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increased difficulty

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jens
2026-06-04 11:15:17 +02:00
parent 4bd516798a
commit 62dd450c9c
1 changed files with 19 additions and 15 deletions
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JayRnn.py
+19 -15
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@@ -24,11 +24,11 @@ from rbm.train import train
from rbm.status import Status
# ── Hyper-parameters ──────────────────────────────────────────────────────────
TEXT = " JAY IS COOL" # the character sequence to learn (matches diagram example)
TEXT = " HALLO SUPER JENS. SUPER! " # the character sequence to learn (matches diagram example)
WIN = 3 # sliding-window width (= N in the diagram)
STRIDE = 1 # sliding-window step size
UNITS = 1
H_SIZE = 64 # hidden units per RBM cell
H_SIZE = 120 # hidden units per RBM cell
TRAIN_PARAMS = TrainingParams(
learning_rate = 0.05,
@@ -71,7 +71,7 @@ class RnnModel(Model):
self.units: list[Entity] = []
for _ in range(UNITS):
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)
unit = Entity((WIN*vocab_size() + H_SIZE, H_SIZE), EntityParams(do_gaussian_visible=False, do_gaussian_hidden=False), training_params=TRAIN_PARAMS)
self.units.append(unit)
def train(self, vc: Mat, status: Status = None):
@@ -91,17 +91,21 @@ class RnnModel(Model):
if __name__ == "__main__":
vec = idx2vec(ch2idx("JENS"))
vec = clamp(vec, axis=1)
indices = vec2idx(vec, axis=1)
ch_str = idx2ch(indices)
print(ch_str)
if 1:
# Test of helper function
# ToDo: move to tests
vec = idx2vec(ch2idx("JENS"))
vec = clamp(vec, axis=1)
indices = vec2idx(vec, axis=1)
ch_str = idx2ch(indices)
print(ch_str)
win_text = to_window(TEXT)
print(win_text)
win_text = to_window(TEXT)
print(win_text)
batch = to_batch(win_text)
print(batch)
batch = to_batch(win_text)
print(batch)
model = RnnModel(name='JayRnn', work_dir='results')
model.init(0.01)
@@ -111,14 +115,14 @@ if __name__ == "__main__":
# context will be updated after training
c_train = np.zeros([len(batch), H_SIZE])
vc_train = concat(batch, c_train, axis=1)
# model.train(vc_train, status=Status())
model.train(vc_train, status=Status())
model.save()
# test the model
seed = str2vec('JA^')
seed = str2vec('HA^')
v_forward = seed.flatten()
context = c_train[0]
for i in range(len(TEXT)):
for i in range(len(TEXT)+5):
print(f"Forward {i:02d}: {vec2str(v_forward.reshape([WIN, vocab_size()]), axis=1)}")
vc_forward = concat(v_forward.reshape([1, WIN*vocab_size()]), context.reshape([1, H_SIZE]), axis=1)
vc_predict = model.forward_step(vc_forward)