classifiers garbage training issue resolved

A13/A13_DeepLearning_Report.ipynb

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2fc1b5f814669ca0e4161b02a6aa29bede0e249da41695bd08473f7ce8088640
-size 100045
+oid sha256:24029afad82f74ccaee01995fcbb7aea986b9131e008a06a1a1e7368657e222c
+size 105227

A13/classification_problems/prepare_classification_data_v2.py

@@ -0,0 +1,257 @@
+#!/usr/bin/env python3
+"""Rebuild the prepared classification arrays from clean raw Kinect data.
+
+Replaces the broken ``prepare_classification_problems.py`` whose
+"first 39 features per frame" slice silently captured 3 metadata columns
+(FrameNo, timestamp, padding-zero) from the 102-feature processed format,
+producing 1.27e9-magnitude garbage in "joint 0" and shifting all later
+joints by one axis. That made the BatchNorm-first-layer model learn on
+fantasy features and always predict "good" when the app fed real
+coordinate-scale inputs.
+
+This v2 reads the original 40-column raw Kinect CSVs directly
+(``A13/kinect_good_vs_bad_not_preprocessed/``) and builds clean
+(10, 13, 3) sequences with real meter-scale joint coordinates.
+
+Outputs to ``A13/classification_problems/prepared_data/`` the exact
+file set expected by ``A13/dl_models/data_loader.py``:
+
+    {A,B}_{Dense,CNN}_{train,train_aug,test}_{X,y}.npy
+    {A,B}_{Dense,CNN}_{train_aug,test}_filenames.npy
+
+Problem A = 3D (Kinect, 13 joints x 3 dims).
+Problem B = 2D (x,y projection of the same Kinect data; the repo
+does not contain PoseNet recordings for the Good-vs-Bad clips, so we
+project rather than guess. The architecture and CV protocol are
+unchanged; only the input channel count differs).
+
+Augmentations applied to the training set only (test never augmented):
+    _mirror      : negate x coordinates
+    _rotate_pos  : +10 deg around vertical (Y) axis
+    _rotate_neg  : -10 deg around vertical (Y) axis
+    _stretch     : isotropic scale by 1.05
+
+Run::
+
+    python -m A13.classification_problems.prepare_classification_data_v2
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+import sys
+
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+# --------------------------------------------------------------------- paths
+THIS_DIR = Path(__file__).resolve().parent
+RAW_DIR = THIS_DIR.parent / "kinect_good_vs_bad_not_preprocessed"
+OUT_DIR = THIS_DIR / "prepared_data"
+
+# --------------------------------------------------------------------- consts
+FRAMES = 10
+JOINTS = 13  # head + 6 upper-body + 6 lower-body, matches the CSV schema
+DIMS = 3
+RANDOM_STATE = 42
+TEST_SIZE = 0.2
+ROT_DEG = 10.0
+STRETCH = 1.05
+
+
+def log(msg: str) -> None:
+    print(msg, flush=True)
+
+
+# ------------------------------------------------------------------ labeling
+def label_from_filename(stem: str) -> int:
+    """G* or A1 -> 1 (good); W* -> 0 (bad). Matches the original spec."""
+    if stem == "A1" or stem.startswith("G"):
+        return 1
+    if stem.startswith("W"):
+        return 0
+    raise ValueError(f"Unknown label for {stem!r}")
+
+
+# ----------------------------------------------------------------- load clip
+def load_clip(csv_path: Path) -> np.ndarray:
+    """Return (FRAMES, JOINTS, DIMS) float32 array of joint coords."""
+    df = pd.read_csv(csv_path)
+    df.columns = [c.strip() for c in df.columns]
+    # Drop FrameNo; the remaining 39 cols are 13 joints x (x, y, z).
+    if "FrameNo" not in df.columns:
+        raise ValueError(f"{csv_path.name}: expected a FrameNo column")
+    coords = df.drop(columns=["FrameNo"]).values.astype("float32")
+    n_rows, n_cols = coords.shape
+    if n_cols != JOINTS * DIMS:
+        raise ValueError(
+            f"{csv_path.name}: expected {JOINTS * DIMS} coord cols, got {n_cols}"
+        )
+
+    # Equidistant subsample to FRAMES; if shorter, pad with last frame.
+    if n_rows >= FRAMES:
+        idx = np.linspace(0, n_rows - 1, FRAMES, dtype=int)
+        seq = coords[idx]
+    else:
+        seq = np.zeros((FRAMES, n_cols), dtype="float32")
+        seq[:n_rows] = coords
+        if n_rows > 0:
+            seq[n_rows:] = coords[-1]
+
+    return seq.reshape(FRAMES, JOINTS, DIMS)
+
+
+# ------------------------------------------------------------- augmentations
+def aug_mirror(seq: np.ndarray) -> np.ndarray:
+    out = seq.copy()
+    out[..., 0] = -out[..., 0]
+    return out
+
+
+def _rotate_y(seq: np.ndarray, deg: float) -> np.ndarray:
+    r = np.deg2rad(deg)
+    c, s = np.cos(r), np.sin(r)
+    out = seq.copy()
+    x = seq[..., 0]
+    z = seq[..., 2]
+    out[..., 0] = c * x + s * z
+    out[..., 2] = -s * x + c * z
+    return out
+
+
+def aug_rotate_pos(seq: np.ndarray) -> np.ndarray:
+    return _rotate_y(seq, +ROT_DEG)
+
+
+def aug_rotate_neg(seq: np.ndarray) -> np.ndarray:
+    return _rotate_y(seq, -ROT_DEG)
+
+
+def aug_stretch(seq: np.ndarray) -> np.ndarray:
+    return seq * STRETCH
+
+
+AUGS = [
+    ("_mirror", aug_mirror),
+    ("_rotate_pos", aug_rotate_pos),
+    ("_rotate_neg", aug_rotate_neg),
+    ("_stretch", aug_stretch),
+]
+
+
+# ----------------------------------------------------------------- pipeline
+def collect_clips() -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    files = sorted(p for p in RAW_DIR.glob("*.csv"))
+    if not files:
+        raise FileNotFoundError(f"No CSVs in {RAW_DIR}")
+    log(f"[1] reading {len(files)} clips from {RAW_DIR}")
+    seqs, labels, names = [], [], []
+    for i, p in enumerate(files):
+        stem = p.stem
+        try:
+            y = label_from_filename(stem)
+        except ValueError as e:
+            log(f"    skip {stem}: {e}")
+            continue
+        seq = load_clip(p)
+        seqs.append(seq)
+        labels.append(y)
+        names.append(stem)
+        if (i + 1) % 25 == 0:
+            log(f"    loaded {i + 1}/{len(files)}")
+    X = np.stack(seqs).astype("float32")          # (N, 10, 13, 3)
+    y = np.asarray(labels, dtype="int32")         # (N,)
+    fn = np.asarray(names, dtype=object)          # (N,)
+    log(f"    -> X {X.shape}  y {y.shape}  good={int(y.sum())}  bad={int((y == 0).sum())}")
+    log(f"    coord scale: min={X.min():.3g}  max={X.max():.3g}  mean={X.mean():.3g}")
+    return X, y, fn
+
+
+def split(X, y, fn):
+    log(f"[2] stratified split test_size={TEST_SIZE} random_state={RANDOM_STATE}")
+    Xtr, Xte, ytr, yte, ftr, fte = train_test_split(
+        X, y, fn, test_size=TEST_SIZE, random_state=RANDOM_STATE, stratify=y
+    )
+    log(f"    train: {Xtr.shape}  good={int(ytr.sum())}/{len(ytr)}")
+    log(f"    test:  {Xte.shape}  good={int(yte.sum())}/{len(yte)}")
+    return Xtr, ytr, ftr, Xte, yte, fte
+
+
+def augment(Xtr, ytr, ftr):
+    log(f"[3] augmenting train (originals + {len(AUGS)} variants each)")
+    X_all = [Xtr]
+    y_all = [ytr]
+    f_all = [ftr]
+    for suf, fn in AUGS:
+        X_all.append(np.stack([fn(s) for s in Xtr]))
+        y_all.append(ytr.copy())
+        f_all.append(np.asarray([f"{n}{suf}" for n in ftr], dtype=object))
+    X = np.concatenate(X_all, axis=0).astype("float32")
+    y = np.concatenate(y_all, axis=0).astype("int32")
+    f = np.concatenate(f_all, axis=0)
+    log(f"    -> aug train: {X.shape}  good={int(y.sum())}/{len(y)}")
+    return X, y, f
+
+
+def save_problem(problem: str, dims_keep: int,
+                 Xtr, ytr, ftr,
+                 Xtr_aug, ytr_aug, ftr_aug,
+                 Xte, yte, fte):
+    """Slice last axis to ``dims_keep`` and write Dense+CNN variants."""
+    def proj(X):
+        return X[..., :dims_keep]
+
+    Xtr_p = proj(Xtr)
+    Xtr_aug_p = proj(Xtr_aug)
+    Xte_p = proj(Xte)
+
+    # Dense = flatten
+    n_feat = FRAMES * JOINTS * dims_keep
+    pairs_dense = {
+        f"{problem}_Dense_train_X":          Xtr_p.reshape(len(Xtr_p), n_feat),
+        f"{problem}_Dense_train_y":          ytr,
+        f"{problem}_Dense_train_aug_X":      Xtr_aug_p.reshape(len(Xtr_aug_p), n_feat),
+        f"{problem}_Dense_train_aug_y":      ytr_aug,
+        f"{problem}_Dense_train_aug_filenames": ftr_aug,
+        f"{problem}_Dense_test_X":           Xte_p.reshape(len(Xte_p), n_feat),
+        f"{problem}_Dense_test_y":           yte,
+        f"{problem}_Dense_test_filenames":   fte,
+    }
+    # CNN = keep (frames, joints, dims)
+    pairs_cnn = {
+        f"{problem}_CNN_train_X":          Xtr_p,
+        f"{problem}_CNN_train_y":          ytr,
+        f"{problem}_CNN_train_aug_X":      Xtr_aug_p,
+        f"{problem}_CNN_train_aug_y":      ytr_aug,
+        f"{problem}_CNN_train_aug_filenames": ftr_aug,
+        f"{problem}_CNN_test_X":           Xte_p,
+        f"{problem}_CNN_test_y":           yte,
+        f"{problem}_CNN_test_filenames":   fte,
+    }
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    for name, arr in {**pairs_dense, **pairs_cnn}.items():
+        np.save(OUT_DIR / f"{name}.npy", arr)
+    log(
+        f"    wrote 16 files for problem {problem} "
+        f"(Dense {n_feat}-feat, CNN {(FRAMES, JOINTS, dims_keep)})"
+    )
+
+
+def main():
+    log("=" * 70)
+    log("prepare_classification_data_v2: clean rebuild from raw Kinect CSVs")
+    log("=" * 70)
+    X, y, fn = collect_clips()
+    Xtr, ytr, ftr, Xte, yte, fte = split(X, y, fn)
+    Xtr_aug, ytr_aug, ftr_aug = augment(Xtr, ytr, ftr)
+
+    log("[4] writing Problem A (3D Kinect, 13x3)")
+    save_problem("A", 3, Xtr, ytr, ftr, Xtr_aug, ytr_aug, ftr_aug, Xte, yte, fte)
+    log("[5] writing Problem B (2D x,y projection of Kinect, 13x2)")
+    save_problem("B", 2, Xtr, ytr, ftr, Xtr_aug, ytr_aug, ftr_aug, Xte, yte, fte)
+    log(f"[6] done. output dir: {OUT_DIR}")
+
+
+if __name__ == "__main__":
+    sys.exit(main() or 0)

A13/classification_problems/prepared_data/A_CNN_test_X.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3c7b1256eefe3202643bd2f752df487f93a18fe5df7cc66671f2aef5d87de7da
-size 71888
+oid sha256:6d4bbbeb93a99d4168eb1d2744f4d6d2ce0d8108005319fd553923e64010cd11
+size 36008

A13/classification_problems/prepared_data/A_CNN_test_filenames.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:20f7880a948535108e4673778cbb6f2e9afa0f1b90c6117b9385b1283c331e7a
+oid sha256:c240cb30b778bfc08d26d107e30381a8c5f38cbdfec32e8b181f1933d618d755
 size 415

A13/classification_problems/prepared_data/A_CNN_test_y.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:76c435af834a77f3a912ec5afe22fc75901f128b95bb8a67bc11a8d947f67a5d
-size 312
+oid sha256:f3ac7391088bfc472768d22710c42efc4fd9098e712869af411c69b6a45852c1
+size 220

A13/classification_problems/prepared_data/A_CNN_train_X.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9a45c8dda4754bae6d5ee1d9365937a2603db23282f321792a23fd44d8f4c5ad
-size 284048
+oid sha256:0f7df257906e9a4be12a3e0c2b89de96171984b1abd9018eef989f6851f9d670
+size 142088

A13/classification_problems/prepared_data/A_CNN_train_aug_X.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8668695ccfef6288e0bb254ebf57d16b9c2fa3709a42ab72dcfe7620d92ae278
-size 1419728
+oid sha256:bf381d97032aac414ea1331776d020eee825b73fbdd05b08611348760954b9ec
+size 709928

A13/classification_problems/prepared_data/A_CNN_train_aug_filenames.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:14d4f22778391dde209b291d64b9132205c3cfec8b7d3b661a0b5e7635b78087
+oid sha256:35b9fa5dea48ab635e8a6398f082da5bc097c8d20acba82ab1c808d4938c0253
 size 6370

A13/classification_problems/prepared_data/A_CNN_train_aug_y.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4a21aa434e31898a110367f743ab182bcde52f9ba4ede528b97a0047dbe5db54
-size 3768
+oid sha256:6459a30e09826ec536fffa677fc3059ed5f024fdb9dded78eae4615d35c24220
+size 1948

A13/classification_problems/prepared_data/A_CNN_train_y.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:999ea205fa7a13f5d1ffd6a03c52ff91d3c2831ba7f9b5f225d4aee052109509
-size 856
+oid sha256:9558d3e120ce247e995be7d7e9f3996d5a62907f29c9a49d896b7922abac7551
+size 492

A13/classification_problems/prepared_data/A_Dense_test_X.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c1899c129364c3c42a056222d552bb8eb8c6a8b6d3014bf293c9586af944f722
-size 71888
+oid sha256:da48faceeda8f2491ff9f10ec34501964a40d81aaac52ad16c19a6a92324c484
+size 36008

A13/classification_problems/prepared_data/A_Dense_test_filenames.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:20f7880a948535108e4673778cbb6f2e9afa0f1b90c6117b9385b1283c331e7a
+oid sha256:c240cb30b778bfc08d26d107e30381a8c5f38cbdfec32e8b181f1933d618d755
 size 415

A13/classification_problems/prepared_data/A_Dense_test_y.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:76c435af834a77f3a912ec5afe22fc75901f128b95bb8a67bc11a8d947f67a5d
-size 312
+oid sha256:f3ac7391088bfc472768d22710c42efc4fd9098e712869af411c69b6a45852c1
+size 220

A13/classification_problems/prepared_data/A_Dense_train_X.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d6384ce1fce90c85f3b28427458dfff7878b06c8deb44b425d297c1669a3fc47
-size 284048
+oid sha256:7c4d02df98c484b1efe2da169aa860fa9ff93ca197179b67922265013086c77e
+size 142088

A13/classification_problems/prepared_data/A_Dense_train_aug_X.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b77ed07ebed808afe23e532bab10305a5ae1d6de98ca3c863e382be81b72c60f
-size 1419728
+oid sha256:219f4311cdc6d54ba71c82442219ea0264c3ecc933117cb80558735639ea2a04
+size 709928

A13/classification_problems/prepared_data/A_Dense_train_aug_filenames.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:14d4f22778391dde209b291d64b9132205c3cfec8b7d3b661a0b5e7635b78087
+oid sha256:35b9fa5dea48ab635e8a6398f082da5bc097c8d20acba82ab1c808d4938c0253
 size 6370

A13/classification_problems/prepared_data/A_Dense_train_aug_y.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4a21aa434e31898a110367f743ab182bcde52f9ba4ede528b97a0047dbe5db54
-size 3768
+oid sha256:6459a30e09826ec536fffa677fc3059ed5f024fdb9dded78eae4615d35c24220
+size 1948

A13/classification_problems/prepared_data/A_Dense_train_y.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:999ea205fa7a13f5d1ffd6a03c52ff91d3c2831ba7f9b5f225d4aee052109509
-size 856
+oid sha256:9558d3e120ce247e995be7d7e9f3996d5a62907f29c9a49d896b7922abac7551
+size 492

A13/classification_problems/prepared_data/B_CNN_test_X.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7439b4d50a585753aa5a2a5d003e81452d3b4a31e90ca55c6a2a8f36ade143a5
-size 47968
+oid sha256:1e87600a78db3f2bcf5239798d39ac73d62fc30c916b34582b1515c140a5d3c5
+size 24048

A13/classification_problems/prepared_data/B_CNN_test_filenames.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:20f7880a948535108e4673778cbb6f2e9afa0f1b90c6117b9385b1283c331e7a
+oid sha256:c240cb30b778bfc08d26d107e30381a8c5f38cbdfec32e8b181f1933d618d755
 size 415

A13/classification_problems/prepared_data/B_CNN_test_y.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:76c435af834a77f3a912ec5afe22fc75901f128b95bb8a67bc11a8d947f67a5d
-size 312
+oid sha256:f3ac7391088bfc472768d22710c42efc4fd9098e712869af411c69b6a45852c1
+size 220

A13/classification_problems/prepared_data/B_CNN_train_X.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5a61cde973c40a9ff45be30a64e1edb7caffa060941a79e9a8f3c411d627cfc5
-size 189408
+oid sha256:2ce7f91555dde94d363baa8fcc2f65f89ce7d988391b522da4a7ef38701863fd
+size 94768

A13/classification_problems/prepared_data/B_CNN_train_aug_X.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:14d4a0c79dbb673cff55446b4eb1bfda4c36583ca769077986b3ed6a6ec6dab1
-size 946528
+oid sha256:a5050af16a32451f9811c8ed2099695cadc37c138c284936947f0c8cac555033
+size 473328

A13/classification_problems/prepared_data/B_CNN_train_aug_filenames.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:14d4f22778391dde209b291d64b9132205c3cfec8b7d3b661a0b5e7635b78087
+oid sha256:35b9fa5dea48ab635e8a6398f082da5bc097c8d20acba82ab1c808d4938c0253
 size 6370

A13/classification_problems/prepared_data/B_CNN_train_aug_y.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4a21aa434e31898a110367f743ab182bcde52f9ba4ede528b97a0047dbe5db54
-size 3768
+oid sha256:6459a30e09826ec536fffa677fc3059ed5f024fdb9dded78eae4615d35c24220
+size 1948

A13/classification_problems/prepared_data/B_CNN_train_y.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:999ea205fa7a13f5d1ffd6a03c52ff91d3c2831ba7f9b5f225d4aee052109509
-size 856
+oid sha256:9558d3e120ce247e995be7d7e9f3996d5a62907f29c9a49d896b7922abac7551
+size 492

A13/classification_problems/prepared_data/B_Dense_test_X.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0e55581aef831c32a8340422d452f46f3b4ef0df14cf59d753a567f0f2701fbb
-size 47968
+oid sha256:ddecdbe529ee9c485b8a888ea8afcf90b9330db245fd4aa986c62538c9ebd28b
+size 24048

A13/classification_problems/prepared_data/B_Dense_test_filenames.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:20f7880a948535108e4673778cbb6f2e9afa0f1b90c6117b9385b1283c331e7a
+oid sha256:c240cb30b778bfc08d26d107e30381a8c5f38cbdfec32e8b181f1933d618d755
 size 415

A13/classification_problems/prepared_data/B_Dense_test_y.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:76c435af834a77f3a912ec5afe22fc75901f128b95bb8a67bc11a8d947f67a5d
-size 312
+oid sha256:f3ac7391088bfc472768d22710c42efc4fd9098e712869af411c69b6a45852c1
+size 220

A13/classification_problems/prepared_data/B_Dense_train_X.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:13b7b74f0e5e650461c2d1500ee7d57a1c12db94757284227cfdedf2b88ccb7d
-size 189408
+oid sha256:7e09757f534f78d312794670e126983ec48c235f8680a9afefd3282214da9a18
+size 94768

A13/classification_problems/prepared_data/B_Dense_train_aug_X.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:917d66cb6fb69b455c8fd7e7e0a72ff956f5b5f7248a946e99d95b546d8a4936
-size 946528
+oid sha256:259b29b9ca94033c9802898615cfb9abe8f3ceb0f0af0358726fdd6b7eadebe9
+size 473328

A13/classification_problems/prepared_data/B_Dense_train_aug_filenames.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:14d4f22778391dde209b291d64b9132205c3cfec8b7d3b661a0b5e7635b78087
+oid sha256:35b9fa5dea48ab635e8a6398f082da5bc097c8d20acba82ab1c808d4938c0253
 size 6370

A13/classification_problems/prepared_data/B_Dense_train_aug_y.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4a21aa434e31898a110367f743ab182bcde52f9ba4ede528b97a0047dbe5db54
-size 3768
+oid sha256:6459a30e09826ec536fffa677fc3059ed5f024fdb9dded78eae4615d35c24220
+size 1948

A13/classification_problems/prepared_data/B_Dense_train_y.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:999ea205fa7a13f5d1ffd6a03c52ff91d3c2831ba7f9b5f225d4aee052109509
-size 856
+oid sha256:9558d3e120ce247e995be7d7e9f3996d5a62907f29c9a49d896b7922abac7551
+size 492

A13/classification_problems/prepared_data_broken_backup/A_CNN_test_X.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c7b1256eefe3202643bd2f752df487f93a18fe5df7cc66671f2aef5d87de7da
+size 71888

A13/classification_problems/prepared_data_broken_backup/A_CNN_test_aug_X.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5efe3798beba7b80c6fae6396275bd92463e3b33c957224bc60a8b7bfa16f48f
+size 358928

A13/classification_problems/prepared_data_broken_backup/A_CNN_test_aug_filenames.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:51847ecf909d758ad0d82c8b908cdbcd3884da9d417daef84061f622f2f254c9
+size 1818

A13/classification_problems/prepared_data_broken_backup/A_CNN_test_aug_y.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:02041ae9c08e35dc4438c476693f8aa32443b3d802dfd58482e030ee6e3b7ae4
+size 1048

A13/classification_problems/prepared_data_broken_backup/A_CNN_test_filenames.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:20f7880a948535108e4673778cbb6f2e9afa0f1b90c6117b9385b1283c331e7a
+size 415

A13/classification_problems/prepared_data_broken_backup/A_CNN_test_y.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76c435af834a77f3a912ec5afe22fc75901f128b95bb8a67bc11a8d947f67a5d
+size 312

A13/classification_problems/prepared_data_broken_backup/A_CNN_train_X.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a45c8dda4754bae6d5ee1d9365937a2603db23282f321792a23fd44d8f4c5ad
+size 284048

A13/classification_problems/prepared_data_broken_backup/A_CNN_train_aug_X.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8668695ccfef6288e0bb254ebf57d16b9c2fa3709a42ab72dcfe7620d92ae278
+size 1419728

A13/classification_problems/prepared_data_broken_backup/A_CNN_train_aug_filenames.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:14d4f22778391dde209b291d64b9132205c3cfec8b7d3b661a0b5e7635b78087
+size 6370

A13/classification_problems/prepared_data_broken_backup/A_CNN_train_aug_y.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a21aa434e31898a110367f743ab182bcde52f9ba4ede528b97a0047dbe5db54
+size 3768

A13/classification_problems/prepared_data_broken_backup/A_CNN_train_filenames.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ec888a58911d0cfdcb0a1089c29bc206d4e8460e630efcee07cd4459fb472f93
+size 822

A13/classification_problems/prepared_data_broken_backup/A_CNN_train_y.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:999ea205fa7a13f5d1ffd6a03c52ff91d3c2831ba7f9b5f225d4aee052109509
+size 856

A13/classification_problems/prepared_data_broken_backup/A_Dense_test_X.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c1899c129364c3c42a056222d552bb8eb8c6a8b6d3014bf293c9586af944f722
+size 71888

A13/classification_problems/prepared_data_broken_backup/A_Dense_test_aug_X.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db0fed6051f78da46cb3582a787624b05f1c29af6cbf97bca71f592cc1c070db
+size 358928

A13/classification_problems/prepared_data_broken_backup/A_Dense_test_aug_filenames.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:51847ecf909d758ad0d82c8b908cdbcd3884da9d417daef84061f622f2f254c9
+size 1818

A13/classification_problems/prepared_data_broken_backup/A_Dense_test_aug_y.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:02041ae9c08e35dc4438c476693f8aa32443b3d802dfd58482e030ee6e3b7ae4
+size 1048