archieve: homework7

homework7/.idea/.gitignore

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+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

homework7/.idea/MarsCodeWorkspaceAppSettings.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="com.codeverse.userSettings.MarscodeWorkspaceAppSettingsState">
+    <option name="ckgOperationStatus" value="SUCCESS" />
+  </component>
+</project>

homework7/.idea/homework7.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="ai-homework" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

homework7/.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

homework7/.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="ai-homework" />
+  </component>
+</project>

homework7/.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/homework7.iml" filepath="$PROJECT_DIR$/.idea/homework7.iml" />
+    </modules>
+  </component>
+</project>

homework7/.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/.." vcs="Git" />
+  </component>
+</project>

homework7/main.py

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+import numpy as np
+# 在文件开头添加兼容性设置
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
+
+# 加载MNIST数据集，通过设置 one_hot=True 来使用独热编码标签
+# 独热编码：对于每个图片的标签 y，10 位中仅有一位的值为 1，其余的为 0。
+mnist = tf.keras.datasets.mnist
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+
+# 权重正态分布初始化函数
+def weight_variable(shape):
+    # 生成截断正态分布随机数,shape表示生成张量的维度，mean是均值(默认=0.0)，stddev是标准差。
+    # 取值范围为 [ mean - 2 * stddev, mean + 2 * stddev ]，这里为[-0.2, 0.2]
+    initial = tf.truncated_normal(shape, stddev=0.1)
+    return tf.Variable(initial)
+
+
+# 偏置量初始化函数
+def bias_variable(shape):
+    initial = tf.constant(0.1, shape=shape)  # value=0.1, shape是张量的维度
+    return tf.Variable(initial)
+
+
+if __name__ == "__main__":
+    # 预处理数据
+    x_train = x_train.reshape(-1, 784).astype('float32') / 255.0
+    x_test = x_test.reshape(-1, 784).astype('float32') / 255.0
+    y_train = tf.keras.utils.to_categorical(y_train, 10)
+    y_test = tf.keras.utils.to_categorical(y_test, 10)
+
+    # 替换原来的mnist变量使用方式
+    mnist_dataset = (x_train, y_train), (x_test, y_test)
+
+    # 后续代码中所有mnist.train.xxx需要改为mnist_dataset[0][0]和mnist_dataset[0][1]
+    # 所有mnist.test.xxx需要改为mnist_dataset[1][0]和mnist_dataset[1][1]
+    print(x_train.shape[0])  # 输出训练集样本数 60000
+    print(x_test.shape[0])   # 输出测试集样本数 10000
+
+    # 为训练数据集的输入 x 和标签 y 创建占位符
+    x = tf.placeholder(tf.float32, [None, 784])  # 保持原样（已通过兼容性导入）
+    y = tf.placeholder(tf.float32, [None, 10])
+    keep_prob = tf.placeholder(tf.float32)
+
+    # 创建神经网络第1层，输入层，激活函数为relu
+    W_layer1 = weight_variable([784, 500])
+    b_layer1 = bias_variable([500])
+    h1 = tf.add(tf.matmul(x, W_layer1), b_layer1)  # W * x + b
+    h1 = tf.nn.relu(h1)
+    # 创建神经网络第2层，隐藏层，激活函数为relu
+    W_layer2 = weight_variable([500, 1000])
+    b_layer2 = bias_variable([1000])
+    h2 = tf.add(tf.matmul(h1, W_layer2), b_layer2)  # W * h1 + b，h1为第1层的输出
+    h2 = tf.nn.relu(h2)
+    # 创建神经网络第3层，隐藏层，激活函数为relu
+    W_layer3 = weight_variable([1000, 300])
+    b_layer3 = bias_variable([300])
+    h3 = tf.add(tf.matmul(h2, W_layer3), b_layer3)  # W * h2 + b，h2为第2层的输出
+    h3 = tf.nn.relu(h3)
+    # 创建神经网络第4层，输出层，激活函数为softmax
+    W_layer4 = weight_variable([300, 10])
+    b_layer4 = bias_variable([10])
+    predict = tf.add(tf.matmul(h3, W_layer4), b_layer4)  # W * h3 + b，h3为第3层的输出
+    y_conv = tf.nn.softmax(tf.matmul(h3, W_layer4) + b_layer4)
+    # 计算交叉熵代价函数
+    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predict, labels=y))
+    # 使用Adam下降算法优化交叉熵代价函数
+    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
+    # 预测是否准确的结果存放在一个布尔型的列表中
+    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y, 1))  # argmax返回的矩阵行中的最大值的索引号
+    # 求预测准确率
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))  # cast将布尔型的数据转换成float型的数据；reduce_mean求平均值
+
+    # 初始化
+    init_op = tf.global_variables_initializer()
+
+    with tf.Session() as sess:
+        sess.run(init_op)
+        for i in range(550):  # 训练样本为55000，分成550批，每批为100个样本
+            # 手动实现next_batch
+            batch_index = np.random.randint(0, x_train.shape[0], 100)
+            batch = (x_train[batch_index], y_train[batch_index])
+            train_accuracy = accuracy.eval(feed_dict={x: batch[0], y: batch[1], keep_prob: 1.0})
+            test_accuracy = accuracy.eval(feed_dict={x: x_test, y: y_test})
+            print('step %d, training accuracy %g, test accuracy %g' % (i, train_accuracy, test_accuracy))
+            # 每一步迭代，都会加载100个训练样本，然后执行一次train_step，并通过feed_dict，用训练数据替代x和y张量占位符。
+            sess.run(train_step, feed_dict={x: batch[0], y: batch[1], keep_prob: 0.5})
+        # 显示最终在测试集上的准确率
+        print(
+            'test accuracy %g' % accuracy.eval(feed_dict={x: x_test, y: y_test, keep_prob: 1.0}))