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import os
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import tensorflow as tf
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import matplotlib.pyplot as plt
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from tensorflow.keras.models import load_model
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import numpy as np
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# 模型路径
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model_path = "./myModel/myModel.h5"
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# 抑制tensorflow，以防显存占用过多报错
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config = tf.compat.v1.ConfigProto(gpu_options=tf.compat.v1.GPUOptions(allow_growth=True))
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sess = tf.compat.v1.Session(config=config)
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# 读取手写数字数据集
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num_mnist = tf.keras.datasets.mnist
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(train_images, train_labels), (test_images, test_labels) = num_mnist.load_data()
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# 读取训练好的模型
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model = load_model(model_path)
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# 打印网络结构
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model.summary()
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# 评估
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model.evaluate(test_images.reshape(-1, 28, 28, 1), test_labels)
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# 可视化预测效果
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show_num = 200
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testShow = test_labels[:show_num]
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pred = model.predict(test_images.reshape(-1, 28, 28, 1))
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predict = []
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for item in pred:
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predict.append(np.argmax(item))
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# 显示折线图
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plt.figure()
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plt.title('Conv Predict')
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plt.ylabel('true number')
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plt.xlabel('img num')
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plt.plot(range(testShow.size), predict[:show_num], marker='^', color='coral', label='predict')
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plt.plot(range(testShow.size), testShow, marker='o', color='deepskyblue', label='result')
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plt.legend()
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# 挑选出预测错误的图片，并显示预测值
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wrongImg = []
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wrongNum = []
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for i in range(testShow.size):
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if (predict[i] != testShow[i]):
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wrongImg.append(test_images[i])
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wrongNum.append(predict[i])
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for i in range(len(wrongImg)):
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plt.figure()
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plt.title(str(wrongNum[i]))
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plt.imshow(wrongImg[i])
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plt.show()
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