for linear regression type of problem, you can simply create the Output layer without any activation function as we are interested in numerical values without any transformation. more info : https://machinelearningmastery.com/regression-tutorial-keras-deep-learning-library-python/ for classification : You can use sigmoid, tanh, Softmax etc.
since ReLU doesn’t have a derivative. No, ReLU has derivative. I assumed you are using ReLU function f(x)=max(0,x). It means if x<=0 then f(x)=0, else f(x)=x. In the first case, when x<0 so the derivative of f(x) with respect to x gives result f'(x)=0. In the second case, it’s clear to compute f'(x)=1.
For binary outputs you can use 1 output unit, so then: self.outputs = nn.Linear(NETWORK_WIDTH, 1) Then you use sigmoid activation to map the values of your output unit to a range between 0 and 1 (of course you need to arrange your training data this way too): def forward(self, x): # other layers omitted x … Read more
This worked for me from keras import backend as K and after predicting my data i inserted this part of code then i had again loaded the model. K.clear_session() i faced this problem in production server, but in my pc it was running fine ……….. from keras import backend as K #Before prediction K.clear_session() #After … Read more
There are definitely a lot of decisions to be made in designing a neural net, and there is no one right answer. However, there are a few general questions that are often helpful to think about: What are you trying to generate as an output? Draughts seems like a challenging game to play with a … Read more
Good question… There is a impressive collection of books and online texts on the subject of neural networks (NN), but many of them are either very technical or focus on somewhat specific domains of application of the NN, artificial vision, pattern recognition and signal processing being the “natural” leaders in this genre. I’d like to … Read more
We used deep convolutional networks on spectrograms for a spoken language identification task. We had around 95% accuracy on a dataset provided in this TopCoder contest. The details are here. Plain convolutional networks do not capture the temporal characteristics, so for example in this work the output of the convolutional network was fed to a … Read more
You can use torchsummary, for instance, for ImageNet dimension(3x224x224): from torchvision import models from torchsummary import summary vgg = models.vgg16() summary(vgg, (3, 224, 224) —————————————————————- Layer (type) Output Shape Param # ================================================================ Conv2d-1 [-1, 64, 224, 224] 1,792 ReLU-2 [-1, 64, 224, 224] 0 Conv2d-3 [-1, 64, 224, 224] 36,928 ReLU-4 [-1, 64, 224, 224] … Read more
The output for the LSTM is the output for all the hidden nodes on the final layer. hidden_size – the number of LSTM blocks per layer. input_size – the number of input features per time-step. num_layers – the number of hidden layers. In total there are hidden_size * num_layers LSTM blocks. The input dimensions are … Read more
For starters, a neural network can model any function (not just linear functions) Have a look at this – http://neuralnetworksanddeeplearning.com/chap4.html. A Neural Network has got non linear activation layers which is what gives the Neural Network a non linear element. The function for relating the input and the output is decided by the neural network … Read more