import paddle
 
import numpy as np
from PIL import Image
from paddle.vision.transforms import Normalize
import os
 
 
transform = Normalize(mean=[127.5], std=[127.5], data_format='CHW')
 
# 下载数据集并初始化 DataSet
 
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
 
test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)
 
 
 
# 模型组网并初始化网络
 
lenet = paddle.vision.models.LeNet(num_classes=10)
 
model = paddle.Model(lenet)
 
 
 
# 模型训练的配置准备，准备损失函数，优化器和评价指标
 
model.prepare(paddle.optimizer.Adam(parameters=model.parameters()),
 
              paddle.nn.CrossEntropyLoss(),
 
              paddle.metric.Accuracy())
 
 
 
# # 模型训练
 
model.fit(train_dataset, epochs=1, batch_size=64, verbose=1)
 
# # 模型评估
 
# model.evaluate(test_dataset, batch_size=64, verbose=1)
 
 
 
# 保存模型
 
model.save('./output/mnist')
 
# 加载模型
 
model.load('output/mnist')
 
 
 
# 从测试集中取出一张图片
 
# img, label = test_dataset[0]
 
# 将图片shape从1*28*28变为1*1*28*28，增加一个batch维度，以匹配模型输入格式要求
 
# img_batch = np.expand_dims(img.astype('float32'), axis=0)
 
path = "E:\\code\\python\\1"
file_list = os.listdir(path)

for file_name in file_list:
    print(file_name)
 
    image = Image.open(os.path.join(path, file_name))

    img = transform(image)
    img_batch = np.expand_dims(img.astype('float32'), axis=0)


    # 执行推理并打印结果，此处predict_batch返回的是一个list，取出其中数据获得预测结果

    out = model.predict_batch(img_batch)[0]

    pred_label = out.argmax()

    print('pred label: {}'.format(pred_label))

# 可视化图片

# from matplotlib import pyplot as plt
# import pylab
# plt.imshow(img[0])
# pylab.show()