def adjustWeights(self, input, target, rate, mrate):
self.setInput(input)
outnodes = self.layers[2]
if len(target) != len(outnodes):
raise ValueError("wrong number of target values")
deltas = {}
#output errors
for k in xrange(len(target)):
error = target[k] - outnodes[k].outval
deltas[outnodes[k]] = dfunc(outnodes[k].outval) * error
for node in self.layers[1]:
error = 0.0
for conn in node.outputs:
error += deltas[conn.dest] * conn.weight
deltas[node] = dfunc(node.outval) * error
for conn in self.tnode.outputs:
change = deltas[conn.dest] * conn.weight * rate
conn.weight += change + self.oldw.get(conn, 0.0) * mrate
self.oldw[conn] = change
conn.output = conn.weight
#find weight changes
for layer in self.layers:
for node in layer:
for conn in node.outputs:
if conn.dest.id != -1 and conn.src.id != -1:
change = deltas[conn.dest] * conn.input * rate
conn.weight += change + self.oldw.get(conn, 0.0) * mrate
self.oldw[conn] = change
#find error
error = 0.0
for k in xrange(len(target)):
error += 1.0/len(target) * (target[k] - outnodes[k].outval)**2.0
return error ** 0.5
def adjustWeights(self, target, rate, mrate):
"""Apply backpropagation through time on network and adjust weights
target (list) :: the list of target outputs
rate (float) :: learning rate
mrate (float) :: momentum rate
I don't think I can explain how backprop through time works here. There are
papers and books for that. But I can explain my implementation and how it ties
into the damned algorithm. 90% of this is standard backprop. Understanding that
is probably a wise prequisite. I'll be mostly commenting on the "through time"
parts. Read on!
"""
outnodes = self.layers[2]
if len(target) != len(outnodes):
raise ValueError("wrong number of target values")
# The keys are a tuple of the node and the time step for that delta (node, time).
# time = 0 is for the current time step. time > 0 is for back in time. It would
# make more sense if time decreased, but it makes the programming a little easier.
deltas = {}
# Calculate output errors. Nothing different here.
for k in xrange(len(target)):
error = target[k] - outnodes[k].outval
deltas[(outnodes[k],0)] = dfunc(outnodes[k].outval) * error
# Calculate output error for hidden nodes at t = 0. Recurrent links aren't used for
# this calculation. Except for that if statement, this is identical to regular
# backprop. The reason that recurrent links aren't used is because it is important
# to remember that hidden state layer and the hidden past state layer are really
# "different" layers. The deltas at t = 0 only depend on the output layer. The
# state layer at t = 1 depends on the state layer at t = 0, t = 2 depends on t = 1,
# and so on. I hope this makes sense. BPTT is really confusing and hard to explain
# without pictures. At least for me.
for node in self.layers[1]:
error = 0.0
for conn in node.outputs:
if conn.delay == 0:
error += deltas[(conn.dest, 0)] * conn.weight
deltas[(node,0)] = dfunc(node.outval) * error
# Now we can calculate deltas for older time steps. Because the t = 0 hidden
# layer relies on the output layer, unlike the t > 0 hidden layers that depend
# on newer time hidden layers, it had to be calculated seperately. But now that we are
# on the t > 0 hidden, we can iteratively move through the time steps. Notice that
# this is for the most part identical to regular backprop, except now we keep moving
# back in time (by increasing t (or i in this case)), the the dependent layer isn't
# the output layer but hidden layer t - 1.
# I'm assuming a delay of 1 here at all times. I should make it more general
for i in xrange(self.history-1):
for node in self.layers[1]:
error = 0.0
for conn in node.outputs:
if conn.delay == 1:
error += deltas[(conn.dest, i)] * conn.weight
#I think this is right because hist[0] will be the same as node.outval
#due to the way I store the history
deltas[(node, i+1)] = dfunc(node.history[i+1]) * error
# Now we can start changing weights. First the threshold nodes.
for conn in self.tnode.outputs:
change = deltas[(conn.dest,0)] * conn.weight * rate
conn.weight += change
conn.output = conn.weight
# Now weight changes for the rest of the nodes. The weight changes for the recurrent
# links are summed over each time step.
for layer in self.layers:
for node in layer:
for conn in node.outputs:
if conn.dest.id != -1 and conn.src.id != -1:
if conn.delay == 0:
change = deltas[(conn.dest,0)] * conn.input * rate
elif conn.delay == 1:
change = 0.0
for i in xrange(self.history-1):
change += deltas[(conn.dest, i)] * conn.src.history[i+1] * rate
conn.weight += change + self.oldw.get(conn, 0.0) * mrate
self.oldw[conn] = change
# find error - RMS
error = 0.0
N = len(target)
for k in xrange(len(target)):
error += 1.0/N * (target[k] - outnodes[k].outval)**2.0
error = error ** 0.5
return error