I am learning Pytorch and trying to understand how the library works for semantic segmentation.
What I've understood so far is that we can use a pre-trained model in pytorch. I've found an article which was using this model in the .eval() mode but I have not been able to find any tutorial on using such a model for training on our own dataset. I have a very small dataset and I need transfer learning to get results. My goal is to only train the FC layers with my own data. How is that achievable in Pytorch without complicating the code with OOP or so many .py files. I have been having a hard time figuring out such repos in github as I am not the most proficient person when it comes to OOP. I have been using Keras for Deep Learning until recently and there everything is easy and straightforward. Do I have the same options in Pycharm?
I appreciate any guidance on this. I need to run a piece of code that does the semantic segmentation and I am really confused about many of the steps I need to take.
Assume you start with a pretrained model called model. All of this occurs before you pass the model any data.
You want to find the layers you want to train by looking at all of them and then indexing them using model.children(). Running this command will show you all of the blocks and layers.
list(model.children())
Suppose you have now found the layers that you want to finetune (your FC layers as you describe). If the layers you want to train are the last 5 you can grab all of the layers except for the last 5 in order to set their requires_grad params to False so they don't train when you run the training algorithm.
list(model.children())[-5:]
Remove those layers:
layer_list = list(model.children())[-5:]
Rebuild model using sequential:
model_small = nn.Sequential(*list(model.children())[:-5])
Set requires_grad params to False:
for param in model_small.parameters():
param.requires_grad = False
Now you have a model called model_small that has all of the layers except the layers you want to train. Now you can reattach the layers that your removed and they will intrinsically have the requires_grad param set to True. Now when you train the model it will only update the weights on those layers.
model_small.avgpool_1 = nn.AdaptiveAvgPool2d()
model_small.lin1 = nn.Linear()
model_small.logits = nn.Linear()
model_small.softmax = nn.Softmax()
model = model_small.to(device)
How can I use the weights of a pre-trained network in my tensorflow project?
I know some theory information about this but no information about coding in tensorflow.
As been pointed out by #Matias Valdenegro in the comments, your first question does not make sense. For your second question however, there are multiple ways to do so. The term that you're searching for is Transfer Learning (TL). TL means transferring the "knowledge" (basically it's just the weights) from a pre-trained model into your model. Now there are several types of TL.
1) You transfer the entire weights from a pre-trained model into your model and use that as a starting point to train your network.
This is done in a situation where you now have extra data to train your model but you don't want to start over the training again. Therefore you just load the weights from your previous model and resume the training.
2) You transfer only some of the weights from a pre-trained model into your new model.
This is done in a situation where you have a model trained to classify between, say, 5 classes of objects. Now, you want to add/remove a class. You don't have to re-train the whole network from the start if the new class that you're adding has somewhat similar features with (an) existing class(es). Therefore, you build another model with the same exact architecture as your previous model except the fully-connected layers where now you have different output size. In this case, you'll want to load the weights of the convolutional layers from the previous model and freeze them while only re-train the fully-connected layers.
To perform these in Tensorflow,
1) The first type of TL can be performed by creating a model with the same exact architecture as the previous model and simply loading the model using tf.train.Saver().restore() module and continue the training.
2) The second type of TL can be performed by creating a model with the same exact architecture for the parts where you want to retain the weights and then specify the name of the weights in which you want to load from the previous pre-trained weights. You can use the parameter "trainable=False" to prevent Tensorflow from updating them.
I hope this helps.
for my current requirement, I'm having a dataset of 10k+ faces from 100 different people from which I have trained a model for recognizing the face(s). The model was trained by getting the 128 vectors from the facenet_keras.h5 model and feeding those vector value to the Dense layer for classifying the faces.
But the issue I'm facing currently is
if want to train one person face, I have to retrain the whole model once again.
How should I get on with this challenge? I have read about a concept called transfer learning but I have no clues about how to implement it. Please give your suggestion on this issue. What can be the possible solutions to it?
With transfer learning you would copy an existing pre-trained model and use it for a different, but similar, dataset from the original one. In your case this would be what you need to do if you want to train the model to recognize your specific 100 people.
If you already did this and you want to add another person to the database without having to retrain the complete model, then I would freeze all layers (set layer.trainable = False for all layers) except for the final fully-connected layer (or the final few layers). Then I would replace the last layer (which had 100 nodes) to a layer with 101 nodes. You could even copy the weights to the first 100 nodes and maybe freeze those too (I'm not sure if this is possible in Keras). In this case you would re-use all the trained convolutional layers etc. and teach the model to recognise this new face.
You can save your training results by saving your weights with:
model.save_weights('my_model_weights.h5')
And load them again later to resume your training after you added a new image to the dataset with:
model.load_weights('my_model_weights.h5')
I'm using faster_rcnn_resnet50 to train a model which will detect corrosions in images and I want to train a model from scratch instead of using transfer learning.
I don't know if this right but the reason I want to do this is that the already existing weights (which are trained on COCO) will affect my model trained on corrosion images.
One way I would like to do this is randomize or unfreeze the weights of the feature extractor on the resnet50 and then train the model on my images.
but there's no function or an option in the resnet50 config file to randomize or unfreeze weights.
I've made a new labelmap with a single label and tried it with transfer learning. It's working but I would like to have a model is trained just on my images and the previous weights shouldn't affect my predictions.
This is the first time I'm working with object detection and transfer learning. Will the weights of the pre-trained model on COCO affect my model which is trained on custom images of corrosion? How do you use tensorflow object-detection API without transfer learning?
I have 10 class dataset with this I got 85% accuracy, got the same accuracy on a saved model.
now I want to add a new class, how to add a new class To the saved model.
I tried by deleting the last layer and train but model get overfit and in prediction every Images show same result (newly added class).
This is what I did
model.pop()
base_model_layers = model.output
pred = Dense(11, activation='softmax')(base_model_layers)
model = Model(inputs=model.input, outputs=pred)
# compile and fit step
I have trained model with 10 class I want to load the model train with class 11 data and give predictions.
Using the model.pop() method and then the Keras Model() API will lead you to an error. The Model() API does not have the .pop() method, so if you want to re-train your model more than once you will have this error.
But the error only occurs if you, after the re-training, save the model and use the new saved model in the next re-training.
Another very wrong and used approach is to use the model.layers.pop(). This time the problem is that function only removes the last layer in the copy it returns. So, the model still has the layer, and just the method's return does not have the layer.
I recommend the following solution:
Admitting you have your already trained model saved in the model variable, something like:
model = load_my_trained_model_function()
# creating a new model
model_2 = Sequential()
# getting all the layers except the output one
for layer in model.layers[:-1]: # just exclude last layer from copying
model_2.add(layer)
# prevent the already trained layers from being trained again
# (you can use layers[:-n] to only freeze the model layers until the nth layer)
for layer in model_2.layers:
layer.trainable = False
# adding the new output layer, the name parameter is important
# otherwise, you will add a Dense_1 named layer, that normally already exists, leading to an error
model_2.add(Dense(num_neurons_you_want, name='new_Dense', activation='softmax'))
Now you should specify the compile and fit methods to train your model and it's done:
model_2.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# model.fit trains the model
model_history = model_2.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_split=0.1)
EDIT:
Note that by adding a new output layer we do not have the weights and biases adjusted in the last training.
Thereby we lost pretty much everything from the previous training.
We need to save the weights and biases of the output layer of the previous training, and then we must add them to the new output layer.
We also must think if we should let all the layers train or not, or even if we should allow the training of only some intercalated layers.
To get the weights and biases from the output layer using Keras we can use the following method:
# weights_training[0] = layer weights
# weights_training[1] = layer biases
weights_training = model.layers[-1].get_weights()
Now you should specify the weights for the new output layer. You can use, for example, the mean of the weights for the weights of the new classes. It's up to you.
To set the weights and biases of the new output layer using Keras we can use the following method:
model_2.layers[-1].set_weights(weights_re_training)
model.pop()
base_model_layers = model.output
pred = Dense(11, activation='softmax')(base_model_layers)
model = Model(inputs=model.input, outputs=pred)
Freeze the first layers, before train it
for layer in model.layers[:-2]:
layer.trainable = False
I am assuming that the problem is singlelabel-multiclass classification i.e. a sample will belong to only 1 of the 11 classes.
This answer will be completely based on implementing the way humans learn into machines. Hence, this will not provide you with a proper code of how to do that but it will tell you what to do and you will be able to easily implement it in keras.
How does a human child learn when you teach him new things? At first, we ask him to forget the old and learn the new. This does not actually mean that the old learning is useless but it means that for the time while he is learning the new, the old knowledge should not interfere as it will confuse the brain. So, the child will only learn the new for sometime.
But the problem here is, things are related. Suppose, the child learned C programming language and then learned compilers. There is a relation between compilers and programming language. The child cannot master computer science if he learns these subjects separately, right? At this point we introduce the term 'intelligence'.
The kid who understands that there is a relation between the things he learned before and the things he learned now is 'intelligent'. And the kid who finds the actual relation between the two things is 'smart'. (Going deep into this is off-topic)
What I am trying to say is:
Make the model learn the new class separately.
And then, make the model find a relation between the previously learned classes and the new class.
To do this, you need to train two different models:
The model which learns to classify on the new class: this model will be a binary classifier. It predicts a 1 if the sample belongs to class 11 and 0 if it doesn't. Now, you already have the training data for samples belonging to class 11 but you might not have data for the samples which doesn't belong to class 11. For this, you can randomly select samples which belong to classes 1 to 10. But note that the ratio of samples belonging to class 11 to that not belonging to class 11 must be 1:1 in order to train the model properly. That means, 50% of the samples must belong to class 11.
Now, you have two separate models: the one which predicts class 1-10 and one which predicts class 11. Now, concatenate the outputs of (the 2nd last layers) these two models with a newly created Dense layer with 11 nodes and let the whole model retrain itself adjusting the weights of pretrained two models and learning new weights of the dense layer. Keep the learning rate low.
The final model is the third model which is a combination of two models (without last Dense layer) + a new Dense layer.
Thank you..