Python convUp 예제들

예제 #1

파일: basic4.py 프로젝트: yucao100/Information-Integration-and-Data-Warehouse

### 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((numFilterColors, filterSizeX, filterSizeX, numFilters))


from cudamat_conv import convUp, convOutp
from cudamat_conv.cudamat_conv_py import convOutp as convOutp_py

T1 = convUp(images, filters, paddingStart=-1)
t1 = convOutp(images, T1, paddingStart=-1)
t1_py = convOutp_py(images, T1, 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 = convUp(images, filters, paddingStart=0)
t2 = convOutp(images, T2, paddingStart=0)
t2_py = convOutp_py(images, T2, paddingStart=0)
assert t2.shape == t2_py.shape

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

예제 #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((numFilterColors, filterSizeX, filterSizeX, numFilters))

from cudamat_conv import convUp
from cudamat_conv.cudamat_conv_py import convUp as convUp_py

t1 = convUp(images, filters, paddingStart=-1)
t1_py = convUp_py(images, 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 = convUp(images, filters, paddingStart=0)
t2_py = convUp_py(images, filters, paddingStart=0)
assert t2.shape == t2_py.shape

print 't2 = ', abs(t2).mean()
print 't2_py = ', abs(t2_py).mean()

예제 #3

파일: basic3.py 프로젝트: virajadduru/deepnet

### 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((numFilterColors, filterSizeX, filterSizeX, numFilters))

from cudamat_conv import convUp, convDown
from cudamat_conv.cudamat_conv_py import convUp as convUp_py, convDown as convDown_py

T1 = convUp(images, filters, paddingStart=-1)
t1 = convDown(T1, filters, paddingStart=-1)
t1_py = convDown_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 = convUp(images, filters, paddingStart=0)
t2 = convDown(T2, filters, paddingStart=0)
t2_py = convDown_py(T2, filters, paddingStart=0)
assert t2.shape == t2_py.shape

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

예제 #4

파일: basic2.py 프로젝트: 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((numFilterColors, filterSizeX, filterSizeX, numFilters))


from cudamat_conv import convUp
from cudamat_conv.cudamat_conv_py import convUp as convUp_py

t1 = convUp(images, filters, paddingStart=-1)
t1_py = convUp_py(images, 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 = convUp(images, filters, paddingStart=0)
t2_py = convUp_py(images, filters, paddingStart=0)
assert t2.shape==t2_py.shape

print 't2 = ',abs(t2).mean()
print 't2_py = ',abs(t2_py).mean()