Question

在keras中，创建模型后，我们可以使用model.input_shape，model.output_shape看到其输入，输出形状。对于权重和配置，我们可以分别使用model.get_weights()和model.get_config()。pytorch有哪些类似的替代方法？检查pytorch模型还需要了解其他功能吗？

作为总结，我知道在pytorch中我们打印模型print(model)，但是所提供的信息少于model.summary()。 pytorch有更好的摘要吗？

Answer 1

pytorch中没有“ model.summary（）”方法。您需要使用内建的方法和模型的字段。

例如，我定制了inception_v3模型。为了获得信息，我需要使用其他许多不同的字段。例如：

IN：

print(model) # print network architecture

输出

Inception3(
  (Conv2d_1a_3x3): BasicConv2d(
    (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False)
    (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
  )
  (Conv2d_2a_3x3): BasicConv2d(
    (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), bias=False)
    (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
  )
  (Conv2d_2b_3x3): BasicConv2d(
    (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
  )
  (Conv2d_3b_1x1): BasicConv2d(
    (conv): Conv2d(64, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (bn): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
  )
  (Conv2d_4a_3x3): BasicConv2d(
    (conv): Conv2d(80, 192, kernel_size=(3, 3), stride=(1, 1), bias=False)
   ...

IN：

for i in model.state_dict().keys():
    print(i) 
#print keys of dict with values of learned weights, bias, parameters

输出：

    Conv2d_1a_3x3.conv.weight
    Conv2d_1a_3x3.bn.weight
    Conv2d_1a_3x3.bn.bias
    Conv2d_1a_3x3.bn.running_mean
    Conv2d_1a_3x3.bn.running_var
    Conv2d_1a_3x3.bn.num_batches_tracked
    Conv2d_2a_3x3.conv.weight
    Conv2d_2a_3x3.bn.weight
    Conv2d_2a_3x3.bn.bias
    Conv2d_2a_3x3.bn.running_mean 
    ...

因此，如果我想获取Conv2d_1a_3x3处CNN层的权重，我会寻找键“ Conv2d_1a_3x3.conv.weight”：

print("model.save_dict()["Conv2d_1a_3x3.conv.weight"])

输出：

tensor([[[[-0.2103, -0.3441, -0.0344],
          [-0.1420, -0.2520, -0.0280],
          [ 0.0736,  0.0183,  0.0381]],

         [[ 0.1417,  0.1593,  0.0506],
          [ 0.0828,  0.0854,  0.0186],
          [ 0.0283,  0.0144,  0.0508]],
...

如果要从优化程序中查看使用的超参数：

optimizer.param_groups

OUT：

[{'dampening': 0,
  'lr': 0.01,
  'momentum': 0.01,
  'nesterov': False,
  'params': [Parameter containing:
   tensor([[[[-0.2103, -0.3441, -0.0344],
             [-0.1420, -0.2520, -0.0280],
             [ 0.0736,  0.0183,  0.0381]],
          ...

pytorch替代kera的input_shape，output_shape，get_weights，get_config和summary的方法

1 个答案: