def main():
parser = make_parser()
args = parser.parse_args()
model = cPickle.load(args.model)
src = model.dataset_yaml_src
test = yaml_parse.load(src)
test = test.get_test_set()
assert test.X.shape[0] == 10000
test.X = test.X.astype('float32')
test.y = test.y.astype('float32')
X = test.X
y = test.y
Xb = model.get_input_space().make_batch_theano()
Xb.name = 'Xb'
yb = model.get_output_space().make_batch_theano()
yb.name = 'yb'
# W/2 network
fn = make_error_fn(Xb, model.fprop(Xb), yb)
mf_test_error = measure_test_error(fn, X, y, batch_size=args.batch_size)
print "Test error: %f" % mf_test_error
num_masks = range(args.low_samples, args.high_samples, args.step_samples)
results = np.empty((args.repeats, len(num_masks)), dtype='float64')
for i, n_masks in enumerate(num_masks):
print "Gathering results for n_masks = %d..." % n_masks
out = sampled_dropout_average(model, Xb, n_masks,
per_example=args.per_example,
input_include_probs={'h0': args.h0_prob},
input_scales={'h0': args.h0_scale})
f = make_error_fn(Xb, out, yb)
for rep in xrange(args.repeats):
print "Repeat %d" % (rep + 1)
results[rep, i] = measure_test_error(f, X, y,
batch_size=args.batch_size)
print "Done."
np.save(args.outfile, results)
def test_sampled_dropout_average():
# This is only a smoke test: verifies that it compiles and runs,
# not any particular value.
inp = theano.tensor.matrix()
mlp = MLP(nvis=2, layers=[Linear(2, 'h0', irange=0.8),
Linear(2, 'h1', irange=0.8),
Softmax(3, 'out', irange=0.8)])
out = sampled_dropout_average(mlp, inp, 5)
f = theano.function([inp], out, allow_input_downcast=True)
f([[2.3, 4.9]])
def main():
parser = make_parser()
args = parser.parse_args()
model = cPickle.load(args.model)
src = model.dataset_yaml_src
test = yaml_parse.load(src)
test = test.get_test_set()
assert test.X.shape[0] == 10000
test.X = test.X.astype('float32')
test.y = test.y.astype('float32')
X = test.X
y = test.y
Xb = model.get_input_space().make_batch_theano()
Xb.name = 'Xb'
yb = model.get_output_space().make_batch_theano()
yb.name = 'yb'
# W/2 network
fn = make_error_fn(Xb, model.fprop(Xb), yb)
mf_test_error = measure_test_error(fn, X, y, batch_size=args.batch_size)
print "Test error: %f" % mf_test_error
num_masks = range(args.low_samples, args.high_samples, args.step_samples)
results = np.empty((args.repeats, len(num_masks)), dtype='float64')
for i, n_masks in enumerate(num_masks):
print "Gathering results for n_masks = %d..." % n_masks
out = sampled_dropout_average(model,
Xb,
n_masks,
per_example=args.per_example,
input_include_probs={'h0': args.h0_prob},
input_scales={'h0': args.h0_scale})
f = make_error_fn(Xb, out, yb)
for rep in xrange(args.repeats):
print "Repeat %d" % (rep + 1)
results[rep, i] = measure_test_error(f,
X,
y,
batch_size=args.batch_size)
print "Done."
np.save(args.outfile, results)