improved context update for JayRnn

JayRnn.py

@@ -81,12 +81,11 @@ class RnnModel(Model):
 			unit = Entity((WIN*vocab_size() + H_SIZE, H_SIZE), EntityParams(do_gaussian_visible=False, do_gaussian_hidden=False), TrainingParams(learning_rate=0.001, momentum=0.9, num_epochs=1000), enable_training=True)
 			self.units.append(unit)
 
-	def train(self, batch: Mat, status: Status = None):
-		c = np.zeros([len(batch), H_SIZE])
+	def train(self, vc: Mat, status: Status = None):
 		for i, unit in enumerate(self.units):
-			vc = concat(batch, c)
 			train(unit, vc, status)
-			c = unit.forward(vc)
+			# Update context portion of vc
+			vc[:, WIN*vocab_size():] = unit.forward(vc)
 
 	def forward_step(self, v_curr: Mat):
 		c = np.zeros([1, H_SIZE])
@@ -108,6 +107,10 @@ if __name__ == "__main__":
 	model = RnnModel(name='JayRnn', work_dir='results')
 	model.init(0.01)
 	model.load()
-	model.train(batch, status=Status())
+
+	# vc contains vis + context
+	# context will be updated after training
+	vc = concat(batch, np.zeros([len(batch), H_SIZE]))
+	model.train(vc, status=Status())
 	model.save()