ValueError: Tensor Tensor("dense_1/Sigmoid:0", shape=(?, 1), dtype=float32) is not an element of this graph
ValueError: Tensor Tensor("dense_1/Sigmoid:0", shape=(?, 1), dtype=float32) is not an element of this graph
我正在使用 tf.keras 加载我之前使用 tf.keras 制作的模型，但是当我尝试进行预测时，我得到了这个
[ERROR] [1560045312.143498]: bad callback: <function callback at 0x7f16fe94b8c0>
Traceback (most recent call last):
File "/opt/ros/kinetic/lib64/python2.7/site-packages/rospy/topics.py", line 750, in _invoke_callback
cb(msg)
File "/home/franky/catkin_ws_kinetic/src/tfm/scripts/nnet_predictor.py", line 50, in callback
true_face.eyes[1].height
File "/usr/lib64/python2.7/site-packages/tensorflow/python/keras/engine/training.py", line 1113, in predict
self, x, batch_size=batch_size, verbose=verbose, steps=steps)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 195, in model_iteration
f = _make_execution_function(model, mode)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 122, in _make_execution_function
return model._make_execution_function(mode)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/keras/engine/training.py", line 1989, in _make_execution_function
self._make_predict_function()
File "/usr/lib64/python2.7/site-packages/tensorflow/python/keras/engine/training.py", line 1979, in _make_predict_function
**kwargs)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/keras/backend.py", line 3201, in function
return GraphExecutionFunction(inputs, outputs, updates=updates, **kwargs)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/keras/backend.py", line 2939, in __init__
with ops.control_dependencies(self.outputs):
File "/usr/lib64/python2.7/site-packages/tensorflow/python/framework/ops.py", line 5028, in control_dependencies
return get_default_graph().control_dependencies(control_inputs)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/framework/ops.py", line 4528, in control_dependencies
c = self.as_graph_element(c)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3478, in as_graph_element
return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3557, in _as_graph_element_locked
raise ValueError("Tensor %s is not an element of this graph." % obj)
ValueError: Tensor Tensor("dense_1/Sigmoid:0", shape=(?, 1), dtype=float32) is not an element of this graph.
我也可能会说我在ros框架中使用这个（机器人操作系统[它不是操作系统，只是有一个超级误导的名字，我在linux]）所以我知道callback() 正在一个线程中被调用，我无法避免使用 ros。
我也测试过，如果我在主线程中使用预测功能，一切正常。
我已经尝试了 with graph.as_default(): 和 clear_session() 解决方案，但没有成功。
我已经检查过每个导入都来自 tf.keras 并且我没有将 tf.keras 与 keras
混用
我还尝试使用 Lock() 来避免同时调用 predict() 函数 2+
#!/usr/bin/python2
from tensorflow import keras
from tensorflow.keras.models import model_from_json
from tfm_msgs.msg import IsLooking
import numpy as np
from tensorflow.keras.backend import clear_session
import tensorflow as tf
from threading import Thread, Lock
# other non relevant imports
def callback(face_array_stamped):
global mutex
mutex.acquire()
try:
global graph
# with graph.as_default():
global my_model
global pub
true_faces = []
for face in face_array_stamped.faces:
if len(face.eyes) == 2:
true_faces.append(face)
if len(true_faces) == 1:
true_face = true_faces[0]
prediction = my_model.predict(np.array([[
#all the data here
]]))[0]
#↑↑↑↑↑It crashes here↑↑↑↑↑↑
#more non relevant stuff
finally:
mutex.release()
if __name__ == '__main__':
# clear_session()
model_dir = str(os.path.dirname(os.path.abspath(__file__))) + "/../nnet_models/"
json_file = open(model_dir+'model.json', 'r')
my_model = model_from_json(json_file.read())
json_file.close()
my_model.load_weights(model_dir+'model.h5')
# my_model._make_predict_function()
my_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# my_model.summary()
我希望代码不会崩溃
我认为你应该添加 graph = tf.get_default_graph() 和 with graph.as_default():
这个呢？
from tensorflow import keras
from tensorflow.keras.models import model_from_json
from tfm_msgs.msg import IsLooking
import numpy as np
from tensorflow.keras.backend import clear_session
import tensorflow as tf
from threading import Thread, Lock
# other non relevant imports
graph = tf.get_default_graph()
def callback(face_array_stamped):
global mutex
mutex.acquire()
try:
global my_model
global pub
true_faces = []
for face in face_array_stamped.faces:
if len(face.eyes) == 2:
true_faces.append(face)
if len(true_faces) == 1:
true_face = true_faces[0]
with graph.as_default():
prediction = my_model.predict(np.array([[
#all the data here
]]))[0]
#↑↑↑↑↑It crashes here↑↑↑↑↑↑
#more non relevant stuff
finally:
mutex.release()
if __name__ == '__main__':
# clear_session()
model_dir = str(os.path.dirname(os.path.abspath(__file__))) + "/../nnet_models/"
json_file = open(model_dir+'model.json', 'r')
my_model = model_from_json(json_file.read())
json_file.close()
my_model.load_weights(model_dir+'model.h5')
# my_model._make_predict_function()
my_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# my_model.summary()
最后我搞不清楚是怎么回事，不知道是不是因为ros的工作原理，但我最终还是使用了解决方案
Execute Python function in Main thread from call in Dummy thread
所以我的代码最终是这样的：
callback_queue = Queue.Queue()
def prediction_callback(true_face, face_header):
#non relevant stuff
prediction = my_model.predict(np.array([[
#all the variables
]])
#more non relevant stuff
def face_callback(face_array_stamped): #this is the original callback
#...
callback_queue.put(lambda: prediction_callback(true_face, face_array_stamped.header))
#...
if __name__ == '__main__':
#...
while not rospy.is_shutdown():
try:
callback_queue.get(True, 2)()
except Queue.Empty:
pass
我遇到了同样的问题，但上面的解决方案对我没有用。我想订阅一张图片并用 keras/tensorflow 预测一些东西。这样做，我遇到了上述相同的错误。
以下解决方案对我有用：
def method_to_predict(msg):
# ...
model.predict(...)
# ...
if __name__ == '__main__':
rospy.init_node('my_node', anonymous=False)
while not rospy.is_shutdown():
msg = rospy.wait_for_message('topic', msg_type)
method_to_predict(msg)
如果上述解决方案不起作用，希望这对您有所帮助。
我正在 ROS 和回调方法调用中订阅一个主题 model.predict()。添加：
import tensorflow as tf
global graph,model
graph = tf.get_default_graph()
和
with graph.as_default():
steering_angle = float(model.predict(cropped[None, :, :, :], batch_size=1))
回调解决了我的问题，正如 Nattaphon 所建议的那样。
Tensorflow 1.12，Keras 2.0.6，Ubuntu18.04
我正在使用 tf.keras 加载我之前使用 tf.keras 制作的模型，但是当我尝试进行预测时，我得到了这个
[ERROR] [1560045312.143498]: bad callback: <function callback at 0x7f16fe94b8c0>
Traceback (most recent call last):
File "/opt/ros/kinetic/lib64/python2.7/site-packages/rospy/topics.py", line 750, in _invoke_callback
cb(msg)
File "/home/franky/catkin_ws_kinetic/src/tfm/scripts/nnet_predictor.py", line 50, in callback
true_face.eyes[1].height
File "/usr/lib64/python2.7/site-packages/tensorflow/python/keras/engine/training.py", line 1113, in predict
self, x, batch_size=batch_size, verbose=verbose, steps=steps)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 195, in model_iteration
f = _make_execution_function(model, mode)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 122, in _make_execution_function
return model._make_execution_function(mode)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/keras/engine/training.py", line 1989, in _make_execution_function
self._make_predict_function()
File "/usr/lib64/python2.7/site-packages/tensorflow/python/keras/engine/training.py", line 1979, in _make_predict_function
**kwargs)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/keras/backend.py", line 3201, in function
return GraphExecutionFunction(inputs, outputs, updates=updates, **kwargs)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/keras/backend.py", line 2939, in __init__
with ops.control_dependencies(self.outputs):
File "/usr/lib64/python2.7/site-packages/tensorflow/python/framework/ops.py", line 5028, in control_dependencies
return get_default_graph().control_dependencies(control_inputs)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/framework/ops.py", line 4528, in control_dependencies
c = self.as_graph_element(c)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3478, in as_graph_element
return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
File "/usr/lib64/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3557, in _as_graph_element_locked
raise ValueError("Tensor %s is not an element of this graph." % obj)
ValueError: Tensor Tensor("dense_1/Sigmoid:0", shape=(?, 1), dtype=float32) is not an element of this graph.
我也可能会说我在ros框架中使用这个（机器人操作系统[它不是操作系统，只是有一个超级误导的名字，我在linux]）所以我知道callback() 正在一个线程中被调用，我无法避免使用 ros。
我也测试过，如果我在主线程中使用预测功能，一切正常。
我已经尝试了 with graph.as_default(): 和 clear_session() 解决方案，但没有成功。
我已经检查过每个导入都来自 tf.keras 并且我没有将 tf.keras 与 keras
混用我还尝试使用 Lock() 来避免同时调用 predict() 函数 2+
#!/usr/bin/python2
from tensorflow import keras
from tensorflow.keras.models import model_from_json
from tfm_msgs.msg import IsLooking
import numpy as np
from tensorflow.keras.backend import clear_session
import tensorflow as tf
from threading import Thread, Lock
# other non relevant imports
def callback(face_array_stamped):
global mutex
mutex.acquire()
try:
global graph
# with graph.as_default():
global my_model
global pub
true_faces = []
for face in face_array_stamped.faces:
if len(face.eyes) == 2:
true_faces.append(face)
if len(true_faces) == 1:
true_face = true_faces[0]
prediction = my_model.predict(np.array([[
#all the data here
]]))[0]
#↑↑↑↑↑It crashes here↑↑↑↑↑↑
#more non relevant stuff
finally:
mutex.release()
if __name__ == '__main__':
# clear_session()
model_dir = str(os.path.dirname(os.path.abspath(__file__))) + "/../nnet_models/"
json_file = open(model_dir+'model.json', 'r')
my_model = model_from_json(json_file.read())
json_file.close()
my_model.load_weights(model_dir+'model.h5')
# my_model._make_predict_function()
my_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# my_model.summary()
我希望代码不会崩溃
我认为你应该添加 graph = tf.get_default_graph() 和 with graph.as_default():
这个呢？
from tensorflow import keras
from tensorflow.keras.models import model_from_json
from tfm_msgs.msg import IsLooking
import numpy as np
from tensorflow.keras.backend import clear_session
import tensorflow as tf
from threading import Thread, Lock
# other non relevant imports
graph = tf.get_default_graph()
def callback(face_array_stamped):
global mutex
mutex.acquire()
try:
global my_model
global pub
true_faces = []
for face in face_array_stamped.faces:
if len(face.eyes) == 2:
true_faces.append(face)
if len(true_faces) == 1:
true_face = true_faces[0]
with graph.as_default():
prediction = my_model.predict(np.array([[
#all the data here
]]))[0]
#↑↑↑↑↑It crashes here↑↑↑↑↑↑
#more non relevant stuff
finally:
mutex.release()
if __name__ == '__main__':
# clear_session()
model_dir = str(os.path.dirname(os.path.abspath(__file__))) + "/../nnet_models/"
json_file = open(model_dir+'model.json', 'r')
my_model = model_from_json(json_file.read())
json_file.close()
my_model.load_weights(model_dir+'model.h5')
# my_model._make_predict_function()
my_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# my_model.summary()
最后我搞不清楚是怎么回事，不知道是不是因为ros的工作原理，但我最终还是使用了解决方案 Execute Python function in Main thread from call in Dummy thread 所以我的代码最终是这样的：
callback_queue = Queue.Queue()
def prediction_callback(true_face, face_header):
#non relevant stuff
prediction = my_model.predict(np.array([[
#all the variables
]])
#more non relevant stuff
def face_callback(face_array_stamped): #this is the original callback
#...
callback_queue.put(lambda: prediction_callback(true_face, face_array_stamped.header))
#...
if __name__ == '__main__':
#...
while not rospy.is_shutdown():
try:
callback_queue.get(True, 2)()
except Queue.Empty:
pass
我遇到了同样的问题，但上面的解决方案对我没有用。我想订阅一张图片并用 keras/tensorflow 预测一些东西。这样做，我遇到了上述相同的错误。
以下解决方案对我有用：
def method_to_predict(msg):
# ...
model.predict(...)
# ...
if __name__ == '__main__':
rospy.init_node('my_node', anonymous=False)
while not rospy.is_shutdown():
msg = rospy.wait_for_message('topic', msg_type)
method_to_predict(msg)
如果上述解决方案不起作用，希望这对您有所帮助。
我正在 ROS 和回调方法调用中订阅一个主题 model.predict()。添加：
import tensorflow as tf
global graph,model
graph = tf.get_default_graph()
和
with graph.as_default():
steering_angle = float(model.predict(cropped[None, :, :, :], batch_size=1))
回调解决了我的问题，正如 Nattaphon 所建议的那样。
Tensorflow 1.12，Keras 2.0.6，Ubuntu18.04