AttributeError：“ Tensor”对象没有属性“ numpy”

时间：2018-09-16 19:09:45

标签： python numpy tensorflow attributeerror tensor

如何解决此错误，我是从GitHub下载此代码的。

predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy()

引发错误

AttributeError: 'Tensor' object has no attribute 'numpy'

请帮助我解决此问题！

我用过：

sess = tf.Session()
    with sess.as_default():
       predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()

我得到这个错误。有人帮我，我只是想让它工作，为什么这么难？

D:\Python>python TextGenOut.py
  File "TextGenOut.py", line 72
    predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
    ^
IndentationError: unexpected indent

D:\Python>python TextGenOut.py
2018-09-16 21:50:57.008663: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-09-16 21:50:57.272973: W T:\src\github\tensorflow\tensorflow\core\framework\op_kernel.cc:1275] OP_REQUIRES failed at resource_variable_ops.cc:480 : Not found: Container localhost does not exist. (Could not find resource: localhost/model/embedding/embeddings)
Traceback (most recent call last):
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1278, in _do_call
    return fn(*args)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1263, in _run_fn
    options, feed_dict, fetch_list, target_list, run_metadata)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1350, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Error while reading resource variable model/dense/kernel from Container: localhost. This could mean that the variable was uninitialized. Not found: Container localhost does not exist. (Could not find resource: localhost/model/dense/kernel)
         [[Node: model/dense/MatMul/ReadVariableOp = ReadVariableOp[dtype=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](model/dense/kernel)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "TextGenOut.py", line 72, in <module>
    predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py", line 680, in eval
    return _eval_using_default_session(self, feed_dict, self.graph, session)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py", line 4951, in _eval_using_default_session
    return session.run(tensors, feed_dict)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 877, in run
    run_metadata_ptr)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1100, in _run
    feed_dict_tensor, options, run_metadata)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1272, in _do_run
    run_metadata)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1291, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Error while reading resource variable model/dense/kernel from Container: localhost. This could mean that the variable was uninitialized. Not found: Container localhost does not exist. (Could not find resource: localhost/model/dense/kernel)
         [[Node: model/dense/MatMul/ReadVariableOp = ReadVariableOp[dtype=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](model/dense/kernel)]]

Caused by op 'model/dense/MatMul/ReadVariableOp', defined at:
  File "TextGenOut.py", line 66, in <module>
    predictions, hidden = model(input_eval, hidden)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\engine\base_layer.py", line 736, in __call__
    outputs = self.call(inputs, *args, **kwargs)
  File "TextGenOut.py", line 39, in call
    x = self.fc(output)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\engine\base_layer.py", line 736, in __call__
    outputs = self.call(inputs, *args, **kwargs)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\layers\core.py", line 943, in call
    outputs = gen_math_ops.mat_mul(inputs, self.kernel)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\gen_math_ops.py", line 4750, in mat_mul
    name=name)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\op_def_library.py", line 510, in _apply_op_helper
    preferred_dtype=default_dtype)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py", line 1094, in internal_convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\resource_variable_ops.py", line 1045, in _dense_var_to_tensor
    return var._dense_var_to_tensor(dtype=dtype, name=name, as_ref=as_ref)  # pylint: disable=protected-access
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\resource_variable_ops.py", line 1000, in _dense_var_to_tensor
    return self.value()
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\resource_variable_ops.py", line 662, in value
    return self._read_variable_op()
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\resource_variable_ops.py", line 745, in _read_variable_op
    self._dtype)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\gen_resource_variable_ops.py", line 562, in read_variable_op
    "ReadVariableOp", resource=resource, dtype=dtype, name=name)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\util\deprecation.py", line 454, in new_func
    return func(*args, **kwargs)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py", line 3155, in create_op
    op_def=op_def)
  File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py", line 1717, in __init__
    self._traceback = tf_stack.extract_stack()

FailedPreconditionError (see above for traceback): Error while reading resource variable model/dense/kernel from Container: localhost. This could mean that the variable was uninitialized. Not found: Container localhost does not exist. (Could not find resource: localhost/model/dense/kernel)
         [[Node: model/dense/MatMul/ReadVariableOp = ReadVariableOp[dtype=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](model/dense/kernel)]]

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9 个答案:

答案 0 :(得分：12)

我怀疑您从中复制代码的地方有eager execution enabled，即在程序开始时调用了tf.enable_eager_execution()。

您可以执行相同的操作。 希望能有所帮助。

答案 1 :(得分：10)

由于接受的答案无法解决我的问题，因此我认为这对于某些已面临tensorflow版本> = 2.2.0并渴望执行的人很有帮助。

问题似乎是在拟合model.fit()期间某些功能 由于性能原因，@ tf.function装饰器禁止执行tensor.numpy()之类的功能。

对我来说，解决方案是将标志run_eagerly = True传递给model.compile（），如下所示：

model.compile（...，run_eagerly = True）

答案 2 :(得分：5)

它发生在旧版的TF中。因此，尝试pip install tensorflow --upgrade

否则运行

import tensorflow as tf
tf.enable_eager_execution()

如果您使用的是Jupyter笔记本，请重新启动内核。

答案 3 :(得分：1)

tf.multinomial返回一个Tensor对象，该对象包含一个2D列表，列表中绘制的形状为[batch_size, num_samples]。预期在该张量对象上调用.eval()会返回一个numpy ndarray。

类似这样的东西：

predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()

您还需要确保会话处于活动状态（否则就没有多大意义了）：

sess = tf.Session()
with sess.as_default():
    predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()

答案 4 :(得分：1)

如果您的代码包装在@ tf.function或Keras层中，则在TF2.0中也会发生这种情况。两者都以图形模式运行。那里有很多秘密破解的代码，因为急切模式和图形模式之间的行为有所不同，并且人们不知道自己正在切换上下文，所以要小心！

答案 5 :(得分：1)

Tensorflow 2具有一个配置选项来“急切地”运行函数，这将使能够通过.numpy()方法获取Tensor值。要启用急切执行，请使用以下命令：

tf.config.run_functions_eagerly(True)

请注意，这主要用于调试。

另请参阅：https://www.tensorflow.org/api_docs/python/tf/config/run_functions_eagerly

答案 6 :(得分：0)

当我运行如下代码时，我看到了类似的错误

tensor = tf.multiply(ndarray, 42)
tensor.numpy()  # throw AttributeError: 'Tensor' object has no attribute 'numpy'

我使用带有Tensorflow 1.14.0的Anaconda 3。我使用以下命令升级了tensorflow 

conda update tensorflow

现在tensorflow是2.0.0，已解决问题。尝试此操作是否可以解决您的问题。

答案 7 :(得分：0)

我在 tf.function() 中遇到了同样的问题：但对我有用的是通过 tf.convert_to_tensor Doku 将 numpy 数组转换为张量流张量，然后继续使用张量流。也许这个技巧对任何人都有用......

答案 8 :(得分：0)

对于在 TF 2.0.0 中仍然存在此问题的人，请运行：tf.config.run_functions_eagerly(True) top of ur program 它完美运行！

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