finished

main.py

@@ -4,6 +4,7 @@ import tensorflow as tf
 import os 
 
 
+
 #Import Data
 PATH = os.path.join(os.getcwd(), "crop-data")
 
@@ -23,12 +24,13 @@ validation_data = tf.keras.utils.image_dataset_from_directory(validation_data,
                                                             batch_size=BATCH_SIZE,
                                                             image_size=IMG_SIZE)
 
-test_data = tf.keras.utils.image_dataset_from_directory(test_data,
+train_size = len(training_data)-len(validation_data)
-                                                            shuffle=True,
+test_data =  training_data.skip(train_size).take(len(validation_data))
-                                                            batch_size=BATCH_SIZE,
-                                                            image_size=IMG_SIZE)
-
 class_names = training_data.class_names
+training_data = training_data.take(train_size)
+
+print(f"Train: {len(training_data)}\nValid: {len(validation_data)}\nTest: {len(test_data)}")
+
 
 #View Data
 plt.figure(figsize=(10,10))
@@ -54,9 +56,6 @@ data_augmentation = tf.keras.Sequential([
     tf.keras.layers.RandomRotation(0.2)
     ])
 
-#Preprocessing
-preprocess_input = tf.keras.applications.mobilenet_v3.preprocess_input
-
 #Create Base Model From MobileNetV3
 IMG_SHAPE = IMG_SIZE + (3,)
 base_model = tf.keras.applications.MobileNetV3Large(
@@ -85,7 +84,6 @@ predication_batch = prediction_layer(feature_batch_avg)
 
 inputs = tf.keras.Input(shape=(160,160,3))
 x = data_augmentation(inputs)
-x = preprocess_input(x)
 x = base_model(x, training=False)
 x = global_avg_layer(x)
 x = tf.keras.layers.Dropout(0.2)(x)
@@ -102,17 +100,13 @@ base_learning_rate = 0.0001
 
 training_data = training_data.map(lambda x,y: (x, tf.one_hot(y,38)))
 validation_data = validation_data.map(lambda x,y: (x, tf.one_hot(y,38)))
-
+test_data = test_data.map(lambda x,y: (x, tf.one_hot(y,38)))
-
 
 optimizer = tf.keras.optimizers.Adam(learning_rate=base_learning_rate)
 loss = tf.keras.losses.CategoricalCrossentropy()
 metrics = [tf.keras.metrics.CategoricalAccuracy()]
 
 
-phy_dev = tf.config.list_physical_devices("GPU")
-for gpu in phy_dev:
-    tf.config.experimental.set_memory_growth(gpu, True)
 
 model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
 
@@ -131,7 +125,7 @@ lr_schedule = tf.keras.callbacks.ReduceLROnPlateau(
         min_lr=1e-6
         )
 
-earky_stopping = tf.keras.callbacks.EarlyStopping(
+early_stopping = tf.keras.callbacks.EarlyStopping(
         monitor="val_loss",
         patience=10,
         restore_best_weights=True
@@ -140,6 +134,15 @@ earky_stopping = tf.keras.callbacks.EarlyStopping(
 history = model.fit(training_data,
                     epochs=initial_epochs,
                     validation_data=validation_data,
-                    callbacks=[lr_schedule])
+                    callbacks=[lr_schedule, early_stopping])
 
-model.save("crop-classifier-callbacks.keras")
+model.save("crop-classifier-better-test.keras")
+
+#Evaluate Model
+results = model.evaluate(validation_data)
+print(f"Validation Loss: {results[0]}")
+print(f"Validation Accuracy: {results[1]}")
+
+results = model.evaluate(test_data)
+print(f"Test Loss: {results[0]}")
+print(f"Test Accuracy: {results[1]}")

results.py

@@ -0,0 +1,42 @@
+import numpy as np
+import tensorflow as tf
+import os
+
+
+#Import Data
+PATH = os.path.join(os.getcwd(), "crop-data")
+
+validation_data = os.path.join(PATH, "valid")
+validation_data_32 = tf.keras.utils.image_dataset_from_directory(validation_data,
+                                                                 shuffle=True,
+                                                                 batch_size=32,
+                                                                 image_size=(160,160))
+
+validation_data_64 = tf.keras.utils.image_dataset_from_directory(validation_data,
+                                                                 shuffle=True,
+                                                                 batch_size=64,
+                                                                 image_size=(160,160))
+
+AUTOTUNE = tf.data.AUTOTUNE
+validation_data_32 = validation_data_32.prefetch(buffer_size=AUTOTUNE)
+validation_data_64 = validation_data_64.prefetch(buffer_size=AUTOTUNE)
+
+validation_data_32 = validation_data_32.map(lambda x,y: (x, tf.one_hot(y,38)))
+validation_data_64 = validation_data_64.map(lambda x,y: (x, tf.one_hot(y,38)))
+
+
+model1 = tf.keras.models.load_model("crop-classifier.keras")
+model2 = tf.keras.models.load_model("crop-classifier-64-batch.keras")
+model3 = tf.keras.models.load_model("crop-classifier-callbacks.keras")
+
+result = model1.evaluate(validation_data_32)
+print(f"Validation Loss: {result[0]}")
+print(f"Accuracy: {result[1]}")
+
+result = model2.evaluate(validation_data_64)
+print(f"Validation Loss: {result[0]}")
+print(f"Accuracy: {result[1]}")
+
+result = model3.evaluate(validation_data_64)
+print(f"Validation Loss: {result[0]}")
+print(f"Accuracy: {result[1]}")

test.py

@@ -1,4 +1,42 @@
-#Load model
+import numpy as np
-#Load test data
+import tensorflow as tf
-#Test
+import os
-#Print results
+
+
+#Import Data
+PATH = os.path.join(os.getcwd(), "crop-data")
+
+test_data = os.path.join(PATH, "test")
+test_data_32 = tf.keras.utils.image_dataset_from_directory(test_data,
+                                                                 shuffle=True,
+                                                                 batch_size=32,
+                                                                 image_size=(160,160))
+
+test_data_64 = tf.keras.utils.image_dataset_from_directory(test_data,
+                                                                 shuffle=True,
+                                                                 batch_size=64,
+                                                                 image_size=(160,160))
+
+AUTOTUNE = tf.data.AUTOTUNE
+test_data_32 = test_data_32.prefetch(buffer_size=AUTOTUNE)
+test_data_64 = test_data_64.prefetch(buffer_size=AUTOTUNE)
+
+test_data_32 = test_data_32.map(lambda x,y: (x, tf.one_hot(y,38)))
+test_data_64 = test_data_64.map(lambda x,y: (x, tf.one_hot(y,38)))
+
+
+model1 = tf.keras.models.load_model("crop-classifier.keras")
+model2 = tf.keras.models.load_model("crop-classifier-64-batch.keras")
+model3 = tf.keras.models.load_model("crop-classifier-callbacks.keras")
+
+result = model1.evaluate(test_data_32)
+print(f"Test Loss: {result[0]}")
+print(f"Test Accuracy: {result[1]}")
+
+result = model2.evaluate(test_data_64)
+print(f"Test Loss: {result[0]}")
+print(f"Test Accuracy: {result[1]}")
+
+result = model3.evaluate(test_data_64)
+print(f"Test Loss: {result[0]}")
+print(f"Test Accuracy: {result[1]}")