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refactored train

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jens
2025-12-21 10:16:08 +01:00
parent 7f4505c1f8
commit 8ed6488b45
2 changed files with 17 additions and 12 deletions
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src/rbm/train.py
+2 -12
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@@ -94,23 +94,13 @@ def train(entity: Entity, batch: Mat, params: TrainingParams, status: Status, cd
if not keep_running:
break
inc_bv = np.zeros(entity.state.b_v.shape)
inc_bh = np.zeros(entity.state.b_h.shape)
inc_whv = np.zeros(entity.state.w_hv.shape)
entity.prepare()
for epochs in range(params.num_epochs):
# Contrastive divergence learning: calculate gradients
dwhv, dbv, dbh, _ = cd_func(entity, mini_batch, params)
# Adjust weight and biases
kl = params.learning_rate/batch.shape[0]
inc_bv = params.momentum*inc_bv + kl*dbv
inc_bh = params.momentum*inc_bh + kl*dbh
inc_whv = params.momentum*inc_whv + kl*dwhv - params.weight_decay*entity.state.w_hv
entity.state.b_v += inc_bv
entity.state.b_h += inc_bh
entity.state.w_hv += inc_whv
entity.adjust(dbv, dbh, dwhv, learning_rate=params.learning_rate/batch.shape[0], momentum=params.momentum, weight_decay=params.weight_decay)
# check if status update is needed
if status.want_report(round(progress)):