From 77da74d98e2cf567462ab6e343f6d1ac71ffb30b 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 10:14:27 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20'file/assignment7/mnist-ol?= =?UTF-8?q?d.py'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- file/assignment7/mnist-old.py | 55 +++++++++++++++++++++++++++++++++++++++++++ file/assignment7/mnist.py | 55 ------------------------------------------- 2 files changed, 55 insertions(+), 55 deletions(-) create mode 100644 file/assignment7/mnist-old.py delete mode 100644 file/assignment7/mnist.py diff --git a/file/assignment7/mnist-old.py b/file/assignment7/mnist-old.py new file mode 100644 index 0000000..2e814da --- /dev/null +++ b/file/assignment7/mnist-old.py @@ -0,0 +1,55 @@ +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="/data/data/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() + +#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() + +#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("/data/output/model_epoch_5") \ No newline at end of file diff --git a/file/assignment7/mnist.py b/file/assignment7/mnist.py deleted file mode 100644 index 2e814da..0000000 --- a/file/assignment7/mnist.py +++ /dev/null @@ -1,55 +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="/data/data/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() - -#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() - -#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("/data/output/model_epoch_5") \ No newline at end of file