Sigmoid function returning 2 Question: I just started learning about neural networks and I’m using the sigmoid function. Here’s the implementation : def sigmoid(x): return 1/1+(np.exp(-x)) then I have my network : def make_predictions2(previousPrice, currentPrice, weight1, weight2, weight3, bias): n11 = np.dot(previousPrice, weight1) + np.dot(currentPrice, weight2) + bias n21 = np.dot(n11, weight3) + bias n31 …
Sigmoid activation output layer produce Many near-1 value Question: 🙂 I have a Datset of ~16,000 .wav recording from 70 bird species. I’m training a model using tensorflow to classify the mel-spectrogram of these recordings using Convolution based architectures. One of the architectures used is simple multi-layer convolutional described below. The pre-processing phase include: extract …
Scaling the sigmoid output Question: I am training a Network on images for binary classification. The input images are normalized to have pixel values in the range[0,1]. Also, the weight matrices are initialized from a normal distribution. However, the output from my last Dense layer with sigmoid activation yields values with a very minute difference …
optimal way of defining a numerically stable sigmoid function for a list in python Question: For a scalar variable x, we know how to write down a numerically stable sigmoid function in python: def sigmoid(x): if x >= 0: return 1. / ( 1. + np.exp(-x) ) else: return exp(x) / ( 1. + np.exp(x) …
How to calculate a logistic sigmoid function in Python? Question: This is a logistic sigmoid function: I know x. How can I calculate F(x) in Python now? Let’s say x = 0.458. F(x) = ? Asked By: Richard Knop || Source Answers: This should do it: import math def sigmoid(x): return 1 / (1 + …
