def test_trained_model_serialization():
batch_size = 2
img_size = 10
num_hid = 10
m = _build_model(batch_size, img_size, num_hid)
global_update_settings = ParamUpdateSettings(
update=lasagne.updates.nesterov_momentum,
learning_rate=0.1,
momentum=0.2)
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_hid).astype(theano.config.floatX)
batch_dict = dict(
# learning_rate_default=0.1,
# momentum_default=0.5,
pixels=pixels,
emotions=emotions)
outs = trainer.train_step(batch_dict)
preds = m.predict(batch_dict)
dirpath = tempfile.mkdtemp()
filepath = os.path.join(dirpath, 'testtrainerout')
m.save_model(filepath)
m2 = Model('load')
m2.load_model(filepath)
shutil.rmtree(dirpath)
global_update_settings = ParamUpdateSettings(
update=lasagne.updates.nesterov_momentum,
learning_rate=0.1,
momentum=0.2)
trainer2 = Trainer(m2, global_update_settings)
preds2 = m.predict(batch_dict)
outs = trainer.train_step(batch_dict)
outs2 = trainer2.train_step(batch_dict)
assert m.outputs.keys() == m2.outputs.keys()
for p, p2 in zip(preds.values(), preds2.values()):
assert np.allclose(p, p2)
for o, o2 in zip(outs, outs2):
assert np.allclose(o, o2)
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_trained_model_serialization():
batch_size = 2
img_size = 10
num_hid = 10
m = _build_model(batch_size,img_size,num_hid)
global_update_settings = ParamUpdateSettings(update=lasagne.updates.nesterov_momentum,learning_rate=0.1, momentum=0.2)
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_hid).astype(theano.config.floatX)
batch_dict = dict(
# learning_rate_default=0.1,
# momentum_default=0.5,
pixels=pixels,
emotions=emotions
)
outs = trainer.train_step(batch_dict)
preds = m.predict(batch_dict)
dirpath = tempfile.mkdtemp()
filepath = os.path.join(dirpath, 'testtrainerout')
m.save_model(filepath)
m2 = Model('load')
m2.load_model(filepath)
shutil.rmtree(dirpath)
global_update_settings = ParamUpdateSettings(update=lasagne.updates.nesterov_momentum,learning_rate=0.1, momentum=0.2)
trainer2 = Trainer(m2,global_update_settings)
preds2 = m.predict(batch_dict)
outs = trainer.train_step(batch_dict)
outs2 = trainer2.train_step(batch_dict)
assert m.outputs.keys()==m2.outputs.keys()
for p,p2 in zip(preds.values(),preds2.values()):
assert np.allclose(p,p2)
for o,o2 in zip(outs,outs2):
assert np.allclose(o,o2)
