import numpy as np def loaddata(): X = np.array([[1,'S'],[1,'M'],[1,'M'],[1,'S'], [1, 'S'], [2, 'S'], [2, 'M'], [2, 'M'], [2, 'L'], [2, 'L'], [3, 'L'], [3, 'M'], [3, 'M'], [3, 'L'], [3, 'L']]) y = np.array([-1,-1,1,1,-1,-1,-1,1,1,1,1,1,1,1,-1]) return X, y def Train(trainset,train_labels): m = trainset.shape[0] n = trainset.shape[1] prior_probability = {}# 先验概率 key是类别值，value是类别的概率值 conditional_probability ={}# 条件概率 key的构造：类别，特征,特征值 #类别的可能取值 labels = set(train_labels) # 计算先验概率(此时没有除以总数据量m) for label in labels: prior_probability[label] = len(train_labels[train_labels == label])+1 #计算条件概率 for i in range(m): for j in range(n): # key的构造：类别，特征,特征值 #补充计算条件概率的代码-1； key = str(train_labels[i])+','+str(j)+','+str(trainset[i][j]) conditional_probability[key] = (conditional_probability[key]+1 if (key in conditional_probability) else 1) conditional_probability_final = {} for key in conditional_probability: #补充计算条件概率的代码-2； label = key.split(',')[0] conditional_probability[key]+=1 key1 = int(key.split(',')[1]) Ni = len(set(trainset[:,key1])) conditional_probability_final[key] =conditional_probability[key]/(prior_probability[int(label)]+Ni) # 最终的先验概率(此时除以总数据量m) for label in labels: prior_probability[label] = prior_probability[label]/ (m+len(labels)) return prior_probability,conditional_probability_final,labels def predict(data): result={} for label in train_labels_set: temp=1.0 #补充预测代码； print('result=',result) #排序返回标签值 result[label] = temp*prior_probability[label] for i in range (len(data)): key = str(label)+ ','+str(i)+','+str(data[i]) result[label]*=conditional_probability_final[key] print('result=',result) #排序返回标签值 return sorted(result.items(), key=lambda x: x[1],reverse=True)[0][0] X,y = loaddata() prior_probability,conditional_probability,train_labels_set = Train(X,y) r_label = predict([2,'S']) print(' r_label =', r_label)运行次python代码

Python numpy.array详解：创建、读取与操作

"numpy.array函数简介" 在Python的科学计算库Numpy中，numpy.array是核心的构造函数，用于创建多维数组，也被称为数组对象或 Ndarray。Ndarray 是一种高效的数据结构，它能够存储同类型的元素，包括整数、浮点数...

深入浅出NumPy数组操作：掌握np.newaxis与reshape技巧

资源摘要信息:"NumPy是Python中用于科学计算的重要库，它为Python提供了一个强大的多维数组对象ndarray，用于处理大型多维数组和矩阵。NumPy数组具有类型一致性，即同一数组内数据类型需保持一致，这有助于提高数组...

import pickle import math import numpy as np from torch.utils.data import DataLoader, TensorDataset def sample_data(filepath, idx, path): f = open(filepath, 'rb') bi = pickle.load(f) data = np.arange(0, idx) data = data.tolist() samples = [] bs = [] a = path r = math.ceil(len(data) / a) for i in range(a): if len(data) > r: sample = np.random.choice(data, r, replace=False) else: sample = np.random.choice(data, len(data), replace=False) b = [bi[j] for j in list(sample)] bs.append(b) samples.append(sample) for s in sample: data.remove(s) samples = [np.array(s) for s in samples] samples = np.concatenate(samples, axis=0) bs = [np.array(e) for e in bs] bs = np.concatenate(bs, axis=0) return samples, bs如何让每一次for循环中的b值都能单独进行一次深度学习的dataloader，最后遍历完for循环中所有的b值

import numpy as np import torch from torch.utils.data import DataLoader, TensorDataset def sample_data(filepath, idx, path, batch_size): f = open(filepath, 'rb') bi = pickle.load(f) data = np...

import numpy as np import pickle import math f = open(r'C:\Users\sdnugeo\Desktop\target2.pkl','rb') bi = pickle.load(f) # bi = np.array(bi) data = np.arange(0, 156) print(data) data = data.tolist() # print(data) # 每次抽取1000个数值，共抽取10次 samples = [] c = [] a = 11 r = math.ceil(len(data)/a) print(r) for i in range(a): if len(data) > r : sample = np.random.choice(data, r, replace=False) else: sample = np.random.choice(data, len(data), replace=False) # for s in sample: # data.remove(s) # continue # print(type(sample)) # sample2 = np.array(sample) b = [bi[j] for j in list(sample)] # d = np.array(b) print(type(b)) c.append(b) # c = np.concatenate(c, axis=0) # c = bi[0] print(sample) # print(b) samples.append(sample) for s in sample: data.remove(s) # sample = [s for s in sample if s in data] samples = [np.array(s) for s in samples] samples = np.concatenate(samples, axis=0) c = [np.array(e) for e in c] c = np.concatenate(c, axis=0) # samples = np.array(samples).reshape(-1) print(samples) print(c.shape) 请帮我把这段代码修改成可调用的函数

import numpy as np import pickle import math def sample_data(filepath): f = open(filepath, 'rb') bi = pickle.load(f) data = np.arange(0, 156) data = data.tolist() samples = [] c = [] a = 11 r...

def sample_data(filepath,idx,path): f = open(filepath, 'rb') bi = pickle.load(f) data = np.arange(0, idx) data = data.tolist() samples = [] bs = [] a = path r = math.ceil(len(data)/a) for i in range(a): if len(data) > r: sample = np.random.choice(data, r, replace=False) else: sample = np.random.choice(data, len(data), replace=False) b = [bi[j] for j in list(sample)] bs.append(b) samples.append(sample) for s in sample: data.remove(s) samples = [np.array(s) for s in samples] samples = np.concatenate(samples, axis=0) bs = [np.array(e) for e in bs] bs = np.concatenate(bs, axis=0) return samples, bs请帮我输出每一次的b值并且将导入dataload里面可以用于深度学习

import numpy as np def sample_data(filepath, idx, path): f = open(filepath, 'rb') bi = pickle.load(f) data = np.arange(0, idx) data = data.tolist() samples = [] bs = [] a = path r = math....

import os import cv2 import numpy as np def load_data(file_dir): all_num = 4000 train_num = int(all_num * 0.75) cats = [] label_cats = [] dogs = [] label_dogs = [] for file in os.listdir(file_dir): file="\\"+file name = file.split(sep='.') if 'cat' in name[0]: cats.append(file_dir + file) label_cats.append(0) else: if 'dog' in name[0]: dogs.append(file_dir + file) label_dogs.append(1) image_list = np.hstack((cats,dogs)) label_list = np.hstack((label_cats, label_dogs)) temp = np.array([image_list, label_list]) # 矩阵转置 temp = temp.transpose() # 打乱顺序 np.random.shuffle(temp) # print(temp) # 取出第一个元素作为 image 第二个元素作为 label image_list = temp[:, 0] label1_train = temp[:train_num, 1] # print(label1_train) # 单出，去掉单字符 label_train = [int(y) for y in label1_train] # print(label_train) label1_test = temp[train_num:, 1] label_test = [int(y) for y in label1_test] data_test=[] data_train = [] for i in range (all_num): if i <train_num: image= image_list[i] image = cv2.imread(image) image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #将图片转换成RGB格式 image = cv2.resize(image, (28, 28)) image = image.astype('float32') image = np.array(image)/255#归一化[0,1] image=image.reshape(-1,28,28) data_train.append(image) # label_train.append(label_list[i]) else: image = image_list[i] image = cv2.imread(image) image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) image = cv2.resize(image, (28, 28)) image = image.astype('float32') image = np.array(image) / 255 image = image.reshape(-1, 28, 28) data_test.append(image) # label_test.append(label_list[i]) data_train=np.array(data_train) label_train = np.array(label_train) data_test = np.array(data_test) label_test = np.array(label_test) return data_train,label_train,data_test, label_test

这段代码是定义了一个函数load_data，用来加载数据集，并将数据集划分为训练集和测试集。函数接收一个参数file_dir，表示数据集所在的路径。首先，定义了all_num和train_num两个变量，用来表示数据集的总数和训练集...

def return_values(): import numpy as np list1=[1,2,4,6,7,8] N1=np.array(list1) tup1=(1,2,3,4,5,6) N2=np.array(tup1) N3=np.array([1,1,1,1,1,1]) N4=np.vstack((N1,N2,N3)) np.save('data',N4) np.load('data.npy') return N4，这是N4

接着，代码使用 NumPy 库中的 array() 函数将 list1 和 tup1 转换为两个一维数组 N1 和 N2，并创建一个全为 1 的一维数组 N3。然后，代码使用 NumPy 库中的 vstack() 函数将 N1、N2 和 N3 垂直堆叠成一个二维数组 N4...

上一关是这样的，def return_values(): import numpy as np list1=[1,2,4,6,7,8] N1=np.array(list1) tup1=(1,2,3,4,5,6) N2=np.array(tup1) N3=np.array([1,1,1,1,1,1]) N4=np.vstack((N1,N2,N3)) np.save('data',N4) np.load('data.npy') return N4

这个函数实现了以下几个步骤： ...5. 使用 np.load() 函数从文件 data.npy 中加载数据，并返回加载的数组 N4 所以当你调用 return_values() 函数时，它会返回一个由 N1、N2 和全为 1 的 numpy 数组组成的二维数组 N4。

#chapter 3.svm multi classification exercise """ reference: https://www.jianshu.com/p/ba59631855a3 https://www.jianshu.com/p/ce96f1a04b72 https://blog.csdn.net/Big_Pai/article/details/89482752 """ import numpy as np import matplotlib.pyplot as plt import svm_bi_classification as svm_bi import svm_multi_classification as svm_multi def load_data(filename): with open(filename) as f: data = [] f.readline() # 跳过第一行 for line in f: line = line.strip().split() x1_f = float(line[0]) x2_f = float(line[1]) t_f = int(line[2]) data.append([x1_f, x2_f, t_f]) ret_f = np.array(data) np.random.shuffle(ret_f) return ret_f，这段代码的含义是什么

这段代码的含义是定义了一个函数load_data，用于从文件中读取数据，并将其转换为Numpy数组格式。具体来说，该函数的输入参数为文件名filename，输出为Numpy数组ret_f。在函数内部，该函数首先打开指定文件，然后跳过...

修改代码使其能够正确运行。import pandas as pd import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from sklearn.preprocessing import MinMaxScaler import cv2 import open3d as o3d from skimage import color import colour from scipy.spatial import ConvexHull def convert_data(data): res=[] data=data.tolist() for d in data: res.append(tuple(d)) # print(res) return res def load_data_and_plot_scatter(path1="1号屏srgb+rgb16预热10分钟切换0.5s.csv"): df1 = pd.read_csv(path1)[["X", "Y", "Z", "R", "G", "B"]] X1 = df1["X"].values Y1 = df1["Y"].values Z1 = df1["Z"].values df1_c = df1[["R", "G", "B"]].values / 255.0 XYZT = np.array([X1,Y1,Z1]) XYZ = np.transpose(XYZT) ABL = colour.XYZ_to_Lab(XYZ) LABT = np.array([ABL[:,1], ABL[:,2], ABL[:,0]]) LAB = np.transpose(LABT) # 将 numpy 数组转换为 open3d 中的 PointCloud 类型 pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(LAB) # 估计点云法向量 pcd.estimate_normals() # 计算点云的凸包表面 mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_alpha_shape(pcd, alpha=0.1) mesh.compute_vertex_normals() # 获取凸包表面上的点的坐标 surface_points = np.asarray(mesh.vertices) # 显示点云的凸包表面 o3d.visualization.draw_geometries([mesh]) # 创建一个 3D 坐标 fig = plt.figure() # ax = Axes3D(fig) ax = plt.axes(projection='3d') ax.scatter(LAB[:,0], LAB[:,1], LAB[:,2], c=df1_c) # # 设置坐标轴标签 ax.set_xlabel('a* Label') ax.set_ylabel('b* Label') ax.set_zlabel('L Label') # 显示图形 plt.show() if name == "main": load_data_and_plot_scatter()

def load_data_and_plot_scatter(path1="1号屏srgb+rgb16预热10分钟切换0.5s.csv"): df1 = pd.read_csv(path1)[["X", "Y", "Z", "R", "G", "B"]] X1 = df1["X"].values Y1 = df1["Y"].values Z1 = df1["Z"]....

补全下面代码中模型预测部分import paho.mqtt.client as mqtt import numpy as np import json import tensorflow as tf from tensorflow.python.keras.backend import set_session model = tf.keras.models.load_model('flowers.hd5') classes=["daisy","dandelion","roses","sunflowers","tulips"] def on_connect(client,userdata,flags,rc): if rc==0: print("successfully connected to broker.") client.subscribe("Group_04/IMAGE/classify") else: print("Connection failed with code: %d." %rc) def classify_flower(filename,data): print("Start classifying") model.predict(data) print("Done.") return {"filename":filename,"prediction":classes[win],"score":0.99,"index":win} def on_message(client,userdata,msg): recv_dict=json.loads(msg.payload) img_data=np.array(recv_dict["data"]) result=classify_flower(recv_dict["filename"],img_data) print("Sending results: ",result) client.publish("Group_04/IMAGE/predict",json.dumps(result)) def setup(hostname): client=mqtt.Client() client.on_connect=on_connect client.on_message=on_message client.connect(hostname) client.loop_start() return client def main(): model = tf.keras.models.load_model('flowers.hd5') client=setup("172.17.0.3") for filename in os.listdir(samples_dir): file_path=os.path.join(samples_dir,filename) classify_flower(file_path) while True: pass if name=='main': main()

def classify_flower(filename, data): print("Start classifying") # 预处理图像数据 processed_img = preprocess_image(data) # 将图像数据转换为模型可接受的输入格式 input_data = convert_to_model_...

import pandas as pd import numpy as np import matplotlib.pyplot as plt import math data = np.load('dataset_em.npy') mu0 = np.array([np.mean(data) - 1, np.mean(data) + 1]) sg0 = np.array([1.0, 1.0]) w0 = [0.5, 0.5] ary_mu = [mu0] ary_sg = [sg0] ary_w = [w0] x = np.arange(-10, 15, 0.1) def pdf_gauss(x, mu, sg): c = 1 / np.sqrt(2 * np.pi) p = c / sg * np.exp(-0.5 * ((x - mu) / sg) ** 2) return p def gauss_mix_pdf(w, x, mu, sg): p = w[0] * pdf_gauss(x, mu[0], sg[0]) + w[1] * pdf_gauss(x, mu[1], sg[1]) return p for j in range(0, 100): mu = ary_mu[j] sg = ary_sg[j] w = ary_w[j] gama = [] for i in (0, 1): b = w[i] * pdf_gauss(data, mu[i], sg[i]) c = gauss_mix_pdf(w, data, mu, sg) gama.append(b / c) gama_sum0 = np.sum(gama[0]) gama_sum1 = np.sum(gama[1]) new_mu = [np.sum(data * gama[0]) / gama_sum0, np.sum(data * gama[1]) / gama_sum1] new_sg = [np.sqrt(np.sum(gama[0] * np.power(data - new_mu[0], 2)) / gama_sum0), np.sqrt(np.sum(gama[1] * np.power(data - new_mu[1], 2)) / gama_sum1)] # 第二个数太大了 new_w = [gama_sum0 / data.shape[0], gama_sum1 / data.shape[0]] ary_mu.extend([new_mu]) ary_sg.extend([new_sg]) ary_w.extend([new_w]) del gama y = gauss_mix_pdf(ary_w[100], x, ary_mu[100], ary_sg[100]) plt.figure() plt.hist(data, bins=100, facecolor="blue", edgecolor="black", alpha=0.7, density=True) plt.plot(x, y) plt.show() 请逐句详细解释这段代码

import numpy as np import matplotlib.pyplot as plt import math 导入所需要的库。 python data = np.load('dataset_em.npy') 从文件中加载数据。 python mu0 = np.array([np.mean(data) - 1, np....

import pickle import numpy as np import os # from scipy.misc import imread def load_CIFAR_batch(filename): with open(filename, 'rb') as f: datadict = pickle.load(f, encoding='bytes') X = datadict[b'data'] Y = datadict[b'labels'] X = X.reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).astype("float") Y = np.array(Y) return X, Y def load_CIFAR10(ROOT): xs = [] ys = [] for b in range(1, 2): f = os.path.join(ROOT, 'data_batch_%d' % (b,)) X, Y = load_CIFAR_batch(f) xs.append(X) ys.append(Y) Xtr = np.concatenate(xs) Ytr = np.concatenate(ys) del X, Y Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch')) return Xtr, Ytr, Xte, Yte def get_CIFAR10_data(num_training=5000, num_validation=500, num_test=500): cifar10_dir = r'D:\daima\cifar-10-python\cifar-10-batches-py' X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir) print(X_train.shape) mask = range(num_training, num_training + num_validation) X_val = X_train[mask] y_val = y_train[mask] mask = range(num_training) X_train = X_train[mask] y_train = y_train[mask] mask = range(num_test) X_test = X_test[mask] y_test = y_test[mask] mean_image = np.mean(X_train, axis=0) X_train -= mean_image X_val -= mean_image X_test -= mean_image X_train = X_train.transpose(0, 3, 1, 2).copy() X_val = X_val.transpose(0, 3, 1, 2).copy() X_test = X_test.transpose(0, 3, 1, 2).copy() return { 'X_train': X_train, 'y_train': y_train, 'X_val': X_val, 'y_val': y_val, 'X_test': X_test, 'y_test': y_test, } def load_models(models_dir): models = {} for model_file in os.listdir(models_dir): with open(os.path.join(models_dir, model_file), 'rb') as f: try: models[model_file] = pickle.load(f)['model'] except pickle.UnpicklingError: continue return models这是一个加载cifar10数据集的函数，如何修改使其能加载mnist数据集，不使用TensorFlow

import numpy as np def load_mnist(path, kind='train'): labels_path = os.path.join(path, '%s-labels-idx1-ubyte.gz' % kind) images_path = os.path.join(path, '%s-images-idx3-ubyte.gz' % kind) with ...

""" This code is based on the Torchvision repository, which was licensed under the BSD 3-Clause. """ import os import pickle import sys import numpy as np import torch from PIL import Image from torch.utils.data import Dataset from utils.mypath import MyPath from torchvision.datasets.utils import check_integrity, download_and_extract_archive class simclr_c10(Dataset): base_folder = 'D:/wjd/simclr_c10' filename = "simclr_c10" def init(self, root=MyPath.db_root_dir('wjd/simclr_c10/'), train=True, transform=None, download=False): super('wjd/simclr_c10/', self).init() self.data = None self.root = root self.transform = transform self.train = train # training set or test set self.classes = ['Al', 'Ag', 'Au', 'Cu', 'W', 'V', 'Ta', 'Mo'] def getitem(self, index): """ Args: index (int): Index Returns: dict: {'image': image, 'target': index of target class, 'meta': dict} """ img, target = self.data[index], self.targets[index] img_size = (img.shape[0], img.shape[1]) img = Image.fromarray(img) class_name = self.classes[target] if self.transform is not None: img = self.transform(img) out = {'image': img, 'target': target, 'meta': {'im_size': img_size, 'index': index, 'class_name': class_name}} return out def get_image(self, index): img = self.data[index] return img def len(self): return len(self.data) def extra_repr(self): return "Split: {}".format("Train" if self.train is True else "Test") return outside function，这段代码正确吗

import numpy as np import torch from PIL import Image from torch.utils.data import Dataset from utils.mypath import MyPath class simclr_c10(Dataset): base_folder = 'D:/wjd/simclr_c10' filename = ...

def unzip_infer_data(src_path,target_path): ''' 解压预测数据集 ''' if(not os.path.isdir(target_path)): z = zipfile.ZipFile(src_path, 'r') z.extractall(path=target_path) z.close() def load_image(img_path): ''' 预测图片预处理 ''' img = Image.open(img_path) if img.mode != 'RGB': img = img.convert('RGB') img = img.resize((224, 224), Image.BILINEAR) img = np.array(img).astype('float32') img = img.transpose((2, 0, 1)) # HWC to CHW img = img/255 # 像素值归一化 return img infer_src_path = './archive_test.zip' infer_dst_path = './archive_test' unzip_infer_data(infer_src_path,infer_dst_path) para_state_dict = paddle.load("MyDNN") model = MyDNN() model.set_state_dict(para_state_dict) #加载模型参数 model.eval() #验证模式 #展示预测图片 infer_path='./archive_test/alexandrite_18.jpg' img = Image.open(infer_path) plt.imshow(img) #根据数组绘制图像 plt.show() #显示图像 #对预测图片进行预处理 infer_imgs = [] infer_imgs.append(load_image(infer_path)) infer_imgs = np.array(infer_imgs) label_dic = train_parameters['label_dict'] for i in range(len(infer_imgs)): data = infer_imgs[i] dy_x_data = np.array(data).astype('float32') dy_x_data=dy_x_data[np.newaxis,:, : ,:] img = paddle.to_tensor (dy_x_data) out = model(img) lab = np.argmax(out.numpy()) #argmax():返回最大数的索引 print("第{}个样本,被预测为：{},真实标签为：{}".format(i+1,label_dic[str(lab)],infer_path.split('/')[-1].split("_")[0])) print("结束")根据这一段代码续写一段利用这个模型进行宝石预测的GUI界面

img = np.array(img).astype('float32') img = img.transpose((2, 0, 1)) img = img / 255 img = np.array([img]) # 进行预测 img = paddle.to_tensor(img) out = model(img) label = np.argmax(out.numpy...

import random import numpy as np import matplotlib.pyplot as plt 生成随机坐标点 def generate_points(num_points): points = [] for i in range(num_points): x = random.uniform(-10, 10) y = random.uniform(-10, 10) points.append([x, y]) return points 计算欧几里得距离 def euclidean_distance(point1, point2): return np.sqrt(np.sum(np.square(np.array(point1) - np.array(point2)))) K-means算法实现 def kmeans(points, k, num_iterations=100): num_points = len(points) # 随机选择k个点作为初始聚类中心 centroids = random.sample(points, k) # 初始化聚类标签和距离 labels = np.zeros(num_points) distances = np.zeros((num_points, k)) for i in range(num_iterations): # 计算每个点到每个聚类中心的距离 for j in range(num_points): for l in range(k): distances[j][l] = euclidean_distance(points[j], centroids[l]) # 根据距离将点分配到最近的聚类中心 for j in range(num_points): labels[j] = np.argmin(distances[j]) # 更新聚类中心 for l in range(k): centroids[l] = np.mean([points[j] for j in range(num_points) if labels[j] == l], axis=0) return labels, centroids 生成坐标点 points = generate_points(100) 对点进行K-means聚类 k_values = [2, 3, 4] for k in k_values: labels, centroids = kmeans(points, k) # 绘制聚类结果 colors = [‘r’, ‘g’, ‘b’, ‘y’, ‘c’, ‘m’] for i in range(k): plt.scatter([points[j][0] for j in range(len(points)) if labels[j] == i], [points[j][1] for j in range(len(points)) if labels[j] == i], color=colors[i]) plt.scatter([centroid[0] for centroid in centroids], [centroid[1] for centroid in centroids], marker=‘x’, color=‘k’, s=100) plt.title(‘K-means clustering with k={}’.format(k)) plt.show()import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.cluster import KMeans from sklearn.datasets import load_iris 载入数据集 iris = load_iris() X = iris.data y = iris.target K-means聚类 kmeans = KMeans(n_clusters=3, random_state=0).fit(X) 可视化结果 plt.scatter(X[:, 0], X[:, 1], c=kmeans.labels_) plt.xlabel(‘Sepal length’) plt.ylabel(‘Sepal width’) plt.title(‘K-means clustering on iris dataset’) plt.show()从聚类算法的评价指标对结果进行分析

2. Silhouette Coefficient（轮廓系数）：用于衡量样本聚类的密集程度，取值范围在[-1,1]之间，越接近1表示聚类效果越好。 3. Calinski-Harabasz Index（方差比率准则）：通过计算类间离散度与类内离散度的比值来...

import os import random import numpy as np import cv2 import keras from create_unet import create_model img_path = 'data_enh/img' mask_path = 'data_enh/mask' # 训练集与测试集的切分 img_files = np.array(os.listdir(img_path)) data_num = len(img_files) train_num = int(data_num * 0.8) train_ind = random.sample(range(data_num), train_num) test_ind = list(set(range(data_num)) - set(train_ind)) train_ind = np.array(train_ind) test_ind = np.array(test_ind) train_img = img_files[train_ind] # 训练的数据 test_img = img_files[test_ind] # 测试的数据 def get_mask_name(img_name): mask = [] for i in img_name: mask_name = i.replace('.jpg', '.png') mask.append(mask_name) return np.array(mask) train_mask = get_mask_name(train_img) test_msak = get_mask_name(test_img) def generator(img, mask, batch_size): num = len(img) while True: IMG = [] MASK = [] for i in range(batch_size): index = np.random.choice(num) img_name = img[index] mask_name = mask[index] img_temp = os.path.join(img_path, img_name) mask_temp = os.path.join(mask_path, mask_name) temp_img = cv2.imread(img_temp) temp_mask = cv2.imread(mask_temp, 0)/255 temp_mask = np.reshape(temp_mask, [256, 256, 1]) IMG.append(temp_img) MASK.append(temp_mask) IMG = np.array(IMG) MASK = np.array(MASK) yield IMG, MASK # train_data = generator(train_img, train_mask, 32) # temp_data = train_data.next() # 计算dice系数 def dice_coef(y_true, y_pred): y_true_f = keras.backend.flatten(y_true) y_pred_f = keras.backend.flatten(y_pred) intersection = keras.backend.sum(y_true_f * y_pred_f) area_true = keras.backend.sum(y_true_f * y_true_f) area_pred = keras.backend.sum(y_pred_f * y_pred_f) dice = (2 * intersection + 1)/(area_true + area_pred + 1) return dice # 自定义损失函数，dice_loss def dice_coef_loss(y_true, y_pred): return 1 - dice_coef(y_true, y_pred) # 模型的创建 model = create_model() # 模型的编译 model.compile(optimizer='Adam', loss=dice_coef_loss, metrics=[dice_coef]) # 模型的训练 history = model.fit_generator(generator(train_img, train_mask, 4), steps_per_epoch=100, epochs=10, validation_data=generator(test_img, test_msak, 4), validation_steps=4 ) # 模型的保存 model.save('unet_model.h5') # 模型的读取 model = keras.models.load_model('unet_model.h5', custom_objects={'dice_coef_loss': dice_coef_loss, 'dice_coef': dice_coef}) # 获取测试数据 test_generator = generator(test_img, test_msak, 32) img, mask = test_generator.next() # 模型的测试 model.evaluate(img, mask) # [0.11458712816238403, 0.885412871837616] 94%

上面这段代码是在导入...它导入了 OS 库，Random 库，NumPy 库，CV2 库，Keras 库，以及一个叫做 Create_unet 的自定义模块。它还定义了两个字符串变量：img_path 和 mask_path，分别存储了图像数据和掩码数据的路径。

Keras 数据集打乱：使用np.random.shuffle()实例

import numpy as np 然后，假设我们有一个包含训练数据的列表data，我们可以这样做来打乱数据： python data = ... # 填充你的数据 np.random.shuffle(data) 在Keras中，这个步骤通常在构建数据生成...

Keras数据集打乱教程：使用np.random.shuffle()

import numpy as np # 创建训练数据集 X_train = ... y_train = ... # 将数据集的索引打乱 indices = np.arange(X_train.shape[0]) np.random.shuffle(indices) # 对数据集进行重新排列 X_train_shuffled = X_...

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