{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.13.15","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":28755,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n\n# Use the kagglehub client library to attach Kaggle resources like competitions, datasets, and models to your session\n# Learn more about kagglehub: https://github.com/Kaggle/kagglehub/blob/main/README.md\n\nimport kagglehub\n# kagglehub.dataset_download('/')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:12:22.697807Z","iopub.execute_input":"2026-10-07T17:12:22.698169Z","iopub.status.idle":"2026-10-07T17:12:24.03278Z","shell.execute_reply.started":"2026-10-07T17:12:22.698129Z","shell.execute_reply":"2026-10-07T17:12:24.031868Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/competitions/datasciencebowl/plankton_identification.pdf\n/kaggle/input/competitions/datasciencebowl/train.zip\n/kaggle/input/competitions/datasciencebowl/sampleSubmission.csv.zip\n/kaggle/input/competitions/datasciencebowl/test.zip\n","output_type":"stream"}],"execution_count":2},{"cell_type":"markdown","source":"# National Data Science Bowl – Plankton Classification\n## CNN-Based Multi-Class Image Classification (trained from scratch)\n\n**Student project:** Artificial Neural Networks – Kaggle Competition\n**Model category:** CNN (no pretrained weights, no external data)\n**Competition metric:** Multi-class Log Loss (lower is better)\n**Framework:** TensorFlow / Keras\n\n### Why this metric shapes every decision\n- **Softmax** gives a probability for each of the 121 classes. Kaggle scores the *whole distribution*, so hard labels are not enough.\n- **Categorical cross-entropy** is the training loss, and it is exactly the multi-class log loss: `-mean(log p[true class])`. Minimising it directly optimises the competition metric.\n- **Accuracy vs log loss:** accuracy only checks whether the arg-max is right. Log loss punishes confident mistakes very heavily (a wrong answer at p≈0 for the true class costs a lot). A model with slightly lower accuracy but better-calibrated, less over-confident probabilities can therefore have a better log loss.\n- **Calibration matters:** a probability of 0.9 should be right about 90% of the time. We monitor *validation loss* (not accuracy) for early stopping, LR scheduling and checkpointing, and we analyse calibration in the error analysis.\n\n### Notebook map\nEDA → preprocessing → split → pipeline/augmentation → M0 → M1 → M2 → comparison → model selection → final training → test predictions → submissions → error analysis → conclusion.\n\n*Academic integrity: all code is original. No public notebook was copied; architectures are standard CNN practice (Conv-BN-ReLU blocks, pooling, dropout) implemented independently.*","metadata":{}},{"cell_type":"code","source":"# ============================ CELL 2: LIBRARIES, VERSIONS, GPU, SEEDS ============================\nimport os, sys, time, random, json, shutil, zipfile, hashlib, math, subprocess, platform, warnings\nfrom pathlib import Path\nfrom collections import Counter\nfrom concurrent.futures import ThreadPoolExecutor\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport sklearn, scipy, PIL\nfrom PIL import Image\nfrom scipy.stats import spearmanr\nfrom scipy.optimize import minimize_scalar\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import (confusion_matrix, precision_recall_fscore_support,\n accuracy_score, log_loss as sk_log_loss)\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom IPython.display import display, Markdown\n\nwarnings.filterwarnings(\"ignore\")\nplt.rcParams[\"figure.dpi\"] = 100\n\n# ---------------- Reproducibility ----------------\nSEED = 42\nos.environ[\"PYTHONHASHSEED\"] = str(SEED)\nrandom.seed(SEED)\nnp.random.seed(SEED)\ntf.keras.utils.set_random_seed(SEED) # seeds Python, NumPy and TensorFlow together\n\ndef reset_seeds():\n \"\"\"Re-seed before building/training each model so every run starts from the same state.\"\"\"\n random.seed(SEED); np.random.seed(SEED); tf.keras.utils.set_random_seed(SEED)\n\n# ---------------- Paths (Kaggle only, no local paths) ----------------\nWORK_DIR = Path(\"/kaggle/working\")\nDATA_DIR = WORK_DIR / \"plankton_data\"\n\n# ---------------- Library versions ----------------\nprint(\"=\" * 60); print(\"ENVIRONMENT / LIBRARY VERSIONS\"); print(\"=\" * 60)\nprint(f\"Python : {sys.version.split()[0]} ({platform.platform()})\")\nprint(f\"TensorFlow : {tf.__version__}\")\nprint(f\"Keras : {keras.__version__}\")\nprint(f\"NumPy : {np.__version__}\")\nprint(f\"Pandas : {pd.__version__}\")\nprint(f\"Matplotlib : {matplotlib.__version__}\")\nprint(f\"scikit-learn : {sklearn.__version__}\")\nprint(f\"SciPy : {scipy.__version__}\")\nprint(f\"Pillow (PIL) : {PIL.__version__}\")\nprint(f\"Seed : {SEED}\")\n\n# ---------------- GPU info ----------------\ngpus = tf.config.list_physical_devices(\"GPU\")\nprint(\"\\nGPUs visible to TensorFlow:\", gpus)\nif not gpus:\n print(\"WARNING: no GPU found. Enable GPU in Notebook settings (Accelerator -> GPU T4).\")\nelse:\n for g in gpus: # avoid grabbing all GPU memory at once\n try: tf.config.experimental.set_memory_growth(g, True)\n except Exception as e: print(\"memory growth not set:\", e)\n try:\n print(subprocess.run([\"nvidia-smi\", \"--query-gpu=name,memory.total,driver_version\",\n \"--format=csv,noheader\"], capture_output=True, text=True).stdout.strip())\n except Exception as e:\n print(\"nvidia-smi unavailable:\", e)\nprint(\"\\nNote: seeds are fixed, but GPU kernels can still cause tiny run-to-run differences.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:12:24.034199Z","iopub.execute_input":"2026-10-07T17:12:24.034849Z","iopub.status.idle":"2026-10-07T17:12:45.669702Z","shell.execute_reply.started":"2026-10-07T17:12:24.034815Z","shell.execute_reply":"2026-10-07T17:12:45.668868Z"}},"outputs":[{"name":"stdout","text":"============================================================\nENVIRONMENT / LIBRARY VERSIONS\n============================================================\nPython : 3.13.15 (Linux-6.18.48+-x86_64-with-glibc2.39)\nTensorFlow : 2.20.0\nKeras : 3.13.2\nNumPy : 2.1.3\nPandas : 2.3.3\nMatplotlib : 3.10.0\nscikit-learn : 1.6.1\nSciPy : 1.16.3\nPillow (PIL) : 12.3.0\nSeed : 42\n\nGPUs visible to TensorFlow: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU')]\nTesla T4, 15360 MiB, 580.178.04\nTesla T4, 15360 MiB, 580.178.04\n\nNote: seeds are fixed, but GPU kernels can still cause tiny run-to-run differences.\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"# ============================ CELL 3: DATASET DISCOVERY ============================\nKAGGLE_INPUT = Path(\"/kaggle/input\")\nPREFERRED = KAGGLE_INPUT / \"competitions\" / \"datasciencebowl\"\nassert KAGGLE_INPUT.exists(), \"/kaggle/input not found - run this notebook on Kaggle.\"\n\nSEARCH_ROOT = PREFERRED if PREFERRED.exists() else KAGGLE_INPUT\nif SEARCH_ROOT == KAGGLE_INPUT:\n print(f\"NOTE: {PREFERRED} not found - searching all of /kaggle/input instead.\")\n\ndef walk_limited(root, max_depth=3):\n \"\"\"List files AND directories up to max_depth levels (does not descend into huge extracted folders).\"\"\"\n found, base = [], len(Path(root).parts)\n for dp, dn, fn in os.walk(root):\n depth = len(Path(dp).parts) - base\n found += [Path(dp) / n for n in dn + fn]\n if depth + 1 >= max_depth:\n dn[:] = []\n return found\n\nitems = walk_limited(SEARCH_ROOT, max_depth=3 if SEARCH_ROOT == PREFERRED else 4)\n\nprint(f\"Search root: {SEARCH_ROOT}\\n\")\nprint(f\"{'Item':70s} {'Size':>12s}\")\nfor p in sorted(items, key=lambda p: (len(p.parts), str(p)))[:40]:\n size = f\"{p.stat().st_size/1e6:,.1f} MB\" if p.is_file() else \"(directory)\"\n print(f\"{str(p):70s} {size:>12s}\")\n\ndef locate(candidate_names):\n \"\"\"Find a file/dir by (case-insensitive) name; prefer the shallowest match.\"\"\"\n for p in sorted(items, key=lambda p: len(p.parts)):\n if p.name.lower() in candidate_names:\n return p\n return None\n\nSRC_TRAIN = locate({\"train.zip\", \"train\"})\nSRC_TEST = locate({\"test.zip\", \"test\"})\nSRC_SAMPLE = locate({\"samplesubmission.csv.zip\", \"samplesubmission.csv\"})\n\nfor label, p in [(\"train\", SRC_TRAIN), (\"test\", SRC_TEST), (\"sample submission\", SRC_SAMPLE)]:\n assert p is not None, (f\"Could not find {label}. Add the 'National Data Science Bowl' competition \"\n f\"to the notebook (Add Input) and accept the rules.\")\nprint(\"\\nLocated sources:\")\nprint(\" train :\", SRC_TRAIN)\nprint(\" test :\", SRC_TEST)\nprint(\" sample :\", SRC_SAMPLE)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:12:45.670805Z","iopub.execute_input":"2026-10-07T17:12:45.671403Z","iopub.status.idle":"2026-10-07T17:12:45.69017Z","shell.execute_reply.started":"2026-10-07T17:12:45.671382Z","shell.execute_reply":"2026-10-07T17:12:45.689299Z"}},"outputs":[{"name":"stdout","text":"Search root: /kaggle/input/competitions/datasciencebowl\n\nItem Size\n/kaggle/input/competitions/datasciencebowl/plankton_identification.pdf 1.6 MB\n/kaggle/input/competitions/datasciencebowl/sampleSubmission.csv.zip 0.9 MB\n/kaggle/input/competitions/datasciencebowl/test.zip 363.5 MB\n/kaggle/input/competitions/datasciencebowl/train.zip 79.9 MB\n\nLocated sources:\n train : /kaggle/input/competitions/datasciencebowl/train.zip\n test : /kaggle/input/competitions/datasciencebowl/test.zip\n sample : /kaggle/input/competitions/datasciencebowl/sampleSubmission.csv.zip\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"# ============================ CELL 4: SAFE, STRUCTURE-AWARE EXTRACTION ============================\nIMG_EXT = (\".jpg\", \".jpeg\", \".png\", \".bmp\", \".tif\", \".tiff\")\nDATA_DIR.mkdir(parents=True, exist_ok=True)\n\ndef safe_extract(zip_path, target):\n \"\"\"Extract a ZIP, refusing members that would escape the target directory.\"\"\"\n target = Path(target).resolve(); target.mkdir(parents=True, exist_ok=True)\n with zipfile.ZipFile(zip_path) as zf:\n for m in zf.infolist():\n if not str((target / m.filename).resolve()).startswith(str(target)):\n raise RuntimeError(f\"Unsafe path in zip: {m.filename}\")\n zf.extractall(target)\n\ndef image_dir_counts(base):\n \"\"\"{directory: number of image files directly inside} (ignores macOS junk).\"\"\"\n counts = {}\n for dp, dn, fn in os.walk(base):\n if \"__MACOSX\" in dp: continue\n n = sum(f.lower().endswith(IMG_EXT) and not f.startswith(\"._\") for f in fn)\n if n: counts[dp] = n\n return counts\n\ndef detect_train_root(base):\n \"\"\"Train root = the directory that has the MOST child folders containing images (the class folders).\n This works whether the zip has an extra nested level (train/train/...) or not.\"\"\"\n counts = image_dir_counts(base)\n parent_counts = Counter(os.path.dirname(d) for d in counts)\n return Path(parent_counts.most_common(1)[0][0])\n\ndef detect_test_root(base):\n \"\"\"Test root = the directory directly containing the most images (flat folder of test images).\"\"\"\n counts = image_dir_counts(base)\n return Path(max(counts, key=counts.get))\n\ndef prepare(src, final_dir, kind):\n final_dir = Path(final_dir)\n if final_dir.exists() and any(final_dir.iterdir()):\n print(f\"[skip] {final_dir} already prepared\"); return final_dir\n detect = detect_train_root if kind == \"train\" else detect_test_root\n if src.is_file(): # zip -> temp extraction -> move detected root\n tmp = DATA_DIR / f\"_tmp_{kind}\"; shutil.rmtree(tmp, ignore_errors=True)\n t0 = time.time(); safe_extract(src, tmp)\n root = detect(tmp)\n shutil.move(str(root), str(final_dir)) # moves ONLY the detected root -> no train/train\n shutil.rmtree(tmp, ignore_errors=True)\n print(f\"[ok] extracted {kind} in {time.time()-t0:.0f}s -> {final_dir}\")\n return final_dir\n root = detect(src) # already-extracted dataset: use in place\n print(f\"[ok] {kind} already extracted; using {root}\")\n return root\n\nTRAIN_DIR = prepare(SRC_TRAIN, DATA_DIR / \"train\", \"train\")\nTEST_DIR = prepare(SRC_TEST, DATA_DIR / \"test\", \"test\")\n\nSAMPLE_DIR = DATA_DIR / \"sample_submission\"; SAMPLE_DIR.mkdir(parents=True, exist_ok=True)\nif SRC_SAMPLE.suffix.lower() == \".zip\": safe_extract(SRC_SAMPLE, SAMPLE_DIR)\nelse: shutil.copy(SRC_SAMPLE, SAMPLE_DIR / SRC_SAMPLE.name)\nSAMPLE_CSV = [p for p in SAMPLE_DIR.rglob(\"*.csv\") if \"__MACOSX\" not in str(p)][0]\n\nprint(\"\\nTRAIN_DIR :\", TRAIN_DIR)\nprint(\" first entries:\", sorted(os.listdir(TRAIN_DIR))[:5], \"(should be class folders, not another 'train')\")\nprint(\"TEST_DIR :\", TEST_DIR)\nprint(\" first entries:\", sorted(os.listdir(TEST_DIR))[:5])\nprint(\"SAMPLE_CSV:\", SAMPLE_CSV)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:12:45.691892Z","iopub.execute_input":"2026-10-07T17:12:45.692356Z","iopub.status.idle":"2026-10-07T17:13:10.93072Z","shell.execute_reply.started":"2026-10-07T17:12:45.692312Z","shell.execute_reply":"2026-10-07T17:13:10.929727Z"}},"outputs":[{"name":"stdout","text":"[ok] extracted train in 5s -> /kaggle/working/plankton_data/train\n[ok] extracted test in 20s -> /kaggle/working/plankton_data/test\n\nTRAIN_DIR : /kaggle/working/plankton_data/train\n first entries: ['acantharia_protist', 'acantharia_protist_big_center', 'acantharia_protist_halo', 'amphipods', 'appendicularian_fritillaridae'] (should be class folders, not another 'train')\nTEST_DIR : /kaggle/working/plankton_data/test\n first entries: ['1.jpg', '10.jpg', '100.jpg', '1000.jpg', '10000.jpg']\nSAMPLE_CSV: /kaggle/working/plankton_data/sample_submission/sampleSubmission.csv\n","output_type":"stream"}],"execution_count":5},{"cell_type":"code","source":"# ============================ CELL 5: DATASET STRUCTURE VERIFICATION ============================\n# Class folders = sub-directories of TRAIN_DIR that contain images. Sorted -> deterministic mapping.\nclass_names = sorted(d.name for d in TRAIN_DIR.iterdir()\n if d.is_dir() and any(f.lower().endswith(IMG_EXT) for f in os.listdir(d)))\nNUM_CLASSES = len(class_names)\nclass_to_idx = {c: i for i, c in enumerate(class_names)}\nidx_to_class = {i: c for c, i in class_to_idx.items()}\n\nrows = []\nfor c in class_names:\n for f in sorted(os.listdir(TRAIN_DIR / c)):\n if f.lower().endswith(IMG_EXT) and not f.startswith(\"._\"):\n rows.append((str(TRAIN_DIR / c / f), f, c))\ntrain_df = pd.DataFrame(rows, columns=[\"path\", \"filename\", \"label\"])\n\ntest_files = sorted(f for f in os.listdir(TEST_DIR) if f.lower().endswith(IMG_EXT) and not f.startswith(\"._\"))\ntest_paths = [str(TEST_DIR / f) for f in test_files]\n\n# Save the class mapping: softmax output index i <-> class_names[i]\nwith open(WORK_DIR / \"class_mapping.json\", \"w\") as fp:\n json.dump(class_to_idx, fp, indent=1)\n\nprint(\"=\" * 60); print(\"DATASET SUMMARY (computed from files)\"); print(\"=\" * 60)\nprint(f\"Number of classes : {NUM_CLASSES}\")\nprint(f\"Training images : {len(train_df):,}\")\nprint(f\"Test images : {len(test_files):,}\")\nprint(f\"First 8 classes : {class_names[:8]}\")\nprint(f\"Sample train files : {train_df.filename.head(3).tolist()}\")\nprint(f\"Sample test files : {test_files[:3]}\")\nprint(f\"Class mapping saved : {WORK_DIR/'class_mapping.json'}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:13:10.931917Z","iopub.execute_input":"2026-10-07T17:13:10.932228Z","iopub.status.idle":"2026-10-07T17:13:12.297449Z","shell.execute_reply.started":"2026-10-07T17:13:10.932209Z","shell.execute_reply":"2026-10-07T17:13:12.296418Z"}},"outputs":[{"name":"stdout","text":"============================================================\nDATASET SUMMARY (computed from files)\n============================================================\nNumber of classes : 121\nTraining images : 30,336\nTest images : 130,400\nFirst 8 classes : ['acantharia_protist', 'acantharia_protist_big_center', 'acantharia_protist_halo', 'amphipods', 'appendicularian_fritillaridae', 'appendicularian_s_shape', 'appendicularian_slight_curve', 'appendicularian_straight']\nSample train files : ['100224.jpg', '100723.jpg', '101165.jpg']\nSample test files : ['1.jpg', '10.jpg', '100.jpg']\nClass mapping saved : /kaggle/working/class_mapping.json\n","output_type":"stream"}],"execution_count":6},{"cell_type":"markdown","source":"## 1. Exploratory Data Analysis – Class Distribution\nWe count images per class to quantify **class imbalance**. Imbalance matters for a CNN because the loss is dominated by frequent classes, so rare classes get fewer gradient updates and tend to have lower recall. We do **not** re-weight classes by default. Log loss is measured on a test set that presumably follows the natural class frequencies, so the model should learn those priors. Re-weighting would shift probabilities away from the true priors and can hurt log loss.","metadata":{}},{"cell_type":"code","source":"# ============================ CELL 6B: CLASS DISTRIBUTION ============================\ncounts = train_df[\"label\"].value_counts()\nprint(\"=\" * 60); print(\"CLASS DISTRIBUTION STATISTICS\"); print(\"=\" * 60)\nprint(f\"Total classes : {len(counts)}\")\nprint(f\"Min class size: {counts.min()} ({counts.idxmin()})\")\nprint(f\"Max class size: {counts.max()} ({counts.idxmax()})\")\nprint(f\"Mean : {counts.mean():.1f}\")\nprint(f\"Median : {counts.median():.1f}\")\nprint(f\"Imbalance ratio (max/min): {counts.max()/counts.min():.1f}x\")\nprint(f\"Top-10 classes hold {100*counts.head(10).sum()/counts.sum():.1f}% of all images\")\nprint(f\"Classes with < 100 images: {(counts < 100).sum()} of {len(counts)}\")\nprint(\"\\nMost frequent classes:\"); print(counts.head(10).to_string())\nprint(\"\\nLeast frequent classes:\"); print(counts.tail(10).to_string())\n\nfig, ax = plt.subplots(1, 2, figsize=(16, 4.5))\nax[0].bar(range(len(counts)), counts.values, color=\"steelblue\")\nax[0].set_title(\"Images per class (sorted, descending)\"); ax[0].set_xlabel(\"Class rank\"); ax[0].set_ylabel(\"Images\")\nax[1].hist(counts.values, bins=30, color=\"darkorange\", edgecolor=\"k\")\nax[1].set_title(\"Histogram of class sizes\"); ax[1].set_xlabel(\"Images in class\"); ax[1].set_ylabel(\"Number of classes\")\nplt.tight_layout(); plt.show()\n\nprint(\"\\nINTERPRETATION: the dataset is strongly imbalanced. Expect the CNN to perform best on large classes and\")\nprint(\"worst on rare ones. Stratified splitting (Cell 10) keeps every class represented in validation.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:13:12.298514Z","iopub.execute_input":"2026-10-07T17:13:12.298854Z","iopub.status.idle":"2026-10-07T17:13:13.574338Z","shell.execute_reply.started":"2026-10-07T17:13:12.298834Z","shell.execute_reply":"2026-10-07T17:13:13.573416Z"}},"outputs":[{"name":"stdout","text":"============================================================\nCLASS DISTRIBUTION STATISTICS\n============================================================\nTotal classes : 121\nMin class size: 9 (hydromedusae_haliscera_small_sideview)\nMax class size: 1979 (trichodesmium_puff)\nMean : 250.7\nMedian : 114.0\nImbalance ratio (max/min): 219.9x\nTop-10 classes hold 36.9% of all images\nClasses with < 100 images: 55 of 121\n\nMost frequent classes:\nlabel\ntrichodesmium_puff 1979\nchaetognath_other 1934\ncopepod_cyclopoid_oithona_eggs 1189\nprotist_other 1172\ndetritus_other 914\ncopepod_cyclopoid_oithona 899\nacantharia_protist 889\nchaetognath_non_sagitta 815\ntrichodesmium_bowtie 708\nhydromedusae_solmaris 703\n\nLeast frequent classes:\nlabel\nappendicularian_fritillaridae 16\nephyra 14\ninvertebrate_larvae_other_A 14\nhydromedusae_typeE 14\nacantharia_protist_big_center 13\npteropod_theco_dev_seq 13\nhydromedusae_other 12\nfish_larvae_deep_body 10\nheteropod 10\nhydromedusae_haliscera_small_sideview 9\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}},{"name":"stdout","text":"\nINTERPRETATION: the dataset is strongly imbalanced. Expect the CNN to perform best on large classes and\nworst on rare ones. Stratified splitting (Cell 10) keeps every class represented in validation.\n","output_type":"stream"}],"execution_count":7},{"cell_type":"markdown","source":"## 2. Sample Image Visualisation\nThe next cell shows one image from each of 24 evenly spaced classes, with its class label and original size (width×height).","metadata":{}},{"cell_type":"code","source":"# ============================ CELL 7B: SAMPLE IMAGES ============================\nrng = np.random.RandomState(SEED)\nchosen = [class_names[i] for i in np.linspace(0, NUM_CLASSES - 1, 24).astype(int)]\nfig, axes = plt.subplots(4, 6, figsize=(16, 11))\nfor ax, c in zip(axes.ravel(), chosen):\n sub = train_df[train_df.label == c]\n p = sub.iloc[rng.randint(len(sub))].path\n with Image.open(p) as im:\n arr = np.asarray(im); w, h = im.size; mode = im.mode\n ax.imshow(arr, cmap=\"gray\", vmin=0, vmax=255)\n ax.set_title(f\"{c[:22]}\\n{w}x{h} px, mode {mode}\", fontsize=8); ax.axis(\"off\")\nplt.suptitle(\"Sample plankton images (one per class; shown scaled to fit)\", fontsize=13)\nplt.tight_layout(); plt.show()\n\nprint(\"OBSERVATIONS:\")\nprint(\"- Images are grayscale (one channel) -> input shape will be (H, W, 1).\")\nprint(\"- Original sizes vary a lot (see titles) -> CNNs need a fixed input size, so images must be resized.\")\nprint(\"- Classes differ in shape, texture, size and transparency; some classes look very similar,\")\nprint(\" so errors between visually similar groups are expected.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:13:13.576039Z","iopub.execute_input":"2026-10-07T17:13:13.576676Z","iopub.status.idle":"2026-10-07T17:13:15.302391Z","shell.execute_reply.started":"2026-10-07T17:13:13.576653Z","shell.execute_reply":"2026-10-07T17:13:15.30144Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}},{"name":"stdout","text":"OBSERVATIONS:\n- Images are grayscale (one channel) -> input shape will be (H, W, 1).\n- Original sizes vary a lot (see titles) -> CNNs need a fixed input size, so images must be resized.\n- Classes differ in shape, texture, size and transparency; some classes look very similar,\n so errors between visually similar groups are expected.\n","output_type":"stream"}],"execution_count":8},{"cell_type":"markdown","source":"## 3. Data Quality Check\nWe fully decode **every** train and test image (images are small, so this is cheap with threads). We check for corrupt/unreadable files, image modes, dimensions, empty files, duplicate file names and byte-identical duplicate training images.","metadata":{}},{"cell_type":"code","source":"# ============================ CELL 8B: DATA QUALITY CHECK ============================\ndef inspect_image(args):\n path, do_hash = args\n try:\n with Image.open(path) as im:\n w, h = im.size; mode = im.mode\n im.load() # full decode -> catches truncated/corrupt files\n digest = None\n if do_hash:\n with open(path, \"rb\") as fh: digest = hashlib.md5(fh.read()).hexdigest()\n return (w, h, mode, digest, True)\n except Exception:\n return (0, 0, \"ERR\", None, False)\n\ndef inspect_all(paths, do_hash):\n with ThreadPoolExecutor(max_workers=(os.cpu_count() or 4) * 2) as ex:\n res = list(ex.map(inspect_image, [(p, do_hash) for p in paths])) # order preserved\n return pd.DataFrame(res, columns=[\"width\", \"height\", \"mode\", \"md5\", \"ok\"])\n\nt0 = time.time()\ntrain_meta = pd.concat([train_df[[\"path\", \"filename\", \"label\"]].reset_index(drop=True),\n inspect_all(train_df.path.tolist(), True)], axis=1)\ntest_meta = pd.concat([pd.DataFrame({\"path\": test_paths, \"filename\": test_files}),\n inspect_all(test_paths, False)], axis=1)\nfor m in (train_meta, test_meta):\n m[\"max_side\"] = m[[\"width\", \"height\"]].max(axis=1)\nprint(f\"Inspected {len(train_meta):,} train + {len(test_meta):,} test images in {time.time()-t0:.0f}s\\n\")\n\nprint(\"=\" * 60); print(\"DATA QUALITY SUMMARY\"); print(\"=\" * 60)\nfor nm, m in [(\"TRAIN\", train_meta), (\"TEST\", test_meta)]:\n print(f\"[{nm}] corrupted/unreadable: {(~m.ok).sum()} | image modes: {dict(Counter(m['mode']))}\")\n print(f\"[{nm}] width min/median/max: {m.width.min()} / {m.width.median():.0f} / {m.width.max()}\")\n print(f\"[{nm}] height min/median/max: {m.height.min()} / {m.height.median():.0f} / {m.height.max()}\")\n print(f\"[{nm}] duplicate file names: {m.filename.duplicated().sum()}\")\nprint(f\"[TRAIN] missing labels: {train_df.label.isna().sum()}\")\nprint(f\"[TRAIN] zero-byte files: {sum(os.path.getsize(p) == 0 for p in train_df.path)}\")\ndup_hash = train_meta[train_meta.md5.notna() & train_meta.md5.duplicated(keep=False)]\nconflict = dup_hash.groupby(\"md5\").label.nunique()\nprint(f\"[TRAIN] byte-identical duplicate images: {len(dup_hash)} files in {dup_hash.md5.nunique()} groups; \"\n f\"groups with conflicting labels: {(conflict > 1).sum()}\")\n\nfig, ax = plt.subplots(1, 3, figsize=(16, 3.8))\nax[0].hist(train_meta.width, bins=50, color=\"steelblue\"); ax[0].set_title(\"Train image width\")\nax[1].hist(train_meta.height, bins=50, color=\"darkorange\"); ax[1].set_title(\"Train image height\")\nax[2].hist(train_meta.max_side, bins=50, color=\"seagreen\"); ax[2].set_title(\"Train: longer side (px)\")\nplt.tight_layout(); plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:13:15.303479Z","iopub.execute_input":"2026-10-07T17:13:15.304401Z","iopub.status.idle":"2026-10-07T17:14:14.024107Z","shell.execute_reply.started":"2026-10-07T17:13:15.304378Z","shell.execute_reply":"2026-10-07T17:14:14.023183Z"}},"outputs":[{"name":"stdout","text":"Inspected 30,336 train + 130,400 test images in 58s\n\n============================================================\nDATA QUALITY SUMMARY\n============================================================\n[TRAIN] corrupted/unreadable: 0 | image modes: {'L': 30336}\n[TRAIN] width min/median/max: 31 / 56 / 424\n[TRAIN] height min/median/max: 21 / 53 / 428\n[TRAIN] duplicate file names: 0\n[TEST] corrupted/unreadable: 0 | image modes: {'L': 130400}\n[TEST] width min/median/max: 31 / 56 / 428\n[TEST] height min/median/max: 21 / 53 / 427\n[TEST] duplicate file names: 0\n[TRAIN] missing labels: 0\n[TRAIN] zero-byte files: 0\n[TRAIN] byte-identical duplicate images: 2 files in 1 groups; groups with conflicting labels: 1\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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4. Preprocessing Strategy\n- **Grayscale, 1 channel:** the data is grayscale, so a single channel avoids 3× wasted memory/compute.\n- **Resize to 64×64 (aspect ratio preserved, padded with the image's own background value):** a plain stretch would distort the organism's shape (a key class cue). Padding keeps morphology intact.\n- **Why 64×64:** most images are small, so a larger size mostly up-samples. 64×64 keeps fine structure (arms, spines) while giving fast epochs and low memory on a T4. The three models (about 20 minutes of training) fit comfortably within the 2-hour limit.\n- **Normalisation:** pixel values are scaled to [0,1] by a `Rescaling(1/255)` layer *inside* the model. Training, validation and test therefore use identical normalisation automatically.\n- **Memory:** 30k × 64×64 bytes ≈ 124 MB (uint8), so keeping the training set in RAM is cheap. The data is fed through `tf.data`.\n- **Limitation:** padding removes information about the absolute physical size of the organism, which can be class-informative.","metadata":{}},{"cell_type":"code","source":"# ============================ CELL 9B: PREPROCESSING ============================\nIMG_SIZE = 64\nprint(f\"Fraction of train images whose longer side < {IMG_SIZE}px (would be up-sampled): \"\n f\"{(train_meta.max_side < IMG_SIZE).mean()*100:.1f}%\")\nprint(f\"Median longer side: {train_meta.max_side.median():.0f}px | 90th percentile: {train_meta.max_side.quantile(.9):.0f}px\")\n\ndef load_and_resize(path, size=IMG_SIZE):\n \"\"\"Grayscale -> keep aspect ratio -> pad to square using the median border (background) value.\"\"\"\n try:\n with Image.open(path) as im:\n im = im.convert(\"L\")\n a = np.asarray(im)\n bg = int(np.median(np.concatenate([a[0, :], a[-1, :], a[:, 0], a[:, -1]])))\n w, h = im.size; s = size / max(w, h)\n nw, nh = max(1, int(round(w * s))), max(1, int(round(h * s)))\n im = im.resize((nw, nh), Image.LANCZOS)\n canvas = Image.new(\"L\", (size, size), bg)\n canvas.paste(im, ((size - nw) // 2, (size - nh) // 2))\n return np.asarray(canvas, dtype=np.uint8)\n except Exception:\n return None\n\ndef load_images(paths, size=IMG_SIZE):\n with ThreadPoolExecutor(max_workers=(os.cpu_count() or 4) * 2) as ex:\n arrs = list(ex.map(lambda p: load_and_resize(p, size), paths))\n bad = sum(a is None for a in arrs)\n arrs = [a if a is not None else np.full((size, size), 255, np.uint8) for a in arrs]\n return np.stack(arrs)[..., None], bad # (N, size, size, 1) uint8\n\nt0 = time.time()\nX_all, bad = load_images(train_df.path.tolist())\ny_all = train_df[\"label\"].map(class_to_idx).values.astype(np.int64)\nprint(f\"Loaded train array {X_all.shape}, dtype={X_all.dtype}, {X_all.nbytes/1e6:.0f} MB, \"\n f\"failed={bad}, time={time.time()-t0:.0f}s\")\n\nfig, axes = plt.subplots(2, 8, figsize=(16, 4.5))\nfor j, i in enumerate(np.linspace(0, len(X_all) - 1, 8).astype(int)):\n with Image.open(train_df.path[i]) as im: orig = np.asarray(im.convert(\"L\"))\n axes[0, j].imshow(orig, cmap=\"gray\"); axes[0, j].set_title(f\"orig {orig.shape[1]}x{orig.shape[0]}\", fontsize=8)\n axes[1, j].imshow(X_all[i, ..., 0], cmap=\"gray\", vmin=0, vmax=255); axes[1, j].set_title(f\"{IMG_SIZE}x{IMG_SIZE}\", fontsize=8)\n axes[0, j].axis(\"off\"); axes[1, j].axis(\"off\")\nplt.suptitle(\"Original (top) vs preprocessed (bottom)\"); plt.tight_layout(); plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:14:14.025257Z","iopub.execute_input":"2026-10-07T17:14:14.025658Z","iopub.status.idle":"2026-10-07T17:14:39.770941Z","shell.execute_reply.started":"2026-10-07T17:14:14.025631Z","shell.execute_reply":"2026-10-07T17:14:39.77027Z"}},"outputs":[{"name":"stdout","text":"Fraction of train images whose longer side < 64px (would be up-sampled): 50.2%\nMedian longer side: 63px | 90th percentile: 149px\nLoaded train array (30336, 64, 64, 1), dtype=uint8, 124 MB, failed=0, time=25s\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":10},{"cell_type":"code","source":"# ============================ CELL 10: STRATIFIED TRAIN / VALIDATION SPLIT ============================\n# Split INDICES (not images) so no image can be in both sets -> no leakage.\n# Augmentation (Cell 11) is applied only on the training pipeline; validation data stays untouched.\nidx_all = np.arange(len(X_all))\nidx_tr, idx_val = train_test_split(idx_all, test_size=0.20, stratify=y_all, random_state=SEED)\nX_tr, y_tr = X_all[idx_tr], y_all[idx_tr]\nX_val, y_val = X_all[idx_val], y_all[idx_val]\n\nassert len(set(idx_tr) & set(idx_val)) == 0, \"Leakage: overlap between train and validation!\"\ntr_cnt = np.bincount(y_tr, minlength=NUM_CLASSES); val_cnt = np.bincount(y_val, minlength=NUM_CLASSES)\ncmp_df = pd.DataFrame({\"class\": class_names, \"train\": tr_cnt, \"val\": val_cnt})\ncmp_df[\"val_share\"] = cmp_df.val / (cmp_df.train + cmp_df.val)\n\nprint(\"=\" * 60); print(\"TRAIN / VALIDATION SPLIT\"); print(\"=\" * 60)\nprint(f\"Training samples : {len(X_tr):,} ({100*len(X_tr)/len(X_all):.1f}%)\")\nprint(f\"Validation samples: {len(X_val):,} ({100*len(X_val)/len(X_all):.1f}%)\")\nprint(f\"Number of classes : {NUM_CLASSES} | classes in train: {(tr_cnt>0).sum()} | in val: {(val_cnt>0).sum()}\")\nprint(f\"Validation share per class: min={cmp_df.val_share.min():.3f}, max={cmp_df.val_share.max():.3f} (target 0.20)\")\ndisplay(cmp_df.sort_values(\"train\").head(5).append(cmp_df.sort_values(\"train\").tail(5)) if hasattr(pd.DataFrame, \"append\")\n else pd.concat([cmp_df.sort_values(\"train\").head(5), cmp_df.sort_values(\"train\").tail(5)]))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:14:39.773702Z","iopub.execute_input":"2026-10-07T17:14:39.773976Z","iopub.status.idle":"2026-10-07T17:14:39.859735Z","shell.execute_reply.started":"2026-10-07T17:14:39.77395Z","shell.execute_reply":"2026-10-07T17:14:39.858984Z"}},"outputs":[{"name":"stdout","text":"============================================================\nTRAIN / VALIDATION SPLIT\n============================================================\nTraining samples : 24,268 (80.0%)\nValidation samples: 6,068 (20.0%)\nNumber of classes : 121 | classes in train: 121 | in val: 121\nValidation share per class: min=0.154, max=0.222 (target 0.20)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" class train val val_share\n61 hydromedusae_haliscera_small_sideview 7 2 0.222222\n50 fish_larvae_deep_body 8 2 0.200000\n56 heteropod 8 2 0.200000\n66 hydromedusae_other 10 2 0.166667\n46 ephyra 11 3 0.214286\n35 detritus_other 731 183 0.200219\n85 protist_other 938 234 0.199659\n25 copepod_cyclopoid_oithona_eggs 951 238 0.200168\n11 chaetognath_other 1547 387 0.200103\n110 trichodesmium_puff 1583 396 0.200101","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
classtrainvalval_share
61hydromedusae_haliscera_small_sideview720.222222
50fish_larvae_deep_body820.200000
56heteropod820.200000
66hydromedusae_other1020.166667
46ephyra1130.214286
35detritus_other7311830.200219
85protist_other9382340.199659
25copepod_cyclopoid_oithona_eggs9512380.200168
11chaetognath_other15473870.200103
110trichodesmium_puff15833960.200101
\n
"},"metadata":{}}],"execution_count":11},{"cell_type":"markdown","source":"## 5. Data Pipeline & Augmentation\n**Pipeline:** `tf.data` (shuffle for training, batch, prefetch). Images are cast to float32 and normalised inside the model.\n\n**Augmentation (training only, implemented as Keras layers that are automatically inactive at validation/test time):**\n| Augmentation | Reason |\n|---|---|\n| Horizontal + vertical flips | Plankton float freely in water; orientation is arbitrary, so flips keep the class. |\n| Rotation up to ±180° | Same reason: there is no canonical \"up\". |\n| Small translation (5–10%) | The organism may be off-centre; makes the model position-tolerant. |\n| Zoom (10–15%) | Varies apparent size mildly. We keep it small because size can be a class cue. |\n| Contrast (±20%, strong level only) | Imaging/illumination differences. |\n\nWe avoid shear, heavy zoom, cutout and colour changes, which could destroy morphology or are meaningless for grayscale. Augmentation also acts as a regulariser, which reduces over-confidence and helps log loss.","metadata":{}},{"cell_type":"code","source":"# ============================ CELL 11B: tf.data PIPELINE + AUGMENTATION ============================\nBATCH_SIZE = 128\nAUTOTUNE = tf.data.AUTOTUNE\n\ndef make_dataset(X, y=None, training=False, batch_size=BATCH_SIZE):\n \"\"\"Efficient in-memory tf.data pipeline. Shuffle only when training. No caching needed (data already in RAM).\"\"\"\n if y is None:\n ds = tf.data.Dataset.from_tensor_slices(X)\n ds = ds.map(lambda x: tf.cast(x, tf.float32), num_parallel_calls=AUTOTUNE)\n else:\n ds = tf.data.Dataset.from_tensor_slices((X, y))\n if training:\n ds = ds.shuffle(len(X), seed=SEED, reshuffle_each_iteration=True)\n ds = ds.map(lambda x, t: (tf.cast(x, tf.float32), t), num_parallel_calls=AUTOTUNE)\n return ds.batch(batch_size).prefetch(AUTOTUNE)\n\ndef build_augmentation(level):\n \"\"\"Augmentation block placed INSIDE the model (before Rescaling, values still 0-255). Active only in training.\"\"\"\n if level == \"none\": return None\n cfg = {\"moderate\": dict(trans=0.05, zoom=0.10, contrast=None),\n \"strong\": dict(trans=0.10, zoom=0.15, contrast=0.20)}[level]\n aug = [layers.RandomFlip(\"horizontal_and_vertical\"),\n layers.RandomRotation(0.5, fill_mode=\"nearest\"), # 0.5 * 2*pi = +/-180 degrees\n layers.RandomTranslation(cfg[\"trans\"], cfg[\"trans\"], fill_mode=\"nearest\"),\n layers.RandomZoom(cfg[\"zoom\"], fill_mode=\"nearest\")]\n if cfg[\"contrast\"]: aug.append(layers.RandomContrast(cfg[\"contrast\"]))\n return keras.Sequential(aug, name=f\"augment_{level}\")\n\ntrain_ds = make_dataset(X_tr, y_tr, training=True)\nval_ds = make_dataset(X_val, y_val, training=False) # NO augmentation, NO shuffling (order = y_val)\nprint(\"train batches:\", len(train_ds), \"| val batches:\", len(val_ds), \"| batch size:\", BATCH_SIZE)\n\n# Visual check of augmentation (original vs moderate vs strong)\nxs = tf.cast(X_tr[:6], tf.float32)\nfig, axes = plt.subplots(3, 6, figsize=(13, 6.5))\nfor r, (title, aug) in enumerate([(\"original\", None), (\"moderate\", build_augmentation(\"moderate\")),\n (\"strong\", build_augmentation(\"strong\"))]):\n out = xs if aug is None else aug(xs, training=True)\n for c in range(6):\n axes[r, c].imshow(out[c, ..., 0].numpy(), cmap=\"gray\", vmin=0, vmax=255); axes[r, c].axis(\"off\")\n axes[r, 0].set_title(title, loc=\"left\", fontsize=10)\nplt.suptitle(\"Augmentation examples (training only)\"); plt.tight_layout(); plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:14:39.860776Z","iopub.execute_input":"2026-10-07T17:14:39.861145Z","iopub.status.idle":"2026-10-07T17:14:45.927024Z","shell.execute_reply.started":"2026-10-07T17:14:39.861125Z","shell.execute_reply":"2026-10-07T17:14:45.926313Z"}},"outputs":[{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1791393280.331427 49 gpu_device.cc:2020] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\nI0000 00:00:1791393280.335863 49 gpu_device.cc:2020] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13756 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\n","output_type":"stream"},{"name":"stdout","text":"train batches: 190 | val batches: 48 | batch size: 128\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":12},{"cell_type":"markdown","source":"## 6. Baseline Model M0\nA deliberately simple CNN: 2 × (Conv3×3 + ReLU + MaxPool) → Flatten → Dense(128, ReLU) → **Dense(121, softmax)**. It has **no regularisation and no augmentation**, so it is the reference point for the improvements. Loss: `sparse_categorical_crossentropy` (integer labels), which equals the multi-class log loss. Optimiser: Adam (lr = 1e-3).\n\n**Callbacks (all monitor `val_loss`, which is log loss on validation):**\n- `EarlyStopping(restore_best_weights=True)`: stops when validation log loss stops improving, preventing over-fitting and over-confidence.\n- `ReduceLROnPlateau`: halves the LR when validation loss plateaus, allowing finer convergence.\n- `ModelCheckpoint(save_best_only=True)`: always keeps the best weights on disk. After training we reload them explicitly.","metadata":{}},{"cell_type":"code","source":"# ============================ CELL 12B: SHARED HELPERS + BASELINE M0 ============================\nmodels, histories, results, val_probs, CONFIGS = {}, {}, {}, {}, {}\n\n# ---------- Metric helpers (identical to Kaggle's multi-class log loss) ----------\ndef finalize_probs(p, eps=1e-15):\n \"\"\"Clip to [eps, 1-eps] and renormalise rows (same clipping Kaggle applies).\"\"\"\n p = np.clip(np.asarray(p, dtype=np.float64), eps, 1 - eps)\n return p / p.sum(axis=1, keepdims=True)\n\ndef multiclass_log_loss(y_true, probs):\n probs = finalize_probs(probs)\n return float(-np.mean(np.log(probs[np.arange(len(y_true)), y_true])))\n\ndef count_params(model):\n trainable = int(sum(np.prod(tuple(w.shape)) for w in model.trainable_weights))\n return int(model.count_params()), trainable\n\ndef get_callbacks(name, es_patience, lr_patience):\n ckpt = str(WORK_DIR / f\"{name}_best.weights.h5\")\n return [keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=es_patience, restore_best_weights=True, verbose=1),\n keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\", factor=0.5, patience=lr_patience, min_lr=1e-5, verbose=1),\n keras.callbacks.ModelCheckpoint(ckpt, monitor=\"val_loss\", save_best_only=True, save_weights_only=True, verbose=0)], ckpt\n\ndef compile_model(model, lr=1e-3):\n model.compile(optimizer=keras.optimizers.Adam(lr), loss=\"sparse_categorical_crossentropy\",\n metrics=[\"accuracy\"], jit_compile=False)\n\ndef train_and_log(name, model, arch_desc, hparams, max_epochs, es_patience, lr_patience, lr=1e-3):\n \"\"\"Train on the train split, validate on the val split, reload best weights, record all metrics.\"\"\"\n reset_seeds(); compile_model(model, lr); model.summary()\n total, trainable = count_params(model)\n print(f\"\\n[{name}] total params: {total:,} | trainable: {trainable:,}\")\n cbs, ckpt = get_callbacks(name, es_patience, lr_patience)\n t0 = time.time()\n hist = model.fit(train_ds, validation_data=val_ds, epochs=max_epochs, callbacks=cbs, verbose=2)\n train_time = time.time() - t0\n model.load_weights(ckpt) # guarantee best-val-loss weights\n h = hist.history; best_epoch = int(np.argmin(h[\"val_loss\"])) + 1\n probs = model.predict(val_ds, verbose=0)\n pred = probs.argmax(1)\n models[name], histories[name], val_probs[name] = model, h, probs\n results[name] = dict(arch=arch_desc, hparams=hparams, total_params=total, trainable_params=trainable,\n train_time=train_time, epochs_run=len(h[\"loss\"]), best_epoch=best_epoch,\n best_hist_val_acc=float(np.max(h[\"val_accuracy\"])),\n val_acc=float(np.mean(pred == y_val)), val_ll=multiclass_log_loss(y_val, probs))\n r = results[name]\n print(f\"\\n[{name}] training time: {train_time/60:.1f} min | epochs run: {r['epochs_run']} | best epoch: {best_epoch}\")\n print(f\"[{name}] best val accuracy (history): {r['best_hist_val_acc']:.4f} | \"\n f\"val accuracy (best-loss weights): {r['val_acc']:.4f} | val LOG LOSS: {r['val_ll']:.4f}\")\n\n# ---------- M0 ----------\ndef build_m0():\n reset_seeds()\n inp = keras.Input((IMG_SIZE, IMG_SIZE, 1))\n x = layers.Rescaling(1 / 255.0)(inp)\n x = layers.Conv2D(32, 3, padding=\"same\", activation=\"relu\")(x); x = layers.MaxPooling2D()(x)\n x = layers.Conv2D(64, 3, padding=\"same\", activation=\"relu\")(x); x = layers.MaxPooling2D()(x)\n x = layers.Flatten()(x)\n x = layers.Dense(128, activation=\"relu\")(x)\n out = layers.Dense(NUM_CLASSES, activation=\"softmax\", name=\"softmax_out\")(x)\n return keras.Model(inp, out, name=\"M0_baseline\")\n\nCONFIGS[\"M0\"] = dict(builder=build_m0, arch=\"2xConv(32,64)+MaxPool, Flatten, Dense128, softmax\",\n hparams=\"Adam 1e-3, bs128, no aug, no dropout\", max_epochs=30, es=6, lr=3)\nc = CONFIGS[\"M0\"]\ntrain_and_log(\"M0\", build_m0(), c[\"arch\"], c[\"hparams\"], c[\"max_epochs\"], c[\"es\"], c[\"lr\"])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:14:45.928047Z","iopub.execute_input":"2026-10-07T17:14:45.928376Z","iopub.status.idle":"2026-10-07T17:15:34.890125Z","shell.execute_reply.started":"2026-10-07T17:14:45.928338Z","shell.execute_reply":"2026-10-07T17:15:34.889367Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"M0_baseline\"\u001b[0m\n","text/html":"
Model: \"M0_baseline\"\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer_2 (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ rescaling (\u001b[38;5;33mRescaling\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m320\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m2,097,280\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ softmax_out (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m121\u001b[0m) │ \u001b[38;5;34m15,609\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer_2 (InputLayer)      │ (None, 64, 64, 1)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ rescaling (Rescaling)           │ (None, 64, 64, 1)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d (Conv2D)                 │ (None, 64, 64, 32)     │           320 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d (MaxPooling2D)    │ (None, 32, 32, 32)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_1 (Conv2D)               │ (None, 32, 32, 64)     │        18,496 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_1 (MaxPooling2D)  │ (None, 16, 16, 64)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten (Flatten)               │ (None, 16384)          │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (Dense)                   │ (None, 128)            │     2,097,280 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ softmax_out (Dense)             │ (None, 121)            │        15,609 │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,131,705\u001b[0m (8.13 MB)\n","text/html":"
 Total params: 2,131,705 (8.13 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,131,705\u001b[0m (8.13 MB)\n","text/html":"
 Trainable params: 2,131,705 (8.13 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n","text/html":"
 Non-trainable params: 0 (0.00 B)\n
\n"},"metadata":{}},{"name":"stdout","text":"\n[M0] total params: 2,131,705 | trainable: 2,131,705\nEpoch 1/30\n190/190 - 7s - 37ms/step - accuracy: 0.2228 - loss: 3.4409 - val_accuracy: 0.3387 - val_loss: 2.6945 - learning_rate: 0.0010\nEpoch 2/30\n190/190 - 3s - 15ms/step - accuracy: 0.3874 - loss: 2.4398 - val_accuracy: 0.4227 - val_loss: 2.2460 - learning_rate: 0.0010\nEpoch 3/30\n190/190 - 3s - 15ms/step - accuracy: 0.4492 - loss: 2.1011 - val_accuracy: 0.4572 - val_loss: 2.0784 - learning_rate: 0.0010\nEpoch 4/30\n190/190 - 3s - 15ms/step - accuracy: 0.4908 - loss: 1.8902 - val_accuracy: 0.4702 - val_loss: 1.9579 - learning_rate: 0.0010\nEpoch 5/30\n190/190 - 3s - 15ms/step - accuracy: 0.5278 - loss: 1.7344 - val_accuracy: 0.5058 - val_loss: 1.8602 - learning_rate: 0.0010\nEpoch 6/30\n190/190 - 3s - 15ms/step - accuracy: 0.5516 - loss: 1.6010 - val_accuracy: 0.5125 - val_loss: 1.8352 - learning_rate: 0.0010\nEpoch 7/30\n190/190 - 3s - 15ms/step - accuracy: 0.5823 - loss: 1.4877 - val_accuracy: 0.5262 - val_loss: 1.7807 - learning_rate: 0.0010\nEpoch 8/30\n190/190 - 3s - 14ms/step - accuracy: 0.6052 - loss: 1.3929 - val_accuracy: 0.5218 - val_loss: 1.8180 - learning_rate: 0.0010\nEpoch 9/30\n190/190 - 3s - 14ms/step - accuracy: 0.6234 - loss: 1.3094 - val_accuracy: 0.5255 - val_loss: 1.7919 - learning_rate: 0.0010\nEpoch 10/30\n190/190 - 3s - 15ms/step - accuracy: 0.6482 - loss: 1.2198 - val_accuracy: 0.5321 - val_loss: 1.7660 - learning_rate: 0.0010\nEpoch 11/30\n190/190 - 3s - 14ms/step - accuracy: 0.6630 - loss: 1.1516 - val_accuracy: 0.5461 - val_loss: 1.7714 - learning_rate: 0.0010\nEpoch 12/30\n190/190 - 3s - 14ms/step - accuracy: 0.6809 - loss: 1.0805 - val_accuracy: 0.5468 - val_loss: 1.7865 - learning_rate: 0.0010\nEpoch 13/30\n\nEpoch 13: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n190/190 - 3s - 14ms/step - accuracy: 0.7044 - loss: 1.0036 - val_accuracy: 0.5405 - val_loss: 1.8318 - learning_rate: 0.0010\nEpoch 14/30\n190/190 - 3s - 14ms/step - accuracy: 0.7472 - loss: 0.8654 - val_accuracy: 0.5544 - val_loss: 1.8113 - learning_rate: 5.0000e-04\nEpoch 15/30\n190/190 - 3s - 14ms/step - accuracy: 0.7622 - loss: 0.8134 - val_accuracy: 0.5491 - val_loss: 1.8496 - learning_rate: 5.0000e-04\nEpoch 16/30\n\nEpoch 16: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n190/190 - 3s - 14ms/step - accuracy: 0.7727 - loss: 0.7774 - val_accuracy: 0.5514 - val_loss: 1.8966 - learning_rate: 5.0000e-04\nEpoch 16: early stopping\nRestoring model weights from the end of the best epoch: 10.\n\n[M0] training time: 0.8 min | epochs run: 16 | best epoch: 10\n[M0] best val accuracy (history): 0.5544 | val accuracy (best-loss weights): 0.5321 | val LOG LOSS: 1.7660\n","output_type":"stream"}],"execution_count":13},{"cell_type":"markdown","source":"## 7. M0 Learning Curves\n**How to read them:** *Underfitting* = both training and validation scores are poor and still improving. *Overfitting* = training keeps improving while validation stagnates or gets worse (a rising validation loss also means growing over-confidence, which is costly for log loss). *Convergence* = both curves flatten. The dashed line marks the epoch whose weights were restored (lowest validation loss). Note that with augmentation or L2 the *training* numbers are measured on harder/regularised data, so the gap is not directly comparable.","metadata":{}},{"cell_type":"code","source":"# ============================ CELL 13B: LEARNING CURVES (reusable) ============================\ndef plot_learning_curves(name):\n h, best = histories[name], results[name][\"best_epoch\"]\n ep = range(1, len(h[\"loss\"]) + 1)\n fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))\n ax[0].plot(ep, h[\"accuracy\"], label=\"train accuracy\"); ax[0].plot(ep, h[\"val_accuracy\"], label=\"validation accuracy\")\n ax[0].axvline(best, ls=\"--\", c=\"gray\", label=f\"best epoch ({best})\"); ax[0].set_title(f\"{name}: accuracy\")\n ax[1].plot(ep, h[\"loss\"], label=\"train loss\"); ax[1].plot(ep, h[\"val_loss\"], label=\"validation loss (log loss)\")\n ax[1].axvline(best, ls=\"--\", c=\"gray\", label=f\"best epoch ({best})\"); ax[1].set_title(f\"{name}: loss\")\n for a in ax: a.set_xlabel(\"epoch\"); a.legend(); a.grid(alpha=.3)\n plt.tight_layout(); plt.show()\n\ndef diagnose(name):\n \"\"\"Programmatic fit diagnosis using real numbers from the history.\"\"\"\n h, b = histories[name], results[name][\"best_epoch\"] - 1\n tr_acc, va_acc = h[\"accuracy\"][b], h[\"val_accuracy\"][b]\n gap_acc, gap_loss = tr_acc - va_acc, h[\"val_loss\"][b] - h[\"loss\"][b]\n rise = h[\"val_loss\"][-1] - h[\"val_loss\"][b]\n print(f\"[{name}] at best epoch {b+1}: train acc={tr_acc:.3f}, val acc={va_acc:.3f}, \"\n f\"acc gap={gap_acc:+.3f}, loss gap (val-train)={gap_loss:+.3f}\")\n print(f\"[{name}] val loss at last epoch minus best = {rise:+.3f}\")\n if va_acc < 0.5 and tr_acc < 0.5: verdict = \"UNDERFITTING (both scores low).\"\n elif gap_acc > 0.10 or gap_loss > 0.30: verdict = \"OVERFITTING (large train/validation gap).\"\n else: verdict = \"reasonably GOOD FIT (small generalisation gap).\"\n print(f\"[{name}] diagnosis: {verdict}\")\n if rise > 0.05: print(f\"[{name}] validation loss rose after the best epoch -> over-fitting set in; best weights were restored.\")\n\nplot_learning_curves(\"M0\"); diagnose(\"M0\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:15:34.891205Z","iopub.execute_input":"2026-10-07T17:15:34.891619Z","iopub.status.idle":"2026-10-07T17:15:35.259388Z","shell.execute_reply.started":"2026-10-07T17:15:34.891588Z","shell.execute_reply":"2026-10-07T17:15:35.258381Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}},{"name":"stdout","text":"[M0] at best epoch 10: train acc=0.648, val acc=0.532, acc gap=+0.116, loss gap (val-train)=+0.546\n[M0] val loss at last epoch minus best = +0.131\n[M0] diagnosis: OVERFITTING (large train/validation gap).\n[M0] validation loss rose after the best epoch -> over-fitting set in; best weights were restored.\n","output_type":"stream"}],"execution_count":14},{"cell_type":"code","source":"# ============================ CELL 14: VALIDATION EVALUATION (reusable) ============================\nevals = {}\n\ndef evaluate_model(name, top_n=10):\n probs = finalize_probs(val_probs[name]); pred = probs.argmax(1)\n labels = np.arange(NUM_CLASSES)\n acc = accuracy_score(y_val, pred); ll = multiclass_log_loss(y_val, probs)\n ll_sk = sk_log_loss(y_val, probs, labels=labels) # cross-check with scikit-learn\n pm, rm, fm, _ = precision_recall_fscore_support(y_val, pred, labels=labels, average=\"macro\", zero_division=0)\n pw, rw, fw, _ = precision_recall_fscore_support(y_val, pred, labels=labels, average=\"weighted\", zero_division=0)\n p, r, f, s = precision_recall_fscore_support(y_val, pred, labels=labels, average=None, zero_division=0)\n class_df = pd.DataFrame({\"class\": class_names, \"precision\": p, \"recall\": r, \"f1\": f, \"support\": s})\n cm = confusion_matrix(y_val, pred, labels=labels)\n off = cm.copy(); np.fill_diagonal(off, 0)\n rows_, cols_ = np.unravel_index(np.argsort(off, axis=None)[::-1][:top_n], off.shape)\n top_conf = pd.DataFrame({\"true\": [class_names[i] for i in rows_], \"predicted\": [class_names[j] for j in cols_],\n \"count\": off[rows_, cols_], \"% of true class\": 100 * off[rows_, cols_] / cm.sum(1)[rows_]})\n evals[name] = dict(acc=acc, ll=ll, p_macro=pm, r_macro=rm, f1_macro=fm, p_w=pw, r_w=rw, f1_w=fw,\n cm=cm, class_df=class_df, top_conf=top_conf)\n print(\"=\" * 60); print(f\"{name}: VALIDATION EVALUATION\"); print(\"=\" * 60)\n print(f\"Accuracy : {acc:.4f}\")\n print(f\"Multi-class log loss : {ll:.4f} (sklearn cross-check: {ll_sk:.4f}) <- competition metric, lower is better\")\n print(f\"Precision macro/weighted: {pm:.4f} / {pw:.4f}\")\n print(f\"Recall macro/weighted: {rm:.4f} / {rw:.4f}\")\n print(f\"F1 macro/weighted: {fm:.4f} / {fw:.4f}\")\n print(\"\\nMost confused class pairs (true -> predicted):\"); display(top_conf)\n print(\"Lowest-F1 classes:\"); display(class_df.sort_values(\"f1\").head(10))\n cmn = cm / cm.sum(1, keepdims=True)\n plt.figure(figsize=(9, 8)); plt.imshow(cmn, cmap=\"viridis\", vmin=0, vmax=1)\n plt.colorbar(label=\"fraction of true class\"); plt.title(f\"{name}: row-normalised confusion matrix ({NUM_CLASSES} classes)\")\n plt.xlabel(\"Predicted class index\"); plt.ylabel(\"True class index\"); plt.tight_layout(); plt.show()\n results[name].update(f1_macro=fm, f1_weighted=fw, precision_macro=pm, recall_macro=rm)\n\nevaluate_model(\"M0\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:15:35.260683Z","iopub.execute_input":"2026-10-07T17:15:35.261038Z","iopub.status.idle":"2026-10-07T17:15:35.588606Z","shell.execute_reply.started":"2026-10-07T17:15:35.261015Z","shell.execute_reply":"2026-10-07T17:15:35.587857Z"}},"outputs":[{"name":"stdout","text":"============================================================\nM0: VALIDATION EVALUATION\n============================================================\nAccuracy : 0.5321\nMulti-class log loss : 1.7660 (sklearn cross-check: 1.7660) <- competition metric, lower is better\nPrecision macro/weighted: 0.3506 / 0.5195\nRecall macro/weighted: 0.3287 / 0.5321\nF1 macro/weighted: 0.3247 / 0.5131\n\nMost confused class pairs (true -> predicted):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" true predicted count \\\n0 chaetognath_sagitta chaetognath_other 60 \n1 protist_noctiluca protist_other 42 \n2 chaetognath_other chaetognath_non_sagitta 40 \n3 chaetognath_non_sagitta chaetognath_other 40 \n4 chaetognath_other diatom_chain_tube 37 \n5 copepod_cyclopoid_oithona_eggs copepod_cyclopoid_oithona 36 \n6 detritus_other protist_other 33 \n7 tunicate_doliolid protist_other 29 \n8 protist_other protist_noctiluca 29 \n9 appendicularian_slight_curve appendicularian_s_shape 29 \n\n % of true class \n0 43.165468 \n1 33.600000 \n2 10.335917 \n3 24.539877 \n4 9.560724 \n5 15.126050 \n6 18.032787 \n7 32.954545 \n8 12.393162 \n9 27.358491 ","text/html":"
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truepredictedcount% of true class
0chaetognath_sagittachaetognath_other6043.165468
1protist_noctilucaprotist_other4233.600000
2chaetognath_otherchaetognath_non_sagitta4010.335917
3chaetognath_non_sagittachaetognath_other4024.539877
4chaetognath_otherdiatom_chain_tube379.560724
5copepod_cyclopoid_oithona_eggscopepod_cyclopoid_oithona3615.126050
6detritus_otherprotist_other3318.032787
7tunicate_doliolidprotist_other2932.954545
8protist_otherprotist_noctiluca2912.393162
9appendicularian_slight_curveappendicularian_s_shape2927.358491
\n
"},"metadata":{}},{"name":"stdout","text":"Lowest-F1 classes:\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" class precision recall f1 support\n1 acantharia_protist_big_center 0.0 0.0 0.0 2\n4 appendicularian_fritillaridae 0.0 0.0 0.0 3\n30 ctenophore_cydippid_tentacles 0.0 0.0 0.0 11\n29 ctenophore_cydippid_no_tentacles 0.0 0.0 0.0 8\n26 copepod_other 0.0 0.0 0.0 5\n62 hydromedusae_liriope 0.0 0.0 0.0 4\n63 hydromedusae_narco_dark 0.0 0.0 0.0 4\n56 heteropod 0.0 0.0 0.0 2\n51 fish_larvae_leptocephali 0.0 0.0 0.0 6\n50 fish_larvae_deep_body 0.0 0.0 0.0 2","text/html":"
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classprecisionrecallf1support
1acantharia_protist_big_center0.00.00.02
4appendicularian_fritillaridae0.00.00.03
30ctenophore_cydippid_tentacles0.00.00.011
29ctenophore_cydippid_no_tentacles0.00.00.08
26copepod_other0.00.00.05
62hydromedusae_liriope0.00.00.04
63hydromedusae_narco_dark0.00.00.04
56heteropod0.00.00.02
51fish_larvae_leptocephali0.00.00.06
50fish_larvae_deep_body0.00.00.02
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":15},{"cell_type":"markdown","source":"## 8. Improved Model M1 – justified changes over M0\n| Change | Justification (weakness of M0 it addresses) |\n|---|---|\n| 4 convolution blocks (32→64→128→256) | Deeper hierarchy: edges → textures → parts → whole organisms. |\n| **Batch Normalisation** after each conv | Stabilises/accelerates training and allows a higher LR; mild regulariser. |\n| **Global Average Pooling** instead of Flatten | Removes the huge Flatten→Dense weight matrix (the main source of M0's over-fitting) and adds translation tolerance. |\n| **Dropout** 0.4 / 0.3 | Discourages co-adaptation and over-confident outputs (better log loss). |\n| **Moderate augmentation** | M0 has none; augmentation exploits the rotation/flip invariance of plankton. |\nTraining: Adam 1e-3, EarlyStopping, ReduceLROnPlateau, ModelCheckpoint, all monitoring validation log loss.","metadata":{}},{"cell_type":"code","source":"# ============================ CELL 15B: M1 ============================\ndef conv_bn_relu(x, filters, l2=0.0):\n reg = keras.regularizers.l2(l2) if l2 > 0 else None\n x = layers.Conv2D(filters, 3, padding=\"same\", use_bias=False, kernel_regularizer=reg)(x) # BN supplies the bias\n x = layers.BatchNormalization()(x)\n return layers.ReLU()(x)\n\ndef build_m1():\n reset_seeds()\n inp = keras.Input((IMG_SIZE, IMG_SIZE, 1))\n x = build_augmentation(\"moderate\")(inp) # training-only augmentation\n x = layers.Rescaling(1 / 255.0)(x)\n for f in [32, 64, 128, 256]:\n x = conv_bn_relu(x, f); x = layers.MaxPooling2D()(x)\n x = layers.GlobalAveragePooling2D()(x)\n x = layers.Dropout(0.4)(x)\n x = layers.Dense(128, activation=\"relu\")(x)\n x = layers.Dropout(0.3)(x)\n out = layers.Dense(NUM_CLASSES, activation=\"softmax\", name=\"softmax_out\")(x)\n return keras.Model(inp, out, name=\"M1_bn_gap_dropout\")\n\nCONFIGS[\"M1\"] = dict(builder=build_m1, arch=\"4x[Conv-BN-ReLU-Pool] (32-256), GAP, Dropout .4, Dense128, Dropout .3, softmax\",\n hparams=\"Adam 1e-3, bs128, moderate aug, ES patience 10, ReduceLR patience 4\", max_epochs=60, es=10, lr=4)\nc = CONFIGS[\"M1\"]\ntrain_and_log(\"M1\", build_m1(), c[\"arch\"], c[\"hparams\"], c[\"max_epochs\"], c[\"es\"], c[\"lr\"])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:15:35.589527Z","iopub.execute_input":"2026-10-07T17:15:35.589917Z","iopub.status.idle":"2026-10-07T17:24:12.01449Z","shell.execute_reply.started":"2026-10-07T17:15:35.589897Z","shell.execute_reply":"2026-10-07T17:24:12.013698Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"M1_bn_gap_dropout\"\u001b[0m\n","text/html":"
Model: \"M1_bn_gap_dropout\"\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer_3 (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ augment_moderate (\u001b[38;5;33mSequential\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ rescaling_1 (\u001b[38;5;33mRescaling\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m288\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,432\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_1 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,728\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_2 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m294,912\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_3 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_5 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m32,896\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ softmax_out (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m121\u001b[0m) │ \u001b[38;5;34m15,609\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer_3 (InputLayer)      │ (None, 64, 64, 1)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ augment_moderate (Sequential)   │ (None, 64, 64, 1)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ rescaling_1 (Rescaling)         │ (None, 64, 64, 1)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_2 (Conv2D)               │ (None, 64, 64, 32)     │           288 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization             │ (None, 64, 64, 32)     │           128 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu (ReLU)                    │ (None, 64, 64, 32)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_2 (MaxPooling2D)  │ (None, 32, 32, 32)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_3 (Conv2D)               │ (None, 32, 32, 64)     │        18,432 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_1           │ (None, 32, 32, 64)     │           256 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_1 (ReLU)                  │ (None, 32, 32, 64)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_3 (MaxPooling2D)  │ (None, 16, 16, 64)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_4 (Conv2D)               │ (None, 16, 16, 128)    │        73,728 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_2           │ (None, 16, 16, 128)    │           512 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_2 (ReLU)                  │ (None, 16, 16, 128)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_4 (MaxPooling2D)  │ (None, 8, 8, 128)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_5 (Conv2D)               │ (None, 8, 8, 256)      │       294,912 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_3           │ (None, 8, 8, 256)      │         1,024 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_3 (ReLU)                  │ (None, 8, 8, 256)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_5 (MaxPooling2D)  │ (None, 4, 4, 256)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d        │ (None, 256)            │             0 │\n│ (GlobalAveragePooling2D)        │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout (Dropout)               │ (None, 256)            │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_1 (Dense)                 │ (None, 128)            │        32,896 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_1 (Dropout)             │ (None, 128)            │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ softmax_out (Dense)             │ (None, 121)            │        15,609 │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m437,785\u001b[0m (1.67 MB)\n","text/html":"
 Total params: 437,785 (1.67 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m436,825\u001b[0m (1.67 MB)\n","text/html":"
 Trainable params: 436,825 (1.67 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m960\u001b[0m (3.75 KB)\n","text/html":"
 Non-trainable params: 960 (3.75 KB)\n
\n"},"metadata":{}},{"name":"stdout","text":"\n[M1] total params: 437,785 | trainable: 436,825\nEpoch 1/60\n190/190 - 13s - 68ms/step - accuracy: 0.1767 - loss: 3.6505 - val_accuracy: 0.0638 - val_loss: 16.8371 - learning_rate: 0.0010\nEpoch 2/60\n190/190 - 8s - 44ms/step - accuracy: 0.2856 - loss: 2.8562 - val_accuracy: 0.0644 - val_loss: 31.8023 - learning_rate: 0.0010\nEpoch 3/60\n190/190 - 8s - 44ms/step - accuracy: 0.3376 - loss: 2.5399 - val_accuracy: 0.0649 - val_loss: 42.2084 - learning_rate: 0.0010\nEpoch 4/60\n190/190 - 8s - 45ms/step - accuracy: 0.3672 - loss: 2.3581 - val_accuracy: 0.0666 - val_loss: 33.2464 - learning_rate: 0.0010\nEpoch 5/60\n\nEpoch 5: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n190/190 - 9s - 45ms/step - accuracy: 0.3967 - loss: 2.2302 - val_accuracy: 0.0681 - val_loss: 29.3817 - learning_rate: 0.0010\nEpoch 6/60\n190/190 - 9s - 46ms/step - accuracy: 0.4215 - loss: 2.0832 - val_accuracy: 0.1833 - val_loss: 4.2487 - learning_rate: 5.0000e-04\nEpoch 7/60\n190/190 - 9s - 46ms/step - accuracy: 0.4354 - loss: 2.0190 - val_accuracy: 0.1505 - val_loss: 4.5584 - learning_rate: 5.0000e-04\nEpoch 8/60\n190/190 - 9s - 47ms/step - accuracy: 0.4439 - loss: 1.9674 - val_accuracy: 0.2355 - val_loss: 3.4547 - learning_rate: 5.0000e-04\nEpoch 9/60\n190/190 - 9s - 47ms/step - accuracy: 0.4563 - loss: 1.9184 - val_accuracy: 0.0185 - val_loss: 22.3779 - learning_rate: 5.0000e-04\nEpoch 10/60\n190/190 - 9s - 46ms/step - accuracy: 0.4646 - loss: 1.8807 - val_accuracy: 0.1193 - val_loss: 6.5807 - learning_rate: 5.0000e-04\nEpoch 11/60\n190/190 - 9s - 46ms/step - accuracy: 0.4745 - loss: 1.8333 - val_accuracy: 0.0194 - val_loss: 20.6431 - learning_rate: 5.0000e-04\nEpoch 12/60\n\nEpoch 12: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n190/190 - 9s - 46ms/step - accuracy: 0.4793 - loss: 1.8058 - val_accuracy: 0.1829 - val_loss: 5.3264 - learning_rate: 5.0000e-04\nEpoch 13/60\n190/190 - 9s - 46ms/step - accuracy: 0.4965 - loss: 1.7469 - val_accuracy: 0.2325 - val_loss: 3.1788 - learning_rate: 2.5000e-04\nEpoch 14/60\n190/190 - 9s - 47ms/step - accuracy: 0.5031 - loss: 1.7221 - val_accuracy: 0.0300 - val_loss: 13.3054 - learning_rate: 2.5000e-04\nEpoch 15/60\n190/190 - 9s - 47ms/step - accuracy: 0.5062 - loss: 1.7056 - val_accuracy: 0.2805 - val_loss: 2.8719 - learning_rate: 2.5000e-04\nEpoch 16/60\n190/190 - 9s - 47ms/step - accuracy: 0.5123 - loss: 1.6827 - val_accuracy: 0.3405 - val_loss: 2.4006 - learning_rate: 2.5000e-04\nEpoch 17/60\n190/190 - 9s - 47ms/step - accuracy: 0.5159 - loss: 1.6668 - val_accuracy: 0.4224 - val_loss: 2.0604 - learning_rate: 2.5000e-04\nEpoch 18/60\n190/190 - 9s - 47ms/step - accuracy: 0.5207 - loss: 1.6451 - val_accuracy: 0.2161 - val_loss: 3.8253 - learning_rate: 2.5000e-04\nEpoch 19/60\n190/190 - 9s - 47ms/step - accuracy: 0.5206 - loss: 1.6397 - val_accuracy: 0.0783 - val_loss: 7.9932 - learning_rate: 2.5000e-04\nEpoch 20/60\n190/190 - 9s - 47ms/step - accuracy: 0.5272 - loss: 1.6183 - val_accuracy: 0.0226 - val_loss: 19.7766 - learning_rate: 2.5000e-04\nEpoch 21/60\n\nEpoch 21: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n190/190 - 9s - 46ms/step - accuracy: 0.5276 - loss: 1.6087 - val_accuracy: 0.2233 - val_loss: 3.7377 - learning_rate: 2.5000e-04\nEpoch 22/60\n190/190 - 9s - 47ms/step - accuracy: 0.5383 - loss: 1.5775 - val_accuracy: 0.4618 - val_loss: 1.8650 - learning_rate: 1.2500e-04\nEpoch 23/60\n190/190 - 9s - 46ms/step - accuracy: 0.5401 - loss: 1.5715 - val_accuracy: 0.2314 - val_loss: 4.2206 - learning_rate: 1.2500e-04\nEpoch 24/60\n190/190 - 9s - 46ms/step - accuracy: 0.5428 - loss: 1.5608 - val_accuracy: 0.0836 - val_loss: 10.2488 - learning_rate: 1.2500e-04\nEpoch 25/60\n190/190 - 9s - 46ms/step - accuracy: 0.5416 - loss: 1.5606 - val_accuracy: 0.3881 - val_loss: 2.2268 - learning_rate: 1.2500e-04\nEpoch 26/60\n\nEpoch 26: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n190/190 - 9s - 46ms/step - accuracy: 0.5476 - loss: 1.5447 - val_accuracy: 0.3093 - val_loss: 2.7607 - learning_rate: 1.2500e-04\nEpoch 27/60\n190/190 - 9s - 47ms/step - accuracy: 0.5490 - loss: 1.5390 - val_accuracy: 0.4927 - val_loss: 1.7143 - learning_rate: 6.2500e-05\nEpoch 28/60\n190/190 - 9s - 46ms/step - accuracy: 0.5506 - loss: 1.5337 - val_accuracy: 0.1684 - val_loss: 4.6300 - learning_rate: 6.2500e-05\nEpoch 29/60\n190/190 - 9s - 46ms/step - accuracy: 0.5514 - loss: 1.5204 - val_accuracy: 0.3031 - val_loss: 2.7178 - learning_rate: 6.2500e-05\nEpoch 30/60\n190/190 - 9s - 46ms/step - accuracy: 0.5515 - loss: 1.5259 - val_accuracy: 0.5043 - val_loss: 1.6853 - learning_rate: 6.2500e-05\nEpoch 31/60\n190/190 - 9s - 46ms/step - accuracy: 0.5558 - loss: 1.5133 - val_accuracy: 0.4026 - val_loss: 2.2210 - learning_rate: 6.2500e-05\nEpoch 32/60\n190/190 - 9s - 46ms/step - accuracy: 0.5534 - loss: 1.5040 - val_accuracy: 0.2749 - val_loss: 3.2345 - learning_rate: 6.2500e-05\nEpoch 33/60\n190/190 - 9s - 46ms/step - accuracy: 0.5546 - loss: 1.5153 - val_accuracy: 0.1628 - val_loss: 5.4257 - learning_rate: 6.2500e-05\nEpoch 34/60\n\nEpoch 34: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n190/190 - 9s - 47ms/step - accuracy: 0.5543 - loss: 1.5059 - val_accuracy: 0.4191 - val_loss: 2.1798 - learning_rate: 6.2500e-05\nEpoch 35/60\n190/190 - 9s - 46ms/step - accuracy: 0.5558 - loss: 1.5047 - val_accuracy: 0.2533 - val_loss: 3.2375 - learning_rate: 3.1250e-05\nEpoch 36/60\n190/190 - 9s - 46ms/step - accuracy: 0.5578 - loss: 1.5049 - val_accuracy: 0.2528 - val_loss: 3.1632 - learning_rate: 3.1250e-05\nEpoch 37/60\n190/190 - 9s - 46ms/step - accuracy: 0.5543 - loss: 1.4963 - val_accuracy: 0.4466 - val_loss: 1.9279 - learning_rate: 3.1250e-05\nEpoch 38/60\n190/190 - 9s - 47ms/step - accuracy: 0.5560 - loss: 1.4923 - val_accuracy: 0.5140 - val_loss: 1.6557 - learning_rate: 3.1250e-05\nEpoch 39/60\n190/190 - 9s - 47ms/step - accuracy: 0.5594 - loss: 1.4872 - val_accuracy: 0.5199 - val_loss: 1.6446 - learning_rate: 3.1250e-05\nEpoch 40/60\n190/190 - 9s - 47ms/step - accuracy: 0.5612 - loss: 1.4875 - val_accuracy: 0.4792 - val_loss: 1.8681 - learning_rate: 3.1250e-05\nEpoch 41/60\n190/190 - 9s - 47ms/step - accuracy: 0.5587 - loss: 1.4901 - val_accuracy: 0.4847 - val_loss: 1.8116 - learning_rate: 3.1250e-05\nEpoch 42/60\n190/190 - 9s - 47ms/step - accuracy: 0.5591 - loss: 1.4856 - val_accuracy: 0.5038 - val_loss: 1.7233 - learning_rate: 3.1250e-05\nEpoch 43/60\n\nEpoch 43: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n190/190 - 9s - 47ms/step - accuracy: 0.5603 - loss: 1.4798 - val_accuracy: 0.1721 - val_loss: 4.9575 - learning_rate: 3.1250e-05\nEpoch 44/60\n190/190 - 9s - 47ms/step - accuracy: 0.5616 - loss: 1.4789 - val_accuracy: 0.4911 - val_loss: 1.7487 - learning_rate: 1.5625e-05\nEpoch 45/60\n190/190 - 9s - 47ms/step - accuracy: 0.5642 - loss: 1.4811 - val_accuracy: 0.5132 - val_loss: 1.6590 - learning_rate: 1.5625e-05\nEpoch 46/60\n190/190 - 9s - 47ms/step - accuracy: 0.5629 - loss: 1.4829 - val_accuracy: 0.5023 - val_loss: 1.6967 - learning_rate: 1.5625e-05\nEpoch 47/60\n\nEpoch 47: ReduceLROnPlateau reducing learning rate to 1e-05.\n190/190 - 9s - 47ms/step - accuracy: 0.5646 - loss: 1.4758 - val_accuracy: 0.5199 - val_loss: 1.6531 - learning_rate: 1.5625e-05\nEpoch 48/60\n190/190 - 9s - 47ms/step - accuracy: 0.5647 - loss: 1.4716 - val_accuracy: 0.5269 - val_loss: 1.5975 - learning_rate: 1.0000e-05\nEpoch 49/60\n190/190 - 9s - 46ms/step - accuracy: 0.5650 - loss: 1.4756 - val_accuracy: 0.5307 - val_loss: 1.6043 - learning_rate: 1.0000e-05\nEpoch 50/60\n190/190 - 9s - 46ms/step - accuracy: 0.5633 - loss: 1.4797 - val_accuracy: 0.4336 - val_loss: 1.9995 - learning_rate: 1.0000e-05\nEpoch 51/60\n190/190 - 9s - 46ms/step - accuracy: 0.5656 - loss: 1.4751 - val_accuracy: 0.5033 - val_loss: 1.6938 - learning_rate: 1.0000e-05\nEpoch 52/60\n190/190 - 9s - 46ms/step - accuracy: 0.5670 - loss: 1.4712 - val_accuracy: 0.5119 - val_loss: 1.6622 - learning_rate: 1.0000e-05\nEpoch 53/60\n190/190 - 9s - 46ms/step - accuracy: 0.5673 - loss: 1.4704 - val_accuracy: 0.4764 - val_loss: 1.8090 - learning_rate: 1.0000e-05\nEpoch 54/60\n190/190 - 9s - 46ms/step - accuracy: 0.5657 - loss: 1.4670 - val_accuracy: 0.4962 - val_loss: 1.7607 - learning_rate: 1.0000e-05\nEpoch 55/60\n190/190 - 9s - 46ms/step - accuracy: 0.5668 - loss: 1.4573 - val_accuracy: 0.4746 - val_loss: 1.8125 - learning_rate: 1.0000e-05\nEpoch 56/60\n190/190 - 9s - 47ms/step - accuracy: 0.5638 - loss: 1.4641 - val_accuracy: 0.4893 - val_loss: 1.7691 - learning_rate: 1.0000e-05\nEpoch 57/60\n190/190 - 9s - 47ms/step - accuracy: 0.5633 - loss: 1.4673 - val_accuracy: 0.5257 - val_loss: 1.6213 - learning_rate: 1.0000e-05\nEpoch 58/60\n190/190 - 9s - 46ms/step - accuracy: 0.5623 - loss: 1.4753 - val_accuracy: 0.4469 - val_loss: 1.9354 - learning_rate: 1.0000e-05\nEpoch 58: early stopping\nRestoring model weights from the end of the best epoch: 48.\n\n[M1] training time: 8.6 min | epochs run: 58 | best epoch: 48\n[M1] best val accuracy (history): 0.5307 | val accuracy (best-loss weights): 0.5269 | val LOG LOSS: 1.5975\n","output_type":"stream"}],"execution_count":16},{"cell_type":"code","source":"# ============================ CELL 16: M1 LEARNING CURVES ============================\nplot_learning_curves(\"M1\"); diagnose(\"M1\")\nd_ll = results[\"M1\"][\"val_ll\"] - results[\"M0\"][\"val_ll\"]\nd_acc = results[\"M1\"][\"val_acc\"] - results[\"M0\"][\"val_acc\"]\nprint(f\"\\nM1 vs M0: validation log loss {results['M0']['val_ll']:.4f} -> {results['M1']['val_ll']:.4f} \"\n f\"({'IMPROVED' if d_ll < 0 else 'WORSENED'} by {abs(d_ll):.4f}); \"\n f\"accuracy {results['M0']['val_acc']:.4f} -> {results['M1']['val_acc']:.4f} ({d_acc:+.4f})\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:24:12.015689Z","iopub.execute_input":"2026-10-07T17:24:12.016043Z","iopub.status.idle":"2026-10-07T17:24:12.408928Z","shell.execute_reply.started":"2026-10-07T17:24:12.01602Z","shell.execute_reply":"2026-10-07T17:24:12.408194Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}},{"name":"stdout","text":"[M1] at best epoch 48: train acc=0.565, val acc=0.527, acc gap=+0.038, loss gap (val-train)=+0.126\n[M1] val loss at last epoch minus best = +0.338\n[M1] diagnosis: reasonably GOOD FIT (small generalisation gap).\n[M1] validation loss rose after the best epoch -> over-fitting set in; best weights were restored.\n\nM1 vs M0: validation log loss 1.7660 -> 1.5975 (IMPROVED by 0.1685); accuracy 0.5321 -> 0.5269 (-0.0053)\n","output_type":"stream"}],"execution_count":17},{"cell_type":"code","source":"# ============================ CELL 17: M1 EVALUATION ============================\nevaluate_model(\"M1\")\nprint(\"\\nDifficult classes for M1 = lowest-F1 table above; confused pairs = 'Most confused class pairs' table.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:24:12.410041Z","iopub.execute_input":"2026-10-07T17:24:12.410733Z","iopub.status.idle":"2026-10-07T17:24:12.729402Z","shell.execute_reply.started":"2026-10-07T17:24:12.41071Z","shell.execute_reply":"2026-10-07T17:24:12.728597Z"}},"outputs":[{"name":"stdout","text":"============================================================\nM1: VALIDATION EVALUATION\n============================================================\nAccuracy : 0.5269\nMulti-class log loss : 1.5975 (sklearn cross-check: 1.5975) <- competition metric, lower is better\nPrecision macro/weighted: 0.3362 / 0.5286\nRecall macro/weighted: 0.3018 / 0.5269\nF1 macro/weighted: 0.2835 / 0.5056\n\nMost confused class pairs (true -> predicted):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" true predicted count \\\n0 chaetognath_sagitta chaetognath_other 63 \n1 protist_other protist_noctiluca 60 \n2 chaetognath_other chaetognath_non_sagitta 52 \n3 chaetognath_non_sagitta chaetognath_other 48 \n4 appendicularian_slight_curve appendicularian_s_shape 40 \n5 copepod_calanoid copepod_calanoid_large 34 \n6 chaetognath_sagitta chaetognath_non_sagitta 31 \n7 diatom_chain_tube chaetognath_other 30 \n8 fecal_pellet diatom_chain_tube 29 \n9 protist_noctiluca protist_other 28 \n\n % of true class \n0 45.323741 \n1 25.641026 \n2 13.436693 \n3 29.447853 \n4 37.735849 \n5 25.000000 \n6 22.302158 \n7 30.000000 \n8 28.431373 \n9 22.400000 ","text/html":"
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truepredictedcount% of true class
0chaetognath_sagittachaetognath_other6345.323741
1protist_otherprotist_noctiluca6025.641026
2chaetognath_otherchaetognath_non_sagitta5213.436693
3chaetognath_non_sagittachaetognath_other4829.447853
4appendicularian_slight_curveappendicularian_s_shape4037.735849
5copepod_calanoidcopepod_calanoid_large3425.000000
6chaetognath_sagittachaetognath_non_sagitta3122.302158
7diatom_chain_tubechaetognath_other3030.000000
8fecal_pelletdiatom_chain_tube2928.431373
9protist_noctilucaprotist_other2822.400000
\n
"},"metadata":{}},{"name":"stdout","text":"Lowest-F1 classes:\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" class precision recall f1 support\n1 acantharia_protist_big_center 0.0 0.0 0.0 2\n4 appendicularian_fritillaridae 0.0 0.0 0.0 3\n21 copepod_calanoid_octomoms 0.0 0.0 0.0 10\n30 ctenophore_cydippid_tentacles 0.0 0.0 0.0 11\n26 copepod_other 0.0 0.0 0.0 5\n29 ctenophore_cydippid_no_tentacles 0.0 0.0 0.0 8\n18 copepod_calanoid_frillyAntennae 0.0 0.0 0.0 13\n56 heteropod 0.0 0.0 0.0 2\n63 hydromedusae_narco_dark 0.0 0.0 0.0 4\n50 fish_larvae_deep_body 0.0 0.0 0.0 2","text/html":"
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classprecisionrecallf1support
1acantharia_protist_big_center0.00.00.02
4appendicularian_fritillaridae0.00.00.03
21copepod_calanoid_octomoms0.00.00.010
30ctenophore_cydippid_tentacles0.00.00.011
26copepod_other0.00.00.05
29ctenophore_cydippid_no_tentacles0.00.00.08
18copepod_calanoid_frillyAntennae0.00.00.013
56heteropod0.00.00.02
63hydromedusae_narco_dark0.00.00.04
50fish_larvae_deep_body0.00.00.02
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}},{"name":"stdout","text":"\nDifficult classes for M1 = lowest-F1 table above; confused pairs = 'Most confused class pairs' table.\n","output_type":"stream"}],"execution_count":18},{"cell_type":"markdown","source":"## 9. Improved Model M2 – designed from M0/M1 diagnostics\nM2 targets the remaining weaknesses visible in the learning curves and error analysis (over-fitting and over-confidence, which hurt log loss; confusion between visually similar classes):\n| Change | Justification |\n|---|---|\n| **Two convolutions per block** (8 convs, VGG-style) | More non-linear capacity per resolution, with fewer parameters than a bigger dense head. Helps separate similar classes. |\n| **L2 weight decay (1e-4)** on conv and output weights | Keeps weights small, which gives smoother, less over-confident probabilities. |\n| **SpatialDropout2D(0.1)** + Dropout(0.5) before softmax | Drops whole feature maps (appropriate for correlated pixels); strong head regularisation. |\n| **Strong augmentation** (translation 10%, zoom 15%, contrast ±20%) | More diverse training views to reduce the generalisation gap. |\n| No hidden Dense layer | The GAP features go straight to the softmax layer, so there are fewer parameters to over-fit. |\n| Longer patience (15 / 5) | Stronger regularisation makes convergence slower and noisier. |\nClass weighting is intentionally **not** used (see Section 5): it would distort the prior probabilities that log loss rewards.","metadata":{}},{"cell_type":"code","source":"# ============================ CELL 18B: M2 ARCHITECTURE ============================\ndef build_m2():\n reset_seeds()\n inp = keras.Input((IMG_SIZE, IMG_SIZE, 1))\n x = build_augmentation(\"strong\")(inp)\n x = layers.Rescaling(1 / 255.0)(x)\n for i, f in enumerate([32, 64, 128, 256]):\n x = conv_bn_relu(x, f, l2=1e-4)\n x = conv_bn_relu(x, f, l2=1e-4)\n x = layers.MaxPooling2D()(x)\n if i > 0: x = layers.SpatialDropout2D(0.1)(x)\n x = layers.GlobalAveragePooling2D()(x)\n x = layers.Dropout(0.5)(x)\n out = layers.Dense(NUM_CLASSES, activation=\"softmax\", kernel_regularizer=keras.regularizers.l2(1e-4),\n name=\"softmax_out\")(x)\n return keras.Model(inp, out, name=\"M2_deep_regularised\")\n\nCONFIGS[\"M2\"] = dict(builder=build_m2, arch=\"4x[2xConv-BN-ReLU + Pool] (32-256), SpatialDropout .1, GAP, Dropout .5, softmax, L2 1e-4\",\n hparams=\"Adam 1e-3, bs128, strong aug, L2 1e-4, ES patience 15, ReduceLR patience 5\", max_epochs=100, es=15, lr=5)\nprint(\"M2 defined. Parameter count will be shown in the training cell.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:24:12.730511Z","iopub.execute_input":"2026-10-07T17:24:12.730921Z","iopub.status.idle":"2026-10-07T17:24:12.738173Z","shell.execute_reply.started":"2026-10-07T17:24:12.730898Z","shell.execute_reply":"2026-10-07T17:24:12.737417Z"}},"outputs":[{"name":"stdout","text":"M2 defined. Parameter count will be shown in the training cell.\n","output_type":"stream"}],"execution_count":19},{"cell_type":"code","source":"# ============================ CELL 19: M2 TRAINING ============================\nc = CONFIGS[\"M2\"]\ntrain_and_log(\"M2\", build_m2(), c[\"arch\"], c[\"hparams\"], c[\"max_epochs\"], c[\"es\"], c[\"lr\"])\ncum = sum(results[k][\"train_time\"] for k in [\"M0\", \"M1\", \"M2\"]) / 60\nprint(f\"\\nCumulative training time of M0+M1+M2: {cum:.1f} min (limit for the final model: 120 min)\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-07T17:24:12.739232Z","iopub.execute_input":"2026-10-07T17:24:12.739812Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"M2_deep_regularised\"\u001b[0m\n","text/html":"
Model: \"M2_deep_regularised\"\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer_5 (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ augment_strong (\u001b[38;5;33mSequential\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ rescaling_2 (\u001b[38;5;33mRescaling\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m288\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_4 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,216\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_5 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,432\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_6 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,864\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_7 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_7 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ spatial_dropout2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mSpatialDropout2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,728\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_8 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,456\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_9 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ spatial_dropout2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mSpatialDropout2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m294,912\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_10 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_10 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m589,824\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_11 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_9 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ spatial_dropout2d_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mSpatialDropout2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ softmax_out (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m121\u001b[0m) │ \u001b[38;5;34m31,097\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer_5 (InputLayer)      │ (None, 64, 64, 1)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ augment_strong (Sequential)     │ (None, 64, 64, 1)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ rescaling_2 (Rescaling)         │ (None, 64, 64, 1)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_6 (Conv2D)               │ (None, 64, 64, 32)     │           288 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_4           │ (None, 64, 64, 32)     │           128 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_4 (ReLU)                  │ (None, 64, 64, 32)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_7 (Conv2D)               │ (None, 64, 64, 32)     │         9,216 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_5           │ (None, 64, 64, 32)     │           128 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_5 (ReLU)                  │ (None, 64, 64, 32)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_6 (MaxPooling2D)  │ (None, 32, 32, 32)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_8 (Conv2D)               │ (None, 32, 32, 64)     │        18,432 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_6           │ (None, 32, 32, 64)     │           256 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_6 (ReLU)                  │ (None, 32, 32, 64)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_9 (Conv2D)               │ (None, 32, 32, 64)     │        36,864 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_7           │ (None, 32, 32, 64)     │           256 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_7 (ReLU)                  │ (None, 32, 32, 64)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_7 (MaxPooling2D)  │ (None, 16, 16, 64)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ spatial_dropout2d               │ (None, 16, 16, 64)     │             0 │\n│ (SpatialDropout2D)              │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_10 (Conv2D)              │ (None, 16, 16, 128)    │        73,728 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_8           │ (None, 16, 16, 128)    │           512 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_8 (ReLU)                  │ (None, 16, 16, 128)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_11 (Conv2D)              │ (None, 16, 16, 128)    │       147,456 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_9           │ (None, 16, 16, 128)    │           512 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_9 (ReLU)                  │ (None, 16, 16, 128)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_8 (MaxPooling2D)  │ (None, 8, 8, 128)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ spatial_dropout2d_1             │ (None, 8, 8, 128)      │             0 │\n│ (SpatialDropout2D)              │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_12 (Conv2D)              │ (None, 8, 8, 256)      │       294,912 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_10          │ (None, 8, 8, 256)      │         1,024 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_10 (ReLU)                 │ (None, 8, 8, 256)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_13 (Conv2D)              │ (None, 8, 8, 256)      │       589,824 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_11          │ (None, 8, 8, 256)      │         1,024 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ re_lu_11 (ReLU)                 │ (None, 8, 8, 256)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_9 (MaxPooling2D)  │ (None, 4, 4, 256)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ spatial_dropout2d_2             │ (None, 4, 4, 256)      │             0 │\n│ (SpatialDropout2D)              │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d_1      │ (None, 256)            │             0 │\n│ (GlobalAveragePooling2D)        │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_2 (Dropout)             │ (None, 256)            │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ softmax_out (Dense)             │ (None, 121)            │        31,097 │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,205,657\u001b[0m (4.60 MB)\n","text/html":"
 Total params: 1,205,657 (4.60 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,203,737\u001b[0m (4.59 MB)\n","text/html":"
 Trainable params: 1,203,737 (4.59 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,920\u001b[0m (7.50 KB)\n","text/html":"
 Non-trainable params: 1,920 (7.50 KB)\n
\n"},"metadata":{}},{"name":"stdout","text":"\n[M2] total params: 1,205,657 | trainable: 1,203,737\nEpoch 1/100\n","output_type":"stream"},{"name":"stderr","text":"E0000 00:00:1791393857.999433 49 meta_optimizer.cc:967] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inStatefulPartitionedCall/M2_deep_regularised_1/spatial_dropout2d_1/stateless_dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\n","output_type":"stream"},{"name":"stdout","text":"190/190 - 28s - 147ms/step - accuracy: 0.2113 - loss: 3.5366 - val_accuracy: 0.0171 - val_loss: 7.2250 - learning_rate: 0.0010\nEpoch 2/100\n190/190 - 20s - 106ms/step - accuracy: 0.3138 - loss: 2.7909 - val_accuracy: 0.0338 - val_loss: 7.2150 - learning_rate: 0.0010\nEpoch 3/100\n190/190 - 20s - 104ms/step - accuracy: 0.3748 - loss: 2.4822 - val_accuracy: 0.1037 - val_loss: 5.8842 - learning_rate: 0.0010\nEpoch 4/100\n190/190 - 20s - 104ms/step - accuracy: 0.4203 - loss: 2.2635 - val_accuracy: 0.1785 - val_loss: 4.6903 - learning_rate: 0.0010\nEpoch 5/100\n190/190 - 20s - 104ms/step - accuracy: 0.4481 - loss: 2.1214 - val_accuracy: 0.0486 - val_loss: 6.1877 - learning_rate: 0.0010\nEpoch 6/100\n190/190 - 20s - 105ms/step - accuracy: 0.4726 - loss: 2.0088 - val_accuracy: 0.0738 - val_loss: 6.4430 - learning_rate: 0.0010\nEpoch 7/100\n190/190 - 20s - 104ms/step - accuracy: 0.4964 - loss: 1.9158 - val_accuracy: 0.0316 - val_loss: 6.9114 - learning_rate: 0.0010\nEpoch 8/100\n190/190 - 20s - 103ms/step - accuracy: 0.5154 - loss: 1.8353 - val_accuracy: 0.0676 - val_loss: 10.9332 - learning_rate: 0.0010\nEpoch 9/100\n\nEpoch 9: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n190/190 - 20s - 103ms/step - accuracy: 0.5325 - loss: 1.7745 - val_accuracy: 0.0084 - val_loss: 13.8355 - learning_rate: 0.0010\nEpoch 10/100\n190/190 - 20s - 103ms/step - accuracy: 0.5639 - loss: 1.6524 - val_accuracy: 0.1027 - val_loss: 6.5599 - learning_rate: 5.0000e-04\nEpoch 11/100\n190/190 - 20s - 103ms/step - accuracy: 0.5789 - loss: 1.6021 - val_accuracy: 0.0615 - val_loss: 9.6188 - learning_rate: 5.0000e-04\nEpoch 12/100\n190/190 - 20s - 103ms/step - accuracy: 0.5865 - loss: 1.5600 - val_accuracy: 0.0292 - val_loss: 8.8961 - learning_rate: 5.0000e-04\nEpoch 13/100\n190/190 - 19s - 103ms/step - accuracy: 0.5931 - loss: 1.5337 - val_accuracy: 0.1122 - val_loss: 5.9892 - learning_rate: 5.0000e-04\nEpoch 14/100\n\nEpoch 14: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n190/190 - 20s - 103ms/step - accuracy: 0.5979 - loss: 1.5133 - val_accuracy: 0.0153 - val_loss: 13.6538 - learning_rate: 5.0000e-04\nEpoch 15/100\n190/190 - 20s - 103ms/step - accuracy: 0.6167 - loss: 1.4498 - val_accuracy: 0.1078 - val_loss: 6.8246 - learning_rate: 2.5000e-04\nEpoch 16/100\n190/190 - 20s - 103ms/step - accuracy: 0.6237 - loss: 1.4212 - val_accuracy: 0.0204 - val_loss: 7.7553 - learning_rate: 2.5000e-04\nEpoch 17/100\n190/190 - 20s - 104ms/step - accuracy: 0.6293 - loss: 1.3901 - val_accuracy: 0.1907 - val_loss: 3.8641 - learning_rate: 2.5000e-04\nEpoch 18/100\n190/190 - 20s - 104ms/step - accuracy: 0.6297 - loss: 1.3920 - val_accuracy: 0.2182 - val_loss: 3.8396 - learning_rate: 2.5000e-04\nEpoch 19/100\n190/190 - 20s - 103ms/step - accuracy: 0.6422 - loss: 1.3517 - val_accuracy: 0.1305 - val_loss: 5.4029 - learning_rate: 2.5000e-04\nEpoch 21/100\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ============================ CELL 20: M2 LEARNING CURVES ============================\nplot_learning_curves(\"M2\"); diagnose(\"M2\")\nprint(\"\\nNOTE: M2's reported training loss includes the L2 penalty and its training accuracy is measured on\")\nprint(\"strongly augmented images, so the train/validation gap looks smaller than for an un-regularised model.\")","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================ CELL 21: M2 EVALUATION ============================\nevaluate_model(\"M2\")\nprint(\"\\nError analysis for M2: see 'Most confused class pairs' and 'Lowest-F1 classes' above; \"\n \"a deeper cross-model analysis follows in Cell 32.\")","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================ CELL 22: MODEL COMPARISON TABLE ============================\ncomp_num = pd.DataFrame([{\n \"Model\": k, \"Architecture\": results[k][\"arch\"], \"Parameters\": results[k][\"total_params\"],\n \"Best Val Accuracy\": results[k][\"best_hist_val_acc\"], \"Val Accuracy (best-loss weights)\": results[k][\"val_acc\"],\n \"Val Log Loss\": results[k][\"val_ll\"], \"Macro F1\": results[k][\"f1_macro\"],\n \"Best Epoch\": results[k][\"best_epoch\"], \"Training Time (min)\": results[k][\"train_time\"] / 60,\n \"Key hyperparameters\": results[k][\"hparams\"]} for k in [\"M0\", \"M1\", \"M2\"]]).set_index(\"Model\")\npd.set_option(\"display.max_colwidth\", 90); pd.set_option(\"display.width\", 250)\nprint(\"=\" * 60); print(\"MODEL COMPARISON (values computed by this notebook; lower log loss = better)\"); print(\"=\" * 60)\ndisplay(comp_num.style.format({\"Parameters\": \"{:,}\", \"Best Val Accuracy\": \"{:.4f}\", \"Val Accuracy (best-loss weights)\": \"{:.4f}\",\n \"Val Log Loss\": \"{:.4f}\", \"Macro F1\": \"{:.4f}\", \"Training Time (min)\": \"{:.1f}\"})\n .highlight_min(subset=[\"Val Log Loss\"], color=\"lightgreen\"))\ncomp_num.to_csv(WORK_DIR / \"model_comparison.csv\")","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================ CELL 23: FINAL MODEL SELECTION ============================\n# Primary criterion = validation log loss (the competition metric). Other criteria are shown for context.\nsel_tbl = pd.DataFrame({\n \"val_log_loss\": comp_num[\"Val Log Loss\"],\n \"val_accuracy\": comp_num[\"Val Accuracy (best-loss weights)\"],\n \"macro_F1\": comp_num[\"Macro F1\"],\n \"train-val acc gap\": [histories[k][\"accuracy\"][results[k][\"best_epoch\"] - 1] -\n histories[k][\"val_accuracy\"][results[k][\"best_epoch\"] - 1] for k in comp_num.index],\n \"train_time_min\": comp_num[\"Training Time (min)\"]})\nsel_tbl[\"rank_logloss\"] = sel_tbl.val_log_loss.rank()\nsel_tbl[\"rank_accuracy\"] = sel_tbl.val_accuracy.rank(ascending=False)\nsel_tbl[\"rank_F1\"] = sel_tbl.macro_F1.rank(ascending=False)\ndisplay(sel_tbl.round(4))\n\nFINAL_NAME = sel_tbl[\"val_log_loss\"].idxmin()\nbest_acc_name = sel_tbl[\"val_accuracy\"].idxmax()\nprint(f\"\\nSELECTED MODEL: {FINAL_NAME} (lowest validation log loss = {results[FINAL_NAME]['val_ll']:.4f})\")\nprint(f\"Architecture : {results[FINAL_NAME]['arch']}\")\nprint(f\"Accuracy {results[FINAL_NAME]['val_acc']:.4f} | macro-F1 {results[FINAL_NAME]['f1_macro']:.4f} | \"\n f\"{results[FINAL_NAME]['total_params']:,} params | {results[FINAL_NAME]['train_time']/60:.1f} min training\")\nif best_acc_name != FINAL_NAME:\n print(f\"Note: {best_acc_name} has the highest accuracy, but {FINAL_NAME} has the better log loss -> \"\n f\"its probabilities are better calibrated, which is what the competition scores.\")\nelse:\n print(\"This model also has the highest accuracy, so accuracy and log loss agree.\")\nprint(\"Final check after Kaggle: if public/private scores contradict this choice, revisit using the Kaggle evidence.\")","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 10. Final Model / Retraining Strategy\n1. All model selection and all reported validation metrics (Cells 12–23) use the **80/20 split**.\n2. If `RETRAIN_ON_FULL = True`, the **selected architecture is retrained from scratch on 100% of the labelled data** (about 25% more data, usually better for rare classes). Without a validation set it cannot early-stop, so it uses the **number of epochs (best epoch × 1.15) and the learning-rate schedule recorded in the validation run**.\n3. The retrained model's own validation numbers are **not reported**, because validation images are now training data. The evidence for the choice remains the split-based comparison plus Kaggle scores.\n4. Separate submissions from M0, M1 and M2 (train-split models) are also written in Cell 30. If you prefer, set `RETRAIN_ON_FULL = False` to use the validated model directly.","metadata":{}},{"cell_type":"code","source":"# ============================ CELL 24B: FINAL TRAINING ============================\nRETRAIN_ON_FULL = True\n\nif RETRAIN_ON_FULL:\n sel_hist = histories[FINAL_NAME]; best_ep = results[FINAL_NAME][\"best_epoch\"]\n n_epochs = int(math.ceil(best_ep * 1.15))\n lr_key = \"learning_rate\" if \"learning_rate\" in sel_hist else (\"lr\" if \"lr\" in sel_hist else None)\n final_model = CONFIGS[FINAL_NAME][\"builder\"]()\n reset_seeds(); compile_model(final_model)\n if lr_key:\n lrs = [float(v) for v in sel_hist[lr_key]]\n cbs = [keras.callbacks.LearningRateScheduler(lambda epoch: lrs[min(epoch, len(lrs) - 1)])] # replay recorded schedule\n else:\n cbs = [keras.callbacks.ReduceLROnPlateau(monitor=\"loss\", factor=0.5, patience=3, min_lr=1e-5)]\n full_ds = make_dataset(X_all, y_all, training=True)\n print(f\"Retraining {FINAL_NAME} on ALL {len(X_all):,} labelled images for {n_epochs} epochs (from scratch)...\")\n t0 = time.time()\n final_model.fit(full_ds, epochs=n_epochs, callbacks=cbs, verbose=2)\n FINAL_TRAIN_TIME = time.time() - t0\n final_desc = f\"{FINAL_NAME} retrained on 100% of labelled data ({n_epochs} epochs)\"\nelse:\n final_model = models[FINAL_NAME]; FINAL_TRAIN_TIME = results[FINAL_NAME][\"train_time\"]\n final_desc = f\"{FINAL_NAME} trained on the 80% split (best validation weights)\"\n\nfinal_model.save(WORK_DIR / \"final_cnn.keras\")\ntotal_gpu_min = (FINAL_TRAIN_TIME + (0 if not RETRAIN_ON_FULL else 0)) / 60\nall_runs_min = (sum(results[k][\"train_time\"] for k in [\"M0\", \"M1\", \"M2\"]) + (FINAL_TRAIN_TIME if RETRAIN_ON_FULL else 0)) / 60\nprint(f\"\\nFinal model: {final_desc}\")\nprint(f\"Final-model training time: {FINAL_TRAIN_TIME/60:.1f} min | all training in this notebook: {all_runs_min:.1f} min (limit 120)\")\nif all_runs_min > 120: print(\"WARNING: total time exceeds 2 hours - reduce max_epochs in CONFIGS.\")\nprint(\"Saved:\", WORK_DIR / \"final_cnn.keras\")","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}