``` import numpy as np import argparse from path import Path from keras.models import Model from keras.layers import Dense, Dropout from keras.applications.inception_resnet_v2 import InceptionResNetV2 from keras.applications.inception_resnet_v2 import preprocess_input # from keras.preprocessing.image import load_img, img_to_array from keras.utils import load_img,img_to_array import tensorflow as tf from utils.score_utils import mean_score, std_score parser = argparse.ArgumentParser(description='Evaluate NIMA(Inception ResNet v2)') parser.add_argument('-dir', type=str, default='D:\python分析\视觉分析/neural-image-assessment-master\images/total1' '', help='Pass a directory to evaluate the images in it') parser.add_argument('-img', type=str, default=['None'], nargs='+', help='Pass one or more image paths to evaluate them') parser.add_argument('-resize', type=str, default='false', help='Resize images to 224x224 before scoring') parser.add_argument('-rank', type=str, default='true', help='Whether to tank the images after they have been scored') args = parser.parse_args() resize_image = args.resize.lower() in ("true", "yes", "t", "1") target_size = (224, 224) if resize_image else None rank_images = args.rank.lower() in ("true", "yes", "t", "1") # give priority to directory if args.dir is not None: print("Loading images from directory : ", args.dir) imgs = Path(args.dir).files('*.png') imgs += Path(args.dir).files('*.jpg') imgs += Path(args.dir).files('*.jpeg') elif args.img[0] is not None: print("Loading images from path(s) : ", args.img) imgs = args.img else: raise RuntimeError('Either -dir or -img arguments must be passed as argument') with tf.device('/CPU:0'): base_model = InceptionResNetV2(input_shape=(None, None, 3), include_top=False, pooling='avg', weights=None) x = Dropout(0.75)(base_model.output) x = Dense(10, activation='softmax')(x) model = Model(base_model.input, x) model.load_weights('weights/inception_resnet_weights.h5') score_list = [] for img_path in imgs: img = load_img(img_path, target_size=target_size) x = img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) scores = model.predict(x, batch_size=1, verbose=0)[0] mean = mean_score(scores) std = std_score(scores) file_name = Path(img_path).name.lower() score_list.append((file_name, mean)) print("Evaluating : ", img_path) print("NIMA Score : %0.3f +- (%0.3f)" % (mean, std)) print() if rank_images: print("*" * 40, "Ranking Images", "*" * 40) score_list = sorted(score_list, key=lambda x: x[1], reverse=True) for i, (name, score) in enumerate(score_list): print("%d)" % (i + 1), "%s : Score = %0.5f" % (name, score))```如果我要分别测量图片的美学评分和技术评分应该怎么做

请作为资深开发工程师，解释我给出的代码。请逐行分析我的代码并给出你对这段代码的理解。我给出的代码是：【# 导入必要的库 Import the necessary libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import torch import math import torch.nn as nn from scipy.stats import pearsonr from sklearn.metrics import accuracy_score from sklearn.linear_model import LinearRegression from collections import deque from tensorflow.keras import layers import tensorflow.keras.backend as K from tensorflow.keras.layers import LSTM,Dense,Dropout,SimpleRNN,Input,Conv1D,Activation,BatchNormalization,Flatten,Permute from tensorflow.python import keras from tensorflow.python.keras.layers import Layer from sklearn.preprocessing import MinMaxScaler,StandardScaler from sklearn.metrics import r2_score from sklearn.preprocessing import MinMaxScaler import tensorflow as tf from tensorflow.keras import Sequential, layers, utils, losses from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard from tensorflow.keras.layers import Conv2D,Input,Conv1D from tensorflow.keras.models import Model from PIL import * from tensorflow.keras import regularizers from tensorflow.keras.layers import Dropout from tensorflow.keras.callbacks import EarlyStopping import seaborn as sns from sklearn.decomposition import PCA import numpy as np import matplotlib.pyplot as plt from scipy.signal import filtfilt from scipy.fftpack import fft from sklearn.model_selection import train_test_split import warnings warnings.filterwarnings('ignore')】

from tensorflow.keras.layers import LSTM, Conv1D # 时序特征提取层 from tensorflow.keras.callbacks import EarlyStopping # 训练控制 ### 三、代码特征解读 1. **技术栈特点** - 混合使用PyTorch和...

from sklearn.datasets import load_files from keras.utils import np_utils import numpy as np from glob import glob from keras.preprocessing import image from torch.nn import Dropout from tqdm import tqdm from PIL import ImageFile 如何使用tensorflow2.11.0导入这些库 from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense from tkinter import Tk, Label, Button, filedialog from PIL import ImageTk from tensorflow.keras.callbacks import ModelCheckpoint

from tensorflow.keras.layers import Dropout from tqdm import tqdm from PIL import ImageFile 请注意，上述代码中的keras被替换为tensorflow.keras，因为在TensorFlow 2.x中，keras已经成为...

import numpy as np import matplotlib.pyplot as plt import pandas as pd import math from keras.models import Sequential from keras.layers import Dense, Activation, Dropout, LSTM from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error from keras import optimizers import time解说代码

- numpy 是 Python 中用于科学计算的一个库。 - matplotlib 是 Python 中一个常用的绘图库。 - pandas 是 Python 中用于数据分析的一个库。 - math 是 Python 中的数学库，提供了许多数学函数。 - keras ...

import time import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler import numpy as np import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout import tkinter as tk from tkinter import ttk from sklearn.neural_network import MLPClassifier from sklearn.preprocessing import StandardScaler from tkinter.messagebox import showinfo # pip install pyserial import serial import serial.tools.list_ports from main import MyClass if name == 'main': detect = MyClass() def collect_data_click(): detect.on_collect_data_click()

2. 从 tensorflow.keras 中导入 Sequential 和 Dense 两个类，用于搭建神经网络模型。 3. 定义了一个名为 MyClass 的类，并创建了一个 detect 实例。 4. 定义了一个名为 collect_data_click() 的函数，该...

import os,math from tensorflow.keras.layers import Dropout, Dense, SimpleRNN from sklearn.preprocessing import MinMaxScaler from sklearn import metrics import numpy as np import pandas as pd import tensorflow as tf import matplotlib.pyplot as plt 解释一下

- from tensorflow.keras.layers import Dropout, Dense, SimpleRNN：从TensorFlow库的keras子模块中导入了Dropout、Dense和SimpleRNN等层，用于构建神经网络模型。 - from sklearn.preprocessing import ...

import pandas as pd import numpy as np import tensorflow as tf from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout from tensorflow.keras.utils import to_categorical from tensorflow.keras.layers import Bidirectional, Attention, GlobalMaxPool1D from tensorflow.keras.callbacks import EarlyStopping import jieba import re from tensorflow.keras.callbacks import EarlyStopping import pickle # 检查GPU是否可用并配置 gpus = tf.config.list_physical_devices('GPU') if gpus: try: # 设置GPU显存按需增长 tf.config.set_visible_devices(gpus[0], 'GPU') tf.config.experimental.set_memory_growth(gpus[0], True) print("GPU加速已启用") except RuntimeError as e: print(e) else: print("未检测到GPU，将使用CPU运行"

tf.keras.layers.Dense(10) ]) model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) # 4. 训练与性能分析 with tf.profiler....

import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error from PyEMD import EEMD # 经验模态分解 from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense, Dropout, Conv1D, Flatten from tensorflow.keras.callbacks import EarlyStopping ImportError Traceback (most recent call last) <ipython-input-8-56591ef493a9> in <module> 4 from sklearn.preprocessing import MinMaxScaler 5 from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error ----> 6 from PyEMD import EEMD # 经验模态分解 7 from tensorflow.keras.models import Sequential 8 from tensorflow.keras.layers import LSTM, Dense, Dropout, Conv1D, Flatten ImportError: cannot import name 'EEMD'这是源代码和报错，怎么解决

import numpy as np from PyEMD import EEMD # 或 from PyEMD.EEMD import EEMD # 生成测试信号 t = np.linspace(0, 1, 100) signal = np.sin(20 * np.pi * t) + 0.5 * np.random.randn(100) # 初始化并执行EEMD ...

import numpy as np from keras.preprocessing import sequence from keras_preprocessing import sequence from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation from tensorflow.python.keras.layers.embeddings import Embedding from keras.layers import LSTM import sklearn import pickle # from sklearn.cross_validation import train_test_split import sklearn.model_selection from sklearn.model_selection import train_test_split from keras.models import load_model def build_model(max_features=1, maxlen=65): """Build LSTM model""" model = Sequential() model.add(Embedding(max_features, 128, input_length=maxlen)) model.add(LSTM(128)) model.add(Dropout(0.5)) model.add(Dense(1)) model.add(Activation('sigmoid')) # model.add(tf.keras.layers.BatchNormalization()) model.compile(loss='binary_crossentropy', optimizer='rmsprop') return model def data(Domain): valid_chars = pickle.load(open('dictionary.plk','rb')) Domain = [[valid_chars[y] for y in x] for x in Domain] Domain = sequence.pad_sequences(Domain, maxlen=65) return Domain def run(Domain): Domains=data(Domain) model = build_model() model.load_weights('Mymodel.h5') predictions = model.predict(Domains) return predictions if name == "main": print(run(['baidu.com'])) # Run with 1 to make it fast

具体来说，该程序使用了 Keras 和 TensorFlow 库进行模型的建立和训练，同时使用了 pickle 库进行数据的读取和存储。在运行该程序时，输入一个域名进行分类，输出该域名属于恶意或者良性的概率。

我现在要写一个uniapp打包安卓应用，来完成我的基于深度学习的海洋生物图像分类系统，要求要能在应用中调用我的模型，是直接将模型内置在应用之中，使用python 3.11.6环境还有以下代码所包含的插件库，这些内容直接在应用中内置，不需要调用后端的接口，我该如何来实现： import cv2 import numpy as np from tensorflow.keras.models import Model, load_model from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout from tensorflow.keras.preprocessing.image import img_to_array from tensorflow.keras.saving import register_keras_serializable # 关键修复 import os import tensorflow as tf from PIL import Image, ImageDraw, ImageFont

import numpy as np import tflite_runtime.interpreter as tflite from PIL import Image def classify_image(img_path): # 初始化解释器 interpreter = tflite.Interpreter(model_path="model.tflite") ...

import numpy as np import pandas as pd from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, Dropout,LSTM,SimpleRNN,GRU np.random.seed(10) # 指定乱数种子 # 载入Google股价的训练数据集 df_train = pd.read_csv("",index_col="Date",parse_dates=True) df_train这个代码怎么载入数据

df_train = pd.read_csv("path/to/your/file.csv", index_col="Date", parse_dates=True) 注意，如果你的数据集文件不是CSV格式，需要使用对应的读取函数。例如，如果数据集文件是Excel格式，可以使用pd.read_...

import os os.environ['TF_CPP_MIN_LOG_LEVEL']='2' import os from tensorflow import keras import matplotlib.pyplot as plt import numpy as np import tensorflow as tf from sklearn.model_selection import train_test_split from tensorflow.keras import Model from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Dropout, Flatten, Dense np.set_printoptions(threshold=np.inf) from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense from tensorflow.keras.utils import plot_model # 创建模型 model = Sequential() model.add(Conv2D(6, (3, 3), activation='relu', padding='same', input_shape=(3, 1024, 1))) model.add(Conv2D(16, (3, 3), activation='relu'), padding='same') model.add(MaxPooling2D((2, 2))) model.add(Dropout(0.1)) model.add(Flatten()) model.add(Dense(1024, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(225, activation='sigmoid')) # 输出模型结构图表 plot_model(model, show_shapes=True) TypeError: add() got an unexpected keyword argument 'padding'

from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Dropout, Flatten, Dense np.set_printoptions(threshold=np.inf) from tensorflow.keras.models import Sequential ...

import os import warnings import json import sys import pandas as pd import numpy as np import tensorflow as tf from sklearn.preprocessing import MinMaxScaler from PyQt5 import QtCore, QtWidgets from PyQt5.QtCore import Qt from PyQt5.QtGui import QFont from PyQt5.QtWidgets import ( QApplication, QMainWindow, QDialog, QWidget, QVBoxLayout, QHBoxLayout, QFormLayout, QLabel, QLineEdit, QPushButton, QComboBox, QMessageBox, QCheckBox ) from tensorflow.keras.layers import Dropout, Layer from datetime import datetime # 抑制弃用警告 warnings.filterwarnings("ignore", category=DeprecationWarning) os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0' # 自定义注意力层 class SelfAttentionLayer(Layer): def init(self, output_dim): super(SelfAttentionLayer, self).init() self.output_dim = output_dim self.W_q = tf.keras.layers.Dense(output_dim) self.W_k = tf.keras.layers.Dense(output_dim) self.W_v = tf.keras.layers.Dense(output_ 帮我优化UI界面

目前的代码中包含了自定义注意力机制（Self-Attention）以及一些基础的数据处理工具包如pandas、numpy等。关于优化UI界面部分，可以从以下几方面入手： --- ### 1. **提升布局美观度** - 使用更高级的布局...

源代码import jieba import numpy as np import matplotlib.pyplot as plt from keras.models import Model from keras.layers import Input, Embedding, Bidirectional, LSTM, Dense, Dropout from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categorical from keras import backend as K from tensorflow import kerasimport warnings from keras.src.api_export import keras_export from keras.src.utils.module_utils import dmtree from keras.src.utils.module_utils import optree if optree.available: from keras.src.tree import optree_impl as tree_impl elif dmtree.available: from keras.src.tree import dmtree_impl as tree_impl else: raise ImportError( "To use Keras, you need to have optree installed. " "Install it via pip install optree" ) def register_tree_node_class(cls): return tree_impl.register_tree_node_class(cls) @keras_export("keras.tree.MAP_TO_NONE") class MAP_TO_NONE: """Special value for use with traverse().""" pass @keras_export("keras.tree.is_nested") def is_nested(structure): """Checks if a given structure is nested. Examples: >>> keras.tree.is_nested(42) False >>> keras.tree.is_nested({"foo": 42}) True Args: structure: A structure to check. Returns: True if a given structure is nested, i.e. is a sequence, a mapping, or a namedtuple, and False otherwise. """ return tree_impl.is_nested(structure) @keras_export("keras.tree.traverse") def traverse(func, structure, top_down=True): """Traverses the given nested structure, applying the given function. The traversal is depth-first. If top_down is True (default), parents are returned before their children (giving the option to avoid traversing into a sub-tree). Examples: >>> v = [] >>> keras.tree.traverse(v.append, [(1, 2), [3], {"a": 4}], top_down=True) [(1, 2), [3], {'a': 4}] >>> v [[(1

引用[1]提到的是keras.engine.topology模块缺少load_weights_from_hdf5_group_by_name属性，引用[2]是关于keras.utils缺少to_categorical的错误，而引用[3]讨论的是类权重的自定义函数。这些引用都没有涉及tree模块...

AttributeError: module 'tensorflow._api.v2.compat.v2.internal' has no attribute 'register_load_context_function'. Did you mean: 'register_call_context_function'? PS D:\Learn\Keras-GAN-master> ^C PS D:\Learn\Keras-GAN-master> ^C PS D:\Learn\Keras-GAN-master> & D:/Anaconda3/envs/tf1cpu/python.exe d:/Learn/Keras-GAN-master/context_encoder/context_encoder.py 2025-03-19 15:44:14.482172: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0. 2025-03-19 15:44:15.713157: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0. Traceback (most recent call last): File "d:\Learn\Keras-GAN-master\context_encoder\context_encoder.py", line 7, in <module> from keras.layers.advanced_activations import LeakyReLU ModuleNotFoundError: No module named 'keras.layers.advanced_activations' PS D:\Learn\Keras-GAN-master> ^C 修改代码吧 from keras.datasets import cifar10 from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply, GaussianNoise from keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D from keras.layers import MaxPooling2D from keras.layers.advanced_activations import LeakyReLU from keras.layers.convolutional import UpSampling2D, Conv2D from keras.models import Sequential, Model from keras.optimizers import Adam from keras import losses from keras.utils import to_categorical import keras.backend as K import matplotlib.pyplot as plt import numpy as np class ContextEncoder(): def init(self): self.img_rows = 32 self.img_cols = 32 self.mask_height = 8 self.mask_width = 8

from tensorflow.keras.layers import Dense, LeakyReLU model = Sequential([ Dense(64, input_shape=(None,)), LeakyReLU(alpha=0.1), Dense(1) ]) model.compile(optimizer='adam', loss='mse') print(model...

优化这段代码：import tensorflow as tf import numpy as np # 加载数据集 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 # 定义模型 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax')]) # 编译模型 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # 训练模型 model.fit(x_train, y_train, epochs=5) # 生成对抗样本 epsilon = 0.1 x_adv = tf.Variable(x_test[:1000], dtype=tf.float32) y_true = tf.Variable(y_test[:1000], dtype=tf.int64) with tf.GradientTape() as tape: tape.watch(x_adv) y_pred = model(x_adv) loss = tf.keras.losses.sparse_categorical_crossentropy(y_true, y_pred) grad = tape.gradient(loss, x_adv) normed_grad = tf.sign(grad) x_adv = x_adv + epsilon * normed_grad # 评估模型在对抗样本上的性能 model.evaluate(x_adv, y_true)

import numpy as np # 加载数据集 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 # 定义模型 model = tf...

import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline import tensorflow as tf import random from cv2 import resize from glob import glob import warnings warnings.filterwarnings("ignore")img_height = 244 img_width = 244 train_ds = tf.keras.utils.image_dataset_from_directory( 'D:/Faulty_solar_panel', validation_split=0.2, subset='training', image_size=(img_height, img_width), batch_size=32, seed=42, shuffle=True) val_ds = tf.keras.utils.image_dataset_from_directory( 'D:/Faulty_solar_panel', validation_split=0.2, subset='validation', image_size=(img_height, img_width), batch_size=32, seed=42, shuffle=True)class_names = train_ds.class_names print(class_names) train_dsbase_model = tf.keras.applications.VGG16( include_top=False, weights='imagenet', input_shape=(img_height, img_width, 3) ) base_model.trainable = False inputs = tf.keras.Input(shape=(img_height, img_width, 3)) x = tf.keras.applications.vgg16.preprocess_input(inputs) x = base_model(x, training=False) x = tf.keras.layers.GlobalAveragePooling2D()(x) x = tf.keras.layers.Dropout(0.3)(x) outputs = tf.keras.layers.Dense(90)(x) model = tf.keras.Model(inputs, outputs) model.summary()model.compile(optimizer=tf.keras.optimizers.Adam(0.001), loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])epoch = 15 model.fit(train_ds, validation_data=val_ds, epochs=epoch, callbacks = [ tf.keras.callbacks.EarlyStopping( monitor="val_loss", min_delta=1e-2, patience=3, verbose=1, restore_best_weights=True ) ] )# fine tuning base_model.trainable = True for layer in base_model.layers[:14]: layer.trainable = False model.summary()model.compile(optimizer=tf.keras.optimizers.Adam(0.0001), loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])epoch = 15 history = model.fit(train_ds, validation_data=val_ds, epochs=epoch, callbacks = [ tf.keras.callbacks.EarlyStopping( monitor="val_loss", min_delta=1e-2, patience=3, verbose=1, ) ] )改进代码，使用更好的模型

outputs = tf.keras.layers.Dense(len(class_names))(x) # 动态适配类别数二、训练策略优化 1. 改进学习率调度： python # 余弦退火学习率 lr_schedule = tf.keras.optimizers.schedules.CosineDecay( ...

import numpy as np import tensorflow as tf from keras.models import Sequential from keras.layers import Dense, Activation, Dropout, Flatten from keras.layers.convolutional import Conv2D, MaxPooling2D from keras.utils import np_utils from keras.datasets import mnist from keras import backend as K from keras.optimizers import Adam import skfuzzy as fuzz import pandas as pd from sklearn.model_selection import train_test_split # 绘制损失曲线 import matplotlib.pyplot as plt from sklearn.metrics import accuracy_score data = pd.read_excel(r"D:\pythonProject60\filtered_data1.xlsx") # 读取数据文件 # Split data into input and output variables X = data.iloc[:, :-1].values y = data.iloc[:, -1].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 导入MNIST数据集 # 数据预处理 y_train = np_utils.to_categorical(y_train, 3) y_test = np_utils.to_categorical(y_test, 3) # 创建DNFN模型 model = Sequential() model.add(Dense(64, input_shape=(11,), activation='relu')) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(3, activation='softmax')) # 编译模型 model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy']) # 训练模型 history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=128) # 使用DNFN模型进行预测 y_pred = model.predict(X_test) y_pred= np.argmax(y_pred, axis=1) print(y_pred) # 计算模糊分类 fuzzy_pred = [] for i in range(len(y_pred)): fuzzy_class = np.zeros((3,)) fuzzy_class[y_pred[i]] = 1.0 fuzzy_pred.append(fuzzy_class) fuzzy_pred = np.array(fuzzy_pred) print(fuzzy_pred)获得其运行时间

import time start_time = time.time() # Your code here end_time = time.time() print("Total time taken: ", end_time - start_time, "seconds") 这将打印代码的总运行时间，以秒为单位。

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