Jeroen commited on
Commit
40b9c6d
1 Parent(s): f9a353e

Updated model to latest iteration

Browse files
README.md CHANGED
@@ -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),
@@ -75,7 +85,7 @@ val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
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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=(img_height, img_width, 3)),
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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'),
@@ -87,7 +97,7 @@ model = tf.keras.Sequential([
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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(num_classes, activation='softmax')
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  ])
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  model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
@@ -101,21 +111,57 @@ model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
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  ### Testing Data, Factors & Metrics
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- #### Testing Data
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-
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- Test data is manual retrieved data from Hubble and James web, see manually manipulated data in the files and their accuracy. Of each a log and linear scaling is used.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- ![11 Spiral images classified using this model](./images/11-Spirals-log.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 | 0.96 | 0.98 | 0.96 | 1637 |
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- | one_two | 0.98 | 0.93 | 0.96 | 1740 |
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- | accuracy | | | 0.96 | 3377 |
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- | macro avg | 0.96 | 0.96 | 0.96 | 3377 |
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- | weighted avg| 0.96 | 0.96 | 0.96 | 3377 |
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  ## Environmental Impact
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  - **Hardware Type:** M3 Pro
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- - **Hours used:** 30min
 
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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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+
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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
data/model.keras CHANGED
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data/model_history.json ADDED
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