Update test.py

Made the code more readable, added descriptive variable names and proper indentation

Author: thebigjoe29

I/image-classifier-using-transfer-learning/test.py

@@ -1,41 +1,54 @@
-from train import load_data, create_model, IMAGE_SHAPE, batch_size, np
+import numpy as np
 import matplotlib.pyplot as plt
-# load the data generators
+from train import load_data, create_model, IMAGE_SHAPE, batch_size
+
+# Load the data generators
 train_generator, validation_generator, class_names = load_data()
-# constructs the model
+
+# Construct the model
 model = create_model(input_shape=IMAGE_SHAPE)
-# load the optimal weights
+
+# Load the optimal weights
 model.load_weights("results/MobileNetV2_finetune_last5-loss-0.66.h5")
 
+# Calculate the number of validation steps per epoch
 validation_steps_per_epoch = np.ceil(validation_generator.samples / batch_size)
-# print the validation loss & accuracy
+
+# Print the validation loss & accuracy
 evaluation = model.evaluate_generator(validation_generator, steps=validation_steps_per_epoch, verbose=1)
 print("Val loss:", evaluation[0])
 print("Val Accuracy:", evaluation[1])
 
-# get a random batch of images
+# Get a random batch of images
 image_batch, label_batch = next(iter(validation_generator))
-# turn the original labels into human-readable text
+
+# Convert the original labels into human-readable text
 label_batch = [class_names[np.argmax(label_batch[i])] for i in range(batch_size)]
-# predict the images on the model
+
+# Predict the images using the model
 predicted_class_names = model.predict(image_batch)
 predicted_ids = [np.argmax(predicted_class_names[i]) for i in range(batch_size)]
-# turn the predicted vectors to human readable labels
+
+# Convert the predicted vectors to human-readable labels
 predicted_class_names = np.array([class_names[id] for id in predicted_ids])
 
-# some nice plotting
-plt.figure(figsize=(10,9))
+# Plot the results
+plt.figure(figsize=(10, 9))
 for n in range(30):
-    plt.subplot(6,5,n+1)
-    plt.subplots_adjust(hspace = 0.3)
+    plt.subplot(6, 5, n + 1)
+    plt.subplots_adjust(hspace=0.3)
     plt.imshow(image_batch[n])
+    
+    # Set the title and color based on correctness
     if predicted_class_names[n] == label_batch[n]:
         color = "blue"
         title = predicted_class_names[n].title()
     else:
         color = "red"
         title = f"{predicted_class_names[n].title()}, correct:{label_batch[n]}"
+    
     plt.title(title, color=color)
     plt.axis('off')
+
 _ = plt.suptitle("Model predictions (blue: correct, red: incorrect)")
-plt.show()
+plt.show()