Linked Questions

5
votes
4answers
4k views

Deep Neural Network weight initialization [duplicate]

Given difficult learning task (e.g high dimensionality, inherent data complexity) Deep Neural Networks become hard to train. To ease many of the problems one might: Normalize && handpick ...
0
votes
0answers
2k views

Choice of initial value for weights in Tensorflow MNIST Tutorial [duplicate]

I was tweaking the Tensorflow MNIST Tutorial, and I am not clear why the weights are initialized using tf.zeros. If I switch to using tf.random_normal, or tf.truncated_normal, I see a very significant ...
82
votes
2answers
93k views

tanh activation function vs sigmoid activation function

The tanh activation function is: $$tanh \left( x \right) = 2 \cdot \sigma \left( 2 x \right) - 1$$ Where $\sigma(x)$, the sigmoid function, is defined as: $$\sigma(x) = \frac{e^x}{1 + e^x}$$. ...
3
votes
2answers
3k views

MNIST digit recognition: what is the best we can get with a fully connected NN only? (no CNN)

To fully understand how it works internally, I'm re-writing a neural network from scratch in Python + numpy only. (As it's for learning purposes, performance is not an issue). Before moving to ...
6
votes
0answers
2k views

When should I use the Normal distribution or the Uniform distribution when using Xavier initialization?

Xavier initialization seems to be used quite widely now to initialize connection weights in neural networks, especially deep ones (see What are good initial weights in a neural network?). The ...
2
votes
1answer
2k views

Feed forward Neural Network and MSE issues

I've been implementing a Feed-forward Neural Network in C++ and CUDA. It is a basic Multi-layered Feed Forward ANN, using various activation functions (sigmoid bipolar, tanh, tanh scaled, and soft-...
2
votes
2answers
1k views

What values should initial weights for a ReLU network be?

For a standard feed-forward Neural Network, what range should my initial weights fall under if I'm planning to use Rectified Linear Unit as an activation function? A mathematical justification for the ...
3
votes
2answers
536 views

Why does pre-training help avoid the vanishing gradient problem?

I read that a problem with the Classic approach to deep NN is the vanishing gradient, which is caused by the derivative of the logistic activation function - broadly speaking, the update flowing down ...
0
votes
1answer
386 views

Why my weight Initialization saturates the activation function?

I am trying to initialize the Hidden Layers of my Convolutional Neural Network with 7 hidden layers, all with ELU Activation with Sigmoid Activation at the end for binary classification. For ...
0
votes
1answer
70 views

Neural net: Cost goes down but performance on train isnt! [duplicate]

So, While building a simpel neural net (MLP) for recognizing digits, I ordered my function to print me both the mean cost overall off the train dataset and %currect answers, also over the train ...
1
vote
1answer
76 views

Initialising the weights of a neural network in regards to the Var{y}

So for a neural network, I'm trying to find the best weight $a$ to form the weight interval $[-a, a]$, given the function for the output of a single layer neural network $y = \sigma(Wx)$ where $x \...