Neural Network - #4 Cleanup #2

.gitignore

@@ -16,6 +16,7 @@ lib64/
 parts/
 sdist/
 var/
+Network/Results
 *.egg-info/
 .installed.cfg
 *.egg

Network/Network.py

@@ -4,27 +4,20 @@ from keras.models import Sequential
 from keras.optimizers import Adam
 from keras.utils import to_categorical
 from keras.preprocessing.image import ImageDataGenerator
-import os
-import PIL
-import PIL.Image
-import numpy
-
 
+ # Normalizes the pixel values of an image to the range [0, 1].
+ 
 def normalize(image, label):
     return image / 255, label
-
-# Set the paths to the folders containing the training data
-train_data_dir = "Training/"
-
-
-# Set the number of classes and batch size
+ # Set the paths to the folder containing the training data
+train_data_dir = "Network/Training/"
+ # Set the number of classes and batch size
 num_classes = 3
 batch_size = 32
-
-# Set the image size and input shape
+ # Set the image size and input shape
 img_width, img_height = 100, 100
 input_shape = (img_width, img_height, 1)
-
+ # Load the training and validation data
 train_ds = tf.keras.utils.image_dataset_from_directory(
   train_data_dir,
   validation_split=0.2,
@@ -42,14 +35,13 @@ val_ds = tf.keras.utils.image_dataset_from_directory(
   seed=123,
   image_size=(img_height, img_width),
   batch_size=batch_size)
-
+ # Get the class names
 class_names = train_ds.class_names
 print(class_names)
-
+ # Normalize the training and validation data
 train_ds = train_ds.map(normalize)
 val_ds = val_ds.map(normalize)
-
-# Define the model architecture
+ # Define the model architecture
 model = tf.keras.Sequential([
     layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(img_height, img_width, 1)),
     layers.MaxPooling2D(),
@@ -61,20 +53,16 @@ model = tf.keras.Sequential([
     layers.Dense(128, activation='relu'),
     layers.Dense(num_classes, activation='softmax')
 ])
-
-# Compile the model
+ # Compile the model
 model.compile(optimizer='adam',
               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
               metrics=['accuracy'])
-
+ # Print the model summary
 model.summary()
-
-
+ # Train the model
 epochs=10
-# Train the model
 model.fit(train_ds,
           validation_data=val_ds,
           epochs=epochs)
-
-# Save the trained model
-model.save('trained_model.h5')
+ # Save the trained model
+model.save('Network/trained_model.h5')

Network/TesterRandom.py

@@ -4,13 +4,13 @@ import tensorflow as tf
 from tensorflow import keras
 
 # Load the trained model
-model = keras.models.load_model('trained_model.h5')
+model = keras.models.load_model('Network/trained_model.h5')
 
 # Load the class names
 class_names = ['Table', 'Done','Order']
 
 # Path to the folder containing test images
-test_images_folder = 'Testing/'
+test_images_folder = 'Network/Testing/'
 
 # Iterate over the test images
 i = 0
@@ -27,7 +27,6 @@ for folder_name in os.listdir(test_images_folder):
             true_class = 'Done'
         elif folder_name == 'People':
             true_class = 'Order'
-        true_class = folder_name
         
         # Iterate over the files in the subfolder
         for filename in os.listdir(folder_path):
@@ -35,8 +34,8 @@ for folder_name in os.listdir(test_images_folder):
                 i+=1
                 # Load and preprocess the test image
                 image_path = os.path.join(folder_path, filename)
-                test_image = keras.preprocessingimage.load_img(image_path, target_size=(100, 100))
-                test_image = keras.preprocessingimage.img_to_array(test_image)
+                test_image = keras.preprocessing.image.load_img(image_path, target_size=(100, 100))
+                test_image = keras.preprocessing.image.img_to_array(test_image)
                 test_image = np.expand_dims(test_image, axis=0)
                 test_image = test_image / 255.0  # Normalize the image
             
@@ -48,7 +47,7 @@ for folder_name in os.listdir(test_images_folder):
                 predicted_class_index = np.argmax(predictions[0])
                 predicted_class = class_names[predicted_class_index]
                 
-                direct = 'Results/'
+                direct = 'Network/Results/'
                 filename = str(i) + predicted_class  + '.jpeg'
                 test_image = np.reshape(test_image, (100, 100, 3))
                 tf.keras.preprocessing.image.save_img(direct+filename, test_image)

Network/testerVal.py

@@ -4,13 +4,13 @@ import tensorflow as tf
 from tensorflow import keras
 
 # Load the trained model
-model = keras.models.load_model('trained_model.h5')
+model = keras.models.load_model('Network/trained_model.h5')
 
 # Load the class names
 class_names = ['Table', 'Done','Order']
 
 # Load and preprocess the validation dataset
-data_dir = "Training/"
+data_dir = "Network/Training/"
 image_size = (100, 100)
 batch_size = 32
 
@@ -45,9 +45,9 @@ for i in range(60):
 
     true_class = class_names[test_label]
 
-    direct = 'Results/'
+    direct = 'Network/Results/'
     filename = predicted_class + str(i) + '.jpeg'
-    tf.keras.preprocessing.image.save_img(direct+filename, test_image)
+    tf.keras.preprocessing.image.save_img(direct+filename, val_images[i])
     if predicted_class != true_class:
         errorcount += 1
     print('Image', i+1)