TensorFlow广度和深度学习的教程

"capital_loss", "hours_per_week", "native_country",

"income_bracket"

]

def maybe_download(train_data, test_data):

"""Maybe downloads training data and returns train and test file names."""

if train_data:

train_file_name = train_data

else:

train_file = tempfile.NamedTemporaryFile(delete=False)

urllib.request.urlretrieve(

"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",

train_file.name) # pylint: disable=line-too-long

train_file_name = train_file.name

train_file.close()

print("Training data is downloaded to %s" % train_file_name)

if test_data:

test_file_name = test_data

else:

test_file = tempfile.NamedTemporaryFile(delete=False)

urllib.request.urlretrieve(

"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test",

test_file.name) # pylint: disable=line-too-long

test_file_name = test_file.name

test_file.close()

print("Test data is downloaded to %s"% test_file_name)

return train_file_name, test_file_name

def input_fn(data_file, num_epochs, shuffle):

"""Input builder function."""

df_data = pd.read_csv(

tf.gfile.Open(data_file),

names=CSV_COLUMNS,

skipinitialspace=True,

engine="python",

skiprows=1)

# 移除 NaN 元素

df_data = df_data.dropna(how="any", axis=0)

labels = df_data["income_bracket"].apply(lambda x: ">50K" in x).astype(int)

return tf.estimator.inputs.pandas_input_fn(

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