import pickle import os dataset_dir=os.path.dirname(os.path.abspath("__file__")) save_file=dataset_dir+"/mnist.pkl" def _change_one_hot_label(x): T=np.zeros((X.size,10)) for idx, row in enumerate(T): row[X[idx]]=1 return T def load_mnist(normalize=True, flatten=True, one_hot_label=False): if not os.path.exists(save_file): print("请下载数据") with open(save_file,'rb')as f: dataset=pickle.load(f) if normalize: for key in ('train_img','test_img'): dataset[key]=dataset[key].astype(np.float32) dataset[key] /=255.0 if one_hot_label: dataset['train_label']=_change_one_hot_label(dataset['train_label']) dataset['test_label']=_change_one_hot_label(dataset['test_label']) if not flatten: for key in ('train_img','test.img'): dataset[key]=dataset[key].reshape(-1,1,28,28) return (dataset['train_img'],dataset['train_label']),(dataset['test_img'],dataset['test_label'])

import gzip import os import pickle 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 gzip.open(labels_path, 'rb') as lbpath: labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8) with gzip.open(images_path, 'rb') as imgpath: images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784) return images, labels def get_mnist_data(num_training=5000, num_validation=500, num_test=500): mnist_dir = r'D:\daima\mnist' # 修改为mnist数据集所在的目录 X_train, y_train = load_mnist(mnist_dir, kind='train') X_test, y_test = load_mnist(mnist_dir, kind='t10k') 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] X_train = X_train.astype('float32') / 255 X_val = X_val.astype('float32') / 255 X_test = X_test.astype('float32') / 255 return X_train, y_train, X_val, y_val, X_test, y_testTypeError: tuple indices must be integers or slices, not str

这是因为在load_mnist函数中返回的是一个元组类型的变量(images, labels)，而在get_mnist_data函数中却使用了类似字典的方式访问这个元组变量，如X_train, y_train = load_mnist(mnist_dir, kind='train')，应该改为...

解释以下这段代码：import tensorflow as tf gpus =tf.config.experimental.list_physical_devices(device_type='GPU') tf.config.experimental.set_virtual_device_configuration(gpus[0],[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4096)]) #import scipy.io as sio import pickle import os,random import matplotlib.pyplot as plt #import scipy.stats from tensorflow import losses from tensorflow.keras import Model from tensorflow.keras import layers import matplotlib.pyplot as plt import tensorflow as tf import numpy as np #import scipy.io as sio #import scipy.stats import math import os import pdb from tensorflow import losses from model import ResNet18 from re_dataset_real import train_image1,train_label1,test_image1,test_label1,val_image1,val_label1 from re_dataset_imag import train_image2,train_label2,test_image2,test_label2,val_image2,val_label2 def phsical_loss(y_true, y_pred): y_true =tf.cast(y_true, y_pred.dtype) loss_real=tf.keras.losses.MSE(y_true[0],y_pred[0]) loss_img= tf.keras.losses.MSE(y_true[1],y_pred[1]) amp_ture=tf.pow(y_true[0],2)+tf.pow(y_true[1],2) amp_pred=tf.pow(y_pred[0],2)+tf.pow(y_pred[1],2) loss_amp=tf.keras.losses.MSE(amp_ture,amp_pred) return loss_real+loss_img+loss_amp#两个子模型各加一个完整约束 def angle_loss(y_true, y_pred): y_true = tf.cast(y_true, y_pred.dtype) img_ture=tf.atan2(y_true[1],y_true[0]) img_pred=tf.atan2(y_pred[1],y_pred[0]) return tf.keras.losses.MAE(img_ture,img_pred) def amp_loss(y_true, y_pred): y_true = tf.cast(y_true, y_pred.dtype) amp_ture=tf.pow(y_true[0],2)+tf.pow(y_true[1],2) amp_pred=tf.pow(y_pred[0],2)+tf.pow(y_pred[1],2) loss_phsical=tf.keras.losses.MSE(amp_ture,amp_pred) return loss_phsical model_in=tf.keras.Input((16,16,1)) model_real_out=ResNet18([2,2,2,2])(model_in) model_img_out=ResNet18([2,2,2,2])(model_in) model_all=tf.keras.Model(model_in,[model_real_out,model_img_out]) model_all.compile(loss=phsical_loss, optimizer=tf.keras.optimizers.Adam(tf.keras.optimizers.schedules.InverseTimeDecay( 0.001, decay_steps=250*25, decay_rate=1, staircase=False)), metrics=['mse']) checkpoint_save_path= "C:\\Users\\Root\\Desktop\\bysj\\model_all.ckpt" if os.path.exists(checkpoint_save_path + '.index'): print('------------------load model all---------------------') model_all.load_weights(checkpoint_save_path) cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path, save_weights_only=True,save_best_only=True)

这段代码主要是实现了一个包含两个子模型的神经网络，其中每个子模型都是由 ResNet18 构成的。此外，还定义了三个损失函数 phsical_loss、angle_loss 和 amp_loss，分别用于计算物理约束、角度约束和幅度约束。...

import os from random import shuffle import pickle import librosa import tensorflow.keras as keras import librosa.display import numpy as np from matplotlib import pyplot as plt def load_files(audio_dir): files=os.listdir(audio_dir) wav_files=[] for wav in files: if not wav.endswith('.wav'): continue wav_files.append(wav) if not wav_files: print('未找到数据集') shuffle(wav_files) nfiles=len(wav_files) ntrain=int(nfiles*0.7) return wav_files[:ntrain],wav_files[ntrain:] audio_dir='../data/recordings/' dataset_pickle='../tmp/recordings.pkl' train_files,valid_files=load_files(audio_dir) print('训练集样本数为{}\n验证集样本数为{}'.format(len(train_files),load_files(valid_files)))帮我检查错误

dataset_pickle = '../tmp/recordings.pkl' train_files, valid_files = load_files(audio_dir) print('训练集样本数为{}\n验证集样本数为{}'.format(len(train_files), len(valid_files))) 希望能帮到你！

#TFIDF from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer import pickle # tfidf向量保存 # tfidf = TfidfVectorizer() tfidftransformer_path = './tfidftransformer.pkl' tfidf = pickle.load(open(tfidftransformer_path,"rb")) x_train_df = tfidf.fit_transform(x_train) x_train_df = tfidf.transform(x_train) # tfidf向量保存 # tfidftransformer_path = './tfidftransformer.pkl' # with open(tfidftransformer_path, 'wb') as fw: # pickle.dump(tfidf, fw) # 先前已执行过一次 x_test_df = tfidf.transform(x_test)

其中，使用 pickle 库对 tfidf 变量进行保存和加载，这样可以避免每次执行程序时都需要重新计算 tfidf。通过调用 fit_transform() 和 transform() 方法，将训练数据集和测试数据集转换成 tfidf 特征向量。这些特征...

import torch, os, pickle, random import numpy as np from yaml import safe_load as yaml_load from json import dumps as json_dumps def load_data(data_path): with open(data_path, 'rb') as f: data = pickle.load(f) return data def save_model(model, save_path, optimizer=None): os.makedirs(os.path.dirname(save_path), exist_ok=True) data2save = { 'state_dict': model.state_dict(), 'optimizer': optimizer.state_dict(), } torch.save(data2save, save_path) def load_model(model, load_path, optimizer=None): data2load = torch.load(load_path, map_location='cpu') model.load_state_dict(data2load['state_dict']) if optimizer is not None and data2load['optimizer'] is not None: optimizer = data2load['optimizer'] def fix_random_seed_as(seed): random.seed(seed) torch.random.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False if name == "main": pass

save_model 函数用于保存模型及其优化器的状态。它接受一个模型对象、保存路径和可选的优化器对象作为输入，并将模型的状态字典和优化器的状态字典保存到指定的路径中。 load_model 函数用于加载模型及其优化器...

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 pickle 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' % ...

import pickle from os import path import jieba import jieba.analyse import matplotlib.pyplot as plt from wordcloud import WordCloud,STOPWORDS,ImageColorGenerator import sys sys.path.append('C:\\Users\\李肖\\PycharmProjects\\pythonProject\\text.txt') with open('C:\\Users\\李肖\\PycharmProjects\\pythonProject\\text.txt','r',encoding='utf-8')as fin: text=fin.read() background_Image=plt.imread('main.jpg') print('加载图片成功!') '''设置词云样式''' wc = WordCloud(background_color="black", mask=background_Image, font_path='msyh.ttc', max_words=200, stopwords=STOPWORDS, max_font_size=50, random_state=30) wc.generate_from_text(text) print('开始加载文本') plt.imshow(wc) plt.axis('off') plt.show() d=path.dirname(file) wc.to_file(path.join(d,"h11.jpg")) print('生成词云成功！')不能运行

1. 将第一行代码改为import pickle, os，这是因为你同时引入了pickle和os两个模块，需要用逗号分隔开。 2. 在第5行代码中，sys.path.append()应该加入的是文件所在目录，而不是文件本身，可以将其修改为...

X_train,T_train=idx2numpy.convert_from_file('emnist/emnist-letters-train-images-idx3-ubyte'),idx2numpy.convert_from_file('emnist/emnist-letters-train-labels-idx1-ubyte')转化为相同形式train_num = 60000 test_num = 10000 img_dim = (1, 28, 28) img_size = 784 def _download(file_name): file_path = dataset_dir + "/" + file_name if os.path.exists(file_path): return print("Downloading " + file_name + " ... ") urllib.request.urlretrieve(url_base + file_name, file_path) print("Done") def download_mnist(): for v in key_file.values(): _download(v) def _load_label(file_name): file_path = dataset_dir + "/" + file_name print("Converting " + file_name + " to NumPy Array ...") with gzip.open(file_path, 'rb') as f: labels = np.frombuffer(f.read(), np.uint8, offset=8) print("Done") return labels def _load_img(file_name): file_path = dataset_dir + "/" + file_name print("Converting " + file_name + " to NumPy Array ...") with gzip.open(file_path, 'rb') as f: data = np.frombuffer(f.read(), np.uint8, offset=16) data = data.reshape(-1, img_size) print("Done") return data def _convert_numpy(): dataset = {} dataset['train_img'] = _load_img(key_file['train_img']) dataset['train_label'] = _load_label(key_file['train_label']) dataset['test_img'] = _load_img(key_file['test_img']) dataset['test_label'] = _load_label(key_file['test_label']) return dataset def init_mnist(): download_mnist() dataset = _convert_numpy() print("Creating pickle file ...") with open(save_file, 'wb') as f: pickle.dump(dataset, f, -1) print("Done!") def _change_one_hot_label(X): T = np.zeros((X.size, 10)) for idx, row in enumerate(T): row[X[idx]] = 1 return T def load_mnist(normalize=True, flatten=True, one_hot_label=False): """读入MNIST数据集 Parameters ---------- normalize : 将图像的像素值正规化为0.0~1.0 one_hot_label : one_hot_label为True的情况下，标签作为one-hot数组返回 one-hot数组是指[0,0,1,0,0,0,0,0,0,0]这样的数组 flatten : 是否将图像展开为一维数组 Returns ------- (训练图像, 训练标签), (测试图像, 测试标签) """ if not os.path.exists(save_file): init_mnist() with open(save_file, 'rb') as f: dataset = pickle.load(f) if normalize: for key in ('train_img', 'test_img'): dataset[key] = dataset[key].astype(np.float32) dataset[key] /= 255.0 if one_hot_label: dataset['train_label'] = _change_one_hot_label(dataset['train_label']) dataset['test_label'] = _change_one_hot_label(dataset['test_label']) if not flatten: for key in ('train_img', 'test_img'): dataset[key] = dataset[key].reshape(-1, 1, 28, 28) return (dataset['train_img'], dataset['train_label']), (dataset['test_img'], dataset['test_label']) if name == 'main': init_mnist()模仿这段代码将获取同样形式

import idx2numpy import numpy as np def load_emnist(): X_train = idx2numpy.convert_from_file('emnist/emnist-letters-train-images-idx3-ubyte') T_train = idx2numpy.convert_from_file('emnist/emnist-...

请问下述代码的问题在哪里import numpy as np file_path="C:\Users\me\shixi\\eeg-predictive_train.npz" poem=np.load(file_path,allow_pickle=True) poem.files

例如：file_path = r"C:\Users\me\shixi\eeg-predictive_train.npz" 2. 使用双反斜杠（\\）来表示单个反斜杠。例如：file_path = "C:\\Users\\me\\shixi\\eeg-predictive_train.npz" 3. 使用正斜杠（/）来替代...

def get_notes(): # """ # 从music_midi目录中的所有MIDI文件里读取note，chord # Note样例：B4，chord样例[C3,E4,G5],多个note的集合，统称“note” # """ notes = [] for midi_file in glob.glob("MID/*.mid"): # 读取MID文件夹中所有的mid文件,file表示每一个文件 stream = converter.parse(midi_file) # midi文件的读取，解析，输出stream的流类型 # 获取所有的乐器部分，开始测试的都是单轨的 parts = instrument.partitionByInstrument(stream) if parts: # 如果有乐器部分，取第一个乐器部分 notes_to_parse = parts.parts[0].recurse() # 递归 else: notes_to_parse = stream.flat.notes # 纯音符组成 for element in notes_to_parse: # notes本身不是字符串类型 # 如果是note类型，取它的音高(pitch) if isinstance(element, note.Note): # 格式例如：E6 notes.append(str(element.pitch)) elif isinstance(element, chord.Chord): # 转换后格式：45.21.78(midi_number) notes.append('.'.join(str(n) for n in element.normalOrder)) # 获取D盘根目录的绝对路径 root_path = os.path.abspath("D:/pythonProject") # 拼接要创建的文件夹路径 data_path = os.path.join(root_path, "data04") # 创建文件夹 if not os.path.exists(data_path): os.mkdir(data_path) # 将数据写入data01/notes with open(os.path.join(data_path, "notes"), 'wb') as filepath: pickle.dump(notes, filepath) return notes # 返回提取出来的notes列表

这是一个Python函数，用于从MIDI文件中提取音符和和弦，并将它们保存到一个列表中。该函数首先使用glob.glob...最后，该函数使用pickle.dump()函数将notes列表保存到一个名为"notes"的二进制文件中，并返回notes列表。

给下面代码每一行给上注释 with open(r'mat_pickle/init_mat.pkl', "rb") as f0: init_mat = np.array(list(pickle.load(f0).values())) with open(r'mat_pickle/trans_mat.pkl', "rb") as f1: init_trans_mat = np.array(list(pickle.load(f1).values())) with open(r'mat_pickle/emit_mat.pkl', "rb") as f2: init_emit_mat = pickle.load(f2) catalog = list(init_emit_mat) trans_mat = []

init_mat = np.array(list(pickle.load(f0).values())) # Load the initial transition matrix from the pickle file with open(r'mat_pickle/trans_mat.pkl', "rb") as f1: init_trans_mat = np.array(list...

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文件格式.pkl表明数据集是以Python的pickle格式保存，这意味着数据集可能包含多种复杂的数据结构，如字典、列表、类的实例等，便于在Python环境中进行序列化和反序列化操作。标签'fm_close'强调了该文件集的核心内容...

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