Tag: activation function

I have used a 4 layered fully connected network to learn a complex classifier boundary. I have used tanh activations throughout except the last layer where I used sigmoid activation for binary classification. I train for 10K iterations with 100K examples (my data points are 3 dimensional and I initialized my weights to 0 to begin with). I see that my network is unable to fit the training data and is leading to a high training error. What is the first thing I try ?

Posted on by MLNerds

  Increase the number of training iterations Make a more complex network – increase hidden layer size Initialize weights to a random small value instead of zeros Change tanh activations to relu     Ans : (3) . I will initialize weights to a non zero value since changing all the weights in the same…

I have designed a 2 layered deep neural network for a classifier with 2 units in the hidden layer. I use linear activation functions with a sigmoid at the final layer. I use a data visualization tool and see that the decision boundary is in the shape of a sine curve. I have tried to train with 200 data points with known class labels and see that the training error is too high. What do I do ?

Posted on by MLNerds

Increase number of units in the hidden layer Increase number of hidden layers  Increase data set size Change activation function to tanh Try all of the above The answer is d. When I use a linear activation function, the deep neural network is realizing a linear combination of linear  functions which leads to modeling only…