def train_model1(sess, train_op, n_epoch, opt, model1, X_dataset, Y_dataset, Z_dataset, qvars, pvars, dvars, qvars_y, pvars_y, dvars_y):
train_gen_op_x = train_op.get_train_gen_op_x()
train_disc_op_x = train_op.get_train_disc_op_x()
train_gen_op_y = train_op.get_train_gen_op_y()
train_disc_op_y = train_op.get_train_disc_op_y()
x = model1.get_x()
y = model1.get_y()
z = model1.get_z()
d1 = model1.get_d1()
d2 = model1.get_d2()
FG_x = []
FG_y = []
FD_x = []
FD_y = []
DG_xz = DG()
DG_xz.initial()
DG_yz = DG()
DG_yz.initial()
dmb1 = [[1]*2]*128
dmb2 = [[0]*2]*128
for epoch in tqdm( range(n_epoch), total=n_epoch):
X_dataset = shuffle(X_dataset)
Y_dataset = shuffle(Y_dataset)
Z_dataset = shuffle(Z_dataset)
i = 0
#print(dmb)
for xmb, ymb, zmb in iter_data(X_dataset, Y_dataset, Z_dataset, size=batch_size):
#print(xmb)
i = i + 1
for _ in range(1):
f_d_x, _ = sess.run([model1.get_disc_loss_x(), train_disc_op_x], feed_dict={x: xmb, y:ymb, z:zmb, d1:dmb1, d2:dmb2})
for _ in range(5):
f_g_x, _ = sess.run([[model1.get_gen_loss_x(), model1.get_gen_loss_xz(), model1.get_cost_x(), model1.get_cost_xz()], train_gen_op_x], feed_dict={x: xmb, y:ymb, z:zmb, d1:dmb1, d2:dmb2})
FG_x.append(f_g_x)
FD_x.append(f_d_x)
for _ in range(1):
f_d_y, _ = sess.run([model1.get_disc_loss_y(), train_disc_op_y], feed_dict={x: xmb, y:ymb, z:zmb, d1:dmb1, d2:dmb2})
for _ in range(5):
f_g_y, _ = sess.run([[model1.get_gen_loss_y(), model1.get_gen_loss_yz(), model1.get_cost_y(), model1.get_cost_yz()], train_gen_op_y], feed_dict={x: xmb, y:ymb, z:zmb, d1:dmb1, d2:dmb2})
FG_y.append(f_g_y)
FD_y.append(f_d_y)
print_xz(epoch, i, f_d_x, f_g_x[0], f_g_x[1], f_g_x[2], f_g_x[3])
print_yz(epoch, i, f_d_y, f_g_y[0], f_g_y[1], f_g_y[2], f_g_y[3])
DG_xz.set_FD(FD_x)
DG_xz.set_FG(FG_x)
DG_yz.set_FD(FD_y)
DG_yz.set_FG(FG_y)
return sess, DG_xz, DG_yz, model1
def train_model2(train_op, n_epoch_2, opt, model1, sess, X_dataset, Y_dataset, W_dataset, Z_dataset):
train_gen_op_xyw = train_op.get_train_gen_op_xyw()
train_disc_op_xyw = train_op.get_train_disc_op_xyw()
FD_xyw = []
FG_xyw = []
DG_xyw = DG()
DG_xyw.initial()
dmb1 = [[0]*2]*128
dmb2 = [[1]*2]*128
x = model1.get_x()
y = model1.get_y()
w = model1.get_w()
z = model1.get_z()
d1 = model1.get_d1()
d2 = model1.get_d2()
for epoch in tqdm( range(n_epoch_2), total=n_epoch_2):
X_dataset = shuffle(X_dataset)
Y_dataset = shuffle(Y_dataset)
W_dataset = shuffle(W_dataset)
Z_dataset = shuffle(Z_dataset)
i = 0
for xmb, ymb, wmb, zmb in iter_data(X_dataset, Y_dataset, W_dataset, Z_dataset, size=batch_size):
i = i + 1
for _ in range(1):
f_d_xyw, _ = sess.run([model1.get_disc_loss_xyw(), train_disc_op_xyw], feed_dict={x: xmb, y:ymb, w:wmb, z:zmb, d1:dmb1, d2:dmb2})
for _ in range(5):
f_g_xyw, _ = sess.run([[model1.get_gen_loss_xyw(), model1.get_gen_loss_x(), model1.get_cost_x(), model1.get_cost_xz()], train_gen_op_xyw], feed_dict={x: xmb, y:ymb, w:wmb, z:zmb, d1:dmb1, d2:dmb2})
FG_xyw.append(f_g_xyw)
FD_xyw.append(f_d_xyw)
print_xy(epoch, i, f_d_xyw, f_g_xyw[0], f_g_xyw[1], f_g_xyw[2], f_g_xyw[3])
DG_xyw.set_FD(FD_xyw)
DG_xyw.set_FG(FG_xyw)
return sess, DG_xyw, model1
z: zmb
})
for _ in range(5):
f_g_y, _ = sess.run(
[[gen_loss_y, gen_loss_yz, cost_y, cost_yz], train_gen_op_y],
feed_dict={
x: xmb,
y: ymb,
z: zmb
})
FG_y.append(f_g_y)
FD_y.append(f_d_y)
print_xz(epoch, i, f_d_x, f_g_x[0], f_g_x[1], f_g_x[2], f_g_x[3])
print_yz(epoch, i, f_d_y, f_g_y[0], f_g_y[1], f_g_y[2], f_g_y[3])
DG_xz.set_FD(FD_x)
DG_xz.set_FG(FG_x)
DG_yz.set_FD(FD_y)
DG_yz.set_FG(FG_y)
q_xz = x_inference_network(x, latent_dim, n_layer_inf, n_hidden_inf, eps_dim)
p_y = y_generative_network(q_xz, input_dim, n_layer_gen, n_hidden_gen, eps_dim)
q_yz = y_inference_network(y, latent_dim, n_layer_inf, n_hidden_inf, eps_dim)
p_x = x_generative_network(q_yz, input_dim, n_layer_gen, n_hidden_gen, eps_dim)
decoder_logit_x = x_data_network(x,
q_xz,
n_layers=n_layer_disc,
n_hidden=n_hidden_disc)
encoder_logit_x = y_data_network(p_y,