def generate_new_data():
global trn_inputs, trn_shifts, trn_targets
#print "Generating new data..."
trn_inputs, trn_shifts, trn_targets = \
generate_data(cfg.x_len, cfg.s_len, cfg.n_batch)
if use_id_data:
trn_targets = trn_inputs.copy()
loss = T.mean((fsn.output(x,s) - t)**2)
if profile:
f_loss = function(inputs=[ps.flat,x,s,t], outputs=loss, mode=profmode, name='f_loss')
f_dloss = function(inputs=[ps.flat,x,s,t], outputs=T.grad(loss, ps.flat), mode=profmode, name='f_dloss')
else:
f_loss = function(inputs=[ps.flat,x,s,t], outputs=loss)
f_dloss = function(inputs=[ps.flat,x,s,t], outputs=T.grad(loss, ps.flat))
f_trn_loss = lambda p: f_loss(p, trn_inputs, trn_shifts, trn_targets)
f_trn_dloss = lambda p: f_dloss(p, trn_inputs, trn_shifts, trn_targets)
# generate data
print "Generating data..."
trn_inputs, trn_shifts, trn_targets = generate_data(cfg.x_len, cfg.s_len, cfg.n_samples)
val_inputs, val_shifts, val_targets = generate_data(cfg.x_len, cfg.s_len, cfg.n_samples)
tst_inputs, tst_shifts, tst_targets = generate_data(cfg.x_len, cfg.s_len, cfg.n_samples)
print "Done."
# optimizer
if cfg.optimizer == 'lbfgs':
opt = climin.Lbfgs(ps.data, f_trn_loss, f_trn_dloss)
elif cfg.optimizer == 'rprop':
opt = climin.Rprop(ps.data, f_trn_dloss)
elif cfg.optimizer == 'rmsprop':
opt = climin.RmsProp(ps.data, f_trn_dloss,
steprate=cfg.steprate[0],
momentum=cfg.momentum)
elif cfg.optimizer == 'gradientdescent':
opt = climin.GradientDescent(ps.data, f_trn_dloss,
def generate_data(n_samples):
if use_base_data:
inputs, shifts, targets = generate_base_data(n_samples)
else:
inputs, shifts, targets = generate_data(cfg.x_len, cfg.s_len, n_samples)
return inputs, shifts, targets
def generate_base_data(n_samples):
inputs, shifts, targets = generate_data(base_x_len, base_s_len, n_samples)
inputs = gp.dot(doubling_matrix(base_x_len).T, inputs)
targets = gp.dot(doubling_matrix(base_x_len).T, targets)
shifts = gp.dot(shift_doubling_matrix(base_s_len).T, shifts)
return inputs, shifts, targets
def generate_new_training_data():
global trn_inputs, trn_shifts, trn_targets
print "Generating new data..."
trn_inputs, trn_shifts, trn_targets = generate_data(cfg.n_batch)
