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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 = "buraya veri yolunu yazınız"
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("image_classifier.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()
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