I want to learn how to write caffe python layers.
But I only find examples about very simple layers like pyloss.
How to write python caffe with trainable parameters?
For example, how to write a fully connected python layer?
Caffe stores the layer's trainable parameters as a vector of blobs. By default this vector is empty and it is up to you to add parameters blobs to it in the setup of the layer. There is a simple example for a layer with parameters in test_python_layer.py.
See this post for more information about "Python" layers in caffe.
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I read the Xception paper and in this paper it was mentioned in part 4.7 that best results are achivable without any activation. Now I want to use this network on videos using keras toolbox but the model in keras uses 'ReLU' activation function. Does the model in keras returns best model or it is better to omit the relu layers?
You are confusing normal activations used for convolutional and dense layers, with the ones mentioned in the paper. Section 4.7 only deals with varying the activation between depth-wise and point-wise convolutions, the rest of the activations in the architecture are kept unchanged.
I am using a pre-trained model which I want to add Elementwise layer that products the output of two layers: one layer is output of convolution layer 1x1x256x256 and the other is also the output of convolution layer 1x32x256x256. My question is: If we add elementwise layer for multiplying two layers and sending to the next layer, should we train from the scratch because the architecture is modified or still it is possible to use the pretrained model?
Thanks
Indeed making architectural changes puts the learned features at odds.
However, there's no reason not to use the learned weight for layers below the change -- these layers are not affected by the change, so they can benefit from the initialization.
As for the rest of the layers, I suppose init from trained weights should not be worse than random, So why not?
Don't forget to init any new layers with random weights (the default in caffe is zero - and this might cause trouble for learning).
I am a very newbie to caffe.
I have a huge weight vector which contains the weights connecting the neurons of the neural network written in C++. I want to know use this weight vector as to define a neural network in Caffe and these weights will be the initial weights of the connecting neurons. How do I feed these weights into the Caffe blobs which is the fundamental way to hold parameter values like weights and biases in caffe.
After every iteration when the weights get updated, I also want to get their values from the blobs and put them back into this huge weight vector which I will access from the remaining part of code in C++.
Please tell me how to code this in caffe. It is actually a process of serialization and deserialization of the weights vector to and from blobs.
Any help will be greatly appreciated
I'm trying to modify the weights of a caffemodel which is part of a caffe-branch called Deep Lab. Although there is a tutorial on how to do net surgery, when I try to do the same with my custom caffemodel the python kernel dies always on the following line:
# Load the original network and extract the fully connected layers' parameters.
net = caffe.Net('../models/deeplab/train.prototxt',
'../models/deeplab/train.caffemodel',
caffe.TRAIN)
I think its because pycaffe doesn't know their custom layers such as ImageSegData, Silence and SegAccuracy so I removed these layers from the prototxt file, but still the python kernel keeps on dying when I try to load the network model. Does anyone know how to load these weights into python?
I found it already. I had literally to remove every custom layer and especially adapt the data layer such that it could read all the input images and thereby calculate the input dimensions.
I wanna compare the performance of CNN and autoencoder in caffe. I'm completely familiar with cnn in caffe but I wanna is the autoencoder also has deploy.prototxt file ? is there any differences in using this two models rather than the architecture?
Yes it also has a deploy.prototxt.
both train_val.prototxt and 'deploy.prototxt' are cnn architecture description files. The sole difference between them is, train_val.prototxt takes training data and loss as input/output, but 'deploy.prototxt' takes testing image as input, and predicted value as out put.
Here is an example of a cnn and autoencoder for MINST: Caffe Examples. (I have not tried the examples.) Using the models is generally the same. Learning rates etc. depend on the model.
You need to implement an auto-encoder example using python or matlab. The example in Caffe is not true auto-encoder because it doesn't set layer-wise training stage and during training stage, it doesn't fix W{L->L+1} = W{L+1->L+2}^T. It is easily to find a 1D auto-encoder in github, but 2D auto-encoder may be hard to find.
The main difference between the Auto encoders and conventional network is
In Auto encoder your input is your label image for training.
Auto encoder tries to approximate the output similar as input.
Auto encoders does not have softmax layer while training.
It can be used as a pre-trained model for your network which converge faster comparing to other pre-trained models. It is because your network has already extracted the features for your data.
The Conventional training and testing you can perform on pre trained auto encoder network for faster convergence and accuracy.