I wrote a custom Tree-RNN-CELL that can handle several different inputs when they are provided as a tuple. This is working fine, but now I wanted to put it together in a submodel, so that i can sum the 4 lines up in 2 lines and to have a better overview ( the tree gets big so its worth it)
Tag: keras
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
Extracting first-layer weights from a multi-layer Keras NN and transferring them to a single layer NN
I trained a 3-hidden layer NN (3-HL) using Keras (with good results, and I wanted to extract the weights from its first layer (inputs to its first-hidden layer) and use them in a single-hidden layer NN (inputs to its single hidden layer), to train. The 3-HL model summary along with its extracted (hopefully first layer) weight dimensions is as follows:
Incomparable weight shape between caffe and tensorflow / keras
I am trying to convert a caffe model to keras, I have successfully been able to use both MMdnn and even caffe-tensorflow. The output I have are .npy files and .pb files. I have not had much luck with the .pb files, so I stuck to .npy files which contain the weights and biases. I have reconstructed an mAlexNet network
fit_generator() returns NoneType instead of History object in Mask R CNN
I would like to save the loss data while training my Mask R CNN, but I seem to be missing something. The training is working but I’m getting the Error: AttributeError: ‘NoneType’ object has no attribute ‘history’ I’m not even sure if this is the right approach but it seemed easy enough. This part of the code calls this function
Predictions become irrational after adding weights to the fit [closed]
Closed. This question needs debugging details. It is not currently accepting answers. Edit the question to include desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem. This will help others answer the question. Closed last year. Improve this question I have a model with several dense layers that behaves normally in all aspects.
Tensorflow: Incompatible shapes: [1,2] vs. [1,4,4,2048]
I have the following tensorflow model: I have simplified this somewhat in an attempt to narrow down the problem, When I run this I get the following error: This error always seems to occur on a different input image. All my images are exactly the same dimennsions. I am using tensorflow 2.4.1 What am I missing? Answer The ResNet50 model
Incompatibility between input and final Dense Layer (Value Error)
I’m following this tutorial from Nabeel Ahmed to create your own emotion detector using Keras (I’m a noob) and I’ve found a strange behaviour that I’d like to understand. The input data is a bunch of 48×48 images, each one with an integer value between 0 and 6 (each number stands for an emotion label), which represents the emotion present
How to make a Confusion Matrix with Keras?
I have trained my model (multiclass classification) of CNN using keras and now I want to evaluate the model on my test set of images. Is there a way to create confusion matrix? Answer Thanks for answers. I did like this:
Tensorflow MirroredStrategy halves the 2nd dimension, though shape in the object remains right
I’ve recently tried to use MirroredStrategy for training. The relevant code is: dataset print is: which is in the correct dimension, but I get the following error: which is odd, as the documentation says that the strategy will halve the first dimension not the second, it should split the dataset for 2, along the first axis. Does anyone know what