From 264ef3b7c7cbc089553c77ab552b2041e2afcf13 Mon Sep 17 00:00:00 2001 From: xuesong Date: Thu, 26 Nov 2020 14:30:16 +0800 Subject: [PATCH] Upload New File --- file/assignment7/mnist.py | 55 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 55 insertions(+) create mode 100644 file/assignment7/mnist.py diff --git a/file/assignment7/mnist.py b/file/assignment7/mnist.py new file mode 100644 index 0000000..2e814da --- /dev/null +++ b/file/assignment7/mnist.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