def set_scoring_mode(self, scoring_mode):
self._init_task_index()
if (scoring_mode == ScoringMode.OneAndZeros):
self._pos_mxts = B.set_subtensor(
self._pos_mxts[:,self._get_task_index()],
self._active)
self._neg_mxts = B.set_subtensor(
self._neg_mxts[:,self._get_task_index()],
self._active)
elif (scoring_mode == ScoringMode.SoftmaxPreActivation):
#I was getting some weird NoneType errors when I tried
#to compile this piece of the code, hence the shift to
#accomplishing this bit via weight normalisation
#n = self.get_activation_vars().shape[1]
#self._mxts = B.ones_like(self.get_activation_vars())*(-1.0/n)
#self._mxts = B.set_subtensor(self._mxts[:,self._get_task_index()],
# (n-1.0)/n)
raise NotImplementedError(
"Do via mean-normalisation of weights "
"instead; see what I did in "
"models.Model.set_pre_activation_target_layer")
else:
raise RuntimeError("Unsupported scoring_mode "+scoring_mode)
self._set_mxts_updated_true()
def get_smoothen_function(window_size, same_size_return=True):
"""
Returns a function for smoothening inputs with a window
of size window_size.
Returned function has arguments of inp,
batch_size and progress_update
"""
from deeplift import backend as B
inp_tensor = B.tensor_with_dims(2, "inp_tensor")
if (same_size_return):
#do padding so that the output will have the same size as the input
#remember, the output will have length of input length - (window_size-1)
#so, we're going to pad with int(window_size/2), and for even window_size
#we will trim off the value from the front of the output later on
padding = int(window_size/2)
new_dims = [inp_tensor.shape[0], inp_tensor.shape[1]+2*padding]
padded_inp = B.zeros(new_dims)
#fill the middle region with the original input
padded_inp = B.set_subtensor(
padded_inp[:,padding:(inp_tensor.shape[1]+padding)],
inp_tensor)
#duplicate the left end for padding
padded_inp = B.set_subtensor(padded_inp[:,0:padding],
inp_tensor[:,0:padding])
#duplicate the right end for padding
padded_inp = B.set_subtensor(
padded_inp[:,(inp_tensor.shape[1]+padding):],
inp_tensor[:,(inp_tensor.shape[1]-padding):])
else:
padded_inp = inp_tensor
padded_inp = padded_inp[:,None,None,:]
averaged_padded_inp = B.pool2d(
inp=padded_inp,
pool_size=(1,window_size),
strides=(1,1),
border_mode="valid",
ignore_border=True,
pool_mode=B.PoolMode.avg)
#if window_size is even, then we have an extra value in the output,
#so kick off the value from the front
if (window_size%2==0 and same_size_return):
averaged_padded_inp = averaged_padded_inp[:,:,:,1:]
averaged_padded_inp = averaged_padded_inp[:,0,0,:]
smoothen_func = B.function([inp_tensor], averaged_padded_inp)
def smoothen(inp, batch_size, progress_update=None):
return run_function_in_batches(
func=smoothen_func,
input_data_list=[inp],
batch_size=batch_size,
progress_update=progress_update)
return smoothen