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fixed dividing l2-norm by length of vector

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jens
2026-01-06 18:17:45 +01:00
parent 542b8ae9b0
commit 7d3ab02e26
2 changed files with 2 additions and 2 deletions
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src/rbm/entity.py
+1 -1
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@@ -104,7 +104,7 @@ class Entity:
self.grad.b_v = momentum * self.grad.b_v + learning_rate * d_bv self.grad.b_v = momentum * self.grad.b_v + learning_rate * d_bv
self.grad.b_h = momentum * self.grad.b_h + learning_rate * d_bh self.grad.b_h = momentum * self.grad.b_h + learning_rate * d_bh
# compute L2 term and penalize cost function (d_whv) # compute L2 term and penalize cost function (d_whv)
l2_norm = np.sum(np.square(self.state.w_hv))/self.state.w_hv.shape[1] l2_norm = 0.5*np.sum(np.square(self.state.w_hv))
l2_term = l2_lambda*l2_norm*self.state.w_hv l2_term = l2_lambda*l2_norm*self.state.w_hv
self.grad.w_hv = momentum * self.grad.w_hv + learning_rate * (d_whv-l2_term) - learning_rate * weight_decay * self.state.w_hv self.grad.w_hv = momentum * self.grad.w_hv + learning_rate * (d_whv-l2_term) - learning_rate * weight_decay * self.state.w_hv
src/tests/test_norbs.py
+1 -1
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@@ -16,7 +16,7 @@ class TestModel(Model):
else: else:
# Hidden binary # Hidden binary
self.unit1 = Entity((96 * 96, 333), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=False), self.unit1 = Entity((96 * 96, 333), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=False),
TrainingParams(learning_rate=0.001, momentum=0.9, num_epochs=1000, l2_lambda=0.4)) TrainingParams(learning_rate=0.001, momentum=0.9, num_epochs=1000, l2_lambda=0.01))
def forward(self, x: Mat): def forward(self, x: Mat):
x = self.unit1.forward(x) x = self.unit1.forward(x)