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