Python localUp Examples

Example #1

File: basic3L.py Project: virajadduru/deepnet

paddingStart = 0 ## try it without padding for now.

numImgColors = numChannels



# create the images
images = g.randn((numChannels, imSizeX, imSizeX, numImages))
filters = g.randn((numModulesX, numModulesX, numFilterColors, filterSizeX, filterSizeX, numFilters))


from cudamat_conv import localUp, localDown
from cudamat_conv.cudamat_conv_py import localUp as localUp_py, localDown as localDown_py

T1 = localUp(images, filters) #, paddingStart=-1)
t1 = localDown(T1, filters) #, paddingStart=-1)
t1_py = localDown_py(T1, filters)#, paddingStart=-1)
assert t1.shape==t1_py.shape
print 't1 = ',abs(t1).mean()
print 't1_py = ',abs(t1_py).mean()
print 't1_diff = ',abs(t1-t1_py).mean()
print 't1.shape = ', t1.shape
print

# T2 = localUp(images, filters, paddingStart=0)
# t2 = localDown(T2, filters, paddingStart=0)
# t2_py = localDown_py(T2, filters, paddingStart=0)
# assert t2.shape==t2_py.shape

# print 't2 = ',abs(t2).mean()

Example #2

### TODO: ask Alex about moduleStride and numGroups.
### But ignoring these I'm good to go.

paddingStart = 0  ## try it without padding for now.

numImgColors = numChannels

# create the images
images = g.randn((numChannels, imSizeX, imSizeX, numImages))
filters = g.randn((numModulesX, numModulesX, numFilterColors, filterSizeX,
                   filterSizeX, numFilters))

from cudamat_conv import localUp
from cudamat_conv.cudamat_conv_py import localUp as localUp_py

t1 = localUp(images, filters)
t1_py = localUp_py(images, filters)

assert t1.shape == t1_py.shape
print 't1 = ', abs(t1).mean()
print 't1_py = ', abs(t1_py).mean()
print 't1_diff = ', abs(t1 - t1_py).mean()
print 't1.shape = ', t1.shape
print

filters = g.randn((numModulesX + 1, numModulesX + 1, numFilterColors,
                   filterSizeX, filterSizeX, numFilters))
t2 = localUp(images, filters)
t2_py = localUp_py(images, filters)
assert t2.shape == t2_py.shape

Example #3

File: basic2L.py Project: ANB2/deepnet

paddingStart = 0 ## try it without padding for now.

numImgColors = numChannels



# create the images
images = g.randn((numChannels, imSizeX, imSizeX, numImages))
filters = g.randn((numModulesX, numModulesX, numFilterColors, filterSizeX, filterSizeX, numFilters))


from cudamat_conv import localUp
from cudamat_conv.cudamat_conv_py import localUp as localUp_py

t1 = localUp(images, filters)
t1_py = localUp_py(images, filters)

assert t1.shape==t1_py.shape
print 't1 = ',abs(t1).mean()
print 't1_py = ',abs(t1_py).mean()
print 't1_diff = ',abs(t1-t1_py).mean()
print 't1.shape = ', t1.shape
print

filters = g.randn((numModulesX+1, numModulesX+1, numFilterColors, filterSizeX, filterSizeX, numFilters))
t2 = localUp(images, filters)
t2_py = localUp_py(images, filters)
assert t2.shape==t2_py.shape

print 't2 = ',abs(t2).mean()