def test_aggregation():
batch_size = 128
img_size = 10
num_out = 3
m = Model('test model cpu')
l_in = m.add_layer(layers.InputLayer(shape=(batch_size,img_size)), name="l_in")
l_out_mean = m.add_layer(layers.DenseLayer(l_in, num_out), name="l_out_mean")
l_out_sum = m.add_layer(layers.DenseLayer(l_in, num_out), name="l_out_sum")
m.bind_input(l_in, "pixels")
m.bind_output(l_out_mean, lasagne.objectives.squared_error, "emotions", "label", "mean")
m.bind_output(l_out_sum, lasagne.objectives.squared_error, "emotions", "label", "sum")
from pprint import pprint
pprint(m.to_dict())
global_update_settings = ParamUpdateSettings(update=lasagne.updates.nesterov_momentum,learning_rate=0.1, momentum=0.5)
trainer = Trainer(m, global_update_settings)
pixels = np.random.rand(batch_size,img_size).astype(theano.config.floatX)
emotions = np.random.rand(batch_size,num_out).astype(theano.config.floatX)
batch_dict = dict(
# learning_rate_default=0.1,
# momentum_default=0.5,
pixels=pixels,
emotions=emotions,
exmotions=emotions.copy()
)
outs = trainer.train_step(batch_dict)
print "Aggregation test succeeded"
def test_serialization():
m2 = _build_model()
serialized_old = m2.to_dict()
print "original model"
pprint.pprint(serialized_old)
m3 = Model('test serialize')
m3.from_dict(serialized_old)
print "model loaded from dict"
pprint.pprint(m3.to_dict())
data = dict(
pixels=np.random.rand(10, 1, 20, 10).astype(theano.config.floatX))
dirpath = tempfile.mkdtemp()
filepath = os.path.join(dirpath, 'testmodelout')
m3.save_model(filepath)
out3 = m3.predict(data, ['out', 'out2'])
m4 = Model('test convenience')
m4.load_model(filepath)
shutil.rmtree(dirpath)
assert serialized_old == m4.to_dict()
assert np.allclose(m4.layers['DenseLayer_2'].W.get_value(),
m3.layers['DenseLayer_2'].W.get_value())
assert ~np.allclose(m4.layers['DenseLayer_2'].W.get_value(),
m2.layers['DenseLayer_2'].W.get_value())
out4 = m4.predict(data, ['out', 'out2'])
print 'out pre serialization'
print out3['out']
print 'out post serialization'
print out4['out']
print 'out2 pre serialization'
print out3['out2']
print 'out2 post serialization'
print out4['out2']
assert np.allclose(out4['out'], out3['out'])
assert np.allclose(out4['out2'], out3['out2'])
def test_serialization():
m2 = _build_model()
serialized_old = m2.to_dict()
print "original model"
pprint.pprint(serialized_old)
m3 = Model("test serialize")
m3.from_dict(serialized_old)
print "model loaded from dict"
pprint.pprint(m3.to_dict())
data = dict(pixels=np.random.rand(10, 1, 20, 10).astype(theano.config.floatX))
dirpath = tempfile.mkdtemp()
filepath = os.path.join(dirpath, "testmodelout")
m3.save_model(filepath)
out3 = m3.predict(data, ["out", "out2"])
m4 = Model("test convenience")
m4.load_model(filepath)
shutil.rmtree(dirpath)
assert serialized_old == m4.to_dict()
assert np.allclose(m4.layers["DenseLayer_2"].W.get_value(), m3.layers["DenseLayer_2"].W.get_value())
assert ~np.allclose(m4.layers["DenseLayer_2"].W.get_value(), m2.layers["DenseLayer_2"].W.get_value())
out4 = m4.predict(data, ["out", "out2"])
print "out pre serialization"
print out3["out"]
print "out post serialization"
print out4["out"]
print "out2 pre serialization"
print out3["out2"]
print "out2 post serialization"
print out4["out2"]
assert np.allclose(out4["out"], out3["out"])
assert np.allclose(out4["out2"], out3["out2"])
def test_aggregation():
batch_size = 128
img_size = 10
num_out = 3
m = Model('test model cpu')
l_in = m.add_layer(layers.InputLayer(shape=(batch_size, img_size)),
name="l_in")
l_out_mean = m.add_layer(layers.DenseLayer(l_in, num_out),
name="l_out_mean")
l_out_sum = m.add_layer(layers.DenseLayer(l_in, num_out), name="l_out_sum")
m.bind_input(l_in, "pixels")
m.bind_output(l_out_mean, lasagne.objectives.squared_error, "emotions",
"label", "mean")
m.bind_output(l_out_sum, lasagne.objectives.squared_error, "emotions",
"label", "sum")
from pprint import pprint
pprint(m.to_dict())
global_update_settings = ParamUpdateSettings(
update=lasagne.updates.nesterov_momentum,
learning_rate=0.1,
momentum=0.5)
trainer = Trainer(m, global_update_settings)
pixels = np.random.rand(batch_size, img_size).astype(theano.config.floatX)
emotions = np.random.rand(batch_size, num_out).astype(theano.config.floatX)
batch_dict = dict(
# learning_rate_default=0.1,
# momentum_default=0.5,
pixels=pixels,
emotions=emotions,
exmotions=emotions.copy())
outs = trainer.train_step(batch_dict)
print "Aggregation test succeeded"
