Tensorflow Error when using tf.gradients and tf.hessian: TypeError: Fetch argument None has invalid type <type 'NoneType'> - tensorflow

I just started learning tensorflow and came across the following error when using the tf.gradients and tf.hessain functions. Given below is the code and error for tf.gradients.
import tensorflow as tf
a = tf.placeholder(tf.float32,shape = (2,2))
b = [[1.0,2.0],[3.0,4.0]]
c = a[0,0]*a[0,1]*a[1,0] + a[0,1]*a[1,0]*a[1,1]
e = tf.reshape(b,[4])
d = tf.gradients(c,e)
sess = tf.Session()
print(sess.run(d,feed_dict={a:b}))
I get the following error for the last line
>>> print(sess.run(d,feed_dict={a:b}))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/share/apps/tensorflow/20170218/python2.7/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 767, in run
run_metadata_ptr)
File "/share/apps/tensorflow/20170218/python2.7/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 952, in _run
fetch_handler = _FetchHandler(self._graph, fetches, feed_dict_string)
File "/share/apps/tensorflow/20170218/python2.7/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 408, in __init__
self._fetch_mapper = _FetchMapper.for_fetch(fetches)
File "/share/apps/tensorflow/20170218/python2.7/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 230, in for_fetch
return _ListFetchMapper(fetch)
File "/share/apps/tensorflow/20170218/python2.7/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 337, in __init__
self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
File "/share/apps/tensorflow/20170218/python2.7/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 227, in for_fetch
(fetch, type(fetch)))
TypeError: Fetch argument None has invalid type <type 'NoneType'>
any ideas as to how I can debug this?


It is because c is calculated based on a, not e. You could change the line of gradient tensor as below.
d = tf.gradients(c,a)
BTW, in your original code, if you print d, you will find it is a [None]

Related

MissingDataError: exog contains inf or nans

Input:
import statsmodels.api as sm
import pandas as pd
# reading data from the csv
data = pd.read_csv('/Users/justkiddings/Desktop/Python/TM/TM.csv')
# defining the variables
x = data['FSP'].tolist()
y = data['RSP'].tolist()
# adding the constant term
x = sm.add_constant(x)
# performing the regression
# and fitting the model
result = sm.OLS(y, x).fit()
# printing the summary table
print(result.summary())
Output:
runfile('/Users/justkiddings/Desktop/Python/Code/untitled28.py', wdir='/Users/justkiddings/Desktop/Python/Code')
Traceback (most recent call last):
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/spyder_kernels/py3compat.py", line 356, in compat_exec
exec(code, globals, locals)
File "/Users/justkiddings/Desktop/Python/Code/untitled28.py", line 24, in <module>
result = sm.OLS(y, x).fit()
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/regression/linear_model.py", line 890, in __init__
super(OLS, self).__init__(endog, exog, missing=missing,
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/regression/linear_model.py", line 717, in __init__
super(WLS, self).__init__(endog, exog, missing=missing,
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/regression/linear_model.py", line 191, in __init__
super(RegressionModel, self).__init__(endog, exog, **kwargs)
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/model.py", line 267, in __init__
super().__init__(endog, exog, **kwargs)
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/model.py", line 92, in __init__
self.data = self._handle_data(endog, exog, missing, hasconst,
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/model.py", line 132, in _handle_data
data = handle_data(endog, exog, missing, hasconst, **kwargs)
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/data.py", line 673, in handle_data
return klass(endog, exog=exog, missing=missing, hasconst=hasconst,
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/data.py", line 86, in __init__
self._handle_constant(hasconst)
File "/Users/justkiddings/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/data.py", line 132, in _handle_constant
raise MissingDataError('exog contains inf or nans')
MissingDataError: exog contains inf or nans
Some of the Data:
DATE,HOUR,STATION,CO,FSP,NO2,NOX,O3,RSP,SO2
1/1/2022,1,TUEN MUN,75,38,39,40,83,59,2
1/1/2022,2,TUEN MUN,72,35,29,30,90,61,2
1/1/2022,3,TUEN MUN,74,38,28,30,91,66,2
1/1/2022,4,TUEN MUN,76,39,31,32,79,61,2
1/1/2022,5,TUEN MUN,72,38,25,26,83,65,2
1/1/2022,6,TUEN MUN,74,37,24,25,86,60,2
I have removed the N.A. in my dataset and they have converted into blanks. (Eg. 3/1/2022,12,TUEN MUN,85,,53,70,59,,5) Why there is MissingDataError? How to fix it? Thanks.

Reading keys from an .npz file with multiple workers in pytorch dataloader?

I have an .npz file where I have stored a dictionary. The dictionary has some keys and the values are numpy arrays. I want to read the dictionary in my getitem() method of the dataloader. When I set the dataloader num_workers to 1, everything runs fine. But when I increase the num workers, it throws the following error when reading the data from that npz file:
Traceback (most recent call last):
File "scripts/train.py", line 235, in <module>
train(args)
File "scripts/train.py", line 186, in train
solver(args.epoch, args.verbose)
File "/local-scratch/codebase/cap/lib/solver.py", line 174, in __call__
self._feed(self.dataloader["train"], "train", epoch_id)
File "/local-scratch/codebase/cap/lib/solver.py", line 366, in _feed
for data_dict in dataloader:
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 819, in __next__
return self._process_data(data)
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 846, in _process_data
data.reraise()
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/torch/_utils.py", line 369, in reraise
raise self.exc_type(msg)
zipfile.BadZipFile: Caught BadZipFile in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
data = fetcher.fetch(index)
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/local-scratch/codebase/cap/lib/dataset.py", line 947, in __getitem__
other_bbox_feat = self.box_features['{}-{}_{}.{}'.format(scene_id, target_object_id, ann_id, object_id)]
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/numpy/lib/npyio.py", line 255, in __getitem__
pickle_kwargs=self.pickle_kwargs)
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/numpy/lib/format.py", line 763, in read_array
data = _read_bytes(fp, read_size, "array data")
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/site-packages/numpy/lib/format.py", line 892, in _read_bytes
r = fp.read(size - len(data))
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/zipfile.py", line 872, in read
data = self._read1(n)
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/zipfile.py", line 962, in _read1
self._update_crc(data)
File "/local-scratch/anaconda3/envs/scanenv/lib/python3.6/zipfile.py", line 890, in _update_crc
raise BadZipFile("Bad CRC-32 for file %r" % self.name)
zipfile.BadZipFile: Bad CRC-32 for file 'scene0519_00-13_1.0.npy'
As far as I know, pytorch dataloader uses multiprocessing to for data loading. Perhaps the issue is with multiprocessing and .npz files. I really appreciate any help.

Error while sequentially exporting two different inference graphs in tensorflow object detection API

I am using Tensorflow object detection API and I have trained two separate models( FRCNN Inception V2 and SSD Mobilenet V2). In my code flow, when both of the models have been trained, I need to export inference graphs. The following is the code for the same:
# Dependencies
import tensorflow as tf
import glob
import os
import re
from google.protobuf import text_format
from object_detection import exporter
from object_detection.protos import pipeline_pb2
class ExportInferenceGraph():
def __init__(self):
# input parameters for exporter
self.pipeline_config_path = None
self.trained_checkpoint_prefix = None
self.output_directory = None
#############################################################################
'''
code used form export_inference_graph.py file from Tensorflow github
'''
#############################################################################
flags = tf.app.flags
flags.DEFINE_string('input_type', 'image_tensor', 'Type of input node. Can be '
'one of [`image_tensor`, `encoded_image_string_tensor`, '
'`tf_example`]')
flags.DEFINE_string('input_shape', None,
'If input_type is `image_tensor`, this can explicitly set '
'the shape of this input tensor to a fixed size. The '
'dimensions are to be provided as a comma-separated list '
'of integers. A value of -1 can be used for unknown '
'dimensions. If not specified, for an `image_tensor, the '
'default shape will be partially specified as '
'`[None, None, None, 3]`.')
flags.DEFINE_string('config_override', '',
'pipeline_pb2.TrainEvalPipelineConfig '
'text proto to override pipeline_config_path.')
flags.DEFINE_boolean('write_inference_graph', False,
'If true, writes inference graph to disk.')
self.FLAGS = flags.FLAGS
#############################################################################
# method to get latest checkpoint files
def get_latest_checkpoints(self, trainingDir):
# getting list of all meta files
metaFiles = glob.glob(trainingDir + '/*.meta')
tempList = []
# sorting based on num_steps
for _file in metaFiles:
tempList.append(int(re.findall(r'[0-9]+', os.path.basename(_file))[0]))
tempList.sort(reverse = True)
# returning path of latest checkpoint file
return trainingDir + '/model.ckpt-' + str(tempList[0])
# parsing flags and exporting graphs
def export(self, pipeline_config_path, trained_checkpoint_dir, output_directory):
# path to store exported inference graphs
if not os.path.exists(output_directory):
os.makedirs(output_directory)
# getting latest checkpoint file
self.trained_checkpoint_prefix = self.get_latest_checkpoints(trained_checkpoint_dir)
print(self.trained_checkpoint_prefix)
self.pipeline_config_path = pipeline_config_path
self.output_directory = output_directory
#############################################################################
'''
code used form export_inference_graph.py file from Tensorflow
'''
#############################################################################
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
with tf.gfile.GFile(self.pipeline_config_path, 'r') as f:
text_format.Merge(f.read(), pipeline_config)
text_format.Merge(self.FLAGS.config_override, pipeline_config)
if self.FLAGS.input_shape:
input_shape = [
int(dim) if dim != '-1' else None
for dim in self.FLAGS.input_shape.split(',')
]
else:
input_shape = None
exporter.export_inference_graph(
self.FLAGS.input_type, pipeline_config, self.trained_checkpoint_prefix,
self.output_directory, input_shape = input_shape,
write_inference_graph = self.FLAGS.write_inference_graph)
#############################################################################
This is the code snippet which shows how I call the methods for two different models:
export = ExportInferenceGraph()
export.export(pipeline_config_path = TRAIN_CONFIG_FILES[0],
trained_checkpoint_dir = MODEL_TRAIN_DIRS[0],
output_directory = INFERENCE_SUB_DIRS[0])
export.export(pipeline_config_path = TRAIN_CONFIG_FILES[1],
trained_checkpoint_dir = MODEL_TRAIN_DIRS[1],
output_directory = INFERENCE_SUB_DIRS[1])
But I am getting the following error:
2020-01-22 17:42:47.520891: W tensorflow/core/framework/op_kernel.cc:1502] OP_REQUIRES failed at save_restore_v2_ops.cc:184 : Not
found: Key BoxPredictor_0/BoxEncodingPredictor/biases not found in checkpoint
Traceback (most recent call last):
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\client\session.py", line 1356, in _do_call
return fn(*args)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\client\session.py", line 1341, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\client\session.py", line 1429, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.NotFoundError: Key BoxPredictor_0/BoxEncodingPredictor/biases not found in checkpoint
[[{{node save_1/RestoreV2}}]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\training\saver.py", line 1286, in restore
{self.saver_def.filename_tensor_name: save_path})
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\client\session.py", line 950, in run
run_metadata_ptr)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\client\session.py", line 1173, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\client\session.py", line 1350, in _do_run
run_metadata)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\client\session.py", line 1370, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.NotFoundError: Key BoxPredictor_0/BoxEncodingPredictor/biases not found in checkpoint
[[node save_1/RestoreV2 (defined at ../TensorFlow/models/research\object_detection\exporter.py:323) ]]
Original stack trace for 'save_1/RestoreV2':
File "ocr_train.py", line 99, in <module>
output_directory = INFERENCE_SUB_DIRS[1])
File "D:\shubham\wsp\OCR\export_inference_graph.py", line 109, in export
write_inference_graph = self.FLAGS.write_inference_graph)
File "../TensorFlow/models/research\object_detection\exporter.py", line 489, in export_inference_graph
write_inference_graph=write_inference_graph)
File "../TensorFlow/models/research\object_detection\exporter.py", line 418, in _export_inference_graph
trained_checkpoint_prefix=checkpoint_to_use)
File "../TensorFlow/models/research\object_detection\exporter.py", line 323, in write_graph_and_checkpoint
tf.import_graph_def(inference_graph_def, name='')
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\util\deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\framework\importer.py", line 443, in import_graph_def
_ProcessNewOps(graph)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\framework\importer.py", line 236, in _ProcessNewOps
for new_op in graph._add_new_tf_operations(compute_devices=False): # pylint: disable=protected-access
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\framework\ops.py", line 3751, in _add_new_tf_operations
for c_op in c_api_util.new_tf_operations(self)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\framework\ops.py", line 3751, in <listcomp>
for c_op in c_api_util.new_tf_operations(self)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\framework\ops.py", line 3641, in _create_op_from_tf_operation
ret = Operation(c_op, self)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\framework\ops.py", line 2005, in __init__
self._traceback = tf_stack.extract_stack()
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\training\saver.py", line 1296, in restore
names_to_keys = object_graph_key_mapping(save_path)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\training\saver.py", line 1614, in object_graph_key_mapping
object_graph_string = reader.get_tensor(trackable.OBJECT_GRAPH_PROTO_KEY)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 678, in get_tensor
return CheckpointReader_GetTensor(self, compat.as_bytes(tensor_str))
tensorflow.python.framework.errors_impl.NotFoundError: Key _CHECKPOINTABLE_OBJECT_GRAPH not found in checkpoint
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "ocr_train.py", line 99, in <module>
output_directory = INFERENCE_SUB_DIRS[1])
File "D:\shubham\wsp\OCR\export_inference_graph.py", line 109, in export
write_inference_graph = self.FLAGS.write_inference_graph)
File "../TensorFlow/models/research\object_detection\exporter.py", line 489, in export_inference_graph
write_inference_graph=write_inference_graph)
File "../TensorFlow/models/research\object_detection\exporter.py", line 418, in _export_inference_graph
trained_checkpoint_prefix=checkpoint_to_use)
File "../TensorFlow/models/research\object_detection\exporter.py", line 327, in write_graph_and_checkpoint
saver.restore(sess, trained_checkpoint_prefix)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\training\saver.py", line 1302, in restore
err, "a Variable name or other graph key that is missing")
tensorflow.python.framework.errors_impl.NotFoundError: Restoring from checkpoint failed. This is most likely due to a Variable name or other graph key that is missing from the checkpoint. Please ensure that you have not altered the graph expected based on the
checkpoint. Original error:
Key BoxPredictor_0/BoxEncodingPredictor/biases not found in checkpoint
[[node save_1/RestoreV2 (defined at ../TensorFlow/models/research\object_detection\exporter.py:323) ]]
Original stack trace for 'save_1/RestoreV2':
File "ocr_train.py", line 99, in <module>
output_directory = INFERENCE_SUB_DIRS[1])
File "D:\shubham\wsp\OCR\export_inference_graph.py", line 109, in export
write_inference_graph = self.FLAGS.write_inference_graph)
File "../TensorFlow/models/research\object_detection\exporter.py", line 489, in export_inference_graph
write_inference_graph=write_inference_graph)
File "../TensorFlow/models/research\object_detection\exporter.py", line 418, in _export_inference_graph
trained_checkpoint_prefix=checkpoint_to_use)
File "../TensorFlow/models/research\object_detection\exporter.py", line 323, in write_graph_and_checkpoint
tf.import_graph_def(inference_graph_def, name='')
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\util\deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\framework\importer.py", line 443, in import_graph_def
_ProcessNewOps(graph)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\framework\importer.py", line 236, in _ProcessNewOps
for new_op in graph._add_new_tf_operations(compute_devices=False): # pylint: disable=protected-access
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\framework\ops.py", line 3751, in _add_new_tf_operations
for c_op in c_api_util.new_tf_operations(self)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\framework\ops.py", line 3751, in <listcomp>
for c_op in c_api_util.new_tf_operations(self)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\framework\ops.py", line 3641, in _create_op_from_tf_operation
ret = Operation(c_op, self)
File "C:\Users\Lenovo\Anaconda3\envs\wsp\lib\site-packages\tensorflow\python\framework\ops.py", line 2005, in __init__
self._traceback = tf_stack.extract_stack()
NOTE: I could see the first graph exported successfully but it throws the error for the second graph. I interchanged calling of the export methods, it still failed at second export.
I am still new to Tensorflow and need some help here. I guess it is using the same graph which it created for the first model.

I dig deep into the Tensorflow directory and reached to method _export_inference_graph. The path is TensorFlow/models/research/object_detection/exporter.py. Adding this line at the end of the function solved my problem.
tf.reset_default_graph()

Invalid argument: TypeError: an integer is required (got type NoneType) when closing Tensorflow

I'm implementing the IMPALA framework https://github.com/deepmind/scalable_agent which uses tensorflows multiprocessing. Everything seems to work fine as the experiment ran for the specified number of steps... however I start to get a type error when tensorflow is closing processes.
Does someone know what could've contributed to this error and how to solve it? This seems to be something with tensorflow rather than the original code itself.
INFO:tensorflow:Closing all processes.
[750. 450. 25.125]
2020-01-13 17:01:00.624889: W tensorflow/core/framework/op_kernel.cc:1389] Invalid argument: TypeError: an integer is required (got type NoneType)
Traceback (most recent call last):
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/site-packages/tensorflow/python/ops/script_ops.py", line 207, in __call__
ret = func(*args)
File "/home/haianh/anaconda3/envs/lab/scalable_agent/py_process.py", line 86, in py_call
result = self._out.recv()
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/multiprocessing/connection.py", line 250, in recv
buf = self._recv_bytes()
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/multiprocessing/connection.py", line 411, in _recv_bytes
return self._recv(size)
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/multiprocessing/connection.py", line 379, in _recv
chunk = read(handle, remaining)
TypeError: an integer is required (got type NoneType)
INFO:tensorflow:Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.InvalidArgumentError'>, TypeError: an integer is required (got type NoneType)
Traceback (most recent call last):
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/site-packages/tensorflow/python/ops/script_ops.py", line 207, in __call__
ret = func(*args)
File "/home/haianh/anaconda3/envs/lab/scalable_agent/py_process.py", line 86, in py_call
result = self._out.recv()
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/multiprocessing/connection.py", line 250, in recv
buf = self._recv_bytes()
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/multiprocessing/connection.py", line 411, in _recv_bytes
return self._recv(size)
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/multiprocessing/connection.py", line 379, in _recv
chunk = read(handle, remaining)
TypeError: an integer is required (got type NoneType)
[[{{node scan/while/flow_environment_step/step}}]]
INFO:tensorflow:All processes closed.
Traceback (most recent call last):
File "experiment.py", line 689, in <module>
tf.app.run()
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "experiment.py", line 683, in main
train(action_set, level_names)
File "experiment.py", line 630, in train
session.run(enqueue_ops)
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 788, in __exit__
self._close_internal(exception_type)
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 826, in _close_internal
self._sess.close()
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 1082, in close
self._sess.close()
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 1244, in close
ignore_live_threads=True)
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/site-packages/tensorflow/python/training/coordinator.py", line 389, in join
six.reraise(*self._exc_info_to_raise)
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/site-packages/six.py", line 696, in reraise
raise value
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/site-packages/tensorflow/python/training/queue_runner_impl.py", line 257, in _run
enqueue_callable()
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1257, in _single_operation_run
self._call_tf_sessionrun(None, {}, [], target_list, None)
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1407, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: TypeError: an integer is required (got type NoneType)
Traceback (most recent call last):
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/site-packages/tensorflow/python/ops/script_ops.py", line 207, in __call__
ret = func(*args)
File "/home/haianh/anaconda3/envs/lab/scalable_agent/py_process.py", line 86, in py_call
result = self._out.recv()
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/multiprocessing/connection.py", line 250, in recv
buf = self._recv_bytes()
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/multiprocessing/connection.py", line 411, in _recv_bytes
return self._recv(size)
File "/home/haianh/anaconda3/envs/lab/lib/python3.6/multiprocessing/connection.py", line 379, in _recv
chunk = read(handle, remaining)
TypeError: an integer is required (got type NoneType)
[[{{node scan/while/flow_environment_step/step}}]]

type matching error in tensor

I am following a Tensorflow example. However, running the code gives me the following error message.
It seems to me that the following code segment causes the problem. Or the last dimension of h_conv10 has float64. But there is no place where float64 is setup explicitly. Thank you for the suggestions and hints.
# Deconvolution 1
W_deconv_1 = weight_variable_devonc([max_pool_size, max_pool_size, 8*chanel_root, 16*chanel_root])
b_deconv1 = bias_variable([8*chanel_root])
h_deconv1 = tf.nn.relu(deconv2d(h_conv10, W_deconv_1, max_pool_size) + b_deconv1)
h_deconv1_concat = crop_and_concat(h_conv8,h_deconv1,[n_image,(((nx-180)/2+4)/2+4)/2+4,(((ny-180)/2+4)/2+4)/2+4,8*chanel_root])
python u-net.py
Traceback (most recent call last):
File "/experiment/tfw/lib/python3.4/site-
packages/tensorflow/python/ops/op_def_library.py", line 377, in apply_op
as_ref=input_arg.is_ref)
File "/experiment/tfw/lib/python3.4/site-
packages/tensorflow/python/framework/ops.py", line 608, in convert_n_to_tensor
ret.append(convert_to_tensor(value, dtype=dtype, name=n, as_ref=as_ref))
File "/experiment/tfw/lib/python3.4/site-
packages/tensorflow/python/framework/ops.py", line 566, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/experiment/tfw/lib/python3.4/site-
packages/tensorflow/python/framework/ops.py", line 510, in
_TensorTensorConversionFunction
% (dtype.name, t.dtype.name, str(t)))
ValueError: Tensor conversion requested dtype int32 for Tensor with dtype
float64: 'Tensor("truediv:0", shape=(), dtype=float64)'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "u-net.py", line 193, in <module>
h_deconv1 = tf.nn.relu(deconv2d(h_conv10, W_deconv_1, max_pool_size) + b_deconv1)
File "u-net.py", line 77, in deconv2d
output_shape = tf.pack([tf.shape(x)[0], tf.shape(x)[1]*2, tf.shape(x)[2]*2, tf.shape(x)[3]/2])
File "/experiment/tfw/lib/python3.4/site-packages/tensorflow/python/ops/array_ops.py", line 241, in pack
return gen_array_ops._pack(values, name=name)
File "/experiment/tfw/lib/python3.4/site-packages/tensorflow/python/ops/gen_array_ops.py", line 916, in _pack
return _op_def_lib.apply_op("Pack", values=values, name=name)
File "/experiment/tfw/lib/python3.4/site-packages/tensorflow/python/ops/op_def_library.py", line 396, in apply_op
raise TypeError("%s that don't all match." % prefix)
TypeError: Tensors in list passed to 'values' of 'Pack' Op have types [int32, int32, int32, float64] that don't all match.