trainX Y shape is: (4755, 48, 23) (4755, 60, 23) (4755, 5, 1) Hx= 1 Traceback (most recent call last): File ~\anaconda4\envs\tensorflow\lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec exec(code, globals, locals) File d:\lstnet_keras-master\train_batch_a391.py:172 model,normalize = startTrainMult_1H(df,name,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:400 in startTrainMult_1H model = trainModel(trainX1, trainX2 , trainY, config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:323 in trainModel model = LSTNetAttention(trainX1,trainX2,trainY,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:297 in LSTNetAttention res = add([concat,z]) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\layers\merge.py:555 in add return Add(**kwargs)(inputs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py:75 in symbolic_fn_wrapper return func(*args, **kwargs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py:463 in __call__ self.build(unpack_singleton(input_shapes)) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\layers\merge.py:90 in build output_shape = self._compute_elemwise_op_output_shape(output_shape, File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\layers\merge.py:59 in _compute_elemwise_op_output_shape raise ValueError('Operands could not be broadcast ' ValueError: Operands could not be broadcast together with shapes (23, 32) (5,)

Python3 traceback教程：精确定位和调试异常

1. sys.exc_traceback：这是一个内部变量，通常不建议直接使用，但它包含了当前未处理的异常的堆栈跟踪信息。在需要时，可以使用sys.exc_info()获取异常元组，包括etype、value和exc_traceback。 2. traceback...

Python traceback高级处理：sys.exc_info()与traceback详解

通过sys.exc_info()，我们能更细致地查看异常的具体类型（<type 'exceptions.NameError'>）、异常的值（--func1exception--）以及异常发生时的traceback对象的内存地址。traceback对象是一个特殊的对象，...

trainX Y shape is: (4755, 48, 23) (4755, 60, 23) (4755, 5, 1) Hx= 1 Traceback (most recent call last): File ~\anaconda4\envs\tensorflow\lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec exec(code, globals, locals) File d:\lstnet_keras-master\train_batch_a391.py:172 model,normalize = startTrainMult_1H(df,name,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:417 in startTrainMult_1H model = trainModel(trainX1, trainX2 , trainY, config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:340 in trainModel model = LSTNetAttention(trainX1,trainX2,trainY,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:282 in LSTNetAttention bidirectional = Bidirectional(LSTM(units=32, return_sequences=True))(embedding) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\layers\wrappers.py:437 in call return super(Bidirectional, self).call(inputs, **kwargs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py:75 in symbolic_fn_wrapper return func(*args, **kwargs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py:446 in call self.assert_input_compatibility(inputs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py:338 in assert_input_compatibility raise ValueError('Input ' + str(input_index) + ValueError: Input 0 is incompatible with layer bidirectional_1: expected ndim=3, found ndim=4

根据你提供的信息，这个错误是在使用 Keras 框架中进行训练时出现的。错误提示中指出了问题的源头是在 Bidirectional 层的第一个输入，它期望一个三维张量作为输入，但实际上输入的是一个四维张量。...

(64849, 21) (64849, 1) trainX Y shape is: (64599, 48, 21) (64599, 60, 21) (64599, 1) Traceback (most recent call last): File ~\anaconda4\envs\tensorflow\lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec exec(code, globals, locals) File d:\lstnet_keras-master\train_batch_a1.py:148 model,normalize = startTrainMult_1H(data,name,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a38.py:570 in startTrainMult_1H model = trainModel(trainX1, trainX2 , trainY, config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a38.py:500 in trainModel model = LSTNetAttention(trainX1,trainX2,trainY,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a38.py:443 in LSTNetAttention lstm1 = LSTM(64, return_sequences=True)(input1) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\layers\recurrent.py:541 in call return super(RNN, self).call(inputs, **kwargs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py:75 in symbolic_fn_wrapper return func(*args, **kwargs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py:475 in call previous_mask = _collect_previous_mask(inputs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py:1441 in _collect_previous_mask mask = node.output_masks[tensor_index] AttributeError: 'Node' object has no attribute 'output_masks'

这个错误是由于 Keras 版本的问题导致的。在旧版本的 Keras 中，LSTM 层的参数是 return_sequences 和 return_state，而在新版本的 Keras 中，LSTM 层的参数是 return_sequences、return_state 和 return_attention...

def dp(dist_mat): N, M = dist_mat.shape cost_mat = np.zeros((N + 1, M + 1)) for i in range(1, N + 1): cost_mat[i, 0] = np.inf for i in range(1, M + 1): cost_mat[0, i] = np.inf traceback_mat = np.zeros((N, M)) for i in range(N): for j in range(M): penalty = [ cost_mat[i, j], cost_mat[i, j + 1], cost_mat[i + 1, j]] i_penalty = np.argmin(penalty) cost_mat[i + 1, j + 1] = dist_mat[i, j] + penalty[i_penalty] traceback_mat[i, j] = i_penalty i = N - 1 j = M - 1 path = [(i, j)] while i > 0 or j > 0: tb_type = traceback_mat[i, j] if tb_type == 0: i = i - 1 j = j - 1 elif tb_type == 1: i = i - 1 elif tb_type == 2: j = j - 1 path.append((i, j)) cost_mat = cost_mat[1:, 1:] return (path[::-1], cost_mat)解释一下

cost_mat是一个(N+1)*(M+1)的矩阵，用于存储最小编辑距离。traceback_mat是一个N*M的矩阵，用于记录路径。算法的核心是通过递推的方式，计算出cost_mat和traceback_mat。最后，通过回溯traceback_mat，可以得到最小...

[00:00:50]: [string "scripts/prefabs/weed_plants.lua"]:116: bad argument #1 to 'random' (interval is empty) LUA ERROR stack traceback: =[C]:-1 in (field) random (C) <-1--1> scripts/prefabs/weed_plants.lua:116 in (upvalue) PlayStageAnim (Lua) <113-124> inst = 120066 - weed_ivy (valid:false) anim = picked custom_pre = nil scripts/prefabs/weed_plants.lua:203 in (field) fn (Lua) <192-207> inst = 120066 - weed_ivy (valid:false) stage = 1 stage_data = table: 000000003A02CC30 scripts/components/growable.lua:179 in (method) SetStage (Lua) <171-181> self = OnLoad = function - ../mods/workshop-2484725102/scripts/upgrade_component.lua:151 growoffscreen = true StartGrowing = function - ../mods/workshop-1645013096/modmain.lua:1 usetimemultiplier = false pausereasons = table: 00000000A3834B40 magicgrowable = true stages = table: 000000003A02DE00 inst = 120066 - weed_ivy (valid:false) stage = 1 domagicgrowthfn = function - scripts/prefabs/weed_plants.lua:357 stage = 1

根据堆栈跟踪信息可以看出，错误发生在“scripts/prefabs/weed_plants.lua”的第116行，具体错误信息是“bad argument #1 to 'random' (interval is empty)”。这个错误提示意味着在调用random函数时，传入的参数...

Traceback (most recent call last): File "C:/yolov5-v6.2/1.py", line 7, in <module> if len(img.shape) == 2: AttributeError: 'NoneType' object has no attribute 'shape'

这个错误通常表示在读取图像文件时出现了问题，导致 img 变量为 None，而 None 没有 shape 属性，因此出现了 AttributeError 的错误。可以检查以下几个方面来解决这个问题： 1. 检查文件路径是否正确：请检查文件...

def convert(size, box): if size[0] != 0: dw = 1. / size[0] else: dw = 0 # 或者你可以选择其他的处理方式 if size[0] != 0: dh = 1. / size[1] else: dw = 0 x = (box[0] + box[1]) / 2.0 y = (box[2] + box[3]) / 2.0 w = box[1] - box[0] h = box[3] - box[2] x = x * dw w = w * dw y = y * dh h = h * dh return (x, y, w, h) 请你根据报错修改这段代码 Traceback (most recent call last): File "D:\hyh\pythonProject\main.py", line 148, in <module> convert_annotation(nameWithoutExtention) # convert label File "D:\hyh\pythonProject\main.py", line 66, in convert_annotation bb = convert((w, h), b) File "D:\hyh\pythonProject\main.py", line 43, in convert y = y * dh UnboundLocalError: local variable 'dh' referenced before assignment 这是报错

根据报错信息，这段代码中的错误是 UnboundLocalError: local variable 'dh' referenced before assignment... y = y * dh h = h * dh return (x, y, w, h) 这样修改后，再次运行代码应该就不会出现该报错了。

上述代码报错了，报错信息：Traceback (most recent call last): File "E:/PyCharm Community Edition 2020.2.2/Project/WordDict/newsim.py", line 327, in <module> similarities = cosine_similarity(vectors, np.array([tokenizer.encode(word, return_tensors='pt')[0].numpy() for word in seed_words])) ValueError: Incompatible dimension for X and Y matrices: X.shape[1] == 768 while Y.shape[1] == 4 怎么修改，给出修改代码

text_vector = torch.mean(hidden_states, dim=1) # 将文本向量转换为numpy数组 text_vector = text_vector.numpy()[0] # 将文本向量添加到向量列表中 vectors.append(text_vector) # 将种子词的词向量与所有...

VIDEOIO ERROR: V4L2: Pixel format of incoming image is unsupported by OpenCV Unable to stop the stream: Device or resource busy Traceback (most recent call last): File "3_d.py", line 27, in <module> if img.shape[2] == 1: AttributeError: 'NoneType' object has no attribute 'shape'

if img.shape[2] == 1: img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) elif img.shape[2] == 4: img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGR) # 转换像素格式 img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #...

--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Input In [27], in <cell line: 11>() 9 model.add(LSTM(units=32, input_shape=(sequence_length, 4))) 10 model.add(Dropout(0.2)) ---> 11 model.add(LSTM(units=32)) 12 model.add(Dense(units=1, activation='sigmoid')) 14 # 编译模型 File ~/anaconda3/lib/python3.9/site-packages/tensorflow/python/trackable/base.py:204, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs) 202 self._self_setattr_tracking = False # pylint: disable=protected-access 203 try: --> 204 result = method(self, *args, **kwargs) 205 finally: 206 self._self_setattr_tracking = previous_value # pylint: disable=protected-access File ~/anaconda3/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs) 67 filtered_tb = _process_traceback_frames(e.traceback) 68 # To get the full stack trace, call: 69 # tf.debugging.disable_traceback_filtering() ---> 70 raise e.with_traceback(filtered_tb) from None 71 finally: 72 del filtered_tb File ~/anaconda3/lib/python3.9/site-packages/keras/src/engine/input_spec.py:235, in assert_input_compatibility(input_spec, inputs, layer_name) 233 ndim = shape.rank 234 if ndim != spec.ndim: --> 235 raise ValueError( 236 f'Input {input_index} of layer "{layer_name}" ' 237 "is incompatible with the layer: " 238 f"expected ndim={spec.ndim}, found ndim={ndim}. " 239 f"Full shape received: {tuple(shape)}" 240 ) 241 if spec.max_ndim is not None: 242 ndim = x.shape.rank ValueError: Input 0 of layer "lstm_8" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 32)

这个错误提示说明你的 LSTM 层期望输入的张量维度为 3，但是实际的输入张量维度只有 2。具体来说，LSTM 层期望的输入张量维度应该为 (batch_size, sequence_length, input_dim)，其中 batch_size 表示输入的样本数目...

==== RESTART: C:/Users/huawei/AppData/Local/Programs/Python/Python310/字符画.py === Traceback (most recent call last): File "C:/Users/huawei/AppData/Local/Programs/Python/Python310/字符画.py", line 1, in <module> from Pillow import Image ModuleNotFoundError: No module named 'Pillow'

根据您提供的错误信息，您在执行字符画.py 文件时遇到了错误。错误信息显示找不到名为 'Pillow' 的模块。这个错误通常是由于未安装 Pillow 库导致的。Pillow 是一个流行的 Python 图像处理库，可能需要先安装它...

Traceback (most recent call last): File "E:/360zip/pythonProject/pythonProject/pythonProjectdaodao/demo1.py", line 8, in <module> height, width, channels = image1.shape ValueError: not enough values to unpack (expected 3, got 2)什么意思

这个错误通常意味着图像的形状（shape）不是您所期望的形状。在这种情况下，您期望的形状是一个三元组（height，width，channels），但是实际上您得到的是一个二元组（height，width），因为该图像可能是单通道...

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

in_flow = np.reshape(in_flow, (D1.shape[0], 5, 109, 109)) out_flow = np.reshape(out_flow, (D1.shape[0], 5, 109)) c1 = np.reshape(c1, (D1.shape[0], 5, 109)) 这样就可以正确地将数据重塑为指定的形状...

ValueError Traceback (most recent call last) <ipython-input-54-536a68c200e5> in <module> 52 return model 53 # lstm network ---> 54 model = create_LSTM_model(X_train,n_steps,n_length, n_features) 55 # summary 56 print(model.summary()) <ipython-input-54-536a68c200e5> in create_LSTM_model(X_train, n_steps, n_length, n_features) 22 X_train = X_train.reshape((X_train.shape[0], n_steps, 1, n_length, n_features)) 23 ---> 24 model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', 25 input_shape=(n_steps, 1, n_length, n_features))) 26 model.add(Flatten()) ~\anaconda3\lib\site-packages\tensorflow\python\trackable\base.py in _method_wrapper(self, *args, **kwargs) 203 self._self_setattr_tracking = False # pylint: disable=protected-access 204 try: --> 205 result = method(self, *args, **kwargs) 206 finally: 207 self._self_setattr_tracking = previous_value # pylint: disable=protected-access ~\anaconda3\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs) 68 # To get the full stack trace, call: 69 # tf.debugging.disable_traceback_filtering() ---> 70 raise e.with_traceback(filtered_tb) from None 71 finally: 72 del filtered_tb ~\anaconda3\lib\site-packages\keras\engine\input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name) 233 ndim = shape.rank 234 if ndim != spec.ndim: --> 235 raise ValueError( 236 f'Input {input_index} of layer "{layer_name}" ' 237 "is incompatible with the layer: " ValueError: Input 0 of layer "conv_lstm2d_12" is incompatible with the layer: expected ndim=5, found ndim=3. Full shape received: (None, 10, 5)解决该错误

你可以尝试将输入张量 X_train 从 shape=(samples, timesteps, features) 转换为 shape=(samples, timesteps, 1, n_length, n_features)，再将其作为模型的输入。代码如下： def create_LSTM_model(X_train, n_...

改代码 # 提取C原子的坐标 C_coords = coords[coords[:, 3] == 6][:, :3] Traceback (most recent call last): File "symmetry.py", line 13, in <module> C_coords = coords[coords[:, 3] == 6][:, :3] IndexError: index 3 is out of bounds for axis 1 with size 3

这个错误是因为你的数组coords只有3列，而你尝试获取第四列进行条件筛选，导致出现了索引错误。因此，你需要检查数组coords的列数是否正确，并根据实际情况修改索引值。假设你想要提取coords数组中第4列等于...

改进代码ValueError Traceback (most recent call last) Input In [15], in <cell line: 26>() 24 y.append(row[-1]) 25 le = LabelEncoder() ---> 26 X = le.fit_transform(X) 27 y = le.fit_transform(y)# 生成决策树 28 clf = DecisionTreeClassifier(criterion='entropy') File C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\_label.py:115, in LabelEncoder.fit_transform(self, y) 102 def fit_transform(self, y): 103 """Fit label encoder and return encoded labels. 104 105 Parameters (...) 113 Encoded labels. 114 """ --> 115 y = column_or_1d(y, warn=True) 116 self.classes_, y = _unique(y, return_inverse=True) 117 return y File C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py:1038, in column_or_1d(y, warn) 1029 warnings.warn( 1030 "A column-vector y was passed when a 1d array was" 1031 " expected. Please change the shape of y to " (...) 1034 stacklevel=2, 1035 ) 1036 return np.ravel(y) -> 1038 raise ValueError( 1039 "y should be a 1d array, got an array of shape {} instead.".format(shape) 1040 ) ValueError: y should be a 1d array, got an array of shape (15, 4) instead.

解决这个问题的方法是将 y 转换成一维数组，可以使用 numpy 的 flatten() 方法或者 reshape(-1) 方法将 y 转换成一维数组。修改后的代码如下： import numpy as np from sklearn.tree import ...

Traceback (most recent call last): File "C:/Users/asus/Desktop/UI界面/YoloV5_PyQt5-main(原版)/detect_logical.py", line 168, in button_image_open info_show = self.detect(name_list, img) File "C:/Users/asus/Desktop/UI界面/YoloV5_PyQt5-main(原版)/detect_logical.py", line 124, in detect img = letterbox(img, new_shape=self.opt.img_size)[0] File "C:\Users\asus\Desktop\UI界面\YoloV5_PyQt5-main(原版)\utils\datasets.py", line 820, in letterbox shape = img.shape[:2] # current shape [height, width] AttributeError: 'NoneType' object has no attribute 'shape'

具体来说，img 变量的值为 NoneType，因此无法访问其 shape 属性。请检查您的代码，并确保正确传递了 img 变量。此外，也要确保 img 变量是正确的类型和格式。如果问题仍然存在，请提供更多上下文和代码细节以便更好...

Traceback (most recent call last): File "C:\Users\Adminis\Desktop\practice\暗通道先验方法\23.07.13(1).py", line 92, in <module> img_min=darkchannel(img_arr) #计算每个通道的最小值 File "C:\Users\Adminis\Desktop\practice\暗通道先验方法\23.07.13(1).py", line 9, in darkchannel rows,cols,channels=Image.shape ValueError: not enough values to unpack (expected 3, got 2)

这个错误是由于你的Image变量的形状只有两个值，而不是三个（行、列和通道数），导致无法按照预期的方式进行解包。这可能是因为你的img_arr是一个灰度图像，而不是彩色图像。灰度图像只有两个维度（行和列），...

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==== RESTART: C:/Users/huawei/AppData/Local/Programs/Python/Python310/字符画.py === Traceback (most recent call last): File "C:/Users/huawei/AppData/Local/Programs/Python/Python310/字符画.py", line 1, in <module> from Pillow import Image ModuleNotFoundError: No module named 'Pillow'

Traceback (most recent call last): File "E:/360zip/pythonProject/pythonProject/pythonProjectdaodao/demo1.py", line 8, in <module> height, width, channels = image1.shape ValueError: not enough values to unpack (expected 3, got 2)什么意思

改代码 # 提取C原子的坐标 C_coords = coords[coords[:, 3] == 6][:, :3] Traceback (most recent call last): File "symmetry.py", line 13, in <module> C_coords = coords[coords[:, 3] == 6][:, :3] IndexError: index 3 is out of bounds for axis 1 with size 3

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