What is going wrong with this code? I have generated adversarial images using cleverhans API - generate_np method. And using the default cleverhans CNN classifier to classify the images. The test accuracy is very low as expected when I use the model after generating the images. But if I save and reload the model, the accuracy is too high. Please check the code here.
https://github.com/csesivakumar/Adversarial_Defense/blob/master/Cleverhans_generatenp.ipynb
Python: 3.6
Pasting my answer from the GitHub issue tracker in case others are facing the same issue:
From your code it looks like you are initializing the model's weights, defining the tf session, etc... after having trained the model using Keras. My guess is that the adv_x array does not contain images that are adversarial. This would explain why the accuracy output by [22] is close to random---because the model weights are random. When you restore the model, its weights are set again to the values learned during training so the accuracy is restored (because the images are not adversarial).
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I have a pretrained CNN (Resnet-18) trained on Imagenet, now i want to extend it on my own dataset of video frames , now the point is all tutorials i found on Finetuning required dataset to be organised in classes like
class1/train/
class1/test/
class2/train/
class2/test/
but i have only frames on many videos , how will i train my CNN on it.
So can anyone point me in right direction , any tutorial or paper etc ?
PS: My final task is to get deep features of all frames that i provide at the time of testing
for training network, you should have some 'label'(sometimes called y) of your input data. from there, network calculate loss between logit(answer of network) and the given label.
And the network will self-revise using that loss value by backpropagating. that process is what we call 'training'.
Because you only have input data, not label, so you can get the logit only. that means a loss cannot be calculated.
Fine tuning is almost same word with 'additional training', so that you cannot fine tuning your pre-trained network without labeled data.
About train set & test set, that is not the problem right now.
If you have enough labeled input data, you can divide it with some ratio.
(e.g. 80% of data for training, 20% of data for testing)
the reason why divide data into these two sets, we want to check the performance of our trained network more general, unseen situation.
However, if you just input your data into pre-trained network(encoder part), it will give a deep feature. It doesn't exactly fit to your task, still it is deep feature.
Added)
Unsupervised pre-training for convolutional neural network in theano
here is the method you need, deep feature encoder in unsupervised situation. I hope it will help.
Newbie to Caffe.
I am trying to use the trained Convolutional neural network on MNIST dataset using Caffe deep learning framework.
Following the official tutorial.
Steps taken successfully:
./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh
./examples/mnist/train_lenet.sh
Model was trained and stopped with the following message:
I1203 solver.cpp:133] Snapshotting solver state to lenet_iter_10000.solverstate
I1203 solver.cpp:78] Optimization Done.
Now, I am not sure as how to get a testing image and use the existing trained model which I believe has been snapshot by the name lenet_iter_10000.solverstate to see the predicted scores for each class.
Use the test function of caffe:
<path to caffe root>/caffe test -model <val filename>.prototxt -weights lenet_iter_10000.caffemodel
As you want to test only one image, give that image as input to your test data layer. Use the mean_image as input as well in your <val filename>.protoxt. Test batch size is 1 in this case.
Also note that lenet_iter_10000.solverstate is not your trained model. Your trained model is actually lenet_iter_10000.caffemodel. To know about the diffrence between solverstate and caffemodel files see here.
I trained GoogLeNet model from scratch. But it didn't give me the promising results.
As an alternative, I would like to do fine tuning of GoogLeNet model on my dataset. Does anyone know what are the steps should I follow?
Assuming you are trying to do image classification. These should be the steps for finetuning a model:
1. Classification layer
The original classification layer "loss3/classifier" outputs predictions for 1000 classes (it's mum_output is set to 1000). You'll need to replace it with a new layer with appropriate num_output. Replacing the classification layer:
Change layer's name (so that when you read the original weights from caffemodel file there will be no conflict with the weights of this layer).
Change num_output to the right number of output classes you are trying to predict.
Note that you need to change ALL classification layers. Usually there is only one, but GoogLeNet happens to have three: "loss1/classifier", "loss2/classifier" and "loss3/classifier".
2. Data
You need to make a new training dataset with the new labels you want to fine tune to. See, for example, this post on how to make an lmdb dataset.
3. How extensive a finetuning you want?
When finetuning a model, you can train ALL model's weights or choose to fix some weights (usually filters of the lower/deeper layers) and train only the weights of the top-most layers. This choice is up to you and it ususally depends on the amount of training data available (the more examples you have the more weights you can afford to finetune).
Each layer (that holds trainable parameters) has param { lr_mult: XX }. This coefficient determines how susceptible these weights to SGD updates. Setting param { lr_mult: 0 } means you FIX the weights of this layer and they will not be changed during the training process.
Edit your train_val.prototxt accordingly.
4. Run caffe
Run caffe train but supply it with caffemodel weights as an initial weights:
~$ $CAFFE_ROOT/build/tools/caffe train -solver /path/to/solver.ptototxt -weights /path/to/orig_googlenet_weights.caffemodel
Fine-tuning is a very useful trick to achieve a promising accuracy compared to past manual feature. #Shai already posted a good tutorial for fine-tuning the Googlenet using Caffe, so I just want to give some recommends and tricks for fine-tuning for general cases.
In most of time, we face a task classification problem that new dataset (e.g. Oxford 102 flower dataset or Cat&Dog) has following four common situations CS231n:
New dataset is small and similar to original dataset.
New dataset is small but is different to original dataset (Most common cases)
New dataset is large and similar to original dataset.
New dataset is large but is different to original dataset.
In practice, most of time we do not have enough data to train the network from scratch, but may be enough for pre-trained model. Whatever which cases I mentions above only thing we must care about is that do we have enough data to train the CNN?
If yes, we can train the CNN from scratch. However, in practice it is still beneficial to initialize the weight from pre-trained model.
If no, we need to check whether data is very different from original datasets? If it is very similar, we can just fine-tune the fully connected neural network or fine-tune with SVM. However, If it is very different from original dataset, we may need to fine-tune the convolutional neural network to improve the generalization.
I am using Theano with keras. I have a trained DNN and I have dumped the weight's in a file. I am performing some operations on these weights and again dumping the new converted weights into another file.
Now, I am loading my DNN model with these converted weights and want to compare the results between the two.
I used the keras.evaluate method but I find the accuracy to be exactly same even though the weights are different.
Is there another approach with which I can compare the accuracy?
Thanks.
Keras performs some under the hood operations for your batch_size including normalization. So if you only scaled and translated your image the result will stay the same.
Anyways you can do model.predict(sample, 1) and write your own evaluation metric to circumvent this issue.
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.