How to take the intermediate Transfer learning output. ? Eg: Tried: Answer There’s an unresolved issue in Tensorflow on this problem. According to the issue, you need to pass inputs of both outer model and inner model to get the output of inner model.
Tag: conv-neural-network
Problem with data cast on the GPU in PyTorch
Im trying to do an image classifier, but im having a problem with the data cast on the GPU. Model already is in cuda, but i get error that says Whats the problem with input.to(args[‘device’])? Answer UPDATE: According to the OP, an aditional data.to(device) before the train loop caused this issue. you are probably getting a string like 0 or
RuntimeError: Given groups=1, weight of size [32, 16, 5, 5], expected input[16, 3, 448, 448] to have 16 channels, but got 3 channels instead
I am getting the following error and can’t figure out why. I printed the input size of my torch before it gets fed to the CNN: Here is my error message: I defined a CNN with 5 convolutional layers and two fully connected layers. I am feeding in batches of 16 and have resized the images to be (448×448). The
How to add a traditional classifier(SVM) to my CNN model
here’s my model i want to make svm classifier as my final classifier in this model so how can i do that? also another question i want to know the predicted class of a certain input so when i use it only gives me probabilities so how can i solve that too Answer You can use neural network as feature
Giving output of one neural network as an input to another in pytorch
I have a pretrained convolution neural network which produces and output of shape (X,164) where X is the number of test examples. So output layer has 164 nodes. I want to take this output and give this two another network which is simply a fully connected neural network whereby the first layer has 64 nodes and output layer has 1
What is meaning of separate ‘bias’ weights stored in Keras model?
Post-edit: Turns out I got confused while constantly playing with the three functions below. model.layer(i).get_weights() returns two separate arrays (without any tags) which are kernel and bias if bias exists in the model. model.get_weights() directly returns all the weights without any tags. model.weights returns weights and a bit of info such as name of the layer it belongs to and
How to infer the shape of the output when connecting convolution layer with dense layers?
I am trying to construct a Convolutional Neural Network using pytorch and can not understand how to interpret the input neurons for the first densely connected layer. Say, for example, I have the following architecture: Here X would be the number of neurons in the first linear layer. So, do I need to keep track of the shape of the
Using densenet with fastai
I am trying to train a densenet model using the fast.ai library. I checked the documentation and I managed to make it work for resnet50. However, for densenet, it seems to be unable to find the module. I tried to use arch=models.dn121 as stated by this forum. But I get the same error. Can anyone please help? Here is the
Adaptation module design for stacking two CNNs
I’m trying to stack two different CNNs using an adaptation module to bridge them, but I’m having a hard time determining the adaption module’s layer hyperparameters correctly. To be more precise, I would like to train the adaptation module to bridge two convolutional layers: Layer A with output shape: (29,29,256) Layer B with input shape: (8,8,384) So, after Layer A,
In a convolutional neural network, how do I use Maxout instead of ReLU as an activation function?
How do I use Maxout instead of’relu’ for activation? Answer You can use tensorflow_addons.layers.Maxout to add Maxout Activation function You can install tensorflow_addons by: