def make_gan_phase_2_seen(GAN_in, G_set, D_set, loss, opt, loss_weights):
output_set = []
ATTR_NUM = len(G_set)
for i in range(ATTR_NUM):
G_out = []
for j in range(ATTR_NUM):
if i != j:
set_trainability(G_set[j], False)
else:
set_trainability(G_set[j], True)
x = G_set[j](GAN_in[j])
G_out.append(x)
x = add(G_out)
set_trainability(D_set[i], True)
output_i = D_set[i](x)
output_set.append(output_i)
GAN = Model(GAN_in, output_set)
GAN.compile(loss=loss, optimizer=opt, loss_weights=loss_weights)
return GAN, output_set
def make_gan(inputs, G, D, G_trainable, D_trainable):
set_trainability(G, G_trainable)
set_trainability(D, D_trainable)
x = G(inputs)
output = D(x)
return output
def build_model(ATTR_NUM, CLASS_NUM, feature_dim, hidden_dim, input_shape,
lambda_mat):
model_input, inputs, _, shared_dim = input_model(hidden_dim, input_shape)
G_set_phase_1 = []
D_set_phase_1 = []
for i in range(ATTR_NUM):
G, _ = get_generative(input_dim=shared_dim, out_dim=feature_dim)
G_set_phase_1.append(G)
D_set_sub = []
for j in range(ATTR_NUM):
if i == j:
activation = 'softmax'
else:
activation = 'softmax'
D, _ = get_discriminative(input_dim=feature_dim,
out_dim=CLASS_NUM[j],
activation=activation)
D_set_sub.append(D)
D_set_phase_1.append(D_set_sub)
opt_gan = Adam(lr=0.0002, beta_1=0.5, beta_2=0.999)
opt = Adam(lr=1e-3)
loss_weights = [1.]
loss_weights.extend([0.1 for _ in range(ATTR_NUM - 1)])
set_trainability(model_input, True)
feats = model_input(inputs)
loss = [K.categorical_crossentropy for _ in range(ATTR_NUM)]
GAN_phase_1_task, _ = make_gan_phase_1_task(inputs, feats, G_set_phase_1,
D_set_phase_1, loss, opt,
loss_weights)
for i in range(ATTR_NUM):
loss_weights = [1.]
loss_weights.extend([0.1 for _ in range(ATTR_NUM - 1)])
for j in range(ATTR_NUM):
if i != j:
loss_weights[j] = loss_weights[j] * lambda_mat[j, i]
if i == 0:
loss_w = loss_weights
else:
loss_w.extend(loss_weights)
loss_weights = loss_w
set_trainability(model_input, False)
feats = model_input(inputs)
GAN_phase_1_domain_pos, _ = make_gan_phase_1_domain_pos(
inputs, feats, G_set_phase_1, D_set_phase_1, 'mse', opt_gan,
loss_weights)
set_trainability(model_input, True)
feats = model_input(inputs)
GAN_phase_1_domain_neg, _ = make_gan_phase_1_domain_neg(
inputs, feats, G_set_phase_1, D_set_phase_1, 'mse', opt_gan,
loss_weights)
Model_gen_hidden_feature, _ = make_gan_phase_1_gen_hidden_feature(
inputs, feats, G_set_phase_1)
return GAN_phase_1_task, GAN_phase_1_domain_pos, GAN_phase_1_domain_neg, Model_gen_hidden_feature