[refactor] move rbm.stack* + rbm.rnn_helper → stack/

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

RNN-RBM.ipynb

@@ -25,7 +25,7 @@
     "\n",
     "import numpy as np_cpu\n",
     "import matplotlib.pyplot as plt\n",
-    "from rbm.stack_rnn import StackRnn\n",
+    "from stack.rnn import StackRnn\n",
     "from rbm.matrix import np, convert\n",
     "from rbm.entity import EntityParams, TrainingParams\n",
     "from rbm.status import CheckpointStatus"
@@ -49,7 +49,7 @@
     }
    },
    "source": [
-    "# ── Config ─────────────────────────────────────────────────────────────────\n",
+    "# \u2500\u2500 Config \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "SENSORY_SIZE   = 40      # numVisibleX * numVisibleY = 1 * 40\n",
     "CONTEXT_SIZE   = 256     # numContext = numHidden\n",
     "UNROLL_DEPTH   = 1       # number of time-step entities (1 = shared weights)\n",
@@ -88,7 +88,7 @@
     }
    },
    "source": [
-    "# ── Training sentence ──────────────────────────────────────────────────────\n",
+    "# \u2500\u2500 Training sentence \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "SENTENCE = (\n",
     "    \"Call me Ishmael. Some years ago, never mind how long precisely, \"\n",
     "    \"having little money in my pocket and nothing particular to interest \"\n",
@@ -135,7 +135,7 @@
     }
    },
    "source": [
-    "# ── Visualise one-hot encoding ─────────────────────────────────────────────\n",
+    "# \u2500\u2500 Visualise one-hot encoding \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "step = max(1, N_SAMPLES // 40)    # show at most 40 tick labels\n",
     "tick_pos = range(0, N_SAMPLES, step)\n",
     "\n",
@@ -182,19 +182,19 @@
     }
    },
    "source": [
-    "# ── Prepare sequences for StackRnn ─────────────────────────────────────────\n",
+    "# \u2500\u2500 Prepare sequences for StackRnn \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "# Tile the single sentence N_REPEAT times to form a real batch so GPU/CPU\n",
     "# matrix ops are (N_REPEAT, 168) instead of (1, 168).\n",
     "N_REPEAT  = 500\n",
     "sequences = np_cpu.tile(sensory_np[np_cpu.newaxis, :, :], (N_REPEAT, 1, 1))\n",
-    "print(f\"sequences shape : {sequences.shape}  →  {N_REPEAT} × {N_SAMPLES} chars\")"
+    "print(f\"sequences shape : {sequences.shape}  \u2192  {N_REPEAT} \u00d7 {N_SAMPLES} chars\")"
    ],
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "sequences shape : (500, 180, 40)  →  500 × 180 chars\n"
+      "sequences shape : (500, 180, 40)  \u2192  500 \u00d7 180 chars\n"
      ]
     }
    ],
@@ -216,7 +216,7 @@
     }
    },
    "source": [
-    "# ── Build model ────────────────────────────────────────────────────────────\n",
+    "# \u2500\u2500 Build model \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "rnn = StackRnn(PRJ_NAME, WORK_DIR)\n",
     "for layer in StackRnn.make_unrolled(\n",
     "    UNROLL_DEPTH,\n",
@@ -247,7 +247,7 @@
     "print(f\"Layers     : {rnn.num_layers()} (alternating t % {rnn.num_layers()})\")\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] * rnn.num_layers():,}  ({rnn.num_layers()} × {e.shape[0] * e.shape[1]:,})\")"
+    "print(f\"Parameters : {e.shape[0] * e.shape[1] * rnn.num_layers():,}  ({rnn.num_layers()} \u00d7 {e.shape[0] * e.shape[1]:,})\")"
    ],
    "outputs": [
     {
@@ -258,7 +258,7 @@
       "Layers     : 1 (alternating t % 1)\n",
       "Visible    : 256 (context) + 40 (sensory) = 296\n",
       "Hidden     : 256\n",
-      "Parameters : 75,776  (1 × 75,776)\n"
+      "Parameters : 75,776  (1 \u00d7 75,776)\n"
      ]
     }
    ],
@@ -279,10 +279,10 @@
     }
    },
    "source": [
-    "# ── Train: predict next character from current context ─────────────────────\n",
+    "# \u2500\u2500 Train: predict next character from current context \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "# Unrolled layers alternate: layer[t % num_layers] owns each time-step position.\n",
-    "#   Step 1 — advance context: h_t = layer[t%D].forward([h_{t-1} | x_t])\n",
-    "#   Step 2 — CD on [h_t | x_{t+1}] using the same layer[t%D]\n",
+    "#   Step 1 \u2014 advance context: h_t = layer[t%D].forward([h_{t-1} | x_t])\n",
+    "#   Step 2 \u2014 CD on [h_t | x_{t+1}] using the same layer[t%D]\n",
     "from rbm.train import cd_binary_binary, _to_gpu\n",
     "from rbm.matrix import rms_error_accu\n",
     "\n",
@@ -354,7 +354,7 @@
     }
    },
    "source": [
-    "# ── Reconstruction: teacher-forced ─────────────────────────────────────────\n",
+    "# \u2500\u2500 Reconstruction: teacher-forced \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "# Step through each sample, reconstruct, compare to input.\n",
     "rnn.reset(batch_size=1)\n",
     "recons = []\n",
@@ -388,7 +388,7 @@
     }
    },
    "source": [
-    "# ── Visualise reconstruction ───────────────────────────────────────────────\n",
+    "# \u2500\u2500 Visualise reconstruction \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "step = max(1, N_SAMPLES // 40)\n",
     "tick_pos = range(0, N_SAMPLES, step)\n",
     "\n",
@@ -430,7 +430,7 @@
     }
    },
    "source": [
-    "# ── Next-step prediction ───────────────────────────────────────────────────\n",
+    "# \u2500\u2500 Next-step prediction \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\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",
@@ -486,4 +486,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

moby_rnn.ipynb

@@ -18,7 +18,7 @@
    "source": [
     "import numpy as np_cpu\n",
     "import matplotlib.pyplot as plt\n",
-    "from rbm.stack_rnn import StackRnn\n",
+    "from stack.rnn import StackRnn\n",
     "from rbm.matrix import np, convert\n",
     "from rbm.entity import EntityParams, TrainingParams\n",
     "from rbm.status import CheckpointStatus"
@@ -42,7 +42,7 @@
     }
    },
    "source": [
-    "# ── Configuration ─────────────────────────────────────────────────────────\n",
+    "# \u2500\u2500 Configuration \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "TEMPORAL_DEPTH = 16                  # characters per training sequence (= number of RBMs)\n",
     "NUM_SEQ        = 200                # training sequences (covers first 200k chars)\n",
     "CONTEXT_SIZE   = 128                 # recurrent hidden / context state size\n",
@@ -73,12 +73,12 @@
     }
    },
    "source": [
-    "# ── Load text and build character vocabulary ───────────────────────────────\n",
+    "# \u2500\u2500 Load text and build character vocabulary \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "with open(\"data/moby.txt\", \"r\", encoding=\"utf-8\") as f:\n",
     "    raw = f.read()\n",
     "\n",
     "# Reduce vocabulary: uppercase letters, digits, and separating punctuation.\n",
-    "# Lowercase → uppercase; everything else → space; collapse runs of spaces.\n",
+    "# Lowercase \u2192 uppercase; everything else \u2192 space; collapse runs of spaces.\n",
     "import re\n",
     "ALLOWED = set(' .!?ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789')\n",
     "text = raw.upper()\n",
@@ -131,7 +131,7 @@
     }
    },
    "source": [
-    "# ── Encode text as one-hot sequences ──────────────────────────────────────\n",
+    "# \u2500\u2500 Encode text as one-hot sequences \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "# Each training sequence is TEMPORAL_DEPTH consecutive characters encoded as\n",
     "# one-hot vectors. Non-overlapping; constants defined in Configuration.\n",
     "encoded   = np_cpu.array([char_to_idx[c] for c in text], dtype=np_cpu.int32)\n",
@@ -143,7 +143,7 @@
     "\n",
     "print(f\"sequences shape : {sequences.shape}\")\n",
     "print(f\"memory          : {sequences.nbytes / 1e6:.1f} MB\")\n",
-    "print(f\"covers chars    : 0 – {NUM_SEQ * TEMPORAL_DEPTH - 1:,}\")"
+    "print(f\"covers chars    : 0 \u2013 {NUM_SEQ * TEMPORAL_DEPTH - 1:,}\")"
    ],
    "outputs": [
     {
@@ -152,7 +152,7 @@
      "text": [
       "sequences shape : (200, 16, 40)\n",
       "memory          : 1.0 MB\n",
-      "covers chars    : 0 – 3,199\n"
+      "covers chars    : 0 \u2013 3,199\n"
      ]
     }
    ],
@@ -174,7 +174,7 @@
     }
    },
    "source": [
-    "# ── Build model ────────────────────────────────────────────────────────────\n",
+    "# \u2500\u2500 Build model \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "# Unrolled mode: TEMPORAL_DEPTH separate RBMs, one per position in the sequence.\n",
     "# Each RBM_t has its own W_t, b_v_t, b_h_t.\n",
     "# visible_t = [context_{t-1} (CONTEXT_SIZE) | x_t (vocab_size)]\n",
@@ -234,7 +234,7 @@
     }
    },
    "source": [
-    "# ── Train ──────────────────────────────────────────────────────────────────\n",
+    "# \u2500\u2500 Train \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "# CheckpointStatus saves weights at every progress report.\n",
     "# Re-run this cell to continue training from the last checkpoint.\n",
     "status = CheckpointStatus(rnn.state_save, update_interval=5)\n",
@@ -352,7 +352,7 @@
     }
    },
    "source": [
-    "# ── Generation helpers ─────────────────────────────────────────────────────\n",
+    "# \u2500\u2500 Generation helpers \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "\n",
     "def predict_next(rnn, context, n_gibbs=10, temperature=1.0):\n",
     "    \"\"\"Return a character probability distribution conditioned on context.\n",
@@ -435,7 +435,7 @@
     }
    },
    "source": [
-    "# ── Reconstruction accuracy (teacher-forced) ───────────────────────────────\n",
+    "# \u2500\u2500 Reconstruction accuracy (teacher-forced) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "# At each step the model sees the true x_t, updates h_t, then reconstructs\n",
     "# x_t from h_t.  This measures how well h_t retains the input, not prediction.\n",
     "EVAL_START = NUM_SEQ * TEMPORAL_DEPTH\n",
@@ -482,7 +482,7 @@
     }
    },
    "source": [
-    "# ── Next-step prediction accuracy ─────────────────────────────────────────\n",
+    "# \u2500\u2500 Next-step prediction accuracy \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "# Given context (= h_{t-1}), predict x_t before observing it.\n",
     "rnn.reset(batch_size=1)\n",
     "h         = np.zeros((1, CONTEXT_SIZE))\n",
@@ -534,7 +534,7 @@
     }
    },
    "source": [
-    "# ── Free text generation ───────────────────────────────────────────────────\n",
+    "# \u2500\u2500 Free text generation \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "for temp in [0.5, 1.0, 1.5]:\n",
     "    print(f\"\\n{'='*60}\")\n",
     "    print(f\"Temperature = {temp}\")\n",
@@ -582,7 +582,7 @@
     }
    },
    "source": [
-    "# ── Hidden state trace ─────────────────────────────────────────────────────\n",
+    "# \u2500\u2500 Hidden state trace \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "# Plot the hidden unit activations over one sequence to see what the RNN\n",
     "# has learned to track.\n",
     "rnn.reset(batch_size=1)\n",
@@ -605,7 +605,7 @@
     "plt.colorbar(label='h activation')\n",
     "plt.xlabel('Time step (character)')\n",
     "plt.ylabel('Hidden unit (first 64)')\n",
-    "plt.title(f'Hidden state trace — \"{seq_str[:50]}…\"')\n",
+    "plt.title(f'Hidden state trace \u2014 \"{seq_str[:50]}\u2026\"')\n",
     "tick_pos = range(0, TEMPORAL_DEPTH, 5)\n",
     "plt.xticks(tick_pos, [seq_str[i] for i in tick_pos], fontsize=8)\n",
     "plt.tight_layout()\n",
@@ -655,7 +655,7 @@
     }
    },
    "source": [
-    "# ── Character frequency: data vs model predictions ─────────────────────────\n",
+    "# \u2500\u2500 Character frequency: data vs model predictions \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "# Compare the character distribution in the training data to the distribution\n",
     "# the model assigns when conditioning on a fixed context.\n",
     "true_counts  = np_cpu.bincount(eval_enc[:500].astype(int), minlength=vocab_size).astype(float)\n",
@@ -730,4 +730,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

pyproject.toml

@@ -21,4 +21,4 @@ readme = "README.md"
 
 [tool.setuptools]
 package-dir = {"" = "src"}
-packages = ["rbm", "image"]
+packages = ["rbm", "image", "stack"]

src/stack/deep.py

@@ -1,7 +1,7 @@
-from .status import Status
-from .train import train
-from .stack import Stack, StackType
-from .matrix import Mat, np
+from rbm.status import Status
+from rbm.train import train
+from stack.stack import Stack, StackType
+from rbm.matrix import Mat, np
 
 class StackDeep(Stack):
 	def __init__(self, name: str, work_dir: str = '.'):
@@ -29,4 +29,4 @@ class StackDeep(Stack):
 	def pass_down_up(self, visible: Mat, from_layer_id: int = 0):
 		h = self.pass_up(visible, from_layer_id)
 		v = self.pass_down(h)
-		return v
+		return v

src/stack/factory.py

@@ -1,9 +1,9 @@
 import json
-from .stack import StackType
-from .layer import Layer
-from .entity import EntityParams, TrainingParams
-from .stack_deep import StackDeep
-from .stack_rnn import StackRnn
+from stack.stack import StackType
+from rbm.layer import Layer
+from rbm.entity import EntityParams, TrainingParams
+from stack.deep import StackDeep
+from stack.rnn import StackRnn
 
 class StackFactory:
 	@classmethod

src/stack/rnn.py

@@ -1,11 +1,10 @@
 import numpy as _np_cpu
-from .status import Status
-from .train import _to_gpu, cd_binary_binary, cd_gaussian_binary, cd_gaussian_gaussian, cd_binary_gaussian
-from .stack import Stack, StackType
-from .matrix import Mat, np, rms_error_accu, convert
-from .entity import Entity
-from .layer import Layer
-from .entity import EntityParams, TrainingParams
+from rbm.status import Status
+from rbm.train import _to_gpu, cd_binary_binary, cd_gaussian_binary, cd_gaussian_gaussian, cd_binary_gaussian
+from stack.stack import Stack, StackType
+from rbm.matrix import Mat, np, rms_error_accu, convert
+from rbm.entity import Entity, EntityParams, TrainingParams
+from rbm.layer import Layer
 
 _CD_FUNC = {
     Entity.Type.BB_RBM: cd_binary_binary,

src/stack/rnn_helper.py

@@ -38,4 +38,3 @@ def clamp_one_hot(src_dst: np.ndarray) -> np.ndarray:
 class Rnn:
 	def __init__(self):
 		pass
-

src/stack/stack.py

@@ -1,6 +1,6 @@
 import os
 from enum import Enum
-from .layer import Layer
+from rbm.layer import Layer
 
 class StackType(Enum):
 	Deep = "Deep",

src/tests/test_rbm.py

@@ -1,9 +1,9 @@
 import os.path
 import cv2 as cv
 from argparse import ArgumentParser
-from rbm.stack_factory import StackFactory
+from stack.factory import StackFactory
 from rbm.status import Status
-from rbm.stack_deep import StackDeep
+from stack.deep import StackDeep
 from rbm.matrix import Mat, np, convert, read_armadillo
 from rbm.entity import Entity
 

src/tests/test_rnn.py

@@ -1,6 +1,6 @@
 """Tests for StackRnn — shared-weights and unrolled (own-weights) modes."""
 import numpy as _np_cpu
-from rbm.stack_rnn import StackRnn
+from stack.rnn import StackRnn
 from rbm.matrix import np, convert
 from rbm.entity import EntityParams, TrainingParams
 from rbm.status import Status

src/tests/test_rnn_helper.py

@@ -1,6 +1,6 @@
 import numpy as np
 import pytest
-from rbm.rnn_helper import shift_right, shift_left, shift_up, shift_down, ch2idx, idx2ch, VOCAB
+from stack.rnn_helper import shift_right, shift_left, shift_up, shift_down, ch2idx, idx2ch, VOCAB
 
 M = np.array([
     [1, 2, 3],