def grad_check(self, x, y, outfd=sys.stderr, **kwargs):
bptt_old = self.bptt
self.bptt = len(y)
print >> outfd, "NOTE: temporarily setting self.bptt = len(y) = %d to compute true gradient." % self.bptt
NNBase.grad_check(self, x, y, outfd=outfd, **kwargs)
self.bptt = bptt_old
print >> outfd, "Reset self.bptt = %d" % self.bptt
def grad_check(self, x, y, outfd=sys.stderr, **kwargs):
"""
Wrapper for gradient check on RNNs;
ensures that backprop-through-time is run to completion,
computing the full gradient for the loss as summed over
the input sequence and predictions.
Do not modify this function!
"""
NNBase.grad_check(self, x, y, outfd=outfd, **kwargs)
def grad_check(self, x, y, outfd=sys.stderr, **kwargs):
"""
Wrapper for gradient check on RNNs;
ensures that backprop-through-time is run to completion,
computing the full gradient for the loss as summed over
the input sequence and predictions.
Do not modify this function!
"""
bptt_old = self.bptt
self.bptt = len(y)
print >> outfd, "NOTE: temporarily setting self.bptt = len(y) = %d to compute true gradient." % self.bptt
NNBase.grad_check(self, x, y, outfd=outfd, **kwargs)
self.bptt = bptt_old
print >> outfd, "Reset self.bptt = %d" % self.bptt
def grad_check(self, x, y, outfd=sys.stderr, **kwargs):
"""
Wrapper for gradient check on RNNs;
ensures that backprop-through-time is run to completion,
computing the full gradient for the loss as summed over
the input sequence and predictions.
Do not modify this function!
"""
bptt_old = self.bptt
self.bptt = len(y)
print >> outfd, "NOTE: temporarily setting self.bptt = len(y) = %d to compute true gradient." % self.bptt
NNBase.grad_check(self, x, y, outfd=outfd, **kwargs)
self.bptt = bptt_old
print >> outfd, "Reset self.bptt = %d" % self.bptt
def grad_check(self, X, y, outfd=sys.stderr, **kwargs):
"""
Wrapper for gradient check on RNNs;
ensures that backprop-through-time is run to completion,
computing the full gradient for the loss as summed over
the input sequence and predictions.
Do not modify this function!
"""
# if not recurssive yet this setting of bptt does not matter
bptt_old = self.bptt
# single example
if isinstance(X, ndarray): X = [X]
for i in range(len(X)):
self.bptt = X[i].shape[0]
print("NOTE: temporarily setting self.bptt = len(y) = %d to compute true gradient." % self.bptt, file=outfd)
NNBase.grad_check(self, X[i], y[i], outfd=outfd, **kwargs)
self.bptt = bptt_old
print("Reset self.bptt = %d" % self.bptt, file=outfd)
