def preprocess_data(note, default_value=1): note_to_int = dict((notes, chords) for notes, chords in enumerate(note)) note_to_number = {'C4': 60, 'D4': 62, 'E4': 64, 'F4': 65, 'G4': 67, 'A4': 69, 'B4': 71} # 将音符和和弦分开 notes, chords = [], [] notes.append([note_to_int.get(char, default_value) for char in note]) chords.append([note_to_int.get(char, default_value) for char in note]) # 对音符序列进行one-hot编码 notes = to_categorical(notes, num_classes=NUM_CLASSES) for seq in note: seq = 'C4' if seq.isnumeric(): seq = int(seq) else: # 处理无效的序列字符串 pass seq = note_to_number[seq] seq = int(seq) notes.append(seq[:, :NUM_CLASSES]) chords.append(seq[:, NUM_CLASSES:]) # 对和弦序列进行编码 chords = np.argmax(chords, axis=-1) notes = np.array(notes) chords = np.array(chords) # 对音符和和弦序列进行填充 notes = pad_sequences(notes, maxlen=SEQ_LEN, padding='pre', truncating='pre') chords = pad_sequences(chords, maxlen=SEQ_LEN, padding='pre', truncating='pre') return notes, chords

Preprocess_scale_rotate.zip_preprocess scale_scale_人脸几何校正_旋转校正_缩

标题"Preprocess_scale_rotate.zip"涉及的关键技术主要包括图像的缩放、旋转以及人脸几何校正，这些都是图像预处理中的常见操作。首先，我们来看“缩放”（scale）。在人脸识别中，不同的人脸大小可能会有很大差异...

speex_preprocess.rar_Never Never

标题 "speex_preprocess.rar_Never Never" 暗示我们关注的是一个与 Speex 预处理相关的压缩包文件，而 "Never Never" 可能是某种内部标记或者项目名称，表示该部分代码或功能在正常情况下不应该被直接访问。...

preprocess_data.ktr

gan_args = batch_size, learning_rate, noise_dim, 24, 2, (0, 1), dim def preprocess(data, seq_len): ori_data = data[::-1] scaler = MinMaxScaler().fit(ori_data) ori_data = scaler.transform(ori_data) temp_data = [] for i in range(0, len(ori_data) - seq_len): _x = ori_data[i:i + seq_len] temp_data.append(_x) idx = np.random.permutation(len(temp_data)) data = [] for i in range(len(temp_data)): data.append(temp_data[idx[i]]) return data

首先，代码将原始数据进行了反转（[::-1]），然后使用MinMaxScaler对数据进行归一化处理。归一化后的数据存储在ori_data中。接下来，代码创建了一个空列表temp_data，并通过循环将长度为seq_len的子序列添加到temp...

from scipy.io import loadmat， savemat 将 numpy 导入为 NP 导入作系统 def wgn（x， snr）：信噪比 = 10 （信噪比 / 10.0） xpower = np.sum（np.absolute（x） 2， axis=0） / x.shape[0] npower = xpower / SNR 返回 np.random.standard_normal（x.shape） * np.sqrt（npower） def add_noise（data， snr_num）： rand_data = wgn（数据， snr_num） n_data = 数据 + rand_data 返回 n_data， rand_data def preprocess（d_path， noise=False， snr=0）：文件名 = os.listdir（d_path） def capture（）的文件 = {} 对于文件名中的 i： file_path = os.path.join（d_path， i）文件 = loadmat（file_path） file_keys = file.keys（）对于 Key in file_keys： if 'DE' in key： # DE//FE # 如果 'BJ' 在 key 中： # 如果 key 中包含 'Pader'：文件[i] = 文件[键].ravel（）返回文件 def slice_enc（数据、噪声、SNR）： noised_data_dict1 = {} noise_dict1 = {} 对于 key， data.items（）中的 val：数据 = val 如果噪声： noised_data1，white_noise1 = add_noise（数据，SNR） noised_data_dict1[键] = noised_data1 noise_dict1[键] = white_noise1 返回 noised_data_dict1， noise_dict1 def save_mat（d_path， noised_data_dict1， SNR）：路径 1 = d_path + '_Noised_' + str（snr）如果不是 os.path.exists（path1）： os.mkdir（路径 1）对于 k，dat 在 noised_data_dict1.items（）中： savemat（os.path.join（path1， k）， {'DE'： dat}） # savemat（os.path.join（path1， k）， {'Pader'： dat}） # 从所有.mat文件中读取出数据的字典数据 = 捕获（） noised_data_dict1，noise_dict1 = slice_enc（数据、噪声、SNR） save_mat（d_path、noised_data_dict1、SNR） if name == “main”： path = 'E：/Anaconda3/CNN/datasets/CWRU' # 更改数据集文件夹以拆分数据 preprocess（d_path=路径， noise=True、 snr=-4） # -6 -4 -2 0 2 4 6 z这段代码怎么工作

data = scipy.io.loadmat('data.mat') # 返回字典，键为变量名，值为numpy数组[^1] - **注意事项**：MATLAB结构体可能被转换为嵌套字典，需通过data['key']逐层提取。 #### 2. **numpy 数组操作** - **...

def preprocess_data(map_data, weather_data, param_data):

key: round(value, 2) for key, value in weather_data.items() } # 处理 param_data if param_data is not None: processed_data["parameters"] = param_data return processed_data #### 参数解释 1....

import numpy as np from tensorflow import keras # 加载手写数字图像和标签 def load_data(): train_data = np.loadtxt('train_images.csv', delimiter=',') train_labels = np.loadtxt('train_labels.csv', delimiter=',') test_data = np.loadtxt('test_image.csv', delimiter=',') return train_data, train_labels, test_data # 数据预处理 def preprocess_data(train_data, test_data): # 归一化到 [0, 1] 范围 train_data = train_data / 255.0 test_data = test_data / 255.0 # 将数据 reshape 成适合 CNN 的输入形状 (样本数, 高度, 宽度, 通道数) train_data = train_data.reshape(-1, 28, 28, 1) test_data = test_data.reshape(-1, 28, 28, 1) return train_data, test_data # 构建 CNN 模型 def build_model(): model = keras.Sequential([ keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)), keras.layers.MaxPooling2D(pool_size=(2, 2)), keras.layers.Flatten(), keras.layers.Dense(units=128, activation='relu'), keras.layers.Dense(units=10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) return model # 进行数字识别 def recognize_digit(image, model): probabilities = model.predict(image) digit = np.argmax(probabilities) return digit # 主函数 def main(): # 加载数据 train_data, train_labels, test_data = load_data() # 数据预处理 train_data, test_data = preprocess_data(train_data, test_data) # 构建并训练模型 model = build_model() model.fit(train_data, train_labels, epochs=10, batch_size=32) # 进行数字识别 recognized_digit = recognize_digit(test_data, model) print("识别结果：", recognized_digit) if name == 'main': main()

3. 定义一个函数preprocess_data()，用于对数据进行预处理，包括归一化和reshape操作。 4. 定义一个函数build_model()，用于构建CNN模型。 5. 定义一个函数recognize_digit()，用于进行数字识别。 6. 定义主函数main...

import xarray as xr import numpy as np from scipy.stats import gamma from scipy.special import erfinv import warnings warnings.filterwarnings('ignore') #%% 基础配置 input_path = r'D:\heatwave\data\precip\precip_merged.nc' output_dir = r'D:\heatwave\data\precip' lat_range = [15, 55] lon_range = [105, 140] #%% 步骤1：增强型数据预处理 def preprocess_data(): ds = xr.open_dataset(input_path) # 统一维度命名 rename_dict = {} if 'latitude' in ds.dims: rename_dict['latitude'] = 'lat' if 'longitude' in ds.dims: rename_dict['longitude'] = 'lon' ds = ds.rename(rename_dict) # 空间裁剪 ds_china = ds.sel(lat=slice(lat_range), lon=slice(lon_range)) # 移除闰日 ds_china = ds_china.sel(time=~((ds_china['time.month'] == 2) & (ds_china['time.day'] == 29))) return ds_china #%% 步骤2：修正后的SPI-30计算 def calculate_spi30(precip): # 30天累积降水（保留空间维度） precip_roll = precip.rolling(time=30, min_periods=30, center=False).sum() # Gamma分布拟合（增强数值稳定性） def gamma_fit(data): valid_data = data[~np.isnan(data)] if len(valid_data) < 5 or np.all(valid_data == 0): return (np.nan, 0, 0) try: return gamma.fit(valid_data + 1e-6, floc=0) except: return (np.nan, 0, 0) # 分组计算（显式保留空间维度） precip_group = precip_roll.groupby('time.dayofyear', restore_coord=True) # 核心修正：维度处理 spi30 = xr.apply_ufunc( lambda x, doy: gamma.cdf(x, gamma_fit(precip_group[doy.item()].values)), precip_roll, precip_roll['time.dayofyear'], input_core_dims=[[], []], # 无核心维度 output_core_dims=[[]], # 保持原始维度 vectorize=True, dask='parallelized', output_dtypes=[float] ) # 转换为标准正态分布 spi30 = xr.apply_ufunc( lambda x: np.sqrt(2)erfinv(2*x-1) if not np.isnan(x) else np.nan, spi30, dask='parallelized', output_dtypes=[float] ) return spi30.rename('SPI30').transpose('time', 'lat', 'lon') # 确保维度顺序 #%% 步骤3：增强型主处理流程 def main_process(): ds = preprocess_data() spi30 = calculate_spi30(ds['precip']) # 编码设置（修复维度丢失问题） encoding = { 'SPI30': {'zlib': True, 'complevel': 1, 'dtype': 'float32'}, 'lat': {'dtype': 'float32'}, 'lon': {'dtype': 'float32'}, 'time': {'dtype': 'int32', 'units': 'days since 1900-01-01'} } # 输出文件 spi30.to_netcdf( f'{output_dir}/SPI30_daily.nc', encoding=encoding, unlimited_dims=['time'] ) print(f"处理完成！维度验证：{spi30.dims}") #%% 执行 if name == 'main': main_process()对以上代码报错了Providing a combination of group and **groupers is not supported.，帮我解决并生成完整代码

#%% 步骤1：增强型数据预处理（保持不变） def preprocess_data(): ds = xr.open_dataset(input_path) # 统一维度命名 rename_dict = {} if 'latitude' in ds.dims: rename_dict['latitude'] = 'lat' if '...

有代码如下（1）数据迭代器：class DealDataset_LAI_train: def init(self): data_dir = r"D:\PyCharm\Paper_2025\c1_data_labels.mat" data = sio.loadmat(data_dir) # c1_features train_data = torch.from_numpy(data["c1_features"]) train_label_1 = torch.from_numpy(data["c1_labels"]) train_label = train_label_10.001 self.train_data = np.reshape(train_data, (-1, 1, 23)) self.train_label = train_label self.len = self.train_data.shape[0] pass def getitem(self, index): return self.train_data[index], self.train_label[index] def len(self): return self.len class DealDataset_LAI_test: def init(self): data_dir = r"D:\PyCharm\Paper_2025\c1_data_labels.mat" data = sio.loadmat(data_dir) test_data = torch.from_numpy((data["c1_features"])) self.test_data = np.reshape(test_data, (-1, 1, 23)) test_label_1 = torch.from_numpy(data["c1_labels"]) test_label = test_label_10.001 self.test_label =test_label self.len = self.test_data.shape[0] def getitem(self, index): return self.test_data[index], self.test_label[index] def len(self): return self.len

def preprocess(self): # 统一的预处理逻辑 self.data.dropna(inplace=True) self.data = self.data[self.data['temperature'] > 0] def __len__(self): return len(self.data) def __getitem__(self, idx)...

def preprocess_data(self): if not self.data.empty: # 忽略warning warnings.filterwarnings("ignore", category=UserWarning) # 复制数据集 processed_data = self.data.copy() # 处理字符串列 string_columns = processed_data.select_dtypes(include=['object']).columns for column in string_columns: processed_data[column] = processed_data[column].str.split(';').apply(lambda x: [float(val) for val in x]) # 处理数值列 numeric_columns = processed_data.select_dtypes(include=['float', 'int']).columns imputer = SimpleImputer(strategy='mean') processed_data[numeric_columns] = imputer.fit_transform(processed_data[numeric_columns]) # 处理异常值 isolation_forest = IsolationForest(contamination=0.05) outliers = isolation_forest.fit_predict(processed_data[numeric_columns]) processed_data = processed_data[outliers != -1] # 标准化处理 scaler = StandardScaler() processed_data[numeric_columns] = scaler.fit_transform(processed_data[numeric_columns]) # 创建一个新窗口来显示处理后的数据集 top = tk.Toplevel(self.master) top.title("处理后的数据集") # 创建用于显示处理后的数据集的表格 table = tk.Text(top) table.pack() # 将处理后的数据集转换为字符串并显示在表格中 table.insert(tk.END, str(processed_data)) table.config(state=tk.DISABLED) else: self.path_label.config(text="请先导入数据集")

1. 处理字符串列：将字符串列按分号拆分为多个数值，并将每个数值转换为 float 类型。 2. 处理数值列：使用均值填充缺失值。 3. 处理异常值：使用孤立森林算法检测和过滤异常值。 4. 标准化处理：使用 ...

def preprocess_data(self, x): """ Preprocesses data such that their shapes match the specified structures :param x: A data batch to preprocess :return: A preprocessed data batch """ x_shape = x.shape x = np.reshape(x, (-1, x_shape[-3], x_shape[-2], x_shape[-1])) if self.reverse_channels is True: reverse_photos = np.ones(shape=x.shape) for channel in range(x.shape[-1]): reverse_photos[:, :, :, x.shape[-1] - 1 - channel] = x[:, :, :, channel] x = reverse_photos x = x.reshape(x_shape) return x

2. 将x的形状修改为(-1, x_shape[-3], x_shape[-2], x_shape[-1])，即保留最后一维的长度不变，而将前三维展平为一个维度。 3. 如果reverse_channels为True，则将数据批次x中的通道顺序反转。具体来说，对于每个通道...

from transformers import BertTokenizer, BertForSequenceClassification, TrainingArguments, Trainer from datasets import load_from_disk import numpy as np from sklearn.metrics import accuracy_score def load_processed_data(): dataset = load_from_disk("../data/processed_imdb") return dataset['train'], dataset['test'] # 定义预处理函数 def preprocess_function(examples): return tokenizer( examples['text'], truncation=True, padding='max_length', max_length=256 ) # 定义评估指标 def compute_metrics(p): preds = np.argmax(p.predictions, axis=1) return {'accuracy': accuracy_score(p.label_ids, preds)} if name == 'main': train_data, test_data = load_processed_data() # 加载BERT的分词器 tokenizer = BertTokenizer.from_pretrained("C:/Users/Administrator/bert_cache") # 应用预处理 train_data = train_data.map(preprocess_function, batched=True) test_data = test_data.map(preprocess_function, batched=True) # 重命名标签列（适配HuggingFace格式） train_data = train_data.rename_column('label', 'labels') test_data = test_data.rename_column('label', 'labels') # 设置pytorch格式 train_data.set_format('torch', columns=['input_ids', 'attention_mask', 'labels']) test_data.set_format('torch', columns=['input_ids', 'attention_mask', 'labels']) # 加载预训练模型 model = BertForSequenceClassification.from_pretrained( "C:/Users/Administrator/bert_cache", num_labels=2 ) training_args = TrainingArguments( output_dir='./results', # 输出目录 num_train_epochs=3, # 训练轮次 per_device_train_batch_size=32, # 训练批次大小 per_device_eval_batch_size=128, # 评估批次大小 warmup_steps=500, # 学习率预热步数 weight_decay=0.01, # 权重衰减 logging_dir='./logs', # 日志目录 eval_strategy='epoch', # 每轮评估一次 save_strategy='epoch' ) # 创建Trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_data, eval_dataset=test_data, # 实际应用时应使用验证集 compute_metrics=compute_metrics ) # 开始训练！ trainer.train() # 保存模型 model.save_pretrained('./my_bert_model') tokenizer.save_pretrained('./my_bert_model') # 加载模型 # from_pretrained_model = BertForSequenceClassification.from_pretrained('./my_bert_model')

train_data = train_data.map(preprocess_function, batched=True) 4. **标签列适配** python train_data.rename_column('label', 'labels') # 修改列名适配Transformers格式 5. **模型初始化** ...

def create_argparser(): defaults = dict( clip_denoised=False, num_samples=1, batch_size=2, use_ddim=True, base_samples="", model_path='/root/autodl-tmp/TASK8/improved_diffusion/model_save_FULL/ema_0.9999_036000.pt', ) print(defaults["model_path"]) defaults.update(sr_model_and_diffusion_defaults()) parser = argparse.ArgumentParser() add_dict_to_argparser(parser, defaults) return parser def load_superres_data(batch_size): data = trans_data_preprocess(batch_size = batch_size, shuffle = False, num_workers = 8) for large_batch, model_kwargs in data: large_batch = large_batch[:,:,np.arange(0, 576, 2),:] model_kwargs["raw_img"] = large_batch model_kwargs["low_res"] = F.interpolate(large_batch, [36, 736], mode="nearest") #36->72 res = dict(low_res=model_kwargs["low_res"], raw_img=model_kwargs["raw_img"],index=model_kwargs["index"] ) yield res逐行解释代码

for large_batch, model_kwargs in data: 迭代由上一行产生的 data 数据集，依次获取每一批次的大尺寸图片 large_batch 和附加关键字参数字典 model_kwargs。 --- python large_batch = large_batch...

这段代码有什么问题def preprocess_X_Train(audio_path, sr=22050, n_fft=2048, hop_length=512, n_mels=128): audio_path = r'G:\Master\Papers\Audio Source\train' audio_files = [os.path.join(audio_path, f) for f in os.listdir(audio_path) if f.endswith('.wav')]

这段代码的问题在于，虽然定义了 preprocess_X_Train 函数，但是在函数内部并没有对音频文件进行预处理的代码。函数内部只是定义了一个 audio_path 变量，并将其赋值为指定的路径。因此，这段代码需要进一步完善，以...

逐行详细解释以下代码并加注释from tensorflow import keras import matplotlib.pyplot as plt base_image_path = keras.utils.get_file( "coast.jpg", origin="https://img-datasets.s3.amazonaws.com/coast.jpg") plt.axis("off") plt.imshow(keras.utils.load_img(base_image_path)) #instantiating a model from tensorflow.keras.applications import inception_v3 model = inception_v3.InceptionV3(weights='imagenet',include_top=False) #配置各层对DeepDream损失的贡献 layer_settings = { "mixed4": 1.0, "mixed5": 1.5, "mixed6": 2.0, "mixed7": 2.5, } outputs_dict = dict( [ (layer.name, layer.output) for layer in [model.get_layer(name) for name in layer_settings.keys()] ] ) feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict) #定义损失函数 import tensorflow as tf def compute_loss(input_image): features = feature_extractor(input_image) loss = tf.zeros(shape=()) for name in features.keys(): coeff = layer_settings[name] activation = features[name] loss += coeff * tf.reduce_mean(tf.square(activation[:, 2:-2, 2:-2, :])) return loss #梯度上升过程 @tf.function def gradient_ascent_step(image, learning_rate): with tf.GradientTape() as tape: tape.watch(image) loss = compute_loss(image) grads = tape.gradient(loss, image) grads = tf.math.l2_normalize(grads) image += learning_rate * grads return loss, image def gradient_ascent_loop(image, iterations, learning_rate, max_loss=None): for i in range(iterations): loss, image = gradient_ascent_step(image, learning_rate) if max_loss is not None and loss > max_loss: break print(f"... Loss value at step {i}: {loss:.2f}") return image #hyperparameters step = 20. num_octave = 3 octave_scale = 1.4 iterations = 30 max_loss = 15. #图像处理方面 import numpy as np def preprocess_image(image_path): img = keras.utils.load_img(image_path) img = keras.utils.img_to_array(img) img = np.expand_dims(img, axis=0) img = keras.applications.inception_v3.preprocess_input(img) return img def deprocess_image(img): img = img.reshape((img.shape[1], img.shape[2], 3)) img /= 2.0 img += 0.5 img *= 255. img = np.clip(img, 0, 255).astype("uint8") return img #在多个连续上运行梯度上升 original_img = preprocess_image(base_image_path) original_shape = original_img.shape[1:3] successive_shapes = [original_shape] for i in range(1, num_octave): shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape]) successive_shapes.append(shape) successive_shapes = successive_shapes[::-1] shrunk_original_img = tf.image.resize(original_img, successive_shapes[0]) img = tf.identity(original_img) for i, shape in enumerate(successive_shapes): print(f"Processing octave {i} with shape {shape}") img = tf.image.resize(img, shape) img = gradient_ascent_loop( img, iterations=iterations, learning_rate=step, max_loss=max_loss ) upscaled_shrunk_original_img = tf.image.resize(shrunk_original_img, shape) same_size_original = tf.image.resize(original_img, shape) lost_detail = same_size_original - upscaled_shrunk_original_img img += lost_detail shrunk_original_img = tf.image.resize(original_img, shape) keras.utils.save_img("DeepDream.png", deprocess_image(img.numpy()))

original_shape = original_img.shape[1:3] successive_shapes = [original_shape] for i in range(1, num_octave): shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape]) successive_...

def tokenize_nmt(lines, token='word'): """词元化“英语－汉语”数据数据集""" # def tokenize(lines, token='word'): #@save """将文本行拆分为单词或字符词元""" if token == 'word': return [line.split() for line in lines] elif token == 'char': return [list(line) for line in lines] else: print('错误：未知词元类型：' .format(token)) source, target = tokenize_nmt(text) source[:6], target[:6] def load_data_nmt(batch_size, num_steps, num_examples=600): """返回翻译数据集的迭代器和词表""" text = preprocess_nmt(read_data_nmt()) source, target = tokenize_nmt(text, num_examples) src_vocab = d2l.Vocab(source, min_freq=2, reserved_tokens=['', '<bos>', '<eos>']) tgt_vocab = d2l.Vocab(target, min_freq=2, reserved_tokens=['', '<bos>', '<eos>']) src_array, src_valid_len = build_array_nmt(source, src_vocab, num_steps) tgt_array, tgt_valid_len = build_array_nmt(target, tgt_vocab, num_steps) data_arrays = (src_array, src_valid_len, tgt_array, tgt_valid_len) data_iter = d2l.load_array(data_arrays, batch_size) return data_iter, src_vocab, tgt_vocab train_iter, src_vocab, tgt_vocab = load_data_nmt(batch_size=2, num_steps=8) for X, X_valid_len, Y, Y_valid_len in train_iter: print('X:', X.type(torch.int32)) print('X的有效长度:', X_valid_len) print('Y:', Y.type(torch.int32)) print('Y的有效长度:', Y_valid_len) break TypeError: cannot unpack non-iterable NoneType object

在这个例子中，可能是在load_data_nmt这个函数中，build_array_nmt函数返回了空值，导致整个load_data_nmt函数返回了None。你可以检查一下build_array_nmt这个函数是否正常运行，或者在load_data_nmt函数中添加一些...

修改以下代码使其能够输出模型预测结果： def open_image(self): file_dialog = QFileDialog() file_paths, _ = file_dialog.getOpenFileNames(self, "选择图片", "", "Image Files (.png .jpg .jpeg)") if file_paths: self.display_images(file_paths) def preprocess_images(self, image_paths): data_transform = transforms.Compose([ transforms.CenterCrop(150), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) self.current_image_paths = [] images = [] for image_path in image_paths: image = Image.open(image_path) image = data_transform(image) image = torch.unsqueeze(image, dim=0) images.append(image) self.current_image_paths.append(image_path) return images def predict_images(self): if not self.current_image_paths: return for i, image_path in enumerate(self.current_image_paths): image = self.preprocess_image(image_path) output = self.model(image) predicted_class = self.class_dict[output.argmax().item()] self.result_labels[i].setText(f"Predicted Class: {predicted_class}") self.progress_bar.setValue((i+1)20) def display_images(self, image_paths): for i, image_path in enumerate(image_paths): image = QImage(image_path) image = image.scaled(300, 300, Qt.KeepAspectRatio) if i == 0: self.image_label_1.setPixmap(QPixmap.fromImage(image)) elif i == 1: self.image_label_2.setPixmap(QPixmap.fromImage(image)) elif i == 2: self.image_label_3.setPixmap(QPixmap.fromImage(image)) elif i == 3: self.image_label_4.setPixmap(QPixmap.fromImage(image)) elif i == 4: self.image_label_5.setPixmap(QPixmap.fromImage(image))

请注意，代码中的 preprocess_images 函数和 predict_images 函数中的 self.preprocess_image(image_path) 需要修改为 self.preprocess_images([image_path])。修改后的代码如下： def open_image(self): file...

Traceback (most recent call last): File "D:\pythonProject\gantrain01.py", line 176, in <module> notes, chords = preprocess_data(data) File "D:\pythonProject\gantrain01.py", line 63, in preprocess_data notes.append(seq[:, :NUM_CLASSES]) TypeError: string indices must be integers

seq = seq.astype(int) print(seq) 这个代码将一个字符串类型的数组转换为整数类型的数组，并输出结果 [1 2 3 4 5]。如果无法确定数据的类型，可以使用 type() 函数来检查变量的类型，例如： python...

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这段代码有什么问题def preprocess_X_Train(audio_path, sr=22050, n_fft=2048, hop_length=512, n_mels=128): audio_path = r'G:\Master\Papers\Audio Source\train' audio_files = [os.path.join(audio_path, f) for f in os.listdir(audio_path) if f.endswith('.wav')]

Traceback (most recent call last): File "D:\pythonProject\gantrain01.py", line 176, in <module> notes, chords = preprocess_data(data) File "D:\pythonProject\gantrain01.py", line 63, in preprocess_data notes.append(seq[:, :NUM_CLASSES]) TypeError: string indices must be integers

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