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
I'm developing a device for Freshwater Quality Management which can be used for freshwater bodies such as lakes and rivers. The project is spr
Let's say I have a matrix X with n, m == X.shape in PyTorch. What is the time complexity of calculating the pseudo-inverse with torch.pinverse? In other words,
Why does zero_grad() need to be called during training? | zero_grad(self) | Sets gradients of all model parameters to zero.
Why does zero_grad() need to be called during training? | zero_grad(self) | Sets gradients of all model parameters to zero.
My goal is to tune over possible network architectures that meet the following criteria: Layer 1 can have any number of hidden units from this list: [32, 64, 12
I was completing the first course of the deeplearning specialization, where the first programming assignment was to build a logistic regression model from scrat
I have being trying to fit the error during my Tensorflow course (Section 3: Neural network Regression with Tensorflow) with Udemy. import tensorflow as tf impo
The original question was in regard to TensorFlow implementations specifically. However, the answers are for implementations in general. This general answer is
We can pass the training = False argument while calling the pre-trained model when using Keras Functional API as shown in this tutorial. How to implement the sa