Notebook reconstructs the C++ context33.prj configuration exactly:
- shared-weights StackRnn (1 entity, reused every time step)
- visible = [context(128) | x_t(40)] = 168 units, hidden = 128
- all hyperparams from .prj: lr=0.05, momentum=0.5, epochs=1000,
mini_batch=100, gibbs=3, rao_blackwell=True, weight_decay=0
Training data loaded directly from context33.training.dat (Armadillo format,
12 × 168): sensory one-hot chars decoded as 'XURQLIFB9651'.
Key design: training pairs are (h_t, x_{t+1}) not (h_{t-1}, x_t) —
context is first advanced by seeing x_t, then the model is trained to
predict the next character x_{t+1} from that context. Evaluation using
clamped Gibbs on h_t achieves 100% next-step prediction accuracy.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
573 lines
222 KiB
Plaintext
573 lines
222 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "c33-0001",
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"metadata": {
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"execution": {
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"shell.execute_reply": "2026-05-31T13:05:52.783936Z"
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"ExecuteTime": {
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"end_time": "2026-05-31T13:08:35.912154551Z",
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"start_time": "2026-05-31T13:08:35.458197926Z"
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}
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},
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"source": [
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"# context33.prj → pyRBM\n",
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"#\n",
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"# Training data : /home/jens/work/repos/Rbm/context33.training.dat\n",
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"# Armadillo matrix 12 × 168 — full visible vectors [sensory(40) | context(128)]\n",
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"# saved from the C++ GUI; sensory columns carry one-hot character encoding.\n",
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"#\n",
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"# Architecture : shared-weights StackRnn (1 entity, reused every time step)\n",
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"# visible = [context(128) | x_t(40)] = 168 hidden = 128\n",
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"#\n",
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"# All hyperparameters taken verbatim from context33.prj.\n",
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"\n",
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"import numpy as np_cpu\n",
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"import matplotlib.pyplot as plt\n",
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"from rbm.stack_rnn import StackRnn\n",
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"from rbm.matrix import np, convert, read_armadillo\n",
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"from rbm.entity import EntityParams, TrainingParams\n",
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"from rbm.status import CheckpointStatus"
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],
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"outputs": [],
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"execution_count": 1
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},
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{
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"cell_type": "code",
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"id": "c33-0002",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-05-31T13:05:52.786922Z",
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"shell.execute_reply": "2026-05-31T13:05:52.789951Z"
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},
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"ExecuteTime": {
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"end_time": "2026-05-31T13:08:35.961330083Z",
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"start_time": "2026-05-31T13:08:35.913508062Z"
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}
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},
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"source": [
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"# ── Config from context33.prj ──────────────────────────────────────────────\n",
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"SENSORY_SIZE = 40 # numVisibleX * numVisibleY = 1 * 40\n",
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"CONTEXT_SIZE = 128 # numContext = numHidden\n",
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"LEARNING_RATE = 0.05 # learningRate\n",
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"MOMENTUM = 0.5 # momentum\n",
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"NUM_EPOCHS = 1000 # numEpochs\n",
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"MINI_BATCH = 100 # miniBatchSize\n",
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"NUM_GIBBS = 3 # numGibbs\n",
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"RAO_BLACKWELL = True # doRaoBlackwell\n",
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"L2_LAMBDA = 0.0 # weightDecay\n",
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"PRJ_NAME = \"context33\"\n",
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"WORK_DIR = \"results\"\n",
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"DATA_PATH = \"/home/jens/work/repos/Rbm/context33.training.dat\"\n",
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"\n",
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"# Vocabulary matching numVisibleY=40\n",
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"ALLOWED = set(' .!?ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789')\n",
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"chars = sorted(ALLOWED)\n",
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"idx_to_char = {i: c for i, c in enumerate(chars)}\n",
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"char_to_idx = {c: i for i, c in enumerate(chars)}"
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],
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"outputs": [],
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"execution_count": 2
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},
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{
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"cell_type": "code",
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"id": "c33-0003",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-05-31T13:05:52.792381Z",
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"iopub.status.busy": "2026-05-31T13:05:52.792174Z",
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"iopub.status.idle": "2026-05-31T13:05:52.799998Z",
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"shell.execute_reply": "2026-05-31T13:05:52.799353Z"
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},
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"ExecuteTime": {
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"end_time": "2026-05-31T13:08:36.020495426Z",
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"start_time": "2026-05-31T13:08:35.962722614Z"
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}
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},
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"source": [
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"# ── Load training data from context33.training.dat ─────────────────────────\n",
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"# Armadillo format: 12 rows × 168 cols = [sensory(40) | context(128)]\n",
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"# Context portion was computed by the C++ model; we use only the sensory part.\n",
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"raw_data = read_armadillo(DATA_PATH) # (12, 168) on device\n",
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"raw_np = convert(raw_data) # to numpy for inspection\n",
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"\n",
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"sensory_np = raw_np[:, :SENSORY_SIZE] # (12, 40) one-hot chars\n",
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"\n",
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"N_SAMPLES = sensory_np.shape[0]\n",
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"decoded = ''.join(idx_to_char[int(np_cpu.argmax(sensory_np[i]))] for i in range(N_SAMPLES))\n",
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"\n",
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"print(f\"Training data : {DATA_PATH}\")\n",
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"print(f\"Shape : {raw_np.shape} (rows=samples, cols=visible)\")\n",
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"print(f\"Sensory columns : 0 – {SENSORY_SIZE-1} ({SENSORY_SIZE} dims, one-hot)\")\n",
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"print(f\"Context columns : {SENSORY_SIZE} – 167 ({CONTEXT_SIZE} dims, from C++ model)\")\n",
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"print(f\"Decoded chars : '{decoded}' ({N_SAMPLES} samples)\")"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"shape: [12, 168]\n",
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"Training data : /home/jens/work/repos/Rbm/context33.training.dat\n",
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"Shape : (12, 168) (rows=samples, cols=visible)\n",
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"Sensory columns : 0 – 39 (40 dims, one-hot)\n",
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"Context columns : 40 – 167 (128 dims, from C++ model)\n",
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"Decoded chars : 'XURQLIFB9651' (12 samples)\n"
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]
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}
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],
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"execution_count": 3
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},
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{
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"cell_type": "code",
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"id": "c33-0004",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-05-31T13:05:52.802158Z",
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"iopub.status.busy": "2026-05-31T13:05:52.801973Z",
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"iopub.status.idle": "2026-05-31T13:05:53.137159Z",
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"shell.execute_reply": "2026-05-31T13:05:53.136234Z"
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},
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"ExecuteTime": {
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"end_time": "2026-05-31T13:08:36.362164851Z",
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"start_time": "2026-05-31T13:08:36.021965131Z"
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}
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},
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"source": [
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"# ── Visualise training samples ─────────────────────────────────────────────\n",
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"fig, axes = plt.subplots(1, 2, figsize=(14, 3))\n",
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"\n",
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"axes[0].imshow(sensory_np.T, aspect='auto', cmap='Blues', vmin=0, vmax=1)\n",
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"axes[0].set_xlabel('Sample index')\n",
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"axes[0].set_ylabel('Vocab index')\n",
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"axes[0].set_title(f'Sensory (one-hot chars): \"{decoded}\"')\n",
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"axes[0].set_xticks(range(N_SAMPLES))\n",
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"axes[0].set_xticklabels(list(decoded))\n",
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"\n",
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"axes[1].imshow(raw_np[:, SENSORY_SIZE:].T, aspect='auto', cmap='RdBu', vmin=0, vmax=1)\n",
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"axes[1].set_xlabel('Sample index')\n",
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"axes[1].set_ylabel('Context unit')\n",
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"axes[1].set_title('Context (hidden state from C++ model)')\n",
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"axes[1].set_xticks(range(N_SAMPLES))\n",
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"axes[1].set_xticklabels(list(decoded))\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.show()"
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],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 1400x300 with 2 Axes>"
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],
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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 4
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "c33-0005",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-05-31T13:05:53.138817Z",
|
||
"iopub.status.busy": "2026-05-31T13:05:53.138656Z",
|
||
"iopub.status.idle": "2026-05-31T13:05:53.142080Z",
|
||
"shell.execute_reply": "2026-05-31T13:05:53.141270Z"
|
||
},
|
||
"ExecuteTime": {
|
||
"end_time": "2026-05-31T13:08:36.418708648Z",
|
||
"start_time": "2026-05-31T13:08:36.362936110Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"# ── Prepare sequences for StackRnn ─────────────────────────────────────────\n",
|
||
"# Treat the 12 samples as one sequence of length 12.\n",
|
||
"# Shape required by _train_shared: (num_seq, T, sensory_size)\n",
|
||
"sequences = sensory_np[np_cpu.newaxis, :, :] # (1, 12, 40)\n",
|
||
"print(f\"sequences shape : {sequences.shape} → 1 sequence of {N_SAMPLES} chars\")"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"sequences shape : (1, 12, 40) → 1 sequence of 12 chars\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 5
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "c33-0006",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-05-31T13:05:53.143611Z",
|
||
"iopub.status.busy": "2026-05-31T13:05:53.143463Z",
|
||
"iopub.status.idle": "2026-05-31T13:05:53.266436Z",
|
||
"shell.execute_reply": "2026-05-31T13:05:53.265629Z"
|
||
},
|
||
"ExecuteTime": {
|
||
"end_time": "2026-05-31T13:08:36.533366117Z",
|
||
"start_time": "2026-05-31T13:08:36.419697676Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"# ── Build model ────────────────────────────────────────────────────────────\n",
|
||
"rnn = StackRnn(PRJ_NAME, WORK_DIR)\n",
|
||
"rnn.append(StackRnn.make_layer(\n",
|
||
" \"layer0\",\n",
|
||
" sensory_size=SENSORY_SIZE,\n",
|
||
" h_size=CONTEXT_SIZE,\n",
|
||
" entity_params=EntityParams(\n",
|
||
" do_gaussian_visible=False,\n",
|
||
" do_gaussian_hidden=False,\n",
|
||
" num_gibbs_samples=NUM_GIBBS,\n",
|
||
" ),\n",
|
||
" training_params=TrainingParams(\n",
|
||
" learning_rate=LEARNING_RATE,\n",
|
||
" momentum=MOMENTUM,\n",
|
||
" num_epochs=NUM_EPOCHS,\n",
|
||
" mini_batch_size=MINI_BATCH,\n",
|
||
" num_gibbs_samples=NUM_GIBBS,\n",
|
||
" do_rao_blackwell=RAO_BLACKWELL,\n",
|
||
" l2_lambda=L2_LAMBDA,\n",
|
||
" ),\n",
|
||
"))\n",
|
||
"\n",
|
||
"rnn.state_init(0.01)\n",
|
||
"rnn.state_load()\n",
|
||
"\n",
|
||
"e = rnn.from_index(0).entity\n",
|
||
"print(f\"Mode : {'shared' if rnn.is_shared else 'unrolled'}\")\n",
|
||
"print(f\"Visible : {rnn.h_size()} (context) + {rnn.sensory_size()} (sensory) = {e.shape[0]}\")\n",
|
||
"print(f\"Hidden : {rnn.h_size()}\")\n",
|
||
"print(f\"Parameters : {e.shape[0] * e.shape[1]:,}\")"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Mode : shared\n",
|
||
"Visible : 128 (context) + 40 (sensory) = 168\n",
|
||
"Hidden : 128\n",
|
||
"Parameters : 21,504\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 6
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "c33-0007",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-05-31T13:05:53.268369Z",
|
||
"iopub.status.busy": "2026-05-31T13:05:53.268153Z",
|
||
"iopub.status.idle": "2026-05-31T13:06:53.046024Z",
|
||
"shell.execute_reply": "2026-05-31T13:06:53.045319Z"
|
||
},
|
||
"ExecuteTime": {
|
||
"end_time": "2026-05-31T13:09:38.858147334Z",
|
||
"start_time": "2026-05-31T13:08:36.534644759Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"# ── Train: predict next character from current context ─────────────────────\n",
|
||
"# Pairs used: (h_t, x_{t+1})\n",
|
||
"# Step 1 — advance context: h_t = forward([h_{t-1} | x_t]) (no weight update)\n",
|
||
"# Step 2 — CD update on visible = [h_t | x_{t+1}] (predict next)\n",
|
||
"from rbm.train import cd_binary_binary, _to_gpu\n",
|
||
"from rbm.matrix import rms_error_accu\n",
|
||
"\n",
|
||
"entity = rnn.from_index(0).entity\n",
|
||
"h_sz = rnn.h_size()\n",
|
||
"num_seq, T, s_sz = sequences.shape\n",
|
||
"\n",
|
||
"rnn.state_init(0.01) # fresh weights for the new training objective\n",
|
||
"entity.grad_zero()\n",
|
||
"\n",
|
||
"d_progress = 100.0 / NUM_EPOCHS\n",
|
||
"progress = 0.0\n",
|
||
"status = CheckpointStatus(rnn.state_save, update_interval=10)\n",
|
||
"status.on_change(entity)\n",
|
||
"\n",
|
||
"for epoch in range(NUM_EPOCHS):\n",
|
||
" h = np.zeros((num_seq, h_sz))\n",
|
||
" err_total = 0.0\n",
|
||
"\n",
|
||
" for t in range(T - 1):\n",
|
||
" x_t = _to_gpu(sequences[:, t, :]) # current char x_t\n",
|
||
" x_tp1 = _to_gpu(sequences[:, t+1, :]) # next char x_{t+1}\n",
|
||
"\n",
|
||
" # Step 1: advance context to h_t (no training)\n",
|
||
" h = entity.forward(np.concatenate([h, x_t], axis=1))\n",
|
||
"\n",
|
||
" # Step 2: train on [h_t | x_{t+1}]\n",
|
||
" vis = np.concatenate([h, x_tp1], axis=1)\n",
|
||
" dwhv, dbv, dbh = cd_binary_binary(entity, vis)\n",
|
||
" grad = entity.grad_compute(dbv, dbh, dwhv)\n",
|
||
" entity.state_adjust(grad, 1.0 / num_seq)\n",
|
||
" err_total += rms_error_accu(vis - entity.reconstruct(entity.forward(vis)))\n",
|
||
"\n",
|
||
" progress += d_progress\n",
|
||
" if status.want_report(round(progress)):\n",
|
||
" if not status.on_change(entity, {\n",
|
||
" \"progress\": {\"value\": round(progress), \"unit\": \"%\"},\n",
|
||
" \"err_rms\": {\"value\": err_total / (T - 1), \"unit\": \"\"},\n",
|
||
" }):\n",
|
||
" break\n",
|
||
"\n",
|
||
"rnn.state_save()"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"-------------------------------------------\n",
|
||
"Entity-168x128: progress : 0%\n",
|
||
"Entity-168x128: err_rms : 0.007958603311052534\n",
|
||
"Entity-168x128: l2_norm : 0.13637654998907264\n",
|
||
"-------------------------------------------\n",
|
||
"Entity-168x128: progress : 10%\n",
|
||
"Entity-168x128: err_rms : 0.005571027488307252\n",
|
||
"Entity-168x128: l2_norm : 1.0539395662219193\n",
|
||
"-------------------------------------------\n",
|
||
"Entity-168x128: progress : 20%\n",
|
||
"Entity-168x128: err_rms : 0.004801824929367379\n",
|
||
"Entity-168x128: l2_norm : 2.260130977724225\n",
|
||
"-------------------------------------------\n",
|
||
"Entity-168x128: progress : 30%\n",
|
||
"Entity-168x128: err_rms : 0.002425499380115013\n",
|
||
"Entity-168x128: l2_norm : 4.793825636401381\n",
|
||
"-------------------------------------------\n",
|
||
"Entity-168x128: progress : 40%\n",
|
||
"Entity-168x128: err_rms : 4.08127824901276e-05\n",
|
||
"Entity-168x128: l2_norm : 8.040419553383257\n",
|
||
"-------------------------------------------\n",
|
||
"Entity-168x128: progress : 50%\n",
|
||
"Entity-168x128: err_rms : 0.0002909826181571967\n",
|
||
"Entity-168x128: l2_norm : 8.939925984007596\n",
|
||
"-------------------------------------------\n",
|
||
"Entity-168x128: progress : 60%\n",
|
||
"Entity-168x128: err_rms : 0.0002615975805001939\n",
|
||
"Entity-168x128: l2_norm : 9.486056678843006\n",
|
||
"-------------------------------------------\n",
|
||
"Entity-168x128: progress : 70%\n",
|
||
"Entity-168x128: err_rms : 3.999627289946936e-06\n",
|
||
"Entity-168x128: l2_norm : 9.86542010448088\n",
|
||
"-------------------------------------------\n",
|
||
"Entity-168x128: progress : 80%\n",
|
||
"Entity-168x128: err_rms : 5.989029762404174e-07\n",
|
||
"Entity-168x128: l2_norm : 10.165022347802207\n",
|
||
"-------------------------------------------\n",
|
||
"Entity-168x128: progress : 90%\n",
|
||
"Entity-168x128: err_rms : 0.00038549475938970054\n",
|
||
"Entity-168x128: l2_norm : 10.358664694920805\n",
|
||
"-------------------------------------------\n",
|
||
"Entity-168x128: progress : 100%\n",
|
||
"Entity-168x128: err_rms : 5.93602052160391e-07\n",
|
||
"Entity-168x128: l2_norm : 10.636880125085302\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 7
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "c33-0008",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-05-31T13:06:53.048026Z",
|
||
"iopub.status.busy": "2026-05-31T13:06:53.047810Z",
|
||
"iopub.status.idle": "2026-05-31T13:06:53.075497Z",
|
||
"shell.execute_reply": "2026-05-31T13:06:53.074710Z"
|
||
},
|
||
"ExecuteTime": {
|
||
"end_time": "2026-05-31T13:09:38.912352345Z",
|
||
"start_time": "2026-05-31T13:09:38.859145208Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"# ── Reconstruction: teacher-forced ─────────────────────────────────────────\n",
|
||
"# Step through each sample, reconstruct, compare to input.\n",
|
||
"rnn.reset(batch_size=1)\n",
|
||
"recons = []\n",
|
||
"for i in range(N_SAMPLES):\n",
|
||
" x_t = np.array(sensory_np[i][np_cpu.newaxis, :])\n",
|
||
" h = rnn.step(x_t)\n",
|
||
" rec = convert(rnn.reconstruct(h))[0] # (40,)\n",
|
||
" recons.append(rec)\n",
|
||
"\n",
|
||
"recons_np = np_cpu.array(recons) # (12, 40)\n",
|
||
"decoded_recon = ''.join(idx_to_char[int(np_cpu.argmax(recons_np[i]))] for i in range(N_SAMPLES))\n",
|
||
"\n",
|
||
"print(f\"Input : '{decoded}'\")\n",
|
||
"print(f\"Reconstruction: '{decoded_recon}'\")\n",
|
||
"\n",
|
||
"correct = sum(decoded[i] == decoded_recon[i] for i in range(N_SAMPLES))\n",
|
||
"print(f\"Char accuracy : {correct}/{N_SAMPLES} = {100*correct/N_SAMPLES:.0f}%\")"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Input : 'XURQLIFB9651'\n",
|
||
"Reconstruction: 'RURQLIFB9651'\n",
|
||
"Char accuracy : 11/12 = 92%\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 8
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "c33-0009",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-05-31T13:06:53.077101Z",
|
||
"iopub.status.busy": "2026-05-31T13:06:53.076865Z",
|
||
"iopub.status.idle": "2026-05-31T13:06:53.311532Z",
|
||
"shell.execute_reply": "2026-05-31T13:06:53.310628Z"
|
||
},
|
||
"ExecuteTime": {
|
||
"end_time": "2026-05-31T13:09:39.163096694Z",
|
||
"start_time": "2026-05-31T13:09:38.913165111Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"# ── Visualise reconstruction ───────────────────────────────────────────────\n",
|
||
"fig, axes = plt.subplots(1, 2, figsize=(14, 3))\n",
|
||
"\n",
|
||
"axes[0].imshow(sensory_np.T, aspect='auto', cmap='Blues', vmin=0, vmax=1)\n",
|
||
"axes[0].set_title(f'Input: \"{decoded}\"')\n",
|
||
"axes[0].set_xticks(range(N_SAMPLES))\n",
|
||
"axes[0].set_xticklabels(list(decoded))\n",
|
||
"axes[0].set_ylabel('Vocab index')\n",
|
||
"\n",
|
||
"axes[1].imshow(recons_np.T, aspect='auto', cmap='Blues', vmin=0, vmax=1)\n",
|
||
"axes[1].set_title(f'Reconstruction: \"{decoded_recon}\"')\n",
|
||
"axes[1].set_xticks(range(N_SAMPLES))\n",
|
||
"axes[1].set_xticklabels(list(decoded_recon))\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1400x300 with 2 Axes>"
|
||
],
|
||
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|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 9
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "c33-0010",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-05-31T13:06:53.313104Z",
|
||
"iopub.status.busy": "2026-05-31T13:06:53.312950Z",
|
||
"iopub.status.idle": "2026-05-31T13:06:53.392952Z",
|
||
"shell.execute_reply": "2026-05-31T13:06:53.392059Z"
|
||
},
|
||
"ExecuteTime": {
|
||
"end_time": "2026-05-31T13:09:39.296395123Z",
|
||
"start_time": "2026-05-31T13:09:39.164243277Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"# ── Next-step prediction ───────────────────────────────────────────────────\n",
|
||
"# Given h_t (context after seeing x_t), predict x_{t+1} via clamped Gibbs.\n",
|
||
"def predict_next(rnn, context, n_gibbs=NUM_GIBBS, temperature=1.0):\n",
|
||
" entity = rnn.next_entity()\n",
|
||
" h_sz, s_sz = rnn.h_size(), rnn.sensory_size()\n",
|
||
" x_init = (np.random.rand(1, s_sz) > 0.5).astype(float)\n",
|
||
" visible = np.concatenate([context, x_init], axis=1)\n",
|
||
" for _ in range(n_gibbs):\n",
|
||
" h = entity.forward(visible)\n",
|
||
" visible = entity.reconstruct(h)\n",
|
||
" visible[:, :h_sz] = context\n",
|
||
" probs = convert(visible[:, h_sz:])[0]\n",
|
||
" probs = np_cpu.power(np_cpu.clip(probs, 1e-10, 1.0), 1.0 / temperature)\n",
|
||
" probs /= probs.sum()\n",
|
||
" return probs\n",
|
||
"\n",
|
||
"rnn.reset(batch_size=1)\n",
|
||
"h = np.zeros((1, CONTEXT_SIZE))\n",
|
||
"predicted = ''\n",
|
||
"\n",
|
||
"for i in range(N_SAMPLES - 1):\n",
|
||
" x_t = np.array(sensory_np[i][np_cpu.newaxis, :])\n",
|
||
" h = rnn.step(x_t) # advance to h_t\n",
|
||
" probs = predict_next(rnn, h.copy()) # predict x_{t+1} from h_t\n",
|
||
" predicted += idx_to_char[int(np_cpu.argmax(probs))]\n",
|
||
"\n",
|
||
"print(f\"Input (t+1) : '{decoded[1:]}'\")\n",
|
||
"print(f\"Predicted from h_t : '{predicted}'\")\n",
|
||
"correct = sum(decoded[i+1] == predicted[i] for i in range(N_SAMPLES - 1))\n",
|
||
"print(f\"Next-step accuracy : {correct}/{N_SAMPLES-1} = {100*correct/(N_SAMPLES-1):.0f}%\")"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Input (t+1) : 'URQLIFB9651'\n",
|
||
"Predicted from h_t : 'URQLIFB9651'\n",
|
||
"Next-step accuracy : 11/11 = 100%\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 10
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.12.3"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|