AttributeError: 'Tensor' object has no attribute 'numpy'
AttributeError: 'Tensor' object has no attribute 'numpy'
如何修复此错误我从 GitHub 下载了此代码。
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy()
抛出错误
AttributeError: 'Tensor' object has no attribute 'numpy'
请帮我解决这个问题！
我用过：
sess = tf.Session()
with sess.as_default():
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
我收到了这个错误。有人帮助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)]]
tf.multinomial returns 一个 Tensor 对象，它包含一个二维列表，其中绘制的样本形状为 [batch_size, num_samples]。在该张量对象上调用 .eval() 预计会 return 一个 numpy ndarray。
像这样：
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
您还需要确保您有一个活动会话（否则没有多大意义）：
sess = tf.Session()
with sess.as_default():
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
我怀疑你从中复制代码的地方在程序的开头有 eager execution enabled, i.e. had invoked tf.enable_eager_execution()。
你也可以这样做。
更新：请注意，在 TensorFlow 2.0 中默认启用即时执行。所以上面的答案只适用于 TensorFlow 1.x
当我 运行 编写如下代码时，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 anaconda 3 和 tensorflow 1.14.0。我用下面的命令升级了 tensorflow
conda update tensorflow
现在 tensorflow 是 2.0.0，问题已解决。试试看它是否能解决您的问题。
它发生在旧版本的 TF 中。所以试试 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。
如果您的代码包装在 @tf.function 或 Keras 层内，这也可能发生在 TF2.0 中。这两个 运行 都处于图形模式。那里有很多秘密破坏的代码，因为急切模式和图形模式之间的行为不同，而且人们不知道他们正在切换上下文，所以要小心！
由于接受的答案没有解决我的问题，所以我认为这可能对一些遇到问题并且已经拥有 tensorflow 版本 >= 2.2.0 并启用了 eager execution 的人有所帮助。
问题似乎是在拟合过程中对于某些函数 model.fit()
@tf.function 装饰器出于性能原因禁止执行 tensor.numpy() 等函数。
我的解决方案是像这样将标志 run_eagerly=True 传递给 model.compile()：
model.compile(..., run_eagerly=True)
Tensorflow 2 有一个配置选项 运行 功能“急切”，这将允许通过 .numpy() 方法获取 Tensor 值。要启用即时执行，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
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去领先于tensorflow。也许这个技巧对任何人都有用...
对于在 TF 2.0.0 中仍然遇到此问题的人 运行：tf.config.run_functions_eagerly（正确）您的程序的顶部它运行完美！
如何修复此错误我从 GitHub 下载了此代码。
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy()
抛出错误
AttributeError: 'Tensor' object has no attribute 'numpy'
请帮我解决这个问题！
我用过：
sess = tf.Session()
with sess.as_default():
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
我收到了这个错误。有人帮助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)]]
tf.multinomial returns 一个 Tensor 对象，它包含一个二维列表，其中绘制的样本形状为 [batch_size, num_samples]。在该张量对象上调用 .eval() 预计会 return 一个 numpy ndarray。
像这样：
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
您还需要确保您有一个活动会话（否则没有多大意义）：
sess = tf.Session()
with sess.as_default():
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
我怀疑你从中复制代码的地方在程序的开头有 eager execution enabled, i.e. had invoked tf.enable_eager_execution()。
你也可以这样做。
更新：请注意，在 TensorFlow 2.0 中默认启用即时执行。所以上面的答案只适用于 TensorFlow 1.x
当我 运行 编写如下代码时，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 anaconda 3 和 tensorflow 1.14.0。我用下面的命令升级了 tensorflow
conda update tensorflow
现在 tensorflow 是 2.0.0，问题已解决。试试看它是否能解决您的问题。
它发生在旧版本的 TF 中。所以试试 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。
如果您的代码包装在 @tf.function 或 Keras 层内，这也可能发生在 TF2.0 中。这两个 运行 都处于图形模式。那里有很多秘密破坏的代码，因为急切模式和图形模式之间的行为不同，而且人们不知道他们正在切换上下文，所以要小心！
由于接受的答案没有解决我的问题，所以我认为这可能对一些遇到问题并且已经拥有 tensorflow 版本 >= 2.2.0 并启用了 eager execution 的人有所帮助。
问题似乎是在拟合过程中对于某些函数 model.fit()
@tf.function 装饰器出于性能原因禁止执行 tensor.numpy() 等函数。
我的解决方案是像这样将标志 run_eagerly=True 传递给 model.compile()：
model.compile(..., run_eagerly=True)
Tensorflow 2 有一个配置选项 运行 功能“急切”，这将允许通过 .numpy() 方法获取 Tensor 值。要启用即时执行，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
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去领先于tensorflow。也许这个技巧对任何人都有用...
对于在 TF 2.0.0 中仍然遇到此问题的人 运行：tf.config.run_functions_eagerly（正确）您的程序的顶部它运行完美！