Im trying to create a tool that buffers a feature class (polygon) and then intersects the buffer output layer with a point shapefile. When I run the tool the buffer analysis runs and completes but the intersect analysis does not. The error message is
ExecuteError: Failed to execute. Parameters are not valid.
ERROR 000732: (my buffer output layer & point shapefile) does not exist or is not supported
Failed to execute (Intersect).
All my layers are loaded in my arcmpap so I am not sure why it does that.
import arcpy
in_Path = arcpy.GetParameterAsText(0) # first parameter in the interactive tool
out_Path = arcpy.GetParameterAsText(1) # second parameter in the interactive tool
bufferDistance = arcpy.GetParameterAsText(2)# third parameter in the interactive tool
result_buffer = arcpy.Buffer_analysis(in_Path, out_Path, bufferDistance,"FULL", "ROUND", "ALL", "") #the parameters to able compute the tool
# ^^ assign variable for the buffer analysis tool
in_CitiesShapefile = arcpy.GetParameterAsText(3)# fourth parameter in the interactive tool
out_CitiesWithinBuffer = arcpy.GetParameterAsText(4) # fifth parameter in the interactive tool
arcpy.Intersect_analysis([out_Path, in_CitiesShapefile], out_CitiesWithinBuffer,"ALL", "", "INPUT") #the parameters to able compute the tool
This error often comes up when there is a problem with file/folder paths.
I'd check the parameters that take paths for the following:
Misspelled folder names
Using backslashes instead of forward slashes
Having spaces in the path names
This esri kb article might help.
Related
I have a testbench to test my VHDL device (DUT), but part of the DUT debug output is an ASSERT/REPORT message to the console, which I would like to check for correctness but I can't change the DUT. The only way I can think of is to post-process the output log file.
Is there any way of capturing the console output in the testbench, so I can check the DUT output directly?
I do this as part of the testbench. However, rather than Assert, I use OSVVM alerts, log, and print. OSVVM is both at osvvm.org and github.
Rather than Assert, I use AffirmIf for self-checking/result checking. I use AlertIf for parameter checking.
Step 1 is get OSVVM. Once you have the code, compile it using the script. In either Mentor or Aldec, run the script by doing:
vlib osvvm
vmap osvvm osvvm
do $PATH_TO_OSVVM/osvvm.do $PATH_TO_OSVVM
Use VHDL-2008 and include all of OSVVM in your program by doing:
library osvvm;
context osvvm.OsvvmContext;
Then rather than:
assert Data /= expected report "..." severity error;
Do:
AffirmIf(Data = Expected, "...") ;
Both assert and AffirmIf/AlertIf print. However, the advantage to AffirmIf/AlertIf is that internally it keeps a count of the errors and you can get a pass fail at the end of your test by doing:
ReportAlerts;
The next advantage of OSVVM AffirmIf/AlertIf/Log/Print is that if you want the results in a file, you simply do:
TranscriptOpen("./results/Test1.txt");
If you want to both print to the screen and a file, also do:
SetTranscriptMirror(TRUE);
That ought get you started. I will leave the rest to the user guides. Start by looking at both the AlertLog package user guide and the transcript package user guide.
In my Python code I execute
train_writer = tf.summary.FileWriter(TBOARD_LOGS_DIR)
train_writer.add_graph(sess.graph)
I can see 1.6MB file created in E:\progs\tensorboard_logs (and no other file)
but then when I execute
tensorboard --logdir=E:\progs\tensorboard_logs
it loads, but says: "No graph definition files were found." when I click on Graph.
Additionally, running tensorboard --inspect --logdir=E:\progs\tensorboard_logs
displays
Found event files in:
E:\progs\tensorboard_logs
These tags are in E:\progs\tensorboard_logs:
audio -
histograms -
images -
scalars -
Event statistics for E:\progs\tensorboard_logs:
audio -
graph
first_step 0
last_step 0
max_step 0
min_step 0
num_steps 1
outoforder_steps []
histograms -
images -
scalars -
sessionlog:checkpoint -
sessionlog:start -
sessionlog:stop -
This is TF 1.01 or so, on Windows 10.
I had similar issue. The issue occurred when I specified 'logdir' folder inside single quotes instead of double quotes. Hope this may be helpful to you.
egs: tensorboard --logdir='my_graph' -> Tensorboard didn't detect the graph
tensorboard --logdir="my_graph" -> Tensorboard detected the graph
In Tensorflows dealing with graphs, there are three parts:
1) creating the graph
2) Writing the graph to event file
3) Visualizing the graph in tensorboard
Example: Creating graph in tensorflow
a = tf.constant(5, name="input_a")
b = tf.constant(3, name="input_b")
c = tf.multiply(a,b, name="mul_c")
d = tf.add(a,b, name="add_d")
e = tf.add(c,d, name="add_e")
sess = tf.Session()
sess.run(c) <--check, value should be 15
sess.run(d) <--check, value should be 8
sess.run(e) <--check, value should be 23
Writing graph in event file
writer = tf.summary.FileWriter('./tensorflow_examples', sess.graph)
It is very important to specify a directory(in this case, the directory is tensorflow_examples), where the event file will be written to.
writer = tf.summary.FileWriter('./', sess.graph) didnt work for me, because the shell command => tensorboard --logdir expects a directory name.
After executing this step, verify if event file has been created in specified directory.
Visualizing graph in Tensorboard
Open terminal(bash), under working directory type:
tensorboard --logdir='tensorflow_examples' --host=127.0.0.1
Then open a new browser in http://127.0.0.1:6006/ or http://localhost/6006 and now tensorboard shows the graph successfully.
The problem might be the parameter --logdir. make sure you have type the correct
example:
in the code:
writer = tf.summary.FileWriter('./log/', s.graph)
open powershell
cd to your work directory and type
tensorboard --logdir=log
you can also use --debug to see if there is a problem in finding the log file. if you see:
TensorBoard path_to_run is: {'C:\\Users\\example\\log': None} that means it can not find the file.
You may need to change the powershell directory to your log file. And the logdir need not the single quotation marks.(Double quotation marks or without the quotes will be both OK)
I'd like to build and train a multi-layer LSTM model (stateIsTuple=True) in python, and then load and use it in C++. But I'm having a hard time figuring out how to feed and fetch states in C++, mainly because I don't have string names which I can reference.
E.g. I put the initial state in a named scope such as
with tf.name_scope('rnn_input_state'):
self.initial_state = cell.zero_state(args.batch_size, tf.float32)
and this appears in the graph as below, but how can I feed to these in C++?
Also, how can I fetch the current state in C++? I tried the graph construction code below in python but I'm not sure if it's the right thing to do, because last_state should be a tuple of tensors, not a single tensor (though I can see that the last_state node in tensorboard is 2x2x50x128, which sounds like it just concatenated the states as I have 2 layers, 128 rnn size, 50 mini batch size, and lstm cell - with 2 state vectors).
with tf.name_scope('outputs'):
outputs, last_state = legacy_seq2seq.rnn_decoder(inputs, self.initial_state, cell, loop_function=loop if infer else None)
output = tf.reshape(tf.concat(outputs, 1), [-1, args.rnn_size], name='output')
and this is what it looks like in tensorboard
Should I concat and split the state tensors so there is only ever one state tensor going in and out? Or is there a better way?
P.S. Ideally the solution won't involve hard-coding the number of layers (or rnn size). So I can just have four strings input_node_name, output_node_name, input_state_name, output_state_name, and the rest is derived from there.
I managed to do this by manually concatenating the state into a single tensor. I'm not sure if this is wise, since this is how tensorflow used to handle states, but is now deprecating that and switching to tuple states. Instead of setting state_is_tuple=False and risking my code being obsolete soon, I've added extra ops to manually stack and unstack the states to and from a single tensor. Saying that, it works fine both in python and C++.
The key code is:
# setting up
zero_state = cell.zero_state(batch_size, tf.float32)
state_in = tf.identity(zero_state, name='state_in')
# based on https://medium.com/#erikhallstrm/using-the-tensorflow-multilayered-lstm-api-f6e7da7bbe40#.zhg4zwteg
state_per_layer_list = tf.unstack(state_in, axis=0)
state_in_tuple = tuple(
# TODO make this not hard-coded to LSTM
[tf.contrib.rnn.LSTMStateTuple(state_per_layer_list[idx][0], state_per_layer_list[idx][1])
for idx in range(num_layers)]
)
outputs, state_out_tuple = legacy_seq2seq.rnn_decoder(inputs, state_in_tuple, cell, loop_function=loop if infer else None)
state_out = tf.identity(state_out_tuple, name='state_out')
# running (training or inference)
state = sess.run('state_in:0') # zero state
loop:
feed = {'data_in:0': x, 'state_in:0': state}
[y, state] = sess.run(['data_out:0', 'state_out:0'], feed)
Here is the full code if anyone needs it
https://github.com/memo/char-rnn-tensorflow
i am currently trying to tune the svm function in the e1071 package for R. my input is genomic data (that is each attribute takes a value in the set {-1, 0, 1}) and none of the four kernels currently offered in the package is really good for this kind of data --- i would like to use Hamming distance as my kernel instead.
the svm function, it seems, is written in C++. i have downloaded the source via
download.packages(pkgs = "e1071",
destdir = ".",
type = "source")
found the svm.cpp file containing code for the function and the corresponding kernel portion, where i can potentially add my own custom kernel. has anyone tried doing this? is it possible to do this? once i've finished modifying svm.cpp (provided i figure out how..), how do i make the package "see" the modified file?
You can modify the existing kernel.
I changed the return statement of radial kernel to make the changes..
You can try with that
I am running libsvm through weka. Its output accuracy looks good to me, so I am planning to write a svm model by myself. However, weka didn't generate any training parameter, such as number of support vector. Therefore i cannot do anything. Searching the web, i found somebody said it would generate some parameters like the following:
optimization finished, #iter = 27
nu = 0.058475864943863545
obj = -1.871013102744184, rho = -0.19357337828800944
nSV = 9, nBSV = 0 `enter code here`
Total nSV = 9
but how come i didn't see any of them? any step that i missed? please help me. Thanks a lot.
Weka writes the output you mentioned to stderr.
So if you have started weka.sh or weka.bat from a terminal (or "command window" if you are on Windows), you should see that output appear in your terminal window after clicking "classify"
If you want to have access to this information via scripts, you can
redirect the output to a file and read in that file.
Here is how to edit the startup file weka.sh / weka.bat.
Edit this line (it is probably the last line) in order to write log info to a file instead of the terminal window:
java -cp $CP -Xmx8092m weka.gui.GUIChooser 2>>/opt/weka-stable/weka.log &
You can also add a properties file to your home directory to add more fine-grained behaviour.
https://weka.wikispaces.com/Properties+file
(You probably can also access information via the Weka Java API somehow, but you did not ask for that)