From da3972fcd922dcca44ee869a370610208c4b5f44 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 10:15:16 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20'.py'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .py | 57 ++++++++++++++++++++++++++++++ file/assignment7/file_assignment7_mnist.py | 57 ------------------------------ 2 files changed, 57 insertions(+), 57 deletions(-) create mode 100644 .py delete mode 100644 file/assignment7/file_assignment7_mnist.py diff --git a/.py b/.py new file mode 100644 index 0000000..2adbcc7 --- /dev/null +++ b/.py @@ -0,0 +1,57 @@ +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 diff --git a/file/assignment7/file_assignment7_mnist.py b/file/assignment7/file_assignment7_mnist.py deleted file mode 100644 index 2adbcc7..0000000 --- a/file/assignment7/file_assignment7_mnist.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