İmage Processing de dataset ile nesne tanima

import numpy as np
import keras
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.optimizers import RMSprop
from keras.utils import to_categorical

# Step 1: Load the dataset
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# Step 2: Preprocess the data
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

# Step 3: Create the model
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))

# Step 4: Compile the model
model.compile(loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy'])

# Step 5: Train the model
model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_test, y_test))

# Step 6: Test the model on new data
new_image = ... # load new image
predictions = model.predict(new_image)
print(predictions)