refactored

src/rbm/entity.py

@@ -0,0 +1,94 @@
+import numpy as np
+from collections.abc import Callable
+from params import RbmParams
+from state import RbmState
+from matrix import sample, prob, rms_error_accu
+from status import Status
+
+class Entity:
+	def __init__(self, shape: tuple[int, int], params: RbmParams):
+		self.state = RbmState.from_layer_params(shape)
+		self.params = params
+
+	def train(self, batch: np.ndarray, cd_func: Callable, status: Status):
+		training_remain = batch.shape[0]
+		batch_size = min(self.params.mini_batch_size, training_remain)
+		if batch_size == 0:
+			batch_size = training_remain
+
+		status.on_change({})
+		d_progress = 100.0 / (training_remain/batch_size * self.params.num_epochs)
+		batch_row_index = 0
+		training_seen = 0
+		keep_running = True
+		while training_remain > 0 and keep_running:
+			batch_size_remain = min(batch_size, training_remain)
+			mini_batch = batch[batch_row_index:batch_row_index + batch_size_remain]
+			training_remain -= batch_size_remain
+			batch_row_index += batch_size_remain
+
+			inc_bv = np.zeros(self.state.b_v.shape)
+			inc_bh = np.zeros(self.state.b_h.shape)
+			inc_whv = np.zeros(self.state.w_hv.shape)
+
+			v_states = mini_batch
+			if self.params.do_batch_sample:
+				v_states = sample(mini_batch)
+
+			for epochs in range(self.params.num_epochs):
+				# Contrastive divergence learning: calculate gradients
+				dwhv, dbv, dbh = cd_func(v_states, self.params, self.v_to_ph, self.h_to_pv)
+
+				# Adjust weight and biases
+				kl = self.params.learning_rate/batch_size
+				inc_bv = self.params.momentum*inc_bv + kl*dbv
+				inc_bh = self.params.momentum*inc_bh + kl*dbh
+				inc_whv = self.params.momentum*inc_whv + kl*dwhv - self.params.weight_decay*self.state.w_hv
+
+				self.state.b_v += inc_bv
+				self.state.b_h += inc_bh
+				self.state.w_hv += inc_whv
+
+				# Calculate	error
+				if status.want_report(round(training_seen*d_progress)):
+					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": "%"},
+											 "err_rms": {"value": err_rms, "unit": ""}}):
+						keep_running = False
+						break
+
+				training_seen += 1
+
+	def v_to_ph(self, v: np.ndarray) -> np.ndarray:
+		state = self.state.v_to_h(v)
+		if self.params.do_gaussian_visible:
+			return state
+
+		return prob(state)
+
+	def h_to_pv(self, h: np.ndarray) -> np.ndarray:
+		state = self.state.h_to_v(h)
+		if self.params.do_gaussian_visible:
+			return state
+
+		return prob(state)
+
+
+	def gibbs_v_to_h(self, v: np.ndarray) -> np.ndarray:
+		h = None
+		for i in range(self.params.num_gibbs_samples):
+			h = self.v_to_ph(v)
+			v = self.h_to_pv(h)
+
+		return h
+
+	def gibbs_h_to_v(self, h: np.ndarray) -> np.ndarray:
+		v = None
+		for i in range(self.params.num_gibbs_samples):
+			v = self.h_to_pv(h)
+			h = self.v_to_ph(v)
+
+		return v
+
+if __name__ == "__main__":
+	print("Test: [passed]")