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:srcgithubtensorflowtensorflowcoreplatformcpu_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:srcgithubtensorflowtensorflowcoreframeworkop_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:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonclientsession.py", line 1278, in _do_call
return fn(*args)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonclientsession.py", line 1263, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonclientsession.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:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonframeworkops.py", line 680, in eval
return _eval_using_default_session(self, feed_dict, self.graph, session)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonframeworkops.py", line 4951, in _eval_using_default_session
return session.run(tensors, feed_dict)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonclientsession.py", line 877, in run
run_metadata_ptr)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonclientsession.py", line 1100, in _run
feed_dict_tensor, options, run_metadata)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonclientsession.py", line 1272, in _do_run
run_metadata)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonclientsession.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:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonkerasenginebase_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:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonkerasenginebase_layer.py", line 736, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonkeraslayerscore.py", line 943, in call
outputs = gen_math_ops.mat_mul(inputs, self.kernel)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonopsgen_math_ops.py", line 4750, in mat_mul
name=name)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonframeworkop_def_library.py", line 510, in _apply_op_helper
preferred_dtype=default_dtype)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonframeworkops.py", line 1094, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonopsresource_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:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonopsresource_variable_ops.py", line 1000, in _dense_var_to_tensor
return self.value()
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonopsresource_variable_ops.py", line 662, in value
return self._read_variable_op()
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonopsresource_variable_ops.py", line 745, in _read_variable_op
self._dtype)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonopsgen_resource_variable_ops.py", line 562, in read_variable_op
"ReadVariableOp", resource=resource, dtype=dtype, name=name)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonframeworkop_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonutildeprecation.py", line 454, in new_func
return func(*args, **kwargs)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonframeworkops.py", line 3155, in create_op
op_def=op_def)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonframeworkops.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)]]
Answers:
tf.multinomial returns a Tensor object that contains a 2D list with drawn samples of shape [batch_size, num_samples]. Calling .eval() on that tensor object is expected to return a numpy ndarray.
Something like this:
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
You also need to ensure that you have a session active (doesn’t make a lot of sense otherwise):
sess = tf.Session()
with sess.as_default():
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
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
I saw similar error when I run code something like the following,
tensor = tf.multiply(ndarray, 42)
tensor.numpy() # throw AttributeError: 'Tensor' object has no attribute 'numpy'
I use anaconda 3 with tensorflow 1.14.0. I upgraded tensorflow with the command below
conda update tensorflow
now tensorflow is 2.0.0, issue fixed. Try this to see if it resolves your issue.
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.
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!
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)
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
I had the same issue in a tf.function(): But what has worked for me is to transform the numpy array into a tensorflow tensor via tf.convert_to_tensor Doku and then go ahead with tensorflow. Maybe this trick could be useful for anyone…
For people who still have this problem in TF 2.0.0 run: tf.config.run_functions_eagerly(True) top of ur program it works perfectly!
You can also use tf.get_static_value() to obtain the value of a tensor. This has the benefit of not needing eager mode. See docs here.
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:srcgithubtensorflowtensorflowcoreplatformcpu_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:srcgithubtensorflowtensorflowcoreframeworkop_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:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonclientsession.py", line 1278, in _do_call
return fn(*args)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonclientsession.py", line 1263, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonclientsession.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:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonframeworkops.py", line 680, in eval
return _eval_using_default_session(self, feed_dict, self.graph, session)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonframeworkops.py", line 4951, in _eval_using_default_session
return session.run(tensors, feed_dict)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonclientsession.py", line 877, in run
run_metadata_ptr)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonclientsession.py", line 1100, in _run
feed_dict_tensor, options, run_metadata)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonclientsession.py", line 1272, in _do_run
run_metadata)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonclientsession.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:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonkerasenginebase_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:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonkerasenginebase_layer.py", line 736, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonkeraslayerscore.py", line 943, in call
outputs = gen_math_ops.mat_mul(inputs, self.kernel)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonopsgen_math_ops.py", line 4750, in mat_mul
name=name)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonframeworkop_def_library.py", line 510, in _apply_op_helper
preferred_dtype=default_dtype)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonframeworkops.py", line 1094, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonopsresource_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:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonopsresource_variable_ops.py", line 1000, in _dense_var_to_tensor
return self.value()
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonopsresource_variable_ops.py", line 662, in value
return self._read_variable_op()
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonopsresource_variable_ops.py", line 745, in _read_variable_op
self._dtype)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonopsgen_resource_variable_ops.py", line 562, in read_variable_op
"ReadVariableOp", resource=resource, dtype=dtype, name=name)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonframeworkop_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonutildeprecation.py", line 454, in new_func
return func(*args, **kwargs)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonframeworkops.py", line 3155, in create_op
op_def=op_def)
File "C:UsersfriedAppDataRoamingPythonPython36site-packagestensorflowpythonframeworkops.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 a Tensor object that contains a 2D list with drawn samples of shape [batch_size, num_samples]. Calling .eval() on that tensor object is expected to return a numpy ndarray.
Something like this:
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
You also need to ensure that you have a session active (doesn’t make a lot of sense otherwise):
sess = tf.Session()
with sess.as_default():
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
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
I saw similar error when I run code something like the following,
tensor = tf.multiply(ndarray, 42)
tensor.numpy() # throw AttributeError: 'Tensor' object has no attribute 'numpy'
I use anaconda 3 with tensorflow 1.14.0. I upgraded tensorflow with the command below
conda update tensorflow
now tensorflow is 2.0.0, issue fixed. Try this to see if it resolves your issue.
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.
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!
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)
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
I had the same issue in a tf.function(): But what has worked for me is to transform the numpy array into a tensorflow tensor via tf.convert_to_tensor Doku and then go ahead with tensorflow. Maybe this trick could be useful for anyone…
For people who still have this problem in TF 2.0.0 run: tf.config.run_functions_eagerly(True) top of ur program it works perfectly!
You can also use tf.get_static_value() to obtain the value of a tensor. This has the benefit of not needing eager mode. See docs here.