Python build_model Examples

Example #1

File: gyudon-checker.py Project: kaneshirok/ml-plactice

calories = [656, 658, 768, 836]

# 入力画像をNumpyに変換 --- (※2)
X = []
files = []
for fname in sys.argv[1:]:
    img = Image.open(fname)
    img = img.convert("RGB")
    img = img.resize((image_size, image_size))
    in_data = np.asarray(img)
    X.append(in_data)
    files.append(fname)
X = np.array(X)

# CNNのモデルを構築 --- (※3)
model = gyudon.build_model(X.shape[1:])
model.load_weights("./image/gyudon-model.hdf5")

# データを予測 --- (※4)
html = ""
pre = model.predict(X)
for i, p in enumerate(pre):
    y = p.argmax()
    print("+ 入力:", files[i])
    print("| 牛丼名:", categories[y])
    print("| カロリー:", calories[y])
    html += """
        <h3>入力:{0}</h3>
        <div>
          <p><img src="{1}" width=300></p>
          <p>牛丼名:{2}</p>

Example #2

calories = [656, 658, 768, 836]

# 입력 이미지를 Numpy로 변환하기 --- (※2)
X = []
files = []
for fname in sys.argv[1:]:
    img = Image.open(fname)
    img = img.convert("RGB")
    img = img.resize((image_size, image_size))
    in_data = np.asarray(img)
    X.append(in_data)
    files.append(fname)
X = np.array(X)

# CNN 모델 구축하기 --- (※3)
model = gyudon.build_model(X.shape[1:])
model.load_weights("./image/gyudon-model.hdf5")
# 데이터 예측하기 --- (※4)
html = ""
pre = model.predict(X)
for i, p in enumerate(pre):
    y = p.argmax()
    print("+입력:", files[i])
    print("|규동 이름:", categories[y])
    print("|칼로리:", calories[y])
    html += """
        <h3>입력:{0}</h3>
        <div>
          <p><img src="{1}" width=300></p>
          <p>규동 이름:{2}</p>
          <p>칼로리 :{3}kcal</p>