Convert MLPRegressor of scikit-learn to pytorch model

Q:How to convert sklearn's MLPRegressor to pytorch's MLP model which has the same weights?

To convert a scikit-learn MLPRegressor model to a PyTorch MLP model with the same weights, you can follow these steps:

Next, you will need to extract the weights and biases of the scikit-learn MLPRegressor model. You can do this by accessing the coefs_ and intercepts_ attributes of the model. These attributes contain the weights and biases of the model, respectively.

Once you have the weights and biases, you can create a PyTorch MLP model with the same architecture as the scikit-learn MLPRegressor model. You can do this by defining a PyTorch model class that inherits from nn.Module and implementing the __init__ and forward methods. In the __init__ method, you can define the layers of the MLP model and initialize the weights and biases with the values extracted from the scikit-learn model.

After you have defined the PyTorch MLP model, you can use it just like any other PyTorch model. You can train it, evaluate it, and make predictions with it.

Here is an example of how you might implement this in code:

code:Python

import torch

import torch.nn as nn

class MLP(nn.Module):

def __init__(self, input_size, hidden_sizes, output_size, weights, biases):

super().__init__()

self.input_size = input_size

self.output_size = output_size

self.hidden_sizes = hidden_sizes

self.layers = nn.ModuleList()

self.activation = nn.Sigmoid()

for i in range(1,len(hidden_sizes)-1):

for i, layer in enumerate(self.layers):

def forward(self, x):

x = self.activation(layer(x))

# Load the scikit-learn MLPRegressor model

from sklearn.neural_network import MLPRegressor

sklearn_model = MLPRegressor(...)

# Extract the weights and biases from the scikit-learn model

weights = sklearn_model.coefs_

biases = sklearn_model.intercepts_

# Define the input size and output size of the PyTorch MLP model

# Define the hidden sizes of the PyTorch MLP model

# Create the PyTorch MLP model

model = MLP(input_size, hidden_sizes, output_size, weights, biases)

# You can now use the PyTorch MLP model like any other PyTorch model

output = model(input)

loss = criterion(output, target)

loss.backward()

optimizer.step()

I hope this helps! Let me know if you have any questions.