[JayRnn]

- reduced VOCAB
- changed test text

JayRnn.py

@@ -24,23 +24,21 @@ from rbm.train import train
 from rbm.status import Status
 
 # ── Hyper-parameters ──────────────────────────────────────────────────────────
-TEXT    = "123456789_Call_me_Ishmael._Some_years_ago" # the character sequence to learn (matches diagram example)
+TEXT    = "CHAPTER 1 LOOMINGS CALL ME ISHMAEL SOME YEARS AGO NEVER MIND HOW LONG PRECISELY HAVING LITTLE OR NO" # the character sequence to learn (matches diagram example)
 WIN    = 2         # sliding-window width  (= N in the diagram)
-STRIDE = 1     	   # sliding-window step size
 UNITS  = 5
 H_SIZE = 128         # hidden units per RBM cell
 NUM_EPOCHS = 1000
-NUM_ITERATIONS = 1
 
 TRAIN_PARAMS = TrainingParams(
-	learning_rate    = 0.1,
+	learning_rate    = 0.25,
 	momentum         = 0.5,
 	num_epochs       = NUM_EPOCHS,
-	do_rao_blackwell = False,
-	num_gibbs_samples= 1
+	do_rao_blackwell = True,
+	num_gibbs_samples= 1,
+	do_gibbs_sample_hidden=True
 )
 #WIN=3
-#STRIDE=1
 #UNIT=3
 #WIN |   |
 #U0: "JAY IS COOL"
@@ -168,14 +166,19 @@ if __name__ == "__main__":
 	model.save()
 
 	# test the model
-	seed_str = '1234'
+	seed_str = 'CHAP'
 	state = SPACE*UNITS
 
+	result = seed_str
+	ch = SPACE
 	# Prime
 	for s in seed_str:
 		state, ch = model.forward_step(s, state)
+		print(f'Prime: {ch}')
+
+	for i in range(len(TEXT)):
+		state, ch = model.forward_step(ch, state)
 		print(f'Predict: {ch}')
+		result += ch
 
-	state, ch = model.forward_step('_', state)
-	print(f'Predict: {ch}')
-
+	print(result)