Update cnn_ela_test.py

cnn_ela_test.py

@@ -25,6 +25,12 @@ import matplotlib.pyplot as plt
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
 from copy import deepcopy
 
+
+
+##########################################
+#define the Cnn network as it was in the training file
+
+
 model = Sequential()
 
 model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'valid', 
@@ -54,29 +60,24 @@ model.summary()
 # Load saved weights
 model.load_weights("ELA_CNN_ART_V2.h5")
 
+#set the optimizer
 optimizer = RMSprop(lr=0.0005, rho=0.9, epsilon=1e-08, decay=0.0)
 model.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"])
 
-
-
+###########################################
+#label and order the data
 
 test_real_folder = 'datasets/test_set/real/'
 test_fake_folder = 'datasets/test_set/fake/'
 
-
-
 test_ela_output = 'datasets/training_set/ela_output/'
 
-
-
-
 test_set = labeling(test_real_folder, test_fake_folder)
 X_test = []
 Y_test = []
 
 
-
-# Preprocess test set
+# Preprocess test set using ELA and then store the output
 for index, row in test_set.iterrows():
     X_test.append(array(convert_to_ela_image(row[0], 90, test_ela_output).resize((128, 128))).flatten() / 255.0)
     Y_test.append(row[1])
@@ -127,7 +128,8 @@ def calculate_acc(y_true, y_pred):
     plt.title('Confusion Matrix')
     plt.show()
     
-
+############################################
+# model preformance metrics and plots
 
 # Get predicted probabilities
 Y_pred_prob = model.predict(X_test)
@@ -141,22 +143,3 @@ Y_true = np.argmax(Y_test, axis=1)
 calculate_acc(Y_true, Y_pred)
 
 model.summary()
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-