集合与映射基础讲解

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"D1_1映射与函数.ppt" 本文将详细探讨集合、映射和函数的基本概念,这是数学中的基础理论,尤其在计算机科学和信息技术领域有着广泛的应用。 首先,我们来看集合的定义。集合是由具有某种特定性质的事物组成的整体,这些事物称为元素。如果一个元素属于集合,我们用"∈"表示,如果元素不属于集合,则用"∉"表示。空集是不含任何元素的集合,记作"∅"。在数论中,我们有时会遇到特定的集合表示,如"∗M"表示排除0的集合,而"∅M"表示排除0和负数的集合。 集合的表示方法主要有两种:列举法和描述法。列举法是直接列出集合中的所有元素,例如有限集合{a1, a2, ..., an}。描述法则是通过元素的特性来定义集合,比如自然数集N={0, 1, 2, 3, ...},整数集Z,有理数集Q,以及实数集R。此外,还有区间的概念,如开区间(a, b)和闭区间[a, b]。 集合之间存在多种关系,如包含关系(A⊆B表示A是B的子集),相等关系(A=B表示A和B含有相同的元素),以及空集与任何集合的关系。集合间还可以进行运算,包括并集(A∪B),交集(A∩B),差集(A∖B)和余集(B\A)。直积运算指的是两个集合A和B的所有元素对构成的新集合{(x, y)|x∈A, y∈B}。 接下来,映射是一个规则,它将一个集合的每个元素关联到另一个集合的一个元素。映射通常用f:A→B表示,其中A称为定义域,B称为值域,f(x)表示x在映射下的像。映射可以是一对一的(每个A中的元素对应B中的唯一元素)、一对多的(一个A元素对应B中的多个元素)或多对一的(A中的多个元素对应B中的一个元素)。 最后,函数是一种特殊的映射,它要求A中的每一个元素在B中都有唯一的像。也就是说,对于函数f:A→B,A中的每个元素x,都有唯一的f(x)存在于B中。函数的性质包括单调性、奇偶性、周期性和连续性等,这些都是数学分析的基础。 集合论、映射和函数是数学和计算机科学中的基石,它们在算法设计、数据结构、逻辑推理和问题建模等方面起着至关重要的作用。理解这些基本概念对于深入学习高级数学和信息技术至关重要。

import tensorflow as tf from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropoutfrom tensorflow.keras import Model​# 在GPU上运算时,因为cuDNN库本身也有自己的随机数生成器,所以即使tf设置了seed,也不会每次得到相同的结果tf.random.set_seed(100)​mnist = tf.keras.datasets.mnist(X_train, y_train), (X_test, y_test) = mnist.load_data()X_train, X_test = X_train/255.0, X_test/255.0​# 将特征数据集从(N,32,32)转变成(N,32,32,1),因为Conv2D需要(NHWC)四阶张量结构X_train = X_train[..., tf.newaxis]    X_test = X_test[..., tf.newaxis]​batch_size = 64# 手动生成mini_batch数据集train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(10000).batch(batch_size)test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(batch_size)​class Deep_CNN_Model(Model):    def __init__(self):        super(Deep_CNN_Model, self).__init__()        self.conv1 = Conv2D(32, 5, activation='relu')        self.pool1 = MaxPool2D()        self.conv2 = Conv2D(64, 5, activation='relu')        self.pool2 = MaxPool2D()        self.flatten = Flatten()        self.d1 = Dense(128, activation='relu')        self.dropout = Dropout(0.2)        self.d2 = Dense(10, activation='softmax')        def call(self, X):    # 无需在此处增加training参数状态。只需要在调用Model.call时,传递training参数即可        X = self.conv1(X)        X = self.pool1(X)        X = self.conv2(X)        X = self.pool2(X)        X = self.flatten(X)        X = self.d1(X)        X = self.dropout(X)   # 无需在此处设置training状态。只需要在调用Model.call时,传递training参数即可        return self.d2(X)​model = Deep_CNN_Model()loss_object = tf.keras.losses.SparseCategoricalCrossentropy()optimizer = tf.keras.optimizers.Adam()​train_loss = tf.keras.metrics.Mean(name='train_loss')train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')test_loss = tf.keras.metrics.Mean(name='test_loss')test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')​# TODO:定义单批次的训练和预测操作@tf.functiondef train_step(images, labels):       ......    @tf.functiondef test_step(images, labels):       ......    # TODO:执行完整的训练过程EPOCHS = 10for epoch in range(EPOCHS)补全代码

2023-06-13 上传

import tensorflow as tf import numpy as np from keras import Model from keras.layers import * from sklearn.model_selection import train_test_split in_flow= np.load("X_in_30od.npy") out_flow= np.load("X_out_30od.npy") c1 = np.load("X_30od.npy") D1 = np.load("Y_30od.npy") in_flow = Reshape(in_flow, (D1.shape[0], 5, 109, 109)) out_flow = Reshape(out_flow, (D1.shape[0], 5, 109)) c1 = Reshape(c1, (D1.shape[0], 5, 109)) X_train, X_test, y_train, y_test = train_test_split((in_flow, out_flow, c1), D1, test_size=0.2, random_state=42) X_train, X_val, y_train, y_val = train_test_split(X_train,y_train, test_size=0.2, random_state=42) input_od=Input(shape=(5,109,109)) x1=Reshape((5,109,109,1),input_shape=(5,109,109))(input_od) x1=ConvLSTM2D(filters=64,kernel_size=(3,3),activation='relu',padding='same',input_shape=(5,109,109,1))(x1) x1=Dropout(0.2)(x1) x1=Dense(1)(x1) x1=Reshape((109,109))(x1) input_inflow=Input(shape=(5,109)) x2=Permute((2,1))(input_inflow) x2=LSTM(109,return_sequences=True,activation='sigmoid')(x2) x2=Dense(109,activation='sigmoid')(x2) x2=tf.multiply(x1,x2) x2=Dense(109,activation='sigmoid')(x2) input_inflow2=Input(shape=(5,109)) x3=Permute([2,1])(input_inflow2) x3=LSTM(109,return_sequences=True,activation='sigmoid')(x3) x3=Dense(109,activation='sigmoid')(x3) x3 = Reshape((109, 109))(x3) x3=tf.multiply(x1,x3) x3=Dense(109,activation='sigmoid')(x3) mix=Add()([x2,x3]) mix=Bidirectional(LSTM(109,return_sequences=True,activation='sigmoid'))(mix) mix=Dense(109,activation='sigmoid')(mix) model= Model(inputs=[input_od,input_inflow,input_inflow2],outputs=[mix]) model.compile(optimizer='adam', loss='mean_squared_error') history = model.fit([X_train[:,0:5,:,:], X_train[:,5:10,:], X_train[:,10:15,:]], y_train, validation_data=([X_val[:,0:5,:,:], X_val[:,5:10,:], X_val[:,10:15,:]], y_val), epochs=10, batch_size=32) test_loss = model.evaluate([X_test[:,0:5,:,:], X_test[:,5:10,:], X_test[:,10:15,:]], y_test) print("Test loss:", test_loss) 程序的运行结果为Traceback (most recent call last): File "C:\Users\liaoshuyu\Desktop\python_for_bigginer\5.23.py", line 11, in <module> in_flow = Reshape(in_flow, (D1.shape[0], 5, 109, 109)) TypeError: Reshape.__init__() takes 2 positional arguments but 3 were given 怎么修改

2023-05-24 上传

#include <dummy.h> #include "esp_camera.h" #include <WiFi.h> #define CAMERA_MODEL_AI_THINKER #include "camera_pins.h" const char* ssid = "666"; const char* password = "qqljc123"; void startCameraServer(); void setup() { Serial.begin(115200); Serial.setDebugOutput(true); Serial.println(); camera_config_t config; config.ledc_channel = LEDC_CHANNEL_0; config.ledc_timer = LEDC_TIMER_0; config.pin_d0 = Y2_GPIO_NUM; config.pin_d1 = Y3_GPIO_NUM; config.pin_d2 = Y4_GPIO_NUM; config.pin_d3 = Y5_GPIO_NUM; config.pin_d4 = Y6_GPIO_NUM; config.pin_d5 = Y7_GPIO_NUM; config.pin_d6 = Y8_GPIO_NUM; config.pin_d7 = Y9_GPIO_NUM; config.pin_xclk = XCLK_GPIO_NUM; config.pin_pclk = PCLK_GPIO_NUM; config.pin_vsync = VSYNC_GPIO_NUM; config.pin_href = HREF_GPIO_NUM; config.pin_sscb_sda = SIOD_GPIO_NUM; config.pin_sscb_scl = SIOC_GPIO_NUM; config.pin_pwdn = PWDN_GPIO_NUM; config.pin_reset = RESET_GPIO_NUM; config.xclk_freq_hz = 20000000; config.pixel_format = PIXFORMAT_JPEG; if(psramFound()){ config.frame_size = FRAMESIZE_UXGA; config.jpeg_quality = 10; config.fb_count = 2; } else { config.frame_size = FRAMESIZE_SVGA; config.jpeg_quality = 12; config.fb_count = 1; } #if defined(CAMERA_MODEL_ESP_EYE) pinMode(13, INPUT_PULLUP); pinMode(14, INPUT_PULLUP); #endif esp_err_t err = esp_camera_init(&config); if (err != ESP_OK) { Serial.printf("Camera init failed with error 0x%x", err); return; } sensor_t * s = esp_camera_sensor_get(); if (s->id.PID == OV3660_PID) { s->set_vflip(s, 1); s->set_brightness(s, 1); s->set_saturation(s, -2); } s->set_framesize(s, FRAMESIZE_QVGA); #if defined(CAMERA_MODEL_M5STACK_WIDE) || defined(CAMERA_MODEL_M5STACK_ESP32CAM) s->set_vflip(s, 1); s->set_hmirror(s, 1); #endif WiFi.begin(ssid, password); while (WiFi.status() != WL_CONNECTED) { delay(500); Serial.print("."); } Serial.println(""); Serial.println("WiFi connected"); startCameraServer(); Serial.print("Camera Ready! Use 'http://"); Serial.print(WiFi.localIP()); Serial.println("' to connect"); } void loop() { delay(10000); } 每句代码具体意思解释

2023-07-16 上传