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      file/assignment7/file_assignment7_mnist.py

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import os
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data(path="/mnist.npz") #加载mnist数据集
#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
print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)
#打印训练集中前9张,看看是什么数字
for i in range(9):
plt.subplot(3,3,1+i)
plt.imshow(x_train[i], cmap='gray')
plt.show()
plt.savefig('./mnist/output/1.jpg')
#打印相应的标签
print(y_train[:9])
#基操:将像素标准化一下
x_train, x_test = x_train / 255.0, x_test / 255.0
#搭建一个两层神经网络
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)), #拉伸图像成一维向量
tf.keras.layers.Dense(128, activation='relu'), #第一层全连接+ReLU激活
tf.keras.layers.Dropout(0.2), #dropout层
tf.keras.layers.Dense(10, activation='softmax') #第二层全连接+softmax激活,输出预测标签
])
#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
model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
#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
model.fit(x_train, y_train, epochs=5)
#计算训练了5个epoch的模型在测试集上的表现
model.evaluate(x_test, y_test)
#直观看一下模型预测结果,打印测试集中的前9张图像
for i in range(9):
plt.subplot(3,3,1+i)
plt.imshow(x_test[i], cmap='gray')
plt.show()
plt.savefig('./mnist/output/2.jpg')
#打印模型识别的数字,是否正确?
# np.argmax(model(x_test[:9]).numpy(), axis=1)
#保存训练好的模型
model.save("./mnist/output/model_epoch_5")

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