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significantly improved GB-RBM training

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jens
2026-01-05 14:16:34 +01:00
parent 9f72c1f253
commit fdf4186d6b
3 changed files with 60 additions and 78 deletions
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src/rbm/train.py
+9 -27
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@@ -93,60 +93,42 @@ def cd_binary_binary(entity: Entity, data_pos: Mat):
return dw, dbv, dbh
def cd_gaussian_binary(entity: Entity, data_pos: Mat):
params = entity.training_params
# Positive phase
if params.do_batch_sample:
h_probs_pos = entity.h_given_v(data_pos + sample_gaussian(data_pos))
else:
h_probs_pos = entity.h_given_v(data_pos)
h_probs_pos = prob(entity.h_given_v(data_pos + sample_gaussian(data_pos)))
# Update weights (positive phase)
dw = np.dot(np.transpose(data_pos), h_probs_pos)
dbh = np.sum(h_probs_pos, 0)
dbv = np.sum(data_pos, 0)
if not params.do_rao_blackwell:
# Sample hidden states
h_probs_pos = sample(h_probs_pos)
dbv = np.sum(data_pos-entity.state.b_v, 0)
# Negative phase
data_neg = entity.v_given_h(h_probs_pos)
h_probs_neg = entity.h_given_v(data_neg)
h_probs_neg = prob(entity.h_given_v(data_neg + sample_gaussian(data_neg)))
# Update weights (negative phase)
dw -= np.dot(np.transpose(data_neg), h_probs_neg)
dbh -= np.sum(h_probs_neg, 0)
dbv -= np.sum(data_neg, 0)
dbv -= np.sum(data_neg-entity.state.b_v, 0)
return dw, dbv, dbh
def cd_gaussian_gaussian(entity: Entity, data_pos: Mat):
params = entity.training_params
# Positive phase
if params.do_batch_sample:
h_probs_pos = entity.h_given_v(data_pos + sample_gaussian(data_pos))
else:
h_probs_pos = entity.h_given_v(data_pos)
if not params.do_rao_blackwell:
# Sample hidden states
h_probs_pos += sample_gaussian(h_probs_pos)
h_probs_pos = entity.h_given_v(data_pos)
# Update weights (positive phase)
dw = np.dot(np.transpose(data_pos), h_probs_pos)
dw = np.dot(np.transpose(data_pos), h_probs_pos + sample_gaussian(h_probs_pos))
dbh = np.sum(h_probs_pos, 0)
dbv = np.sum(data_pos, 0)
dbv = np.sum(data_pos-entity.state.b_v, 0)
# Negative phase
data_neg = entity.v_given_h(h_probs_pos)
h_probs_neg = entity.h_given_v(data_neg)
h_probs_neg = entity.h_given_v(data_neg + sample_gaussian(data_neg))
# Update weights (negative phase)
dw -= np.dot(np.transpose(data_neg), h_probs_neg)
dbh -= np.sum(h_probs_neg, 0)
dbv -= np.sum(data_neg, 0)
dbv -= np.sum(data_neg-entity.state.b_v, 0)
return dw, dbv, dbh