Question

我正在尝试从here复制Tensorflow教程代码，该代码应该下载CSV文件并进行预处理（直到将数字数据组合在一起）。

可重现的示例如下：

import tensorflow as tf
print("TF version is: {}".format(tf.__version__))

# Download data
train_url = "https://storage.googleapis.com/tf-datasets/titanic/train.csv"
test_url  = "https://storage.googleapis.com/tf-datasets/titanic/eval.csv"

train_path = tf.keras.utils.get_file("train.csv", train_url)
test_path  = tf.keras.utils.get_file("test.csv",  test_url)


# Get data into batched dataset
def get_dataset(path):
    dataset = tf.data.experimental.make_csv_dataset(path
                                                   ,batch_size=5
                                                   ,num_epochs=1
                                                   ,label_name='survived'
                                                   ,na_value='?'
                                                   ,ignore_errors=True)
    return dataset

raw_train_dataset = get_dataset(train_path)
raw_test_dataset  = get_dataset(test_path)

# Define numerical and categorical column lists
def get_df_batch(dataset):
    for batch,label in dataset.take(1):
        df = pd.DataFrame()
        df['survived'] = label.numpy()
        for key, value in batch.items():
            df[key] = value.numpy()
        return df

dfb = get_df_batch(raw_train_dataset)
num_columns = [i for i in dfb if (dfb[i].dtype != 'O' and i!='survived')]
cat_columns = [i for i in dfb if dfb[i].dtype == 'O']


# Combine numerical columns into one `numerics` column
class Pack():
    def __init__(self,names):
        self.names = names
    def __call__(self,features, labels):
        num_features = [features.pop(name) for name in self.names]
        num_features = [tf.cast(feat, tf.float32) for feat in num_features]
        num_features = tf.stack(num_features, axis=1)
        features["numerics"] = num_features
        return features, labels

packed_train = raw_train_dataset.map(Pack(num_columns))


# Show what we got
def show_batch(dataset):
    for batch, label in dataset.take(1):
        for key, value in batch.items():
            print("{:20s}: {}".format(key,value.numpy()))

show_batch(packed_train)

TF version is: 2.0.0
sex                 : [b'female' b'female' b'male' b'male' b'male']
class               : [b'Third' b'First' b'Second' b'First' b'Third']
deck                : [b'unknown' b'E' b'unknown' b'C' b'unknown']
embark_town         : [b'Queenstown' b'Cherbourg' b'Southampton' b'Cherbourg' b'Queenstown']
alone               : [b'n' b'n' b'y' b'n' b'n']
numerics            : [[ 28.       1.       0.      15.5   ]
 [ 40.       1.       1.     134.5   ]
 [ 32.       0.       0.      10.5   ]
 [ 49.       1.       0.      89.1042]
 [  2.       4.       1.      29.125 ]]

然后我尝试但以失败告终，以一种实用的方式组合了数字特征：

@tf.function
def pack_func(row, num_columns=num_columns):
    features, labels = row
    num_features = [features.pop(name) for name in num_columns]
    num_features = [tf.cast(feat, tf.float32) for feat in num_features]
    num_features = tf.stack(num_features, axis=1)
    features['numerics'] = num_features
    return features, labels

packed_train = raw_train_dataset.map(pack_func)

部分回溯：

ValueError：转换后的代码：：3 pack_func * 功能，标签=行 ValueError：太多值无法解包（预期2）

这里有2个问题：

如何在类features的定义中的labels中分配def __call__(self,features, labels):和Pack。我的直觉是应该将它们作为定义的变量传递，尽管我绝对不明白它们在哪里定义。
当我这样做

for row in raw_train_dataset.take(1):
    print(type(row))
    print(len(row))
    f,l = row
    print(f)
    print(l)

我看到row中的raw_train_dataset是一个元组2，可以成功地将其解压缩为要素和标签。为什么不能通过map API来完成？您能建议以功能方式组合数字特征的正确方法吗？

非常感谢！

Answer 1

经过一些研究和试验，第二个问题的答案似乎是：

def pack_func(features, labels, num_columns=num_columns):
    num_features = [features.pop(name) for name in num_columns]
    num_features = [tf.cast(feat, tf.float32) for feat in num_features]
    num_features = tf.stack(num_features, axis=1)
    features['numerics'] = num_features
    return features, labels

packed_train = raw_train_dataset.map(pack_func)

show_batch(packed_train)

sex                 : [b'male' b'male' b'male' b'female' b'male']
class               : [b'Third' b'Third' b'Third' b'First' b'Third']
deck                : [b'unknown' b'unknown' b'unknown' b'E' b'unknown']
embark_town         : [b'Southampton' b'Southampton' b'Queenstown' b'Cherbourg' b'Queenstown']
alone               : [b'y' b'n' b'n' b'n' b'y']
numerics            : [[24.      0.      0.      8.05  ]
 [14.      5.      2.     46.9   ]
 [ 2.      4.      1.     29.125 ]
 [39.      1.      1.     83.1583]
 [21.      0.      0.      7.7333]]

Tensorflow 2.0：以功能性方式将数据集的数字特征打包在一起

1 个答案: