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

Question

How can I fix this error I downloaded this code from GitHub.

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

throws the error

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

Please help me fix this!

I used:

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

And i get this error. Someone help me i just want it to work why is this so hard?

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)]]

Solution 1

I suspect the place where you copied the code from had eager execution enabled, i.e. had invoked tf.enable_eager_execution() at the start of the program.

You could do the same.

UPDATE: Note that eager execution is enabled by default in TensorFlow 2.0. So the answer above applies only to TensorFlow 1.x

Solution 2

Since the accepted answer did not solve the problem for me so I thought it might be helpful for some people who face the problem and that already have tensorflow version >= 2.2.0 and eager execution enabled.

The issue seems to be that for certain functions during the fitting model.fit() the @tf.function decorator prohibits the execution of functions like tensor.numpy() for performance reasons.

The solution for me was to pass the flag run_eagerly=True to the model.compile() like this:

model.compile(..., run_eagerly=True)

Solution 3

Tensorflow 2 has a config option to run functions "eagerly" which will enable getting Tensor values via .numpy() method. To enable eager execution, use following command:

tf.config.run_functions_eagerly(True)

Note that this is useful mainly for debugging.

See also: https://www.tensorflow.org/api_docs/python/tf/config/run_functions_eagerly

Solution 4

This can also happen in TF2.0 if your code is wrapped in a @tf.function or inside a Keras layer. Both of those run in graph mode. There's a lot of secretly broken code out of there because behavior differs between eager and graph modes and people are not aware that they're switching contexts, so be careful!

Solution 5

It happens in older version of TF. So try pip install tensorflow --upgrade

otherwise run

import tensorflow as tf
tf.enable_eager_execution()

If you are using Jupyter notebook, restart the Kernel.

View more solutions

Comments

[Frieder Hannenheim]

now i get this error:raise ValueError("Cannot evaluate tensor using eval(): No default " ValueError: Cannot evaluate tensor using eval(): No default session is registered. Use with sess.as_default() or pass an explicit session to eval(session=sess)

[Frieder Hannenheim]

Sorry if this is dumb but I'm very new to python.

[Debosmit Ray]

ah - this is because you don't have a session established. updating the post above.

[Debosmit Ray]

@FriederMüller you should also use {this post} as reference

[Frieder Hannenheim]

That's it thanks. What does eager execution do by the way?

[ash]

It changes the TensirFlie APIs so that they execute operations on tensors immediately (as opposed to adding the operations to a graph). See the links in the answer above for details

[Btc Sources]

Could you please add a different reference in order to know how to do it without eager execution?

[Will.Evo]

This is still a problem in TF 2.0.0

[Helen]

Incredible... thank you! I ran into the same while using an official TF tutorial :\

[LKM]

I got an error "ValueError: tf.enable_eager_execution must be called at program startup."

[Smankusors]

the link is broken now, it says 404 page not found :(

[Agustin Barrachina]

Well if you se the response the problem is that it should be eager execution. That's why is "fixed" in TF 2, because it has eager execution by default. But the problem is always the same, you cannot use .numpy() method in NOT eager execution.

[GuitarExtended]

Please explain in what context you got this error.

[jp_]

huh? it worked for me on tensorflow 2.0. ( not without eager execution)

[BioCoder]

This solution works for me, it's perfect !

[whitepanda]

This solves the problem for the latest version of tensorflow. +1

[Admin]

What's the workaround then? I need to use this in a tf.function

[markemus]

@Stark see Alexander's answer: stackoverflow.com/a/63596006/9095840 or perhaps stackoverflow.com/a/68057458/9095840

[Admin]

Thanks for the reply. I did try that solution as well, but it did not work for me. I've found a way to implement what I wanted, without the tensor to numpy conversion however. I realised that I did not require it in the first place. So, it's all well.

[Adri HM]

solve my problem too. Thanks

[ASLAN]

I think this is more up-to-date answer