-0.01491369 0.00353318 -0.00694948 ... 0.01226766 -0.00014423
0.00544263]
[ 0.00212971 0.0019974 -0.00624789 ... -0.01214037 0.00118004
-0.01594325]]
Layer: dense_relu_sequential.0.bias
Size: (512,)
Values : [0. 0.]
Layer: dense_relu_sequential.2.weight
Size: (512, 512)
Values : [[ 0.00565423 0.00354313 0.00637383 ... -0.00352688 0.00262949
0.01157355]
[-0.01284141 0.00657666 -0.01217057 ... 0.00318963 0.00319115
-0.00186801]]
Layer: dense_relu_sequential.2.bias
Size: (512,)
Values : [0. 0.]
Layer: dense_relu_sequential.4.weight
Size: (10, 512)
Values : [[ 0.0087168 -0.00381866 -0.00865665 ... -0.00273731 -0.00391623
0.00612853]
[-0.00593031 0.0008721 -0.0060081 ... -0.00271535 -0.00850481
-0.00820513]]
Layer: dense_relu_sequential.4.bias
Size: (10,)
Values : [0. 0.]
更多内置神经网络层详见mindspore.nn API。