Skip to content

Add neuralnetworks.LinearLayer.set_weights() and .set_biases() #15

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Add neuralnetworks.LinearLayer.set_weights() and .set_biases() #15

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
23 changes: 23 additions & 0 deletions experiments/neuralnetwork/neuralnetworks/components/LinearLayer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,22 @@ def __init__(self, num_inputs: AutoSettable[int], num_outputs: int, dtype: AutoS
self._dtype = dtype
self._use_coopvec = use_coopvec

def set_weights(self, weights_np: np.ndarray[Any, Any]):
self.check_initialized()
if weights_np.shape != (self.num_outputs, self.num_inputs):
raise ValueError(f"LinearLayer weights must have shape ({self.num_outputs}, {self.num_inputs}), rather than {weights_np.shape}")

if self.use_coopvec:
layout = CoopVecMatrixLayout.training_optimal
desc = self.weights.device.coopvec_create_matrix_desc(self.num_outputs, self.num_inputs, layout, self.dtype.sgl(), 0)
weight_count = desc.size // self.dtype.size()

params_np = np.zeros((weight_count, ), dtype=self.dtype.numpy())
self.weights.device.coopvec_convert_matrix_host(weights_np, params_np, dst_layout=layout)
self.weights.storage.copy_from_numpy(params_np)
else:
self.weights.storage.copy_from_numpy(weights_np)

def get_weights(self) -> np.ndarray[Any, Any]:
self.check_initialized()
weights_np = self.weights.to_numpy()
Expand All @@ -48,6 +64,13 @@ def get_weights(self) -> np.ndarray[Any, Any]:
else:
return weights_np

def set_biases(self, biases_np: np.ndarray[Any, Any]):
self.check_initialized()
if biases_np.shape != (self.num_outputs,):
raise ValueError(f"LinearLayer biases must have shape ({self.num_outputs}), rather than {biases_np.shape}")

self.biases.storage.copy_from_numpy(biases_np)

def get_biases(self) -> np.ndarray[Any, Any]:
self.check_initialized()
return self.biases.to_numpy()
Expand Down