Jeroen
commited on
Commit
•
40b9c6d
1
Parent(s):
f9a353e
Updated model to latest iteration
Browse files- README.md +58 -12
- data/model.keras +2 -2
- data/model_history.json +1 -0
- images/11-Spirals-linear.png +0 -0
- images/11-Spirals-log.png +0 -0
- images/26-Spheroids-linear.png +0 -0
- images/26-Spheroids-log.png +0 -0
- images/28-Spheroids-linear.png +0 -0
- images/28-Spheroids-log.png +0 -0
- images/loss-accuracy.png +0 -0
- images/spiral-example.png +0 -0
README.md
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@@ -60,6 +60,16 @@ Furthermore are the image segmented for noice removal
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### Training Procedure
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```python
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data_augmentation = tf.keras.Sequential([
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tf.keras.layers.RandomFlip('horizontal'),
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tf.keras.layers.RandomRotation(0.2),
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model = tf.keras.Sequential([
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data_augmentation,
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tf.keras.layers.Rescaling(1./255, input_shape=(
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tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
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tf.keras.layers.MaxPooling2D(2, 2),
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tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
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tf.keras.layers.Flatten(),
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tf.keras.layers.Dropout(0.5),
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tf.keras.layers.Dense(512, activation='relu'),
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tf.keras.layers.Dense(
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])
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model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
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### Testing Data, Factors & Metrics
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### Results
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| | precision | recall | f1-score | support |
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|-----------|-----------|--------|----------|---------|
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| one_one |
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| one_two |
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| accuracy | | |
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| macro avg |
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| weighted avg| 0.
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## Environmental Impact
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- **Hardware Type:** M3 Pro
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- **Hours used:**
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### Training Procedure
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```python
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train_ds, val_ds = image_dataset_from_directory(
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dataset_directory,
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validation_split=0.2,
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subset="both",
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seed=123,
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image_size=(200, 200),
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batch_size=32,
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color_mode='grayscale'
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)
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data_augmentation = tf.keras.Sequential([
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tf.keras.layers.RandomFlip('horizontal'),
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tf.keras.layers.RandomRotation(0.2),
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model = tf.keras.Sequential([
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data_augmentation,
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tf.keras.layers.Rescaling(1./255, input_shape=(200, 200, 1)),
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tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
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tf.keras.layers.MaxPooling2D(2, 2),
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tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
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tf.keras.layers.Flatten(),
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tf.keras.layers.Dropout(0.5),
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tf.keras.layers.Dense(512, activation='relu'),
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tf.keras.layers.Dense(2, activation='softmax')
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])
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model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
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### Testing Data, Factors & Metrics
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### Model summary
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```text
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
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┃ Layer (type) ┃ Output Shape ┃ Param # ┃
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┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
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│ sequential (Sequential) │ (None, 200, 200, 1) │ 0 │
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├─────────────────────────────────┼────────────────────────┼───────────────┤
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│ rescaling (Rescaling) │ (None, 200, 200, 1) │ 0 │
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├─────────────────────────────────┼────────────────────────┼───────────────┤
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│ conv2d (Conv2D) │ (None, 198, 198, 64) │ 640 │
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├─────────────────────────────────┼────────────────────────┼───────────────┤
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│ max_pooling2d (MaxPooling2D) │ (None, 99, 99, 64) │ 0 │
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├─────────────────────────────────┼────────────────────────┼───────────────┤
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│ conv2d_1 (Conv2D) │ (None, 97, 97, 128) │ 73,856 │
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├─────────────────────────────────┼────────────────────────┼───────────────┤
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│ max_pooling2d_1 (MaxPooling2D) │ (None, 48, 48, 128) │ 0 │
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├─────────────────────────────────┼────────────────────────┼───────────────┤
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│ conv2d_2 (Conv2D) │ (None, 46, 46, 256) │ 295,168 │
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├─────────────────────────────────┼────────────────────────┼───────────────┤
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│ max_pooling2d_2 (MaxPooling2D) │ (None, 23, 23, 256) │ 0 │
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├─────────────────────────────────┼────────────────────────┼───────────────┤
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│ conv2d_3 (Conv2D) │ (None, 21, 21, 256) │ 590,080 │
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├─────────────────────────────────┼────────────────────────┼───────────────┤
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│ max_pooling2d_3 (MaxPooling2D) │ (None, 10, 10, 256) │ 0 │
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├────────────���────────────────────┼────────────────────────┼───────────────┤
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│ flatten (Flatten) │ (None, 25600) │ 0 │
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├─────────────────────────────────┼────────────────────────┼───────────────┤
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│ dropout (Dropout) │ (None, 25600) │ 0 │
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├─────────────────────────────────┼────────────────────────┼───────────────┤
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│ dense (Dense) │ (None, 1024) │ 26,215,424 │
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├─────────────────────────────────┼────────────────────────┼───────────────┤
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│ dense_1 (Dense) │ (None, 2) │ 2,050 │
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└─────────────────────────────────┴────────────────────────┴───────────────┘
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Total params: 81,531,656 (311.02 MB)
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Trainable params: 27,177,218 (103.67 MB)
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Non-trainable params: 0 (0 MB)
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Optimizer params 54,354,438 (207.35 MB)
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```
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### Loss and Accuracy
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![Loss and Accuracy](./images/loss-accuracy.png)
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### Results
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| | precision | recall | f1-score | support |
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|-----------|-----------|--------|----------|---------|
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| one_one | 0.98 | 0.98 | 0.96 | 1637 |
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| one_two | 0.98 | 0.98 | 0.98 | 1740 |
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| accuracy | | | 0.96 | 3377 |
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| macro avg | 0.98 | 0.98 | 0.98 | 3377 |
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| weighted avg| 0.98 | 0.98 | 0.98 | 3377 |
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## Environmental Impact
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- **Hardware Type:** M3 Pro
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- **Hours used:** 72
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data/model.keras
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:a28fe3b200d39a72695b6308b0d5f888e74c0d262c46d16595b1c750bc7eae25
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size 326192489
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data/model_history.json
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