ai-school/exp2/source/train.py

from pathlib import Path
import sys
import tensorflow as tf
import keras
from keras import datasets, layers, models

sys.path.append(str(Path(__file__).resolve().parent.parent.parent))
import tensorflow_gpu_util


class CNN(object):
    def __init__(self):
        model = models.Sequential()
        # 第1层卷积，卷积核大小为3*3，32个，28*28为待训练图片的大小
        model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
        model.add(layers.MaxPooling2D((2, 2)))
        # 第2层卷积，卷积核大小为3*3，64个
        model.add(layers.Conv2D(64, (3, 3), activation='relu'))
        model.add(layers.MaxPooling2D((2, 2)))
        # 第三层卷积，卷积核大小为3*3，64个
        model.add(layers.Conv2D(64, (3, 3), activation='relu'))
        model.add(layers.Flatten())
        model.add(layers.Dense(64, activation='relu'))
        model.add(layers.Dense(10, activation='softmax'))
        model.summary()
        self.model = model

class DataSource(object):
    def __init__(self):
        # mnist数据集存储的位置，如何不存在将自动下载
        data_path = Path(__file__).resolve().parent.parent / 'datasets' / 'mnist.npz'
        (train_images, train_labels), (test_images,
                                       test_labels) = datasets.mnist.load_data(path=data_path)
        # 6万张训练图片，1万张测试图片
        train_images = train_images.reshape((60000, 28, 28, 1))
        test_images = test_images.reshape((10000, 28, 28, 1))
        # 像素值映射到 0 - 1 之间
        train_images, test_images = train_images / 255.0, test_images / 255.0
        self.train_images, self.train_labels = train_images, train_labels
        self.test_images, self.test_labels = test_images, test_labels

class Train:
    def __init__(self):
        self.cnn = CNN()
        self.data = DataSource()
    def train(self):
        check_path = Path(__file__).resolve().parent.parent / 'models' / 'cnn.ckpt'
        # period 每隔5epoch保存一次
        save_model_cb = keras.callbacks.ModelCheckpoint(
            str(check_path), save_weights_only=True, verbose=1, period=5)
        self.cnn.model.compile(optimizer='adam',
                               loss='sparse_categorical_crossentropy',
                               metrics=['accuracy'])
        self.cnn.model.fit(self.data.train_images, self.data.train_labels,
                           epochs=5, batch_size=1000, callbacks=[save_model_cb])
        test_loss, test_acc = self.cnn.model.evaluate(
            self.data.test_images, self.data.test_labels)
        print("准确率: %.4f, 共测试了%d张图片 " % (test_acc, len(self.data.test_labels)))

if __name__ == "__main__":
    tensorflow_gpu_util.print_gpu_availability()
    #app = Train()
    #app.train()