Added useful common features for training & evaluation & testing

README.md

@@ -3,26 +3,30 @@ This project uses TensorFlow2.0 for image classification tasks.
 
 ## How to use
 ### Requirements
-+ **Python 3.x** (My Python version is 3.6.8)<br/>
-+ **TensorFlow version: 2.0.0-beta1**<br/> 
++ **Python 3.x** (My Python version is 3.8.0)<br/>
++ **TensorFlow version: 2.11.0 <br/> 
 + The file directory of the dataset should look like this: <br/>
 ```
 ${dataset_root}
 |——train
-|   |——class_name_0
-|   |——class_name_1
-|   |——class_name_2
-|   |——class_name_3
+|   |——class_dir_0
+|   |  |——image_1.jpg
+|   |  |——image_2.jpg
+|   |  |——image_3.jpg
+|   |  ...
+|   |——class_dir_1
+|   |——class_dir_2
+|   |——class_dir_3
 |——valid
-|   |——class_name_0
-|   |——class_name_1
-|   |——class_name_2
-|   |——class_name_3
+|   |——class_dir_0
+|   |——class_dir_1
+|   |——class_dir_2
+|   |——class_dir_3
 |——test
-    |——class_name_0
-    |——class_name_1
-    |——class_name_2
-    |——class_name_3
+    |——class_dir_0
+    |——class_dir_1
+    |——class_dir_2
+    |——class_dir_3
 ```
 
 ### Train
@@ -40,3 +44,4 @@ The structure of the network is defined in `model_definition.py`, you can change
 ## References
 1. AlexNet : http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf
 2. VGG : https://arxiv.org/abs/1409.1556
+3. Keras : https://keras.io/api/applications/

config.py

@@ -1,12 +1,15 @@
 # some training parameters
-EPOCHS = 50
-BATCH_SIZE = 8
-NUM_CLASSES = 5
-image_height = 224
-image_width = 224
+EPOCHS = 100
+BATCH_SIZE = 128
+NUM_CLASSES = 6
+image_height = 128
+image_width = 128
 channels = 3
-model_dir = "image_classification_model.h5"
-train_dir = "dataset/train"
-valid_dir = "dataset/valid"
-test_dir = "dataset/test"
-test_image_path = ""
+
+model_save_name = "EfficientNetB2"
+model_dir = "trained_models/salmon_crop_128/"+model_save_name+"/" # = save_dir
+
+train_dir = "/home/mirap/0_DATABASE/IMAS_Salmon/7_SalmonTest/train"
+valid_dir = "/home/mirap/0_DATABASE/IMAS_Salmon/7_SalmonTest/valid"
+test_dir = "/home/mirap/0_DATABASE/IMAS_Salmon/7_SalmonTest/test"
+test_image_path = "/home/mirap/0_DATABASE/IMAS_Salmon/7_SalmonTest/test/5/5_108.jpg"

evaluate.py

@@ -1,14 +1,32 @@
 import tensorflow as tf
 import config
 from prepare_data import get_datasets
+from sklearn.metrics import classification_report
+import numpy as np
 
-train_generator, valid_generator, test_generator, \
-train_num, valid_num, test_num= get_datasets()
 
+def eval_model(new_model):
+    # Load data
+    train_generator, valid_generator, test_generator, \
+    train_num, valid_num, test_num= get_datasets()
 
-# Load the model
-new_model = tf.keras.models.load_model(config.model_dir)
-# Get the accuracy on the test set
-loss, acc = new_model.evaluate_generator(test_generator,
-                                         steps=test_num // config.BATCH_SIZE)
-print("The accuracy on test set is: {:6.3f}%".format(acc*100))
+    # Get the accuracy on the test set
+    loss, acc, auc, precision, recall  = new_model.evaluate(test_generator,
+                                             batch_size=config.BATCH_SIZE,
+                                             steps=test_num // config.BATCH_SIZE)
+    print("result of ",config.model_dir)
+    print("The accuracy on test set is: {:6.3f}%".format(acc*100))
+    print("The auc on test set is: {:6.3f}%".format(auc*100))
+    print("The precision on test set is: {:6.3f}%".format(precision*100))
+    print("The recall on test set is: {:6.3f}%".format(recall*100))
+
+    # Evaluate per class
+    lables_array = test_generator.classes
+    predictions = new_model.predict(test_generator)
+    predictions = np.argmax(predictions, axis=1)
+    print(classification_report(lables_array, predictions))
+
+if __name__ == '__main__':
+    # Load the model
+    new_model = tf.keras.models.load_model(config.model_dir+config.model_save_name+".h5")
+    eval_model(new_model)