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
"discriminator_y") opt = tf.train.AdamOptimizer(1e-3, beta1=0.5) train_gen_op_x = opt.minimize(gen_loss_x, var_list=qvars + pvars) train_disc_op_x = opt.minimize(disc_loss_x, var_list=dvars) train_gen_op_y = opt.minimize(gen_loss_y, var_list=qvars_y + pvars_y) train_disc_op_y = opt.minimize(disc_loss_y, var_list=dvars_y) """ training """ config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.1 sess = tf.Session(config=config) sess.run(tf.global_variables_initializer()) DG_xz = DG() DG_xz.initial() DG_yz = DG() DG_yz.initial() DG_xy = DG() DG_xy.initial() FG_x = [] FG_y = [] FD_x = [] FD_y = [] FG_xy = [] FD_xy = [] X_dataset = data_x.get_dataset() Y_dataset = data_y.get_dataset()
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(option, data_x, data_y, data_z):
n_iter = option.n_iter
setattr(tf.GraphKeys, "VARIABLES", "variables")
DG_xz = DG()
DG_xz.initial()
DG_yz = DG()
DG_yz.initial()
DG_xy = DG()
DG_xy.initial()
tf.reset_default_graph()
model1 = Model()
model1.initial(option)
# decoder and encoder
qvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "inference_x")
pvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "generative_x")
dvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
"discriminator_x")
qvars_y = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
"inference_y")
pvars_y = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
"generative_y")
dvars_y = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
"discriminator_y")
opt = tf.train.AdamOptimizer(1e-3, beta1=0.5)
train_op = Trainer()
train_op.initial(model1, opt, qvars, pvars, dvars, qvars_y, pvars_y,
dvars_y)
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.1
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
sess.graph.finalize()
DG_xz = DG()
DG_xz.initial()
DG_yz = DG()
DG_yz.initial()
DG_xy = DG()
DG_xy.initial()
""" training """
X_dataset = data_x.get_dataset()
Y_dataset = data_y.get_dataset()
Z_dataset = data_z.get_dataset()
#[sess, DG_xz, DG_yz, model1] = train_model1(sess, train_op, option.n_epoch, opt, model1, X_dataset, Y_dataset, Z_dataset, qvars, pvars, dvars, qvars_y, pvars_y, dvars_y)
#global summary_writer
#summary_writer = tf.train.SummaryWriter('/tmp/mnist_logs', sess.graph)
[sess, DG_xy,
model1] = train_model2(train_op, option.n_epoch_2, opt, model1, sess,
X_dataset, Y_dataset, Z_dataset, qvars, pvars,
dvars, qvars_y, pvars_y, dvars_y)
'''
for i in range(0,n_iter):
#if(i!=0):
#saver = tf.train.Saver()
#saver.restore(sess=sess,save_path="model/my-model"+str(i-1)+".ckpt")
time_start=time.time()
print("#####################")
print("iteration:")
print(i)
print("#####################")
print("#####################")
print("#####################")
#[sess, DG_xz, DG_yz, model1] = train_model1_3(train_op, option.n_epoch_3, opt, model1, sess, X_dataset, Y_dataset, Z_dataset, qvars, pvars, dvars, qvars_y, pvars_y, dvars_y)
[sess, DG_xz, DG_yz, model1] = train_model1(train_op, option.n_epoch_3, opt, model1, sess, X_dataset, Y_dataset, Z_dataset, qvars, pvars, dvars, qvars_y, pvars_y, dvars_y)
print("#####################")
print("#####################")
[sess, DG_xy, model1] = train_model2(train_op, option.n_epoch_4,opt, model1, sess, X_dataset, Y_dataset, Z_dataset, qvars, pvars, dvars, qvars_y, pvars_y, dvars_y)
print("#####################")
#saver = tf.train.Saver()
#saver.save(sess=sess,save_path="model/my-model"+str(i)+".ckpt")
#summary_writer.add_summary(summary_str, total_step)
time_end=time.time()
print('Model1_2: totally cost',time_end-time_start)
#
[sess, DG_xz, DG_yz, model1] = train_model1(train_op, option.n_epoch_5, opt, model1, sess, X_dataset, Y_dataset, Z_dataset, qvars, pvars, dvars, qvars_y, pvars_y, dvars_y)
[sess, DG_xy, model1] = train_model2(train_op, option.n_epoch_6, opt, model1, sess, X_dataset, Y_dataset, Z_dataset, qvars, pvars, dvars, qvars_y, pvars_y, dvars_y)
'''
return sess, DG_xy, DG_xz, DG_yz, model1