83536 fashion_checker.py

import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import numpy as np
from mnist_test.number_checker import NumberChecker
from mnist_test.fashion_checker import FashionChecker
 
class FashionChecker:
    def __init__(self):
        self.class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
                            'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
 
 
    def create_model(self) -> []:
        fashion_mnist = keras.datasets.fashion_mnist
        (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
 
 
        # plt.figure()
        # plt.imshow(train_images[10])
        # plt.colorbar()
        # plt.grid(False)
        # plt.show()
 
 
        # modeling
        model = keras.Sequential([
            keras.layers.Flatten(input_shape=(28, 28)),
            keras.layers.Dense(128, activation='relu'),
            keras.layers.Dense(10, activation='softmax')
        ])
        model.compile(optimizer='adam',
                      loss = 'sparse_categorical_crossentropy',
                      metrics=['accuracy'])
        # learning
        model.fit(train_images, train_labels, epochs=5)
        # test
        test_loss, test_acc = model.evaluate(test_images, test_labels)
        print('테스트 정확도: {}'.format(test_acc))
 
if __name__ == '__main__':
    # t = NumberChecker()
    t = FashionChecker()
    t.create_model()