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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+chart-7.png filter=lfs diff=lfs merge=lfs -text
+chart-8.png filter=lfs diff=lfs merge=lfs -text

CANet-v1.4.py

@@ -0,0 +1,67 @@
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
+
+
+veriyolu = ""
+
+image_size = (150, 150)
+batch_size = 32
+
+train_datagen = ImageDataGenerator(rescale=1./255, validation_split=0.1)
+train_generator = train_datagen.flow_from_directory(
+    veriyolu,
+    target_size=image_size,
+    batch_size=batch_size,
+    class_mode='categorical',
+    subset='training'
+)
+validation_generator = train_datagen.flow_from_directory(
+    veriyolu,
+    target_size=image_size,
+    batch_size=batch_size,
+    class_mode='categorical',
+    subset='validation'
+)
+
+
+model = keras.Sequential([
+    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
+    layers.MaxPooling2D(2, 2),
+    layers.Conv2D(64, (3, 3), activation='relu'),
+    layers.MaxPooling2D(2, 2),
+    layers.Conv2D(128, (3, 3), activation='relu'),
+    layers.MaxPooling2D(2, 2),
+    layers.Flatten(),
+    layers.Dense(512, activation='relu'),
+    layers.Dense(len(train_generator.class_indices), activation='softmax')
+])
+
+model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+
+model.fit(train_generator, validation_data=validation_generator, epochs=10)
+
+
+model.save("model5.h5")
+
+
+def guess(image_path, model, class_indices):
+    img = load_img(image_path, target_size=(150, 150))
+    img_array = img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    prediction = model.predict(img_array)
+    predicted_class = np.argmax(prediction)
+    class_labels = {v: k for k, v in class_indices.items()}
+    predicted_label = class_labels[predicted_class]
+    
+    plt.imshow(img)
+    plt.title(f"model_guess: {predicted_label}")
+    plt.axis("off")
+    plt.show()
+