将keras模型转换为pytorch

Question

有没有一种简单的方法可以将这样的模型从keras转换为pytorch？

我在keras中有如下代码：

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.regularizers import l2
state_dim = 10
architecture = (256, 256)  # units per layer
learning_rate = 0.0001  # learning rate
l2_reg = 0.00000001  # L2 regularization
trainable = True
num_actions = 3
layers = []
n = len(architecture) # n = 2
for i, units in enumerate(architecture, 1):
layers.append(Dense(units=units,
input_dim=state_dim if i == 1 else None,
activation='relu',
kernel_regularizer=l2(l2_reg),
name=f'Dense_{i}',
trainable=trainable))

layers.append(Dropout(.1))
layers.append(Dense(units=num_actions,
trainable=trainable,
name='Output'))
model = Sequential(layers)
model.compile(loss='mean_squared_error',
optimizer=Adam(lr=learning_rate))

输出如下：

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
Dense_1 (Dense)              (None, 256)               2816      
_________________________________________________________________
Dense_2 (Dense)              (None, 256)               65792     
_________________________________________________________________
dropout_3 (Dropout)          (None, 256)               0         
_________________________________________________________________
Output (Dense)               (None, 3)                 771       
=================================================================
Total params: 69,379
Trainable params: 69,379
Non-trainable params: 0
_________________________________________________________________
None

我必须承认，我有点力不从心，所以任何建议都很感激。我正在努力阅读pytorch文档，如果我能做到的话，我会用一个可能的答案来更新我的问题。

Answer 1

这是我最好的尝试：

state_dim = 10
architecture = (256, 256)  # units per layer
learning_rate = 0.0001  # learning rate
l2_reg = 0.00000001  # L2 regularization
trainable = True
num_actions = 3
import torch
from torch import nn
class CustomModel(nn.Module):
def __init__(self):

super().__init__()

self.layers = nn.Sequential(
nn.Linear(state_dim, architecture[0]),
nn.ReLU(),
nn.Linear(architecture[0], architecture[1]),
nn.ReLU(),
nn.Dropout(0.25),
nn.Linear(architecture[1], num_actions),
)
def forward(self, x):
return self.layers(x)
model = CustomModel()
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

它输出了一个看起来很有前景的输出：

CustomModel(
(layers): Sequential(
(0): Linear(in_features=10, out_features=256, bias=True)
(1): ReLU()
(2): Linear(in_features=256, out_features=256, bias=True)
(3): ReLU()
(4): Dropout(p=0.25, inplace=False)
(5): Linear(in_features=256, out_features=3, bias=True)
)
)

然而，仍有一些项目没有得到解答：

1. 激活是否在正确的位置
2. 如何在前两个线性/密集层中添加kernel_regularizer=l2(l2_reg(
3. 我们如何使这些层可训练

欢迎任何意见。