I am new to tensorflow lite, and I have noticed that many examples are using static tflite::MicroMutableOpResolver < > micro_op_resolver;
So, my question is: how many layers can we add here when deploying the model?
Or can the layers be exactly the same as described in the machine learning model?
What if there are any layers that repeat?
As mentioned here: https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/micro_mutable_op_resolver.h
The Add* functions, as mentioned in the above link, add the various built-in operators to the MicroMutableOpResolver object.
How can I add Conv1D and Maxpool1D to the main_functions.cc code? This is still not clear to me. What if my model contains a layer structure like this?
Conv1D
Conv1D
Dropout
Dense
Dropout
Flatten
Dense
Could you please explain that to me?
Also, would you mind sharing the detailed link or document for MicroMutableOpResolver?
Regards,
Divya
Related
I am new to attention mechanisms and I want to learn more about it by doing some practical examples. I came across a Keras implementation for multi-head attention found it in this website Pypi keras multi-head. I found two different ways to implement it in Keras.
One way is to use a multi-head attention as a keras wrapper layer with either LSTM or CNN.
This is a snippet of implementating multi-head as a wrapper layer with LSTM in Keras. This example is taken from this website keras multi-head"
import keras
from keras_multi_head import MultiHead
model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=100, output_dim=20, name='Embedding'))
model.add(MultiHead(keras.layers.LSTM(units=64), layer_num=3, name='Multi-LSTMs'))
model.add(keras.layers.Flatten(name='Flatten'))
model.add(keras.layers.Dense(units=4, activation='softmax', name='Dense'))
model.build()
model.summary()
The other way is to use it separately as a stand-alone layer.
This is a snippet of the second implementation for multi-head as stand-alone laye, also taken from keras multi-head"
import keras
from keras_multi_head import MultiHeadAttention
input_layer = keras.layers.Input( shape=(2, 3), name='Input',)
att_layer = MultiHeadAttention( head_num=3, name='Multi-Head',)(input_layer)
model = keras.models.Model(inputs=input_layer, outputs=att_layer)
model.compile( optimizer='adam', loss='mse', metrics={},)
I have been trying to find some documents that explain this but I have not found yet.
Update:
What I have found was that the second implementation (MultiHeadAttention) is more like the Transformer paper "Attention All You Need". However, I am still struggling to understand the first implementation which is the wrapper layer.
Does the first one (as a wrapper layer) would combine the output of multi-head with LSTM?.
I was wondering if someone could explain the idea behind them, especially, the wrapper layer.
I understand your confusion. From my experience, what the Multihead (this wrapper) does is that it duplicates (or parallelize) layers to form a kind of multichannel architecture, and each channel can be used to extract different features from the input.
For instance, each channel can have a different configuration, which is later concatenated to make an inference. So, the MultiHead can be used to wrap conventional architectures to form multihead-CNN, multihead-LSTM etc.
Note that the attention layer is different. You may stack attention layers to form a new architecture. You may also parallelize the attention layer (MultiHeadAttention) and configure each layer as explained above. See here for different implementation of the attention layer.
I was wondering if anyone can help by providing me with some guidelines for creating a bald-or-not image classifier.
So far I have a model for face and eye detection and to sum it up, this is my main questions:
Where can I find datasets for this kind of classification without going to google and download thousands of images by hand?
What classification model (i.e. the structure of layers in the network) should be used for this?
Question 1:
You could start by looking at some of the datasets available in Kaggle or Tensor Flow Datasets to see if there is anything available.
If none, you could try using an Image scraper tool to download images quickly compared to by hand.
Question 2:
Typically Image Classification model uses Convolutional Layers and MaxPooling layers. On top of the commonly used Dense Layer for Multi-layer Perceptron.
To get started you can study the Tensor Flow tutorial for Image Classification in this link,
which classifies whether the Image is Cat or Dog.
This example can provide you with the general idea of how to build an Image Classifier.
Hope this helps you. Thanks
I am trying to add an attention layer for my text classification model. The inputs are texts (e.g. movie review), the output is a binary outcome (e.g. positive vs negative).
model = Sequential()
model.add(Embedding(max_features, 32, input_length=maxlen))
model.add(Bidirectional(CuDNNGRU(16,return_sequences=True)))
##### add attention layer here #####
model.add(Dense(1, activation='sigmoid'))
After some searching, I found a couple of read-to-use attention layers for keras. There is the keras.layers.Attention layer that is built-in in Keras. There is also the SeqWeightedAttention and SeqSelfAttention layer in the keras-self-attention package. As a person who is relatively new to the deep learning field, I have a hard time understanding the mechanism behind these layers.
What does each of these lays do? Which one will be the best for my model?
Thank you very much!
If you are using RNN, I would not recommend using the keras.layers.Attention class.
While analysing tf.keras.layers.Attention Github code to better understand how to use the same, the first line I could come across was - "This class is suitable for Dense or CNN networks, and not for RNN networks"
There is another open source version maintained by CyberZHG called
keras-self-attention. To the best of my knowledge this is NOT a part of the Keras or TensorFlow library and seems to be an independent piece of code. This contains the two classes you mentioned - SeqWeightedAttention & SeqSelfAttention layer classes. former returns a 2D value and latter a 3D value. So the SeqWeightedAttention should work for your situation. The former seems to be loosely based on Raffel et al and can be used for Seq classification, The latter seems to be a variation of Bahdanau.
In general, I would suggest you to write your own seq to classification model. The attention piece can be added in less than half a dozen lines of code (bare-bones essence)...much less than the time you would spend in integrating or debugging or understanding the code in these external libraries.
Please refer: Create an LSTM layer with Attention in Keras for multi-label text classification neural network
So tensorflow is extremely useful at creating neural networks that involve perceptron neurons. However, if one wanted to use a new type of neuron instead of the classic perceptron neuron, is this possible through augmenting tensorflow code? I can't seem to find an answer. I understand this would change the forward propagation, and more mathematical calculations, and I am willing to change all the necessary areas.
I am also aware that I can just code from scratch the layers I need, and the neurons I had in mind, but tensorflow nevertheless has GPU integration, so one can see its more ideal to manipulate their code as opposed to creating my own from scratch.
Has anyone experimented with this? My goal is to create neural network structures that use a different type of neuron than the classic perceptron.
If someone who knows where in tensorflow I could look to see where they initialize the perceptron neurons, I would very much appreciate it!
Edit:
To be more specific, is it possible to alter code in tensorflow to use a different neuron type rather than the perceptron to invoke the tensorlfow Module: tf.layers for example? Or tf.nn? (conv2D, batch-norm, max-pool, etc). I can figure out the details. I just need to know where (I'm sure they're a few locations) I would go about changing code for this.
However, if one wanted to use a new type of neuron instead of the classic perceptron neuron, is this possible through augmenting tensorflow code?
Yes. Tensorflow provides you the possibility to define a computational graph. It then can automatically calculate the gradient for that. No need to do it yourself. This is the reason why you define it symbolically. You might want to read the whitepaper or start with a tutorial.
Currently I am trying to use pre-trained LSTM RNN model for feature extraction.
I stumbled across following reference for feature extraction using deep neural nets. That's for images however.
https://www.kernix.com/blog/image-classification-with-a-pre-trained-deep-neural-network_p11
In a similar fashion I would like to use LSTM RNN https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition/? for feature extraction. The code is implemented using Tensorflow BasicLSTMCell.
Is there any way to get the layer like "pool_3:0" as described in the first reference link ?
Any links or references would be helpful.