score.py

from tensorflow import keras
from tensorflow.keras.models import load_model
from tensorflow.keras.datasets import cifar10
import numpy as np
import sys

def score(modelfile):
    # Load the CIFAR10 data.
    (x_train, y_train), (x_test, y_test) = cifar10.load_data()
    num_classes = 10

    # Input image dimensions.
    input_shape = x_train.shape[1:]

    # Normalize data.
    x_train = x_train.astype('float32') / 255
    x_test = x_test.astype('float32') / 255

    subtract_pixel_mean = True
    # If subtract pixel mean is enabled
    if subtract_pixel_mean:
        x_train_mean = np.mean(x_train, axis=0)
        x_train -= x_train_mean
        x_test -= x_train_mean

    print('x_train shape:', x_train.shape)
    print(x_train.shape[0], 'train samples')
    print(x_test.shape[0], 'test samples')
    print('y_train shape:', y_train.shape)

    # Convert class vectors to binary class matrices.
    y_train = keras.utils.to_categorical(y_train, num_classes)
    y_test = keras.utils.to_categorical(y_test, num_classes)

    model = load_model(modelfile)
    # preds = model.predict(x)
    # print(preds)

    # Score trained model.
    scores = model.evaluate(x_test, y_test, verbose=1)
    print('Test loss:', scores[0])
    print('Test accuracy:', scores[1])

if __name__ == '__main__':
    modelfile = sys.argv[1]
    print("score model: ", modelfile)
    score(modelfile)