인류의 복지와 편익을 위한 인프라 건설을 주도하는토목공학과
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Python_deeplearning_mnist(박영훈 교수)
작성일
2022.11.24
작성자
부천대학교 토목과
import tensorflow as tf from keras.datasets import mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data() train_images.shape test_images.shape from keras import models from keras import layers network = models.Sequential() network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,))) network.add(layers.Dense(10, activation='softmax')) network.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy']) train_images = train_images.reshape((60000, 28 * 28)) train_images = train_images.astype('float32') / 255 test_images = test_images.reshape((10000, 28 * 28)) test_images = test_images.astype('float32') / 255 from keras.utils import to_categorical train_labels = to_categorical(train_labels) test_labels = to_categorical(test_labels) network.fit(train_images, train_labels, epochs=5, batch_size=128) test_loss, test_acc = network.evaluate(test_images, test_labels) print('test_acc:', test_acc)