我正在尝试使用这个数据集中的’Name’、’Platform’、’Genre’、’Publisher’和’Year’的值来预测全球销售情况：https://www.kaggle.com/gregorut/videogamesales
这是我用于训练模型的代码：
from __future__ import absolute_import, division, print_function, unicode_literalsimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom IPython.display import clear_outputfrom six.moves import urllibimport tensorflow as tfdftrain = pd.read_csv('./vgsales_eval.csv')dfeval = pd.read_csv('./vgsales_train.csv')print(dftrain[dftrain.isnull().any(axis=1)])y_train = dftrain.pop('Global_Sales')y_eval = dfeval.pop('Global_Sales')CATEGORICAL_COLUMNS = ['Name', 'Platform', 'Genre', 'Publisher']NUMERIC_COLUMNS = ['Year']feature_columns = []for feature_name in CATEGORICAL_COLUMNS: vocabulary = dftrain[feature_name].unique() # gets a list of all unique values from given feature column feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))for feature_name in NUMERIC_COLUMNS: feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.int64))print(feature_columns)def make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32): def input_function(): ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df)) if shuffle: ds = ds.shuffle(1000) ds = ds.batch(batch_size).repeat(num_epochs) return ds return input_function train_input_fn = make_input_fn(dftrain, y_train) eval_input_fn = make_input_fn(dfeval, y_eval, num_epochs=1, shuffle=False)linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)linear_est.train(train_input_fn)我得到了以下错误：
Traceback (most recent call last): File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\data\util\structure.py", line 93, in normalize_element spec = type_spec_from_value(t, use_fallback=False) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\data\util\structure.py", line 466, in type_spec_from_value (element, type(element).__name__))TypeError: Could not build a TypeSpec for 0 Tecmo Koei1 Nippon Ichi Software2 Ubisoft3 Activision4 Atari ...6594 Kemco6595 Infogrames6596 Activision6597 7G//AMES6598 WanadooName: Publisher, Length: 6599, dtype: object with type SeriesDuring handling of the above exception, another exception occurred:Traceback (most recent call last): File "c:\Users\kuhn-\Documents\Github\Tensorflow_Test\VideoGameSales_Test\main.py", line 45, in <module> linear_est.train(train_input_fn) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 349, in train loss = self._train_model(input_fn, hooks, saving_listeners) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1175, in _train_model return self._train_model_default(input_fn, hooks, saving_listeners) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1201, in _train_model_default self._get_features_and_labels_from_input_fn(input_fn, ModeKeys.TRAIN)) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1037, in _get_features_and_labels_from_input_fn self._call_input_fn(input_fn, mode)) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1130, in _call_input_fn return input_fn(**kwargs) File "c:\Users\kuhn-\Documents\Github\Tensorflow_Test\VideoGameSales_Test\main.py", line 34, in input_function ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df)) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py", line 682, in from_tensor_slices return TensorSliceDataset(tensors) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py", line 3001, in __init__ element = structure.normalize_element(element) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\data\util\structure.py", line 98, in normalize_element ops.convert_to_tensor(t, name="component_%d" % i)) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1499, in convert_to_tensor ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\constant_op.py", line 338, in _constant_tensor_conversion_function return constant(v, dtype=dtype, name=name) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\constant_op.py", line 264, in constant allow_broadcast=True) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\constant_op.py", line 282, in _constant_impl allow_broadcast=allow_broadcast)) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 563, in make_tensor_proto append_fn(tensor_proto, proto_values) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 155, in SlowAppendObjectArrayToTensorProto tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values]) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 155, in <listcomp> tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values]) File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\util\compat.py", line 87, in as_bytes (bytes_or_text,))TypeError: Expected binary or unicode string, got nan我在这里做错了什么？这是数据集的问题，还是我需要以不同的方式读取值？
回答：
这基本上是由于你所使用的数据中存在null值，你需要在加载数据时处理这些值。
我做了一些修改。
- 为了删除含有null值的记录，你也可以根据列和需要填充的值的数据类型使用
df.fillna进行填充。 - 我将
Year列的数据类型从float改为int，因为这会导致tensor_slices的另一个问题。
以下是使用你所使用的数据的修改后的代码。
df = pd.read_csv('/content/vgsales.csv')# print(df.head())print(df[df.isnull().any(axis=1)])# df.fillna('', inplace=True)df.dropna(how="any",inplace = True)df.Year = df.Year.astype(int) CATEGORICAL_COLUMNS = ['Name', 'Platform', 'Genre', 'Publisher']NUMERIC_COLUMNS = ['Year'] feature_columns = []for feature_name in CATEGORICAL_COLUMNS: vocabulary = df[feature_name].unique() # gets a list of all unique values from given feature column feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))for feature_name in NUMERIC_COLUMNS: feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.int64))print(feature_columns)def make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32): def input_function(): ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df)) if shuffle: ds = ds.shuffle(1000) ds = ds.batch(batch_size).repeat(num_epochs) return ds return input_function train_input_fn = make_input_fn(df, y_train) linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)