Neural Network - #3

Network3.py

@@ -0,0 +1,63 @@
+import tensorflow as tf
+from tensorflow import keras
+from keras import layers
+
+# Load and preprocess the dataset
+# Assuming you have three folders named 'class1', 'class2', and 'class3'
+# each containing images of their respective classes
+
+data_dir = 'Training/'
+image_size = (100, 100)
+batch_size = 32
+
+train_ds = tf.keras.preprocessing.image_dataset_from_directory(
+    data_dir,
+    validation_split=0.2,
+    subset="training",
+    seed=123,
+    image_size=image_size,
+    batch_size=batch_size,
+)
+
+val_ds = tf.keras.preprocessing.image_dataset_from_directory(
+    data_dir,
+    validation_split=0.2,
+    subset="validation",
+    seed=123,
+    image_size=image_size,
+    batch_size=batch_size,
+)
+
+class_names = train_ds.class_names
+num_classes = len(class_names)
+
+
+# Create the model
+model = keras.Sequential([
+    layers.Rescaling(1./255, input_shape=(100, 100, 3)),
+    layers.Conv2D(16, 3, padding='same', activation='relu'),
+    layers.MaxPooling2D(),
+    layers.Conv2D(32, 3, padding='same', activation='relu'),
+    layers.MaxPooling2D(),
+    layers.Conv2D(64, 3, padding='same', activation='relu'),
+    layers.MaxPooling2D(),
+    layers.Flatten(),
+    layers.Dense(128, activation='relu'),
+    layers.Dense(num_classes)
+])
+
+# Compile the model
+model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+
+# Train the model
+epochs = 10
+model.fit(
+    train_ds,
+    validation_data=val_ds,
+    epochs=epochs
+)
+
+# Save the trained model
+model.save('trained_model')

Tester2.py

@@ -10,7 +10,7 @@ train_ds = tf.keras.utils.image_dataset_from_directory(train_data_dir, validatio
 val_ds = tf.keras.utils.image_dataset_from_directory(train_data_dir, validation_split=0.2,
                                                      subset="validation", seed=123, batch_size=32, image_size=(100, 100))
 
-model = keras.models.load_model("trained_model")
+model = keras.models.load_model("trained_model.h5")
 predictions = model.predict(val_ds.take(32))
 
 

network2.py

@@ -0,0 +1,52 @@
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from keras import layers
+import matplotlib.pyplot as plt
+
+def normalize(image, label):
+    return image / 255, label
+
+train_data_dir = "Training/"
+
+train_ds = tf.keras.utils.image_dataset_from_directory(train_data_dir,
+                                                       validation_split=0.2, subset="training", seed=123, batch_size=32,
+                                                       image_size=(100, 100), color_mode='grayscale')
+
+val_ds = tf.keras.utils.image_dataset_from_directory(train_data_dir,
+                                                     validation_split=0.2, subset="validation", seed=123, batch_size=32,
+                                                     image_size=(100, 100), color_mode='grayscale')
+
+class_names = train_ds.class_names
+print(class_names)
+
+train_ds = train_ds.map(normalize)
+val_ds = val_ds.map(normalize)
+
+
+model = keras.Sequential([
+    layers.Conv2D(64, (3, 3), activation='relu', input_shape=(100, 100, 1)),
+    layers.MaxPool2D((2, 2)),
+
+    layers.Conv2D(128, (3, 3), activation='relu'),
+    layers.MaxPool2D((2, 2)),
+
+    layers.Conv2D(256, (3, 3), activation='relu'),
+    layers.MaxPool2D((2, 2)),
+
+    layers.Flatten(),
+    layers.Dense(1024, activation='relu'),
+    layers.Dense(1, activation='sigmoid')
+])
+
+print(model.summary())
+
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+trainHistory = model.fit(train_ds, epochs=10, validation_data=val_ds)
+
+(loss, accuracy) = model.evaluate(val_ds)
+print(loss)
+print(accuracy)
+
+model.save("mode2.h5")

testerZBS.py

@@ -0,0 +1,57 @@
+import os
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+
+# Load the trained model
+model = keras.models.load_model('trained_model.h5')
+
+# Load the class names
+class_names = ['Empty', 'Food','People']
+
+# Load and preprocess the validation dataset
+data_dir = "Training/"
+image_size = (100, 100)
+batch_size = 32
+
+val_ds = tf.keras.preprocessing.image_dataset_from_directory(
+    data_dir,
+    validation_split=0.2,
+    subset="validation",
+    seed=123,
+    image_size=image_size,
+    batch_size=batch_size,
+)
+
+# Select 20 random images from the validation set
+val_images = []
+val_labels = []
+for images, labels in val_ds.unbatch().shuffle(1000).take(60):
+    val_images.append(images)
+    val_labels.append(labels)
+
+# Make predictions on the random images
+errorcount = 0
+for i in range(60):
+    test_image = val_images[i]
+    test_label = val_labels[i]
+    test_image = np.expand_dims(test_image, axis=0)
+    test_image = test_image / 255.0  # Normalize the image
+
+    # Make predictions
+    predictions = model.predict(test_image)
+    predicted_class_index = np.argmax(predictions[0])
+    predicted_class = class_names[predicted_class_index]
+
+    true_class = class_names[test_label]
+
+    direct = 'Results/'
+    filename = predicted_class + str(i) + '.jpeg'
+    tf.keras.preprocessing.image.save_img(direct+filename, val_images[i])
+    if predicted_class != true_class:
+        errorcount += 1
+    print('Image', i+1)
+    print('True class:', true_class)
+    print('Predicted class:', predicted_class)
+    print()
+print('Error count: ', errorcount)