我在一些天气数据上训练了一个深度神经网络回归器。当我尝试classifier.predict（）时，它返回一个生成器对象。通常我们所做的是将list（）放在对象上以获得预测。
它曾经工作，但我相信在最近更新后，它已不再有效。我目前正在使用tensorflow 1.7.0。我尝试降级到几个版本的tensorflow，找不到有效的版本。
import tensorflow as tf
feature_columns = [tf.feature_column.numeric_column("x", shape=[163])]
classifier = tf.estimator.DNNRegressor(feature_columns=feature_columns,
hidden_units=[200,100,20],
model_dir='model/'
)
onehot,price=load_single_data([[5,18,16,1],'Mostly Sunny','Mostly Sunny',46.5])
prediction= classifier.predict(np.array(onehot))
#This line produced the error
print(list(prediction))
产生的错误是：
Traceback (most recent call last):
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\inspect.py", line 1089, in getfullargspec
sigcls=Signature)
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\inspect.py", line 2156, in _signature_from_callable
raise TypeError('{!r} is not a callable object'.format(obj))
TypeError: array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0]) is not a callable object
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:/Users/5V/PycharmProjects/UberAPI/deep_learning.py", line 41, in <module>
print(list(prediction))
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\estimator\estimator.py", line 486, in predict
input_fn, model_fn_lib.ModeKeys.PREDICT)
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\estimator\estimator.py", line 662, in _get_features_from_input_fn
result = self._call_input_fn(input_fn, mode)
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\estimator\estimator.py", line 763, in _call_input_fn
input_fn_args = util.fn_args(input_fn)
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\estimator\util.py", line 55, in fn_args
args = tf_inspect.getfullargspec(fn).args
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\util\tf_inspect.py", line 67, in getfullargspec
if d.decorator_argspec is not None), spec_fn(target))
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\inspect.py", line 1095, in getfullargspec
raise TypeError('unsupported callable') from ex
TypeError: unsupported callable
这样做会产生：
print(prediction)
>> <generator object Estimator.predict at 0x00000069E3AC0BF8>
好的，这是一个完整的追溯
Traceback (most recent call last):
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\inspect.py", line 1089, in getfullargspec
sigcls=Signature)
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\inspect.py", line 2156, in _signature_from_callable
raise TypeError('{!r} is not a callable object'.format(obj))
TypeError: array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0]) is not a callable object
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:/Users/5V/PycharmProjects/UberAPI/deep_learning.py", line 41, in <module>
print(list(prediction))
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\estimator\estimator.py", line 486, in predict
input_fn, model_fn_lib.ModeKeys.PREDICT)
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\estimator\estimator.py", line 662, in _get_features_from_input_fn
result = self._call_input_fn(input_fn, mode)
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\estimator\estimator.py", line 763, in _call_input_fn
input_fn_args = util.fn_args(input_fn)
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\estimator\util.py", line 55, in fn_args
args = tf_inspect.getfullargspec(fn).args
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\util\tf_inspect.py", line 67, in getfullargspec
if d.decorator_argspec is not None), spec_fn(target))
File "C:\Users\5V\AppData\Local\Programs\Python\Python35\lib\inspect.py", line 1095, in getfullargspec
raise TypeError('unsupported callable') from ex
TypeError: unsupported callable
答案 0 :(得分：0)
我不确定你是否还有其他错误。基于回溯，很明显，这个特定错误是由np.array(onehot)用作input_fn classifier.predict导致的，即numpy数组不是可调用对象。要解决此问题，您可以使用numpy_input_fn：
input_predict= tf.estimator.inputs.numpy_input_fn({'x': np.array(onehot)}, shuffle=False)
print(list(model.predict(input_predict)))
以下是MCVE显示
的一个潜在原因InvalidArgumentError（参见上面的回溯）：重塑的输入是一个张量... ...
import numpy as np
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.INFO)
model = tf.estimator.DNNRegressor(
hidden_units=[20, 20],
feature_columns=[tf.feature_column.numeric_column(key='x')],
model_dir=r"E:\GitHub\miscellaneous\ml-models\foobar\tensorflow\test"
)
input_train= tf.estimator.inputs.numpy_input_fn(
x={'x': np.arange(100)},
y=np.arange(100),
num_epochs=None,
shuffle=True
)
model.train(input_fn=input_train, steps=200)
test = np.array([[200]]) # correct input with shape 1 for each input (row)
#test = np.array([[200, 300]]) # incorrect input with shape 2 for each input (row)
input_predict= tf.estimator.inputs.numpy_input_fn(
{'x': test}, shuffle=False
)
print(list(model.predict(input_predict)))
如代码中所述，正确的输入应该只有一个而不是两个。