Mercurial > ift6266
changeset 375:e36ccffb3870
Changes to cast NIST data to floatX for rbm code
| author | Xavier Glorot <glorotxa@iro.umontreal.ca> |
|---|---|
| date | Sun, 25 Apr 2010 14:53:10 -0400 |
| parents | 846f0678ffe8 |
| children | 0b7e64e8e93f |
| files | deep/rbm/rbm.py |
| diffstat | 1 files changed, 10 insertions(+), 5 deletions(-) [+] |
line wrap: on
line diff
--- a/deep/rbm/rbm.py Sun Apr 25 13:55:07 2010 -0400 +++ b/deep/rbm/rbm.py Sun Apr 25 14:53:10 2010 -0400 @@ -273,9 +273,13 @@ h = open(data_path+'all/all_test_data.ft') i = open(data_path+'all/all_test_labels.ft') - train_set_x = theano.shared(ft.read(f)) + train_set_x_uint8 = theano.shared(ft.read(f)) + test_set_x_uint8 = theano.shared(ft.read(h)) + + + train_set_x = T.cast(train_set_x_uint8/255.,theano.config.floatX) train_set_y = ft.read(g) - test_set_x = ft.read(h) + test_set_x = T.cast(test_set_x_uint8/255.,theano.config.floatX) test_set_y = ft.read(i) f.close() @@ -284,7 +288,6 @@ h.close() #t = len(train_set_x) - print train_set_x.value.shape # revoir la recuperation des donnees ## dataset = load_data(dataset) @@ -296,10 +299,10 @@ batch_size = b_size # size of the minibatch # compute number of minibatches for training, validation and testing - n_train_batches = train_set_x.value.shape[0] / batch_size + n_train_batches = train_set_x_uint8.value.shape[0] / batch_size # allocate symbolic variables for the data - index = T.scalar() # index to a [mini]batch + index = T.lscalar() # index to a [mini]batch x = T.matrix('x') # the data is presented as rasterized images rng = numpy.random.RandomState(123) @@ -332,6 +335,8 @@ print 'yes' # it is ok for a theano function to have no output # the purpose of train_rbm is solely to update the RBM parameters + print type(batch_size) + print index.dtype train_rbm = theano.function([index], cost, updates = updates, givens = { x: train_set_x[index*batch_size:(index+1)*batch_size]})