이미지 분류 프로젝트

분류용 데이터 세트

데이터세트 불러오기

import tensorflow as tf
from tensorflow.keras.datasets import cifar10

# Load dataset
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# Normalize the pixel values to [0, 1]
x_train = x_train / 255.0
x_test = x_test / 255.0

print("Training data shape:", x_train.shape)

CNN 모델 구축하기

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# Define the CNN model
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
])

모델 컴파일하기

# Compile the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

모델 학습하기

# Train the model
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

모델 평가

# Evaluate the model
loss, accuracy = model.evaluate(x_test, y_test)
print(f"Test Accuracy: {accuracy * 100:.2f}%")

예측 수행

# Predict the category of a test image
import numpy as np

sample_image = x_test[0]
sample_label = y_test[0]

prediction = np.argmax(model.predict(sample_image[np.newaxis, ...]))
print(f"Predicted Label: {prediction}, Actual Label: {sample_label[0]}")

이미지 분류의 과제

요약 및 다음 단계