From f26ea381c54049606e020e6d916cca383d3df22b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E9=99=86=E9=9B=AA=E6=9D=BE?= <xslu@dase.ecnu.edu.cn>
Date: Fri, 14 Apr 2023 10:15:31 +0800
Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=20'.py'?=
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---
 .py | 57 ---------------------------------------------------------
 1 file changed, 57 deletions(-)
 delete mode 100644 .py

diff --git a/.py b/.py
deleted file mode 100644
index 2adbcc7..0000000
--- a/.py
+++ /dev/null
@@ -1,57 +0,0 @@
-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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