云计算课程实验
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56 satır
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  1. import os
  2. import tensorflow as tf
  3. import matplotlib.pyplot as plt
  4. import numpy as np
  5. mnist = tf.keras.datasets.mnist
  6. (x_train, y_train),(x_test, y_test) = mnist.load_data(path="/mnist.npz") #加载mnist数据集
  7. #验证mnist数据集大小。x为数据,y为标签。mnist每张图的像素为28*28
  8. print(x_train.shape)
  9. print(y_train.shape)
  10. print(x_test.shape)
  11. print(y_test.shape)
  12. #打印训练集中前9张,看看是什么数字
  13. for i in range(9):
  14. plt.subplot(3,3,1+i)
  15. plt.imshow(x_train[i], cmap='gray')
  16. plt.show()
  17. plt.savefig('./mnist/output/1.jpg')
  18. #打印相应的标签
  19. print(y_train[:9])
  20. #基操:将像素标准化一下
  21. x_train, x_test = x_train / 255.0, x_test / 255.0
  22. #搭建一个两层神经网络
  23. model = tf.keras.models.Sequential([
  24. tf.keras.layers.Flatten(input_shape=(28, 28)), #拉伸图像成一维向量
  25. tf.keras.layers.Dense(128, activation='relu'), #第一层全连接+ReLU激活
  26. tf.keras.layers.Dropout(0.2), #dropout层
  27. tf.keras.layers.Dense(10, activation='softmax') #第二层全连接+softmax激活,输出预测标签
  28. ])
  29. #设置训练超参,优化器为sgd,损失函数为交叉熵,训练衡量指标为accuracy
  30. model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  31. #开始训练,训练5个epoch,一个epoch代表所有图像计算一遍。每一个epoch能观察到训练精度的提升
  32. model.fit(x_train, y_train, epochs=5)
  33. #计算训练了5个epoch的模型在测试集上的表现
  34. model.evaluate(x_test, y_test)
  35. #直观看一下模型预测结果,打印测试集中的前9张图像
  36. for i in range(9):
  37. plt.subplot(3,3,1+i)
  38. plt.imshow(x_test[i], cmap='gray')
  39. plt.show()
  40. plt.savefig('./mnist/output/2.jpg')
  41. #打印模型识别的数字,是否正确?
  42. # np.argmax(model(x_test[:9]).numpy(), axis=1)
  43. #保存训练好的模型
  44. model.save("./mnist/output/model_epoch_5")