程序员优劣：理解之别

高清英文版
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"Code.Simplicity" 是一本由 Max Kanat-Alexander 编著的IT专业书籍，专注于提升编程技能和理解的重要性。这本书以一种清晰简洁的方式探讨了程序员之间的差异，区分了出色的程序员与平庸程序员的关键因素：理解。作者强调，优秀的程序员不仅编写代码，更是真正理解他们所编写的程序和背后的逻辑。书中可能涵盖了诸如软件设计原则、编程范式、代码可读性和维护性等方面的知识，强调了对技术深度和清晰思维的追求。 本书可能包含实用的编程技巧，以及如何通过简化代码来提高效率和减少错误。它可能会探讨如何通过良好的编程习惯和结构化思考来避免常见的编程陷阱，同时提升团队协作和代码复用能力。对于那些希望成为高级工程师或技术领导者的人来说，理解章节将有助于读者深化对编程本质的认识，提升自身在解决复杂问题时的决策力。 "CodeSimplicity" 以其高清英文版的形式呈现，适合英语阅读者，无论是初学者还是经验丰富的开发者，都能从中获益。版权信息显示，本书享有2012年的首次发行权，并且O'Reilly Media作为出版商，提供了在线版本供读者选择。该书还包含了编辑、生产编辑、校对人员和设计师等专业人士的贡献，确保了内容的专业性和质量。 此外，书中可能还包含了一个修订历史记录，注明了首次出版的时间及后续的更新情况，以及O'Reilly Media的常见商标信息。"CodeSimplicity" 的封面设计可能以象征简洁和理解的环颈鸽为特色，旨在激发读者对简化和优雅编程艺术的兴趣。 "Code.Simplicity" 不仅是一本技术指南，更是一个启发思考和提升编程素养的工具，帮助读者建立坚实的编程基础，从而在IT行业中脱颖而出。

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SELECT scm.english_name as englishName, scm.city_simplicity as citySimplicity, scm.initials, scm.city_name as city, scm.city_three as cityCode FROM sd_airport_new san INNER JOIN sys_city_management scm ON scm.city_three = san.city_code WHERE san.del_flag=0 <if test="hotHave !=null and hotHave != ''"> and scm.is_hot = 1 </if> GROUP BY scm.city_three 将这段变成sql语句

2023-06-04 上传
SELECT scm.english_name as englishName, scm.city_simplicity as citySimplicity, scm.initials, scm.city_name as city, scm.city_three as cityCode FROM sd_airport_new san INNER JOIN sys_city_management ...

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import numpy as np import tensorflow as tf from tensorflow import keras import matplotlib.pyplot as plt ## Let us define a plt function for simplicity def plt_loss(x,training_metric,testing_metric,ax,colors = ['b']): ax.plot(x,training_metric,'b',label = 'Train') ax.plot(x,testing_metric,'k',label = 'Test') ax.set_xlabel('Epochs') ax.set_ylabel('Accuarcy')# ax.set_ylabel('Categorical Crossentropy Loss') plt.legend() plt.grid() plt.show() tf.keras.utils.set_random_seed(1) ## We import the Minist Dataset using Keras.datasets (train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data() ## We first vectorize the image (28*28) into a vector （784） train_data = train_data.reshape(train_data.shape[0],train_data.shape[1]train_data.shape[2]) # 60000784 test_data = test_data.reshape(test_data.shape[0],test_data.shape[1]test_data.shape[2]) # 10000784 ## We next change label number to a 10 dimensional vector, e.g., 1->[0,1,0,0,0,0,0,0,0,0] train_labels = keras.utils.to_categorical(train_labels,10) test_labels = keras.utils.to_categorical(test_labels,10) ## start to build a MLP model N_batch_size = 5000 N_epochs = 100 lr = 0.01 ## we build a three layer model, 784 -> 64 -> 10 MLP_4 = keras.models.Sequential([ keras.layers.Dense(128, input_shape=(784,),activation='relu'), keras.layers.Dense(64,activation='relu'), keras.layers.Dense(10,activation='softmax') ]) MLP_4.compile( optimizer=keras.optimizers.Adam(lr), loss= 'categorical_crossentropy', metrics = ['accuracy'] ) History = MLP_4.fit(train_data[:10000],train_labels[:10000], batch_size = N_batch_size, epochs = N_epochs,validation_data=(test_data,test_labels), shuffle=False) train_acc = History.history['accuracy'] test_acc = History.history['val_accuracy']在该模型中加入early stopping，使用monitor='loss', patience = 2设置代码

2023-06-02 上传
## Let us define a plt function for simplicity def plt_loss(x,training_metric,testing_metric,ax,colors = ['b']): ax.plot(x,training_metric,'b',label = 'Train') ax.plot(x,testing_metric,'k',label ...

import numpy as np import tensorflow as tf from tensorflow import keras import matplotlib.pyplot as plt Let us define a plt function for simplicity def plt_loss(x,training_metric,testing_metric,ax,colors = ['b']): ax.plot(x,training_metric,'b',label = 'Train') ax.plot(x,testing_metric,'k',label = 'Test') ax.set_xlabel('Epochs') ax.set_ylabel('Accuracy') plt.legend() plt.grid() plt.show() tf.keras.utils.set_random_seed(1) We import the Minist Dataset using Keras.datasets (train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data() We first vectorize the image (28*28) into a vector （784） train_data = train_data.reshape(train_data.shape[0],train_data.shape[1]train_data.shape[2]) # 60000784 test_data = test_data.reshape(test_data.shape[0],test_data.shape[1]test_data.shape[2]) # 10000784 We next change label number to a 10 dimensional vector, e.g., 1-> train_labels = keras.utils.to_categorical(train_labels,10) test_labels = keras.utils.to_categorical(test_labels,10) start to build a MLP model N_batch_size = 5000 N_epochs = 100 lr = 0.01 we build a three layer model, 784 -> 64 -> 10 MLP_3 = keras.models.Sequential([ keras.layers.Dense(128, input_shape=(784,),activation='relu'), keras.layers.Dense(64, activation='relu'), keras.layers.Dense(10,activation='softmax') ]) MLP_3.compile( optimizer=keras.optimizers.Adam(lr), loss= 'categorical_crossentropy', metrics = ['accuracy'] ) History = MLP_3.fit(train_data,train_labels, batch_size = N_batch_size, epochs = N_epochs,validation_data=(test_data,test_labels), shuffle=False) train_acc = History.history['accuracy'] test_acc = History.history对于该模型，使用不同数量的训练数据（5000，10000，15000，…，60000,公差=5000的等差数列），绘制训练集和测试集准确率（纵轴）关于训练数据大小(横轴)的曲线

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早起的虫子被鸟吃_好名抢光了
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