From 4fd371f066c8828f7891b27092af578edce4123a Mon Sep 17 00:00:00 2001 From: 10225501460 <10225501460@stu.ecnu.edu.cn> Date: Mon, 6 Nov 2023 14:28:40 +0800 Subject: [PATCH] commit source code and model --- code/mnist.py | 57 +++++++++++++++++++++ output/1.jpg | Bin 0 -> 29570 bytes output/2.jpg | Bin 0 -> 29528 bytes output/model_epoch_10/fingerprint.pb | Bin 0 -> 55 bytes output/model_epoch_10/keras_metadata.pb | 8 +++ output/model_epoch_10/saved_model.pb | Bin 0 -> 86008 bytes .../variables/variables.data-00000-of-00001 | Bin 0 -> 1226516 bytes output/model_epoch_10/variables/variables.index | Bin 0 -> 1214 bytes 8 files changed, 65 insertions(+) create mode 100644 code/mnist.py create mode 100644 output/1.jpg create mode 100644 output/2.jpg create mode 100644 output/model_epoch_10/fingerprint.pb create mode 100644 output/model_epoch_10/keras_metadata.pb create mode 100644 output/model_epoch_10/saved_model.pb create mode 100644 output/model_epoch_10/variables/variables.data-00000-of-00001 create mode 100644 output/model_epoch_10/variables/variables.index diff --git a/code/mnist.py b/code/mnist.py new file mode 100644 index 0000000..2902195 --- /dev/null +++ b/code/mnist.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="/home/mnist/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() +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='adam', 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=10) + +#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_10") diff --git a/output/1.jpg b/output/1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..1fc6351762e8c7aef11a404dc82f4540c73fa197 GIT binary patch literal 29570 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