I am trying to train a Deep Neural Network using MNIST data set. BATCH_SIZE = 100 train_data = train_data.batch(BATCH_SIZE) validation_data = validation_data.b
How to change the activation layer of a Pytorch pretrained network? Here is my code : print("All modules") for child in net.children(): if isinstance(chi
I want to use keras to build a neural network regression model from X_train -> Y_train. In this example, however, I need to perform a preprocessing transform
I want to use matrix multiplication inside TF model. My model is a NN with input shape = (1,9). And I want to get a product of this vectors by themself (i.e. I
I have a model, for which i need to compute the gradients of output w.r.t the model's input. But I want to apply some custom gradients for some of the nonlinear
I have written a basic program to understand what's happening in MLP classifier? from sklearn.neural_network import MLPClassifier data: a dataset of body met
is it possible to create my own object detection script with YOLO or create a Neuron Network to implement it in the NAO robot( iknow that there is a box of det
is it possible to create my own object detection script with YOLO or create a Neuron Network to implement it in the NAO robot( iknow that there is a box of det
I am trying to create a copy of a nn.Sequential network. For example, the following is the easiest way to do the same- net = nn.Sequential( nn.Conv2d(16
Suppose, the following is a dataset for solving a regression problem: H -9.118 5.488 5.166 4.852 5.164 4.943 8.103 -9.152 7.470 6.452 6.069 6
I just read about the Keras weight initializers in here. In the documentation, only different initializers has been introduced. Such as: mode
Below is my code: model = Sequential([ Dense(32, input_shape=(32,), activation = 'relu'), Dense(100, activation='relu'), Dense(65, input_shape=(65
I am trying to implement a VAE for MNIST using convolutional layers using TensorFlow-2.6 and Python-3.9. The code I have is: # Specify latent space dimensions-
I am using the Physics Informed Neural Networks (PINNs) methodology to solve non-linear PDEs in high dimension. Specifically, I am using this class https://git
I'm trying to use the neuralnet package to train a model on this data set. However, I'm getting the following error which I can't understand: Error: the err
What does it mean to "unroll a RNN dynamically". I've seen this specifically mentioned in the Tensorflow source code, but I'm looking for a conceptual explanati
If you have both a classification and regression problem that are related and rely on the same input data, is it possible to successfully architect a neural net
My question is about coding a neural network which does regression (and NOT classification) using tflearn. Dataset: fixed acidity volatile acidity citric acid
I am implementing a CNN for an highly unbalanced classification problem and I would like to implement custum metrics in tensorflow to use the Select Best Model
I am trying to solve the 3-bit parity problem using the functional link neural network (Pao,1988). I am performing backpropagation to update the weights and ext