From f26ea381c54049606e020e6d916cca383d3df22b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=99=86=E9=9B=AA=E6=9D=BE?= Date: Fri, 14 Apr 2023 10:15:31 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=20'.py'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .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") \ No newline at end of file