This release contains the training log, results, and also the model weight in h5 format.
Training Conditions:
- Data Augmentation.
- Transfer learning using ImageNet weights.
- All layers are trained.
- Optimizer = SGD lr=0.001, decay=1e-6, momentum=0.9, nesterov=False
The results of this model are as follows:
Inception ResNetV2
Training:
- loss: 0.3823
- accuracy: 0.8906
- precision: 0.5723
- recall: 0.4950
- AUC: 0.8347
Validation:
- loss : 0.3409378457069397
- accuracy : 0.890625
- precision : 0.57225436
- recall : 0.495
- auc : 0.8346987
Final Score:
- Kappa score: 0.46929492039423804
- F-1 score: 0.890625
- AUC value: 0.8381830357142857
- Final Score: 0.7327009853695078
Changes in the model:
# Metrics
defined_metrics = [
tf.keras.metrics.BinaryAccuracy(name='accuracy'),
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(name='recall'),
tf.keras.metrics.AUC(name='auc'),
]
# Added a new dense layer
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(num_classes, activation='sigmoid')(x)
sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
x.compile(optimizer=sgd, loss='binary_crossentropy', metrics=defined_metrics)