Expected more than 1 value per channel when training, got input size torch.Size
因为模型中用了batchnomolization，训练中用batch训练的时候当前batch恰好只含一个sample，而由于BatchNorm操作需要多于一个数据计算平均值，因此造成该错误。
ValueError: Found input variables with inconsistent numbers of samples: [140, 1120] 怎么解决？
这个错误通常发生在机器学习模型的训练中，它表示输入数据的样本数量不一致。在你的情况下，你的输入数据中有两个变量，一个变量的样本数量为140，另一个变量的样本数量为1120，因此这个错误就出现了。
为了解决这个问题，你需要确保所有输入变量的样本数量是相同的。你可以通过以下几种方式来解决这个问题：
检查数据：检查数据是否正确加载，可能会导致数据样本数量不一致。
数据清洗：检查是否有重复的样本或者缺失的样本，如果有则需要对数据进行清洗。
数据对齐：如果你使用了多个数据源，那么你需要对它们进行对齐以确保它们的样本数量一致。
数据重采样：如果数据中有不均衡的样本数量，你可以考虑使用数据重采样方
解决TypeError: tf__update_state() got an unexpected keyword argument ‘sample_weight‘
解决TypeError: tf__update_state() got an unexpected keyword argument ‘sample_weight‘
ValueError: Negative dimension size caused by subtracting 5 from 1 for ‘{{node le_net5/conv2d/Conv2D
ValueError: Negative dimension size caused by subtracting 5 from 1 for ‘{{node le_net5/conv2d/Conv2D
解决Pytorch中RuntimeError: expected scalar type Double but found Float
解决Pytorch中RuntimeError: expected scalar type Double but found Float
ValueError: With n_samples=0, test_size=0.15 and train_size=None, the resulting train set will be em
ValueError: With n_samples=0, test_size=0.15 and train_size=None, the resulting train set will be em
ValueError: Sample larger than population or is negative
ValueError: Sample larger than population or is negative
成功解决lightgbm.basic.LightGBMError: Parameter max_depth should be of type int, got “0.02“
成功解决lightgbm.basic.LightGBMError: Parameter max_depth should be of type int, got “0.02“
成功解决ParserError: Error tokenizing data. C error: Expected 2 fields in line 53, saw 3
成功解决ParserError: Error tokenizing data. C error: Expected 2 fields in line 53, saw 3
成功解决KeyError: “Passing list-likes to .loc or [] with any missing labels is no longer supported. The
成功解决KeyError: “Passing list-likes to .loc or [] with any missing labels is no longer supported. The
成功解决ValueError: With n_samples=0, test_size=0.3 and train_size=None, the resulting train set will be
成功解决ValueError: With n_samples=0, test_size=0.3 and train_size=None, the resulting train set will be
成功解决ValueError: Number of passed names did not match number of header fields in the file
成功解决ValueError: Number of passed names did not match number of header fields in the file
成功解决_catboost.CatBoostError: Invalid cat_features[4] = 8 value: index must be ＜ 8.
成功解决_catboost.CatBoostError: Invalid cat_features[4] = 8 value: index must be ＜ 8.
成功解决ValueError: Could not interpret input day
成功解决ValueError: Could not interpret input day
Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data
The Leaky Pipeline Problem: Making your Mark as a Woman in Big Data
