def _get_fprop(large_network=False, output_layers=[-1], detailed=False):
# arch=[]
arch = _get_architecture(large_network, detailed=detailed)
# remove the last layer..
# arch.pop(-1)
# arch.append(getSemanticLayer())
print arch
expressions, input_var = fuse(arch, output_expressions=output_layers, input_dtype="float32")
fprop = theano.function([input_var], expressions)
return fprop
def _get_fprop(large_network=False, output_layers=[-1], detailed=False):
#arch=[]
arch = _get_architecture(large_network, detailed=detailed)
# remove the last layer..
#arch.pop(-1)
#arch.append(getSemanticLayer())
print arch
expressions, input_var = fuse(arch, output_expressions=output_layers,
input_dtype='float32')
fprop = theano.function([input_var], expressions)
return fprop
def get_overfeat_features(X1):
arch1 = _get_architecture(large_network=True, detailed=False)
output_layers = range(0,25)
expressions1, affine = fuse(arch1, output_expressions=output_layers,input_dtype='float32', entry_expression = X1.transpose(0, 3, 1, 2))
print "Number of layers in overfeat net", len(expressions1)
ds = 0.0
#for element in expressions1:
# ds += T.sum(element)
return expressions1
