commit source code and model

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")

output/model_epoch_10/keras_metadata.pb

@@ -0,0 +1,8 @@
+
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+¹
^root.keras_api.metrics.0"_tf_keras_metric*‚
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 15}2

+ó
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{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "sparse_categorical_accuracy"}, "shared_object_id": 11}2