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fixed progress indication

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jens
2025-12-18 16:36:47 +01:00
parent 1b51861daf
commit 3936a5be27
1 changed files with 6 additions and 6 deletions
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src/rbm/entity.py
+6 -6
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@@ -11,15 +11,15 @@ class Entity:
self.params = params self.params = params
def train(self, batch: np.ndarray, cd_func: Callable, status: Status): def train(self, batch: np.ndarray, cd_func: Callable, status: Status):
training_remain = batch.shape[0] training_remain = batch.shape[1]
batch_size = min(self.params.mini_batch_size, training_remain) batch_size = min(self.params.mini_batch_size, training_remain)
if batch_size == 0: if batch_size == 0:
batch_size = training_remain batch_size = training_remain
status.on_change() status.on_change()
d_progress = 100.0 / (training_remain/batch_size * self.params.num_epochs) d_progress = 100.0 / (training_remain*self.params.num_epochs)
progress = 0
batch_row_index = 0 batch_row_index = 0
training_seen = 0
keep_running = True keep_running = True
while training_remain > 0 and keep_running: while training_remain > 0 and keep_running:
batch_size_remain = min(batch_size, training_remain) batch_size_remain = min(batch_size, training_remain)
@@ -50,15 +50,15 @@ class Entity:
self.state.w_hv += inc_whv self.state.w_hv += inc_whv
# check if status update is needed # check if status update is needed
if status.want_report(round(training_seen*d_progress)): if status.want_report(round(progress)):
# Calculate error # Calculate error
err_rms = rms_error_accu(mini_batch - self.h_to_pv(self.v_to_ph(v_states))) err_rms = rms_error_accu(mini_batch - self.h_to_pv(self.v_to_ph(v_states)))
if not status.on_change({"progress": {"value": round(training_seen * d_progress), "unit": "%"}, if not status.on_change({"progress": {"value": round(progress), "unit": "%"},
"err_rms": {"value": err_rms, "unit": ""}}): "err_rms": {"value": err_rms, "unit": ""}}):
keep_running = False keep_running = False
break break
training_seen += 1 progress += d_progress*batch_size_remain
def v_to_ph(self, v: np.ndarray) -> np.ndarray: def v_to_ph(self, v: np.ndarray) -> np.ndarray:
state = self.state.v_to_h(v) state = self.state.v_to_h(v)