revised status
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+7
-11
@@ -34,8 +34,8 @@ class Layer:
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if batch_size == 0:
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batch_size = training_remain
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status.on_change({})
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d_progress = 100.0 / (training_remain/batch_size * self.params.num_epochs)
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last_progress = 0
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batch_row_index = 0
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training_seen = 0
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keep_running = True
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@@ -67,19 +67,15 @@ class Layer:
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self.state.b_h += inc_bh
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self.state.w_hv += inc_whv
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progress = round(training_seen*d_progress)
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if progress != last_progress:
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# Calculate error
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status.progress = round(progress)
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status.err = rms_error_accu(mini_batch - self.h_to_pv(self.v_to_ph(v_states)))
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if not status.on_change():
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keep_running = False
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break
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# Calculate error
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err_rms = rms_error_accu(mini_batch - self.h_to_pv(self.v_to_ph(v_states)))
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if not status.on_change({"progress": {"value": round(training_seen*d_progress), "unit": "%"}, "err_rms": {"value": err_rms, "unit": ""}}):
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keep_running = False
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break
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last_progress = progress
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training_seen += 1
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status.on_change()
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status.on_change({"progress": {"value": round(training_seen*d_progress), "unit": "%"}, "err_rms": {"value": err_rms, "unit": ""}})
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def v_to_ph(self, v: np.ndarray) -> np.ndarray:
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state = self.state.v_to_h(v)
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+14
-12
@@ -1,16 +1,18 @@
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class Status:
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def __init__(self):
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self.progress = 0
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self.err = -1.0
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self.err_total = -1.0
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self.l1 = -1.0
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self.l2 = -1.0
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self.progress = -1
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def on_change(self) -> bool:
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print("-------------------------------------------")
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print(f"Progress : {self.progress} %")
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print(f"error (per mini batch) : {self.err}")
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print(f"error (total) : {self.err_total}")
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print(f"L1 : {self.l1}")
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print(f"L2 : {self.l2}")
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def on_change(self, status: dict) -> bool:
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if status == {}:
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self.progress = -1
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if "progress" in status.keys():
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progress = status["progress"]['value']
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if self.progress < 0 or progress - self.progress >= 10:
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self.progress = progress
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print("-------------------------------------------")
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for key in status.keys():
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value = status[key]['value']
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unit = status[key]['unit']
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print(f"{key} : {value}{unit}")
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return True
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