,

@horacio_hyatt

You can generate PyTorch models randomly by defining a neural network architecture with random parameters. Here is an example of how you can create a simple fully connected neural network with random weights:

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import torch import torch.nn as nn class RandomNN(nn.Module): def __init__(self): super(RandomNN, self).__init__() # Define the architecture with random parameters self.fc1 = nn.Linear(10, 20) self.fc2 = nn.Linear(20, 30) self.fc3 = nn.Linear(30, 1) # Initialize the parameters randomly self._initialize_weights() def forward(self, x): x = torch.relu(self.fc1(x)) x = torch.relu(self.fc2(x)) x = self.fc3(x) return x def _initialize_weights(self): for p in self.parameters(): p.data.uniform_(-1, 1) # Create an instance of the random neural network model model = RandomNN() |

In this example, we define a custom PyTorch module called `RandomNN`

that consists of three fully connected layers with randomly initialized weights. The `_initialize_weights`

method is used to initialize the weights of the model with random values sampled from a uniform distribution between -1 and 1.

You can adjust the architecture of the neural network, the number of layers, and the size of each layer to create more complex models. Additionally, you can also explore other initialization methods and distributions for the random weights.

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