I need to get several outputs from several layers from keras model instead of getting the output from the last layer.
I know how to adjust the code according to what I need, but don't know how I can use it within keras application. I meant how can I import it then. Do I need to infall the setup.py at keras-application again. I did so but nothing happen. My changes doesn't applied. Is there any different way to get the outputs from different layer within the model?
Actually the solution was so simple, you need just to call the output of specified layer.
new_model= tf.keras.Model(base_model.input, base_model.get_layer(layer_name).output)
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I want to hand write a framework to perform inference of a given neural network. The network is so complicated, so to make sure my implementation is correct, I need to know how exactly the inference process is done on device.
I tried to use torchviz to visualize the network, but what I got seems to be the back propagation compute graph, which is really hard to understand.
Then I tried to convert the pytorch model to ONNX format, following the instruction enter link description here, but when I tried to visualize it, it seems that the original layers of the model had been seperated into very small operators.
I just want to get the result like this
How can I get this? Thanks!
Have you tried saving the model with torch.save (https://pytorch.org/tutorials/beginner/saving_loading_models.html) and opening it with Netron? The last view you showed is a view of the Netron app.
You can try also the package torchview, which provides several features (useful especially for large models). For instance you can set the display depth (depth in nested hierarchy of moduls).
It is also based on forward prop
github repo
Disclaimer: I am the author of the package
Note: The accepted format for tool is pytorch model
I have a model, exported from pytorch, I'll call main_model.onnx. It has an input node I'll call main_input that expects a list of integers. I can load this in onnxruntime and send a list of ints and it works great.
I made another ONNX model I'll call pre_model.onnx with input pre_input and output pre_output. This preprocesses some text so input is the text, and pre_output is a list of ints, exactly as main_model.onnx needs for input.
My goal here is, using the Python onnx.helper tools, create one uber-model that accepts text as input, and runs through my pre-model.onnx, possibly some connector node (Identity maybe?), and then through main_model.onnx all in one big combined.onnx model.
I have tried using pre_model.graph.node+Identity connector+main_model.graph.node as nodes in a new graph, but the parameters exported from pytorch are lost this way. Is there a way to keep all those parameters and everything around, and export this one even larger combined ONNX model?
This is possible to achieve albeit a bit tricky. You can explore the Python APIs offered by ONNX (https://github.com/onnx/onnx/blob/master/docs/PythonAPIOverview.md). This will allow you to load models to memory and you'll have to "compose" your combined model using the APIs exposed (combine both the GraphProto messages into one - this is easier said than done - you' ll have to ensure that you don't violate the onnx spec while doing this) and finally store the new Graphproto in a new ModelProto and you have your combined model. I would also run it through the onnx checker on completion to ensure the model is valid post its creation.
If you have static size inputs, sclblonnx package is an easy solution for merging Onnx models. However, it does not support dynamic size inputs.
For dynamic size inputs, one solution would be writing your own code using ONNX API as stated earlier.
Another solution would be converting the two ONNX models to a framework(Tensorflow or PyTorch) using tools like onnx-tensorflow or onnx2pytorch. Then pass the outputs of one network as inputs of the other network and export the whole network to Onnx format.
I am using the keras and fit_generator to train my model. The current model I am doing is an auto-encoder - which does not render desired results. I would therefore like to create a callback that illustrates the training image and groundtruth image in every 500 batch or so. I therefore want to use on_batch_begin I am however unsure of how I can access the current batch to create a tf.Summary.Image.
Can anybody direct me to some information about this or knows how to get the current batch. Or would it be done in the generator? I just do not see how to attach a callback to that.
I have not been able to find an elegant solution. I have added an array to the callback with the files I want to analyse. Then I randomly choose one image just for illustration, and use the current model and illustrate it on tensorboard. It works but I had hoped it would have been more elegant :)
I have some models saved that have dropout layers. Unfortunately, the dropout_keep_dim value was not given as placeholders. Now when I restore the model for test purpose, it gives random output for each run. So, my question is, is it possible to change the dropout_keep_dim of a saved variable? The dropout layer is added the following way:
tf.nn.dropout(layer_no, dropout_keep_dim)
I have already wasted hours on google and didn't find any working solution. Is there even a solution or are my saved models of no use now? Tf.assign doesn't work as, in my case, dropout_keep_dim is not a variable. Any kind of help is appreciated.
NB. I can restore the dropout_keep_dim value and print it. I want to change it if that's possible and then test with the saved weights.
I'm trying to implement 'multi-threading' to do both training and prediction(testing) at the same time. And I'm gonna use the python module 'threading' as shown in https://www.tensorflow.org/api_docs/python/tf/FIFOQueue
And the followings are questions.
If I use the python module 'threading', does tensorflow use more portion of gpu or more portion of cpu?
Do I have to make two graphs(neural nets which have the same topology) in tensorflow one for prediction and the other for training? Or is it okay to make just one graph?
I'll be very grateful to anyone who can answer these questions! thanks!
If you use python threading module, it will only make use of cpu; also python threading not for run time parallelism, you should use multiprocessing.
In your model if you are using dropout or batch_norm like ops which change based on training and validation, it's a good idea to create separate graphs, reusing (validation graph will reuse all training variables) the common variable for validation/testing.
Note: you can use one graph also, with additional operations which changes behaviors based on training/validation.