Python 在运行以下命令之后；“培训模式”；我得到这个错误_Python_Tensorflow_Machine Learning_Deep Learning_Jupyter Notebook

Python 在运行以下命令之后；“培训模式”；我得到这个错误

python tensorflow machine-learning deep-learning jupyter-notebook

Python 在运行以下命令之后；“培训模式”；我得到这个错误,python,tensorflow,machine-learning,deep-learning,jupyter-notebook,Python,Tensorflow,Machine Learning,Deep Learning,Jupyter Notebook,创建此模型后，我收到一个错误“unsupported callable” 错误： TypeError:不支持的可调用该错误是因为访问精度的正确键为精度（小写为a）为了了解发生了什么，您可以打印result的值如果我们执行命令，print（result），输出将是 {'accuracy': 0.7386364, 'accuracy_baseline': 0.625, 'auc': 0.83783287, 'auc_precision_recall': 0.78672063, 'av

创建此模型后，我收到一个错误“unsupported callable”

错误：

TypeError:不支持的可调用

该错误是因为访问

精度

的正确键为精度
（小写为a）

为了了解发生了什么，您可以打印

result

的值

如果我们执行命令，

print（result）

，输出将是

{'accuracy': 0.7386364, 'accuracy_baseline': 0.625, 'auc': 0.83783287, 
    'auc_precision_recall': 0.78672063, 'average_loss': 0.47509083, 'label/mean': 0.375, 
    'loss': 0.46859953, 'precision': 0.64705884, 'prediction/mean': 0.39348394, 
'recall': 0.6666667, 'global_step': 200}

可以使用

结果['accurity']

访问精度

完整的工作代码如下所示：

import os
import sys

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import clear_output
from six.moves import urllib

import tensorflow.compat.v2.feature_column as fc

import tensorflow as tf

# Load dataset.
dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')
dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')
y_train = dftrain.pop('survived')
y_eval = dfeval.pop('survived')

CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck',
                       'embark_town', 'alone']
NUMERIC_COLUMNS = ['age', 'fare']

feature_columns = []
for feature_name in CATEGORICAL_COLUMNS:
  vocabulary = dftrain[feature_name].unique()
  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.float32))

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)

ds = make_input_fn(dftrain, y_train, batch_size=10)()
for feature_batch, label_batch in ds.take(1):
  print('Some feature keys:', list(feature_batch.keys()))
  print()
  print('A batch of class:', feature_batch['class'].numpy())
  print()
  print('A batch of Labels:', label_batch.numpy())

age_column = feature_columns[7]
tf.keras.layers.DenseFeatures([age_column])(feature_batch).numpy()

gender_column = feature_columns[0]
tf.keras.layers.DenseFeatures([tf.feature_column.indicator_column(gender_column)])(feature_batch).numpy()

linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)
linear_est.train(train_input_fn)
result = linear_est.evaluate(eval_input_fn)

clear_output()
print(result)

print(result['accuracy'])

上述代码的输出为：

{'accuracy': 0.7348485, 'accuracy_baseline': 0.625, 'auc': 0.8331497, 'auc_precision_recall': 0.7953714, 'average_loss': 0.47750932, 'label/mean': 0.375, 'loss': 0.47087723, 'precision': 0.6407767, 'prediction/mean': 0.4067955, 'recall': 0.6666667, 'global_step': 200}

0.7348485

有关更多详细信息，请参阅此

希望这有帮助。学习愉快