Add trained model, metrics, loss graph, and training config

README.md

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+
+# LSTM Sequence Anomaly Detection
+
+This repository contains a trained LSTM Autoencoder model used for anomaly detection in sequential data.
+
+## Model Overview
+The model is an LSTM-based Autoencoder, which is trained to reconstruct sequential data. Anomalies can be detected by measuring the reconstruction error: sequences with high reconstruction loss are considered anomalous.
+
+### Model Architecture:
+- **Type**: LSTM Autoencoder
+- **Encoder**: LSTM with 64 units
+- **Decoder**: LSTM with 64 units
+- **Output Layer**: TimeDistributed Dense with softmax activation
+
+## Training Details:
+- **Optimizer**: Adam
+- **Loss Function**: Categorical Crossentropy
+- **Batch Size**: 128
+- **Epochs**: 50
+- **Early Stopping**: Patience = 5
+
+## Performance Metrics:
+- **Validation Loss (Mean)**: {val_loss.mean():.4f}
+- **Validation Loss (Std Dev)**: {val_loss.std():.4f}
+- **Training/Validation Loss**: See the graph below.
+
+![Training Loss Graph](loss_graph.png)
+
+## How to Use:
+1. Load the model using `tf.keras.models.load_model()`.
+2. Use the model to detect anomalies in new sequences.
+3. Calculate reconstruction loss for each sequence, and use a threshold to classify anomalies.
+
+## Files in this Repo:
+- **model.h5**: The trained LSTM Autoencoder model.
+- **metrics.txt**: Performance metrics for the model.
+- **loss_graph.png**: Loss curve during training.
+- **README.md**: This file.
+- **model_config.json**: Model architecture details.
+- **training_config.json**: Training hyperparameters.

metrics.txt

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+Validation Reconstruction Loss:
+Mean: 0.0015, Std: 0.0030
+Losses: [7.0015353e-04 8.8246615e-04 1.0358144e-03 2.3799071e-03 1.2637556e-03
+ 6.6058309e-04 4.1786637e-04 5.0311431e-04 4.6074213e-04 8.4128020e-05]

model_config.json

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+{
+    "architecture": "LSTM Autoencoder",
+    "input_shape": [
+        10,
+        6
+    ],
+    "lstm_units": 64,
+    "epochs": 50,
+    "batch_size": 128,
+    "optimizer": "adam",
+    "loss_function": "categorical_crossentropy"
+}

trained_lstm_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:598715b511564675aaeebbb04d4f63e86c4fb57bcb6aa23e9ea16292b0f32c6f
+size 656080

training_config.json

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+{
+    "learning_rate": 0.001,
+    "batch_size": 128,
+    "epochs": 50,
+    "optimizer": "adam",
+    "early_stopping": true,
+    "early_stopping_patience": 5
+}