---
# System prepended metadata

title: 5/12 Progress Report
tags: [Report]

---

# 5/12 Progress Report

## Amanda

 - 1. The model structure
![](https://hackmd.io/_uploads/SkD6foVSn.png)

## New
### Training Progress
#### Code could be found:
https://drive.google.com/file/d/1POtvafDfAOLUkXgVJ63StS45SfVyBiR1/view?usp=sharing

 - 1. The model structure
     - Dense layer after flatten is used as an embedding vector with dimension of 100.
 ![](https://hackmd.io/_uploads/B1fJceMB2.png)
     - Accuracy
        Training: 0.9493
        Testing: 0.9412
 - 2. Using embedding vector with Random Forest.
     - Acuuracy with using n_estimators = 20
        Training: 0.9888
        Testing: 0.9300

## Jessica
### Progress
1. Adjusted the sliding window function for the anomaly
2. Finished data pipeline from raw csv to dataset ready for training.
    * `data` containing the raw data slice from the sliding window
    * `anomaly_label` containing 0/1 for each window
    * `cluster_label` containing the soft_label

#### Code could be found:
1. Github repo (my branch)
2. [Google Colab](https://colab.research.google.com/drive/11uF31MiWCmcngnAi-lAdg6pIM6mkhSOb?usp=sharing)

### Best Model (as per 5/12)
According to the feature extraction code written in CODATS paper.
![](https://hackmd.io/_uploads/HkAc5tjV3.png)
```python=
epoch = 5
batch_sz = 32

model_m = Sequential()
model_m.add(Conv1D(filters=128, kernel_size=8, padding='same', use_bias=False, input_shape=(sliding_window, num_sensor)))
model_m.add(BatchNormalization())
model_m.add(Activation("relu"))

model_m.add(Conv1D(filters=256, kernel_size=5, padding='same', use_bias=False))
model_m.add(BatchNormalization())
model_m.add(Activation("relu"))

model_m.add(Conv1D(filters=128, kernel_size=8, padding='same', use_bias=False))
model_m.add(BatchNormalization())
model_m.add(Activation("relu"))

model_m.add(GlobalAveragePooling1D())
model_m.add(Dense(num_class))
```
### Results (Accuracy)
With 8/2 Train/Test Split
Training: 0.9365
Test: 0.9371

###### tags: `Report`