python deep learning代写

You can use the package Theano – a Python version from U. Montreal (Linux/MacOS/Windows), or any other packages or your own implementation, perform the following tasks:

  1. a) On the miniboone dataset, train a neural network with one hidden layer, with

neurons in the hidden layer and ReLU activation functions

for the hidden layer, and no activation function for the output layer. For each k find an appropriate learning rate and minibatch size to obtain a small final loss value on the training set after 100-300 epochs. Report in a table the misclassification errors for the four models on the training and test sets. Observe that since the miniboone data does not have a set training or test set, you should present results as the average of 10 independent random splits, each split using a random subsample of 80% of the data for training and the remaining 20% for testing.

  1. b) Repeat point a) with a neural network with two hidden layers, with 128 neurons in the first layer and neurons in the second layer and ReLU activation functions.
  2. c) Repeat point a) on the madelon For madelon you don’t need to do the random splits, just use the training and test set.
  3. d) Repeat point b) on the madelon

 

Dataset Detail