Goal
I want to compare different types of RNN tflite-micro models, built using tensorflow, on a microcontroller based on their accuracy, model size and inference time. I have also created my own custom RNN cell that I want to compare with the LSTM cell, GRU cell, and SimpleRNN cell. I create the tensorflow model using tf.keras.layers.RNN(Cell(...)).
Problem
I have successfully deployed a keras LSTM-RNN using tf.keras.layers.LSTM(...) but when I create the same model using tf.keras.layers.RNN(tf.keras.layers.LSTMCell(...)) and deploy it to the microcontroller, then it does not work. I trained both networks on a batch size of 64, but then I copy the weights and biases to a model where the batch_size is fixed to 1 as tflite-micro does not support dynamic batch sizes.
When the keras LSTM layer is converted to a tflite model it creates a fused operator called UnidirectionalSequenceLSTM but the network created with an RNN layer using the LSTMCell does not have that UnidirectionalSequenceLSTM operator, instead it has a reshape and while operator. The first network has only 1 subgraph but the second has 3 subgraphs.
When I run that second model on the microcontroller, two things go wrong:
the interpreter returns the same result for different inputs
the interpreter fails on some inputs reporting an error with the while loop saying that int32 is not supported (which is in the while operator, and can't be quantized to int8)
LSTM tflite-model vizualized with Netron
RNN(LSTMCell) tflite-model vizualized with Netron
Bad solution (10x model size)
I figured out that by unrolling the second network I can successfully deploy it and get correct results on the microcontroller. However, that increases the model size 10x which is really bad as we are trying to deploy the model on a resource constrained device.
Better solution?
I have explained the problem using the example of the LSTM layer (works) and LSTM cell in an RNN layer (does not work), but I want to be able to deploy a model using the GRU cell, SimpleRNN cell, and of course the custom cell that I have created. And all those have the same problem as the network created with the LSTM cell.
What can I do?
Do I have to create a special fused operator? Maybe even one for each cell I want to compare? How would I do that?
Can I use the interface into the conversion infrastructure for user-defined RNN implementations mentioned here: https://www.tensorflow.org/lite/models/convert/rnn. How I understand the documentation, is that this would only work for user-defined LSTM implementations, not user-defined RNN implemenations like the title suggests.
Related
Corresponding Tensorflow documentation I trained 3 objects and get result (It can recognize these objects). When I show other objects (not the 3 ones) it doesn't work correctly.
I want to train only one object (example: a cup) and recognize only this object. Is it possible to do via Tensorflow ?
Your question doesn't provide enough details, but as I can guess your trained the network with softmax activation and Categorical or SparseCategorical cross entropy loss. If my guess is right, such network always generates prediction to one of three classess, regardless to actual data, i.e. there is no option of "no-one".
In order to train network to recognize only one class of objects, make the only one output with only one channel and sigmoid activation. Use BinaryCrossEntropy loss to train your model for the specific object. Provide dataset that includes examples with this object and without it.
I have heard that it is possible to use the pretrained Universal Sentence Encoder (USE) (neural language model) from TF-hub as part of a trainable model, e.g. a sentence classifier. Some versions of USE rely on SentencePiece sub-word tokenizer, which I also need. There are minimal instructions online for how to do this.
Here is how to use USE-lite with SentencePiece:
- https://tfhub.dev/google/universal-sentence-encoder-lite/2
Here is how to train a classifier based on a pretrained USE model:
- http://hunterheidenreich.com/blog/google-universal-sentence-encoder-in-keras/
- https://www.youtube.com/watch?v=gnz1CUzb5qo
And here is how to measure sentence similarity using both USE-lite and SentencePiece:
- https://github.com/tensorflow/hub/blob/master/examples/colab/semantic_similarity_with_tf_hub_universal_encoder_lite.ipynb
I have successfully reproduced the above pieces separately. I have then tried to combine the above ideas into a single POC that will build a classifier model based on USE-lite and SentencePiece, but I cannot see how to do it. I am currently stuck on the part where I modify the trainable classifier's first layer(s). I have tried to make it accept either (1) SentencePiece token IDs (in which I tokenize the text outide of the Tensorflow graph) or (2) raw text (using SentencePiece as an Op inside the Tensorflow graph). After that point, it should feed tokenized text forward into the USE-lite model, either in a lambda or in some other way. Finally, the output of USE-lite should be fed into a dense layer (or two?) ending in softmax for computing class probabilities.
I am relatively new to Tensorflow. I imagine that the above sources would be sufficient for a more experienced Tensorflow developer to merge and make work for my use-case. Let me know if you can provide any pointers. Thanks.
As we know, a DNN is comprised of many layers which consist of many neurons applying the same function to different parts of the input. Meanwhile, if we use Tensorflow to execute a DNN task, we will get a dataflow graph generated by Tensorflow automatically and we can use Tensorboard to visualize the dataflow graph as blow. But there is no neuron in the layer. So I wonder what is the relationship between Tensorflow dataflow graph and a DNN? When a neuron of DNN's layer map into dataflow graph, how is it represented?What is the relationship of neuron in DNN and node in tensorflow(representing an operation)? I just started to learn DNN and Tensorflow, please help me arrange thoughts in order. Thanks:) enter image description here
You have to differentiate between the metaphoric representation of a DNN and it's mathematic description. The math behind a classic neuron is the sum of the weighted inputs + a bias (usually calling a activation function on this result)
So in this case you have an input vector mutplied by a weight vector (containing trainable variables) and then summed up with a bias scalar (also trainable)
If you now consider a layer of neurons instead of one, the weights will become a matrix and the bias a vector. So calculating a feed forward layer is nothing more then a matrix multiplication follow by a sum of vectors.
This is the operation you can see in your tensorflow graph.
You can actually build your Neural Network this way without any use of the so called High Level API which use the Layer abstraction. (Many have done this in the early days of tensorflow)
The actual "magic", which tensorflow does for you is calculating and executing the derivatives of this foreword pass in order to calculate the updates for the weights.
I'm using tensorflow to run a cnn for image classification.
I use tensorflow cifar10 cnn implementation.(tensorflow cifar10)
I want to decrease the number of connections, meaning I want to prune the low-weight connections.
How can I create a new graph(subgraph) without some of the nuerones?
Tensorflow does not allow you lock/freeze a particular kernel of a particular layer, that I have found. The only I've found to do this is to use the tf.assign() function as shown in
How to freeze/lock weights of one Tensorflow variable (e.g., one CNN kernel of one layer
It's fairly cave-man but I've seen no other solution that works. Essentially, you have to .assign() the values every so often as you iterate through the data. Since this approach is so inelegant and brute-force, it's very slow. I do the .assign() every 100 batches.
Someone please post a better solution and soon!
The cifar10 model you point to, and for that matter, most models written in TensorFlow, do not model the weights (and hence, connections) of individual neurons directly in the computation graph. For instance, for fully connected layers, all the connections between the two layers, say, with M neurons in the layer below, and 'N' neurons in the layer above, are modeled by one MxN weight matrix. If you wanted to completely remove a neuron and all of its outgoing connections from the layer below, you can simply slice out a (M-1)xN matrix by removing the relevant row, and multiply it with the corresponding M-1 activations of the neurons.
Another way is add an addition mask to control the connections.
The first step involves adding mask and threshold variables to the
layers that need to undergo pruning. The variable mask is the same
shape as the layer's weight tensor and determines which of the weights
participate in the forward execution of the graph.
There is a pruning implementation under tensorflow/contrib/model_pruning to prune the model. Hope this can help you to prune model quickly.
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/model_pruning
I think google has an updated answer here : https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/model_pruning
Removing pruning nodes from the trained graph:
$ bazel build -c opt contrib/model_pruning:strip_pruning_vars
$ bazel-bin/contrib/model_pruning/strip_pruning_vars --checkpoint_path=/tmp/cifar10_train --output_node_names=softmax_linear/softmax_linear_2 --filename=cifar_pruned.pb
I suppose that cifar_pruned.pb will be smaller, since the pruned "or zero masked" variables are removed.
I'd like to train a network that contains a sub network that I need to stay fix during the training. The basic idea is to prepend and append some layers the the pre-trained network (inceptionV3)
new_layers -> pre-trained and fixed sub-net (inceptionv3) -> new_layers
and run the training process for the task I have without changing the pre-trained one.
I also need to branch directly on some layer of the pre-trained network. For example, with the inceptionV3 I like to uses it from the conv 299x299 to the last pool layer or from the conv 79x79 to the last pool layer.
Whether or not a "layer" is trained is determined by whether the variables used in that layer get updated with gradients. If you are using the Optimizer interface to optimize your network, then you can simply not pass the variables used in the layers that you want to keep fixed to the minimize function, i.e.,
opt.minimize(loss, <subset of variables you want to train>)
If you are using tf.gradients function directly, then remove the variables that you want to keep fixed from the second argument to tf.gradients.
Now, how you "branch directly" to a layer of a pre-trained network depends on how that network is implemented. I would simply locate the tf.Conv2D call to the 299x299 layer you are talking about, and pass as its input, the output of your new layer, and on the output side, locate the 79x79 layer, use its output as the input to your new layer.