I'm a newbie of deep learning utilizing tensorflow.
I want to make the own model that predict my custom images that are constructed on the grayscale.
But the only thing that I know is MNIST example utilizing tensorflow.
So I used a converting module from this repo but the error had been occurred such as this.
Images like to convert was constructed as 80,680 of training images, 20,170 of test images.
I really don't know why this error has occurred.
Please help me.
The script you're referring to doesn't correctly set up the headers for the MNIST format. It was addressed in a previous Github issue that has since been deleted, but my modification:
header = array('B')
header.extend([0,0,8,1,0,0])
header.append(int('0x'+hexval[2:][:2],16))
header.append(int('0x'+hexval[2:][2:],16))
to
header = array('B')
header.extend([0,0,8,1])
header.append(int('0x'+hexval[2:][:2],16))
header.append(int('0x'+hexval[4:][:2],16))
header.append(int('0x'+hexval[6:][:2],16))
header.append(int('0x'+hexval[8:][:2],16))
should get it working. Hope this helps!
Related
I follow this tutorial on keras
https://keras.io/examples/nlp/semantic_similarity_with_bert/
I wanted to save the model with this command
model.save("saved_model/my_model")
I got this warnings when i saved the model
enter image description here
Then when i want to load the model to use it with this command
tf.keras.models.load_model('saved_model/my_model')
I got this error
enter image description here
Is this the good way to save the model ?
your first structure is inside a dict. You must extract the item from the dict to be able to get rid of your error. Try checking this out.
I want to reload some of my model variables with the saved weight in the chheckpoint and then export it to the tflite file.
The question is a bit tricky without see code, so I made this Colab jupyter notebook with the complete code to explain it better (All code is working, you can actually copy in a new collab and change if you want):
https://colab.research.google.com/drive/1wSor4CxEz36LgElVi4y_N8uiSt4-j9b2#scrollTo=XKBQzoW_wd4A
I got it working but with two issues:
The exported .tflite file is like 3Ks, so I do not believe it is the entire model with the weights in it. Only the input is an image of 128x128x3, one weight for each is more than 3K.
When I finally import the model in Android, I have this error: "Didn't find custom op for name 'VariableV2' /n Didn't find custom op for name 'ReorderAxes' /n Registration failed."
Maybe the last error is cause the save/restore operations? They look like are there when I save the graph definition.
Thanks in advance.
I realize my problem.. I'm trying to convert to TFLITE a model without previously freezing it, TFLITE do not allow "VariableV2" nodes cause they should not be there..
All the problem is corrected freezing the model like this:
output_graph_def = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), ["output"])
I lost some time looking for that, hope it helps.
I've read some questions on stackoverflow about attention-ocr, and most of them are about the implementation detail of a specific step. What I wanted to know is the pipeline for us to fine-tune this model on our own dataset.
As far as I know, the steps should be:
0) Should we first download FSNS dataset?? I tried to bypass this step and try running inference on just one image, but it always give me error:"ImportError: No module named 'fsns". So I wonder if this error will go away once I set my own dataset up.
1) Store our data in the same format as FSNS. (Links on this topic: How to create dataset in the same format as the FSNS dataset？, how to create cutomized dataset for google tensorflow attention ocr? )
2) Download the pre-trained checkpoint(http://download.tensorflow.org/models/attention_ocr_2017_08_09.tar.gz)
3) Somehow modify the 'model.py' to fit your own purpose.
4) Somehow modify the 'train.py' to train your own module using tensorflow serving.
I am still on the early stage (creating own dataset) on this project now, and confused on how to do it and what's the next stage.
The error was caused by incorrect version of Python. They should be run with Python 2, and you can just change the 'import' sentence to solve this error. Try to change the 'import fsns' to 'from datasets import fsns'.
I am trying to test my model using tensorflow-lite on x86_64 PC.
I coded a c++ test code and succeed interpreting given mobilenet model and executing inference.
I wanted to change some operation in the model to my custom operation.
Before doing that, I checked whether I can convert .tflite to json correctly.
What I did is changing the mobilenet.lite to mobilenet.json using flatc and tensorflow lite's schema (schema.fbs) and re-changing the mobilenet.json to mobilenet_new.lite.
However, when I tested mobilenet_new.lite, error occurs like below : tensorflow/contrib/lite/kernels/kernel_util.cc:35 std::abs(input_product_scale - bias_scale) <= 1e-6 * std::min(input_product_scale, bias_scale) was not true.
When I converted the mobilenet_new.lite to mobilenet_new.json, two JSON files were the same without any difference. Why does this error happen? If the parameter values are the same, how this can be possible?
If you have knowledge about this, please give me help.
Thanks
I has resolved this.
when I debug this problem, it was flatbuffer problem.
flatbuffer change float to string when make json file.
So, it becomes fixed point value with precision 6. This makes float value rounding.
So, when I converted tflite -> json -> tflite, there was some change between two tflite files.
I am reading tutorials about TensorFlow visualization and found out Tensorboard. I would like to know how can I visualize for example, Iris dataset taken from UCI Machine Learning repository. I have been able to run a specified port on localhost which shows TensorBoard, but do not know how to visualize a locally taken dataset there. I searched on google but really could not find how to do. Could you help me, please ?
If i understand you correctly then you wish to use tf.summary.image. The documentation is here: https://www.tensorflow.org/api_docs/python/tf/summary/image
Some example usage from my code is:
x_pl=tf.placeholder(tf.float32, [None,height,width,channels], name="ImageIn")
tf.summary.image('input', x_pl, 10)
x_pl is where I feed my image data in.
In my cummary declaration I say that I want to create a summary called 'input' and to take 10 images from x_pl.
Read the summary-writer example/tutorial here: https://www.tensorflow.org/get_started/summaries_and_tensorboard
You will need to merge your summaries:
merged = tf.summary.merge_all()
You will need to declare a summary-writer a bit like this:
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',sess.graph)
See the above tutorial/example to understand how Tensorboard works. Not that you will want to replace the summaries with image summaries for your purposes.