jensandClaude Sonnet 4.6
248ae4f199
- reduce WIN to 3 and H_SIZE to 32 for faster iteration - increase NUM_EPOCHS to 1000 and collapse NUM_ITERATIONS to a single pass - add README_JayRnn.md with algorithm description and ASCII architecture diagrams Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
143 lines
4.3 KiB
Python
143 lines
4.3 KiB
Python
#!/usr/bin/env python3
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"""Character-level RNN-RBM (unrolled) — see docs/Rnn.drawio.png.
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Diagram recap
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t=0: v[0]={0} v[1:N]=[' ',' ','J'] W[0] → h
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t=1: v[0]=h₀ v[1:N]=[' ','J','A'] W[1] → h
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...
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t=M-1: v[0]=h_{M-2} v[1:N]=['J','A','Y'] W[M-1] → h
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v[0] = recurrent context (previous hidden state, or zeros at t=0)
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v[1:N] = N_WIN one-hot-encoded characters concatenated (the sliding window)
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W[t] = RBM weight matrix for time step t (one per step → unrolled mode)
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"""
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import sys
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import os
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
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from rbm.entity import EntityParams, TrainingParams, Entity
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from rbm.matrix import np, Mat
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from model.model import Model
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from stack.rnn_helper import vocab_size, shift_left, clamp, concat, split, vec2idx, idx2ch, ch2idx, idx2vec
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from rbm.train import train
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from rbm.status import Status
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# ── Hyper-parameters ──────────────────────────────────────────────────────────
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TEXT = "HALLO SUPER JENS UND SUPER MAUSI!" # the character sequence to learn (matches diagram example)
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WIN = 3 # sliding-window width (= N in the diagram)
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STRIDE = 1 # sliding-window step size
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UNITS = 1
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H_SIZE = 32 # hidden units per RBM cell
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NUM_EPOCHS = 1000
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NUM_ITERATIONS = 1
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TRAIN_PARAMS = TrainingParams(
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learning_rate = 0.04,
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momentum = 0.5,
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num_epochs = NUM_EPOCHS,
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do_rao_blackwell = True,
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num_gibbs_samples= 1
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)
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#WIN=3
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#STRIDE=1
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#UNIT=3
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#WIN | |
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#U0: "JAY IS COOL"
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#U1: "AY IS COOL "
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#U2: "Y IS COOL "
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def vec2str(mat: Mat, axis=0):
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return idx2ch(vec2idx(mat, axis))
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def str2vec(ch_str: str) -> Mat:
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return idx2vec(ch2idx(ch_str))
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def to_window(text: str):
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win_text = []
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text_padded = text + ' '*WIN
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for i in range(len(TEXT)):
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win_text.append(text_padded[i:i+WIN])
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return win_text
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def to_batch(_win_str: list[str]):
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_batch = np.zeros([len(TEXT), WIN*vocab_size()])
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for i, win in enumerate(_win_str):
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_batch[i, :] = str2vec(win).flatten()
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return _batch
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class RnnModel(Model):
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def __init__(self, name: str, work_dir: str = '.'):
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super().__init__(name, work_dir)
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self.units: list[Entity] = []
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for _ in range(UNITS):
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unit = Entity((WIN*vocab_size() + H_SIZE, H_SIZE), EntityParams(do_gaussian_visible=False, do_gaussian_hidden=False), training_params=TRAIN_PARAMS)
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self.units.append(unit)
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def train(self, vc: Mat, status: Status = None):
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for unit in self.units:
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train(unit, vc, status)
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# For the next unit: Update context portion of vc
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_c = unit.forward(vc)
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vc[1:, WIN*vocab_size():] = _c[0:-1,:]
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def forward_step(self, _v: Mat, _c: Mat) -> tuple[Mat, Mat]:
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_vc = concat(_v.reshape([1, WIN*vocab_size()]), _c.reshape([1, H_SIZE]), axis=1)
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for unit in self.units:
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_c = unit.forward(_vc)
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_vc = unit.reconstruct(_c)
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return split(_vc.flatten(), H_SIZE)
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def forward(self, x: Mat):
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pass
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if __name__ == "__main__":
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if 1:
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# Test of helper function
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# ToDo: move to tests
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vec = idx2vec(ch2idx("JENS"))
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vec = clamp(vec, axis=1)
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indices = vec2idx(vec, axis=1)
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ch_str = idx2ch(indices)
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print(ch_str)
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win_text = to_window(TEXT)
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print(win_text)
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batch = to_batch(win_text)
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print(batch)
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model = RnnModel(name='JayRnn', work_dir='results')
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model.init(0.01)
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model.load()
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# vc contains vis + context
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# context will be updated after training
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c_train = np.zeros([len(batch), H_SIZE])
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vc_train = concat(batch, c_train, axis=1)
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model.train(vc_train, status=Status())
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model.save()
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# test the model
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seed_str = 'HA^'
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seed_padded = ' '*(WIN-len(seed_str)) + seed_str
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v_forward = str2vec(seed_padded).flatten()
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context = c_train[0]
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text_predict = ''
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for i in range(len(TEXT)+5):
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v_mat = v_forward.reshape([WIN, vocab_size()])
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print(f"Forward {i:02d}: {vec2str(v_mat, axis=1)}")
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v_predict, context = model.forward_step(v_mat, context)
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v_predict_mat = v_predict.reshape([WIN, vocab_size()])
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v_predict_clamp = clamp(v_predict_mat, axis=1)
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v_predict_str = vec2str(v_predict_clamp, axis=1)
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text_predict += v_predict_str[WIN-1]
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print(f"Predict {i:02d}: {v_predict_str}")
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v_forward = shift_left(v_predict_clamp.reshape([1, WIN*vocab_size()]), vocab_size())
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print(seed_padded[0:WIN-1] + text_predict) |