Upload 6 files

.gitattributes

@@ -0,0 +1 @@
+particle_data.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,108 @@
+# Ionic Sphere Quantum Simulator v7.0
+## Real-Time Neural Training System
+
+**Model Name:** IonicQuantumSimulator_v7.0
+**Version:** 7.0
+**Export Date:** 2025-12-31T00:27:29.944Z
+
+### Training Summary
+- **Total Epochs:** 3
+- **Final Loss:** 0.6713
+- **Final Accuracy:** 65.6%
+- **Training Samples:** 800
+- **Simulation Time:** 37.8s
+
+### Dataset Information
+This package contains real-time captured data from the quantum ionic simulation:
+
+**Particle Data:**
+- Frames captured: 29
+- Particles per frame: 10240
+- Total position samples: 890880
+- Time range: 38s
+
+**Features Captured:**
+1. Position (x, y, z) - normalized coordinates
+2. Velocity (x, y) - movement vectors
+3. Timestamp - simulation time
+4. Model state - neural network parameters at capture time
+
+### Model Architecture
+```
+Input(5) → Dense(32, relu) → Dropout(0.2)
+         → Dense(16, relu)
+         → Dense(8, relu)
+         → Output(1, sigmoid)
+```
+
+### Training Configuration
+- **Optimizer:** Adam (learning_rate=0.001)
+- **Loss Function:** Binary Crossentropy
+- **Batch Size:** 32
+- **Validation Split:** 20%
+- **Shuffle:** True
+
+### Simulation Parameters
+- **Ion Count:** 10,240
+- **Ocean Size:** 200x200 units
+- **Physics Engine:** GPU.js accelerated
+- **Render Engine:** Three.js r128
+- **Target FPS:** 60
+
+### File Structure
+```
+ionicsphere_export_v7.0_*.zip/
+├── model_metadata.json     # Model configuration and stats
+├── training_log.json       # Loss/accuracy per epoch
+├── particle_data.json      # Captured particle positions/velocities
+├── screenshots/            # PNG frames from simulation
+│   ├── frame_0_*.png
+│   └── ...
+├── tfjs_model/            # TensorFlow.js model files
+│   ├── model.json
+│   └── weights.bin
+├── README.md              # This file
+├── terminal_log.txt       # CLI interaction history
+└── config.json           # System configuration
+```
+
+### Usage Instructions
+
+**1. Load Model in TensorFlow.js:**
+```javascript
+async function loadModel() {
+    const model = await tf.loadLayersModel('tfjs_model/model.json');
+    const weights = await fetch('tfjs_model/weights.bin');
+    // Load weights and make predictions
+}
+```
+
+**2. Analyze Particle Data:**
+```javascript
+const data = JSON.parse(particleDataJson);
+const positions = data.positions;  // Array of position frames
+const velocities = data.velocities; // Array of velocity frames
+```
+
+**3. Reproduce Simulation:**
+- Use Three.js with provided particle data
+- Apply same physics parameters
+- Feed data into neural network for stability predictions
+
+### Citation
+If you use this data in research, please cite:
+```bibtex
+@dataset{ionic_sphere_2024,
+  title={Real-Time Quantum Ionic Simulation Dataset},
+  author={IONICSPHERE Research Team},
+  year={2024},
+  publisher={IONICSPHERE v7.0},
+  url={https://github.com/ionicsphere/simulator}
+}
+```
+
+### License
+Research Use Only - Attribution Required
+
+### Contact
+For questions or access to newer versions, visit the project repository.

config.json

@@ -0,0 +1,29 @@
+{
+  "simulation": {
+    "ion_count": 10240,
+    "ocean_size": 200,
+    "time_step": 0.016,
+    "physics_engine": "GPU.js"
+  },
+  "neural_network": {
+    "input_shape": [
+      5
+    ],
+    "output_shape": [
+      1
+    ],
+    "layers": [
+      32,
+      16,
+      8
+    ],
+    "activation": "relu",
+    "output_activation": "sigmoid"
+  },
+  "export_info": {
+    "version": "7.0",
+    "format": "JSON/ZIP",
+    "total_size": "varies",
+    "compatible_with": "TensorFlow.js, Three.js"
+  }
+}

model_metadata.json

@@ -0,0 +1,21 @@
+{
+  "name": "IonicQuantumSimulator_v7.0",
+  "version": "7.0",
+  "export_date": "2025-12-31T00:27:28.589Z",
+  "epochs_trained": 3,
+  "final_loss": 0.6712959408760071,
+  "final_accuracy": 0.65625,
+  "particle_count": 10240,
+  "simulation_time": 36.255,
+  "features": [
+    "position_x",
+    "position_y",
+    "position_z",
+    "velocity_x",
+    "velocity_y"
+  ],
+  "architecture": "5→32→16→8→1",
+  "optimizer": "adam",
+  "learning_rate": 0.001,
+  "batch_size": 32
+}

particle_data.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6eec7646b413561178ef4b439d8a2e55e6342738a462e44dcd1f13a399cfb12
+size 46086944

terminal_log.txt

@@ -0,0 +1,111 @@
+========================================
+  REAL-TIME IONIC SIMULATOR v7.0
+  TensorFlow.js + Three.js Integration
+========================================
+Initializing quantum simulation matrix...
+Loading TensorFlow.js neural kernel...
+Generating 10,240 synthetic ions...
+Type 'help' for available commands
+$ █
+[SYSTEM] Booting IONICSPHERE v7.0...
+[TENSORFLOW] Initializing...
+[TENSORFLOW] Backend: webgl
+[TENSORFLOW] Creating neural network...
+[TENSORFLOW] Model created successfully
+[TENSORFLOW] Architecture: 5→32→16→8→1
+[TENSORFLOW] Optimizer: Adam (0.001)
+[DATA] Generating synthetic training data...
+[DATA] Generated 800 training samples
+[DATA] Generated 200 validation samples
+[THREE.JS] Initializing 3D visualization...
+[IONS] Created 10,240 particles
+[GPU.JS] Physics kernel initialized
+[THREE.JS] Visualization ready
+[SYSTEM] Ready. Type "help" for commands.
+[SYSTEM] Real-time training: train/stop
+[SYSTEM] Data export: export
+[SIMULATION] Started real-time quantum simulation
+[TRAINING] Started real-time neural training
+[TRAINING] Using live particle data as input
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[CAPTURE] Stored 10 data frames
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Epoch 1 completed
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Epoch 2 completed
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[CAPTURE] Stored 20 data frames
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Epoch 3 completed
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Error: Cannot start training because another fit() call is ongoing.
+[TRAINING] Stopped
+[EXPORT] Creating unified package...

training_log.json

@@ -0,0 +1,590 @@
+[
+  {
+    "epoch": 0,
+    "batch": 0,
+    "loss": 0.7411002516746521,
+    "accuracy": 0.3125,
+    "timestamp": 1767140820443
+  },
+  {
+    "epoch": 0,
+    "batch": 1,
+    "loss": 0.748737096786499,
+    "accuracy": 0.3125,
+    "timestamp": 1767140820916
+  },
+  {
+    "epoch": 0,
+    "batch": 2,
+    "loss": 0.7897660136222839,
+    "accuracy": 0.25,
+    "timestamp": 1767140821432
+  },
+  {
+    "epoch": 0,
+    "batch": 3,
+    "loss": 0.7086161375045776,
+    "accuracy": 0.40625,
+    "timestamp": 1767140821808
+  },
+  {
+    "epoch": 0,
+    "batch": 4,
+    "loss": 0.717400312423706,
+    "accuracy": 0.375,
+    "timestamp": 1767140822505
+  },
+  {
+    "epoch": 0,
+    "batch": 5,
+    "loss": 0.737858235836029,
+    "accuracy": 0.4375,
+    "timestamp": 1767140823469
+  },
+  {
+    "epoch": 0,
+    "batch": 6,
+    "loss": 0.6953486204147339,
+    "accuracy": 0.4375,
+    "timestamp": 1767140823806
+  },
+  {
+    "epoch": 0,
+    "batch": 7,
+    "loss": 0.7095077037811279,
+    "accuracy": 0.40625,
+    "timestamp": 1767140824237
+  },
+  {
+    "epoch": 0,
+    "batch": 8,
+    "loss": 0.7757364511489868,
+    "accuracy": 0.28125,
+    "timestamp": 1767140824902
+  },
+  {
+    "epoch": 0,
+    "batch": 9,
+    "loss": 0.7191400527954102,
+    "accuracy": 0.4375,
+    "timestamp": 1767140825147
+  },
+  {
+    "epoch": 0,
+    "batch": 10,
+    "loss": 0.7332611680030823,
+    "accuracy": 0.40625,
+    "timestamp": 1767140825360
+  },
+  {
+    "epoch": 0,
+    "batch": 11,
+    "loss": 0.7418195009231567,
+    "accuracy": 0.3125,
+    "timestamp": 1767140825866
+  },
+  {
+    "epoch": 0,
+    "batch": 12,
+    "loss": 0.7675049304962158,
+    "accuracy": 0.25,
+    "timestamp": 1767140826320
+  },
+  {
+    "epoch": 0,
+    "batch": 13,
+    "loss": 0.7316216230392456,
+    "accuracy": 0.3125,
+    "timestamp": 1767140826776
+  },
+  {
+    "epoch": 0,
+    "batch": 14,
+    "loss": 0.6952475309371948,
+    "accuracy": 0.5,
+    "timestamp": 1767140827294
+  },
+  {
+    "epoch": 0,
+    "batch": 15,
+    "loss": 0.7152165770530701,
+    "accuracy": 0.3125,
+    "timestamp": 1767140828054
+  },
+  {
+    "epoch": 0,
+    "batch": 16,
+    "loss": 0.7109678983688354,
+    "accuracy": 0.40625,
+    "timestamp": 1767140828120
+  },
+  {
+    "epoch": 0,
+    "batch": 17,
+    "loss": 0.7420613765716553,
+    "accuracy": 0.375,
+    "timestamp": 1767140828272
+  },
+  {
+    "epoch": 0,
+    "batch": 18,
+    "loss": 0.71675705909729,
+    "accuracy": 0.3125,
+    "timestamp": 1767140828651
+  },
+  {
+    "epoch": 0,
+    "batch": 19,
+    "loss": 0.7275558114051819,
+    "accuracy": 0.3125,
+    "timestamp": 1767140829014
+  },
+  {
+    "epoch": 0,
+    "batch": 20,
+    "loss": 0.7111624479293823,
+    "accuracy": 0.34375,
+    "timestamp": 1767140829324
+  },
+  {
+    "epoch": 0,
+    "batch": 21,
+    "loss": 0.6968860030174255,
+    "accuracy": 0.40625,
+    "timestamp": 1767140829608
+  },
+  {
+    "epoch": 0,
+    "batch": 22,
+    "loss": 0.7071496248245239,
+    "accuracy": 0.53125,
+    "timestamp": 1767140829935
+  },
+  {
+    "epoch": 0,
+    "batch": 23,
+    "loss": 0.6908776760101318,
+    "accuracy": 0.46875,
+    "timestamp": 1767140830271
+  },
+  {
+    "epoch": 0,
+    "batch": 24,
+    "loss": 0.6962689161300659,
+    "accuracy": 0.40625,
+    "timestamp": 1767140830655
+  },
+  {
+    "epoch": 1,
+    "batch": 0,
+    "loss": 0.7090840935707092,
+    "accuracy": 0.375,
+    "timestamp": 1767140831106
+  },
+  {
+    "epoch": 1,
+    "batch": 1,
+    "loss": 0.7116997241973877,
+    "accuracy": 0.5,
+    "timestamp": 1767140831400
+  },
+  {
+    "epoch": 1,
+    "batch": 2,
+    "loss": 0.7077443599700928,
+    "accuracy": 0.375,
+    "timestamp": 1767140831774
+  },
+  {
+    "epoch": 1,
+    "batch": 3,
+    "loss": 0.6939939856529236,
+    "accuracy": 0.5625,
+    "timestamp": 1767140832150
+  },
+  {
+    "epoch": 1,
+    "batch": 4,
+    "loss": 0.6895667314529419,
+    "accuracy": 0.46875,
+    "timestamp": 1767140832515
+  },
+  {
+    "epoch": 1,
+    "batch": 5,
+    "loss": 0.699230432510376,
+    "accuracy": 0.46875,
+    "timestamp": 1767140832989
+  },
+  {
+    "epoch": 1,
+    "batch": 6,
+    "loss": 0.6921590566635132,
+    "accuracy": 0.46875,
+    "timestamp": 1767140833190
+  },
+  {
+    "epoch": 1,
+    "batch": 7,
+    "loss": 0.6988462209701538,
+    "accuracy": 0.4375,
+    "timestamp": 1767140833516
+  },
+  {
+    "epoch": 1,
+    "batch": 8,
+    "loss": 0.7171862721443176,
+    "accuracy": 0.375,
+    "timestamp": 1767140833662
+  },
+  {
+    "epoch": 1,
+    "batch": 9,
+    "loss": 0.6946218013763428,
+    "accuracy": 0.4375,
+    "timestamp": 1767140833819
+  },
+  {
+    "epoch": 1,
+    "batch": 10,
+    "loss": 0.6919615864753723,
+    "accuracy": 0.5,
+    "timestamp": 1767140833964
+  },
+  {
+    "epoch": 1,
+    "batch": 11,
+    "loss": 0.6953616142272949,
+    "accuracy": 0.4375,
+    "timestamp": 1767140834074
+  },
+  {
+    "epoch": 1,
+    "batch": 12,
+    "loss": 0.7070935368537903,
+    "accuracy": 0.375,
+    "timestamp": 1767140834223
+  },
+  {
+    "epoch": 1,
+    "batch": 13,
+    "loss": 0.7055894136428833,
+    "accuracy": 0.375,
+    "timestamp": 1767140834388
+  },
+  {
+    "epoch": 1,
+    "batch": 14,
+    "loss": 0.6903753876686096,
+    "accuracy": 0.5625,
+    "timestamp": 1767140834798
+  },
+  {
+    "epoch": 1,
+    "batch": 15,
+    "loss": 0.6901687383651733,
+    "accuracy": 0.53125,
+    "timestamp": 1767140835068
+  },
+  {
+    "epoch": 1,
+    "batch": 16,
+    "loss": 0.7004857063293457,
+    "accuracy": 0.46875,
+    "timestamp": 1767140835232
+  },
+  {
+    "epoch": 1,
+    "batch": 17,
+    "loss": 0.7013710737228394,
+    "accuracy": 0.375,
+    "timestamp": 1767140835565
+  },
+  {
+    "epoch": 1,
+    "batch": 18,
+    "loss": 0.6903039813041687,
+    "accuracy": 0.46875,
+    "timestamp": 1767140835970
+  },
+  {
+    "epoch": 1,
+    "batch": 19,
+    "loss": 0.6959590315818787,
+    "accuracy": 0.53125,
+    "timestamp": 1767140836436
+  },
+  {
+    "epoch": 1,
+    "batch": 20,
+    "loss": 0.6937905550003052,
+    "accuracy": 0.40625,
+    "timestamp": 1767140837174
+  },
+  {
+    "epoch": 1,
+    "batch": 21,
+    "loss": 0.6821688413619995,
+    "accuracy": 0.59375,
+    "timestamp": 1767140837329
+  },
+  {
+    "epoch": 1,
+    "batch": 22,
+    "loss": 0.6867853999137878,
+    "accuracy": 0.59375,
+    "timestamp": 1767140837479
+  },
+  {
+    "epoch": 1,
+    "batch": 23,
+    "loss": 0.6854528784751892,
+    "accuracy": 0.5625,
+    "timestamp": 1767140837627
+  },
+  {
+    "epoch": 1,
+    "batch": 24,
+    "loss": 0.6779837608337402,
+    "accuracy": 0.6875,
+    "timestamp": 1767140837786
+  },
+  {
+    "epoch": 2,
+    "batch": 0,
+    "loss": 0.6750118732452393,
+    "accuracy": 0.6875,
+    "timestamp": 1767140837989
+  },
+  {
+    "epoch": 2,
+    "batch": 1,
+    "loss": 0.6848226189613342,
+    "accuracy": 0.625,
+    "timestamp": 1767140838171
+  },
+  {
+    "epoch": 2,
+    "batch": 2,
+    "loss": 0.6864094734191895,
+    "accuracy": 0.625,
+    "timestamp": 1767140838361
+  },
+  {
+    "epoch": 2,
+    "batch": 3,
+    "loss": 0.6736193299293518,
+    "accuracy": 0.625,
+    "timestamp": 1767140838547
+  },
+  {
+    "epoch": 2,
+    "batch": 4,
+    "loss": 0.6886866092681885,
+    "accuracy": 0.46875,
+    "timestamp": 1767140838842
+  },
+  {
+    "epoch": 2,
+    "batch": 5,
+    "loss": 0.6872621774673462,
+    "accuracy": 0.5625,
+    "timestamp": 1767140839113
+  },
+  {
+    "epoch": 2,
+    "batch": 6,
+    "loss": 0.6883639097213745,
+    "accuracy": 0.5625,
+    "timestamp": 1767140839328
+  },
+  {
+    "epoch": 2,
+    "batch": 7,
+    "loss": 0.7002809643745422,
+    "accuracy": 0.53125,
+    "timestamp": 1767140839713
+  },
+  {
+    "epoch": 2,
+    "batch": 8,
+    "loss": 0.6715573072433472,
+    "accuracy": 0.625,
+    "timestamp": 1767140839960
+  },
+  {
+    "epoch": 2,
+    "batch": 9,
+    "loss": 0.6938263773918152,
+    "accuracy": 0.40625,
+    "timestamp": 1767140840368
+  },
+  {
+    "epoch": 2,
+    "batch": 10,
+    "loss": 0.6874582767486572,
+    "accuracy": 0.53125,
+    "timestamp": 1767140840631
+  },
+  {
+    "epoch": 2,
+    "batch": 11,
+    "loss": 0.6666799783706665,
+    "accuracy": 0.71875,
+    "timestamp": 1767140841058
+  },
+  {
+    "epoch": 2,
+    "batch": 12,
+    "loss": 0.6758941411972046,
+    "accuracy": 0.65625,
+    "timestamp": 1767140841589
+  },
+  {
+    "epoch": 2,
+    "batch": 13,
+    "loss": 0.6774862408638,
+    "accuracy": 0.6875,
+    "timestamp": 1767140842012
+  },
+  {
+    "epoch": 2,
+    "batch": 14,
+    "loss": 0.6637570261955261,
+    "accuracy": 0.78125,
+    "timestamp": 1767140842240
+  },
+  {
+    "epoch": 2,
+    "batch": 15,
+    "loss": 0.684646487236023,
+    "accuracy": 0.53125,
+    "timestamp": 1767140842587
+  },
+  {
+    "epoch": 2,
+    "batch": 16,
+    "loss": 0.6750656366348267,
+    "accuracy": 0.71875,
+    "timestamp": 1767140842826
+  },
+  {
+    "epoch": 2,
+    "batch": 17,
+    "loss": 0.6863251328468323,
+    "accuracy": 0.5625,
+    "timestamp": 1767140843053
+  },
+  {
+    "epoch": 2,
+    "batch": 18,
+    "loss": 0.7029653191566467,
+    "accuracy": 0.5,
+    "timestamp": 1767140843289
+  },
+  {
+    "epoch": 2,
+    "batch": 19,
+    "loss": 0.6970543265342712,
+    "accuracy": 0.59375,
+    "timestamp": 1767140843429
+  },
+  {
+    "epoch": 2,
+    "batch": 20,
+    "loss": 0.7156466245651245,
+    "accuracy": 0.375,
+    "timestamp": 1767140843686
+  },
+  {
+    "epoch": 2,
+    "batch": 21,
+    "loss": 0.6840257048606873,
+    "accuracy": 0.53125,
+    "timestamp": 1767140843923
+  },
+  {
+    "epoch": 2,
+    "batch": 22,
+    "loss": 0.6979974508285522,
+    "accuracy": 0.5625,
+    "timestamp": 1767140844310
+  },
+  {
+    "epoch": 2,
+    "batch": 23,
+    "loss": 0.685049295425415,
+    "accuracy": 0.53125,
+    "timestamp": 1767140844455
+  },
+  {
+    "epoch": 2,
+    "batch": 24,
+    "loss": 0.6642530560493469,
+    "accuracy": 0.75,
+    "timestamp": 1767140844747
+  },
+  {
+    "epoch": 3,
+    "batch": 0,
+    "loss": 0.6771752834320068,
+    "accuracy": 0.625,
+    "timestamp": 1767140845021
+  },
+  {
+    "epoch": 3,
+    "batch": 1,
+    "loss": 0.659159779548645,
+    "accuracy": 0.625,
+    "timestamp": 1767140845246
+  },
+  {
+    "epoch": 3,
+    "batch": 2,
+    "loss": 0.6672576665878296,
+    "accuracy": 0.6875,
+    "timestamp": 1767140845426
+  },
+  {
+    "epoch": 3,
+    "batch": 3,
+    "loss": 0.6715335845947266,
+    "accuracy": 0.65625,
+    "timestamp": 1767140845957
+  },
+  {
+    "epoch": 3,
+    "batch": 4,
+    "loss": 0.6796213388442993,
+    "accuracy": 0.59375,
+    "timestamp": 1767140846322
+  },
+  {
+    "epoch": 3,
+    "batch": 5,
+    "loss": 0.6706708669662476,
+    "accuracy": 0.53125,
+    "timestamp": 1767140846615
+  },
+  {
+    "epoch": 3,
+    "batch": 6,
+    "loss": 0.6850961446762085,
+    "accuracy": 0.53125,
+    "timestamp": 1767140846815
+  },
+  {
+    "epoch": 3,
+    "batch": 7,
+    "loss": 0.677682638168335,
+    "accuracy": 0.65625,
+    "timestamp": 1767140846956
+  },
+  {
+    "epoch": 3,
+    "batch": 8,
+    "loss": 0.6712959408760071,
+    "accuracy": 0.65625,
+    "timestamp": 1767140847277
+  }
+]