def get_model(inputdim,
outputdim,
regularization_strength=0.01,
lr=0.000,
cosine=False,
**kwargs):
transformation = Dense(inputdim,
init='identity',
W_constraint=Orthogonal())
model = Model()
model.add_input(name='embeddings1', input_shape=(inputdim, ))
model.add_input(name='embeddings2', input_shape=(inputdim, ))
model.add_shared_node(transformation,
name='transformation',
inputs=['embeddings1', 'embeddings2'],
outputs=['transformed1', 'transformed2'])
model.add_node(Lambda(lambda x: x[:, :outputdim]),
input='transformed1',
name='projected1')
model.add_node(Lambda(lambda x: -x[:, :outputdim]),
input='transformed2',
name='negprojected2')
if cosine:
model.add_node(
Lambda(lambda x: x / K.reshape(K.sqrt(K.sum(x * x, axis=1)),
(x.shape[0], 1))),
name='normalized1',
input='projected1')
model.add_node(
Lambda(lambda x: x / K.reshape(K.sqrt(K.sum(x * x, axis=1)),
(x.shape[0], 1))),
name='negnormalized2',
input='negprojected2')
model.add_node(
Lambda(lambda x: K.reshape(K.sum(x, axis=1), (x.shape[0], 1))),
name='distances',
inputs=['normalized1', 'negnormalized2'],
merge_mode='mul')
else:
model.add_node(Lambda(lambda x: K.reshape(K.sqrt(K.sum(x * x, axis=1)),
(x.shape[0], 1))),
name='distances',
inputs=['projected1', 'negprojected2'],
merge_mode='sum')
model.add_output(name='y', input='distances')
model.compile(loss={
'y': lambda y, d: K.mean(y * d)
},
optimizer=SimpleSGD())
return model
