def __init__(self,
n_verts=0,
n_dims=3,
latent_dim=2,
n_layers=2,
n_units=128,
relu=False,
add_random_offsets=False,
dropout=False):
if not n_verts: raise Exception('Please provide the number of vertices `n_verts`')
self.n_verts = n_verts # input vert count
self.n_dims = n_dims # input dimensions
self.relu = relu # whether to add relu layers in encoder/decoder
self.dropout = dropout # whether to add dropout layers in encoder/decoder
self.latent_dim = latent_dim
self.n_layers = n_layers
self.n_units = n_units
self.encoder = self.build_encoder()
self.decoder = self.build_decoder()
# attach the encoder and decoder
i = Input((self.n_verts, self.n_dims))
if add_random_offsets:
random_offsets = K.cast(K.learning_phase(),'float')*K.random_uniform((K.shape(i)[0],1,3))*K.constant([[[1,1,0]]])
offset_layer = Lambda(lambda x: x + random_offsets)
offset_layer.uses_learning_phase = True
i_offset = offset_layer(i)
else:
i_offset = i
z = self.encoder(i_offset) # push observations into latent space
o = self.decoder(z) # project from latent space to feature space
if add_random_offsets:
o = Lambda(lambda x: x - random_offsets)(o)
self.model = Model(inputs=[i], outputs=[o])
self.model.compile(loss='mse', optimizer=optimizers.Adam(lr=1e-4))
def PadSymmetricInTestPhase():
pad = Lambda(lambda x: K.in_train_phase(
x, tf.pad(x, tf.constant([[0, 0], [2, 2], [2, 2], [0, 0]]), 'SYMMETRIC'
)))
pad.uses_learning_phase = True
return pad
