데일리 08.25 - teachablemachine

과제

본인들이 관심 있는 이미지를 가지고 티처블 머신에서 예측모델을 생성한 후 테스트한 결과 영상을 예측모델 소스와 같이 문서로 작성하여 제출하시오.

https://teachablemachine.withgoogle.com

Teachable Machine

 

모델

강아지와 고양이를 분류하는 모델을 만들어보기로 했다.

from keras.models import load_model  # TensorFlow is required for Keras to work
from PIL import Image, ImageOps  # Install pillow instead of PIL
import numpy as np

# Disable scientific notation for clarity
np.set_printoptions(suppress=True)

# Load the model
model = load_model("keras_Model.h5", compile=False)

# Load the labels
class_names = open("labels.txt", "r").readlines()

# Create the array of the right shape to feed into the keras model
# The 'length' or number of images you can put into the array is
# determined by the first position in the shape tuple, in this case 1
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)

# Replace this with the path to your image
image = Image.open("<IMAGE_PATH>").convert("RGB")

# resizing the image to be at least 224x224 and then cropping from the center
size = (224, 224)
image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)

# turn the image into a numpy array
image_array = np.asarray(image)

# Normalize the image
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1

# Load the image into the array
data[0] = normalized_image_array

# Predicts the model
prediction = model.predict(data)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]

# Print prediction and confidence score
print("Class:", class_name[2:], end="")
print("Confidence Score:", confidence_score)

 

결과

학습에 사용한 데이터가 별로 없어서 아직 구분을 잘 못하는 모습을 보여준다.

 

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본 후기는 정보통신산업진흥원(NIPA)에서 주관하는 <AI 서비스완성! AI+웹개발 취업캠프 - 프런트엔드&백엔드> 과정 학습/프로젝트/과제 기록으로 작성되었습니다.