def train_cvae(X_source, X_aux_list, X_target, X_source_t, X_aux_list_t,
X_target_t):
cvae = CVAE(input_shape=58, hidden_layers=1, dims=[45, 30])
input_tensor = tf.compat.v1.placeholder(dtype=tf.float32, shape=(None, 58))
cond_tensor = tf.compat.v1.placeholder(dtype=tf.float32, shape=(None, 4))
label_tensor = tf.compat.v1.placeholder(dtype=tf.float32, shape=(None, 58))
train_tensor = tf.compat.v1.placeholder(dtype=tf.float32, shape=())
train_ds = tf.data.Dataset.from_tensor_slices(
(input_tensor, cond_tensor,
label_tensor)).shuffle(100000).batch(BATCH).repeat()
iterator = train_ds.make_initializable_iterator()
dat, c, lab = iterator.get_next()
#cvae = CVAE(100, hidden_layers=1, dims=[50, 20])
#cvae.init_model(input_tensor, cond_tensor, label_tensor, train_tensor)
cvae.init_model(dat, c, lab, train_tensor)
model = cvae.model_
loss = cvae.loss_
train_op = tf.train.AdamOptimizer().minimize(loss)
X_train = np.vstack([X_source] + X_aux_list)
X_train_t = np.vstack([X_source_t] + X_aux_list_t)
k = len(X_aux_list) + 1
C_train = [k] * X_source.shape[0]
for i in range(len(X_aux_list)):
k -= 1
C_train += [k] * X_aux_list[i].shape[0]
print(len(C_train))
print(X_train.shape)
print(C_train)
C_train = np.array(C_train)
C_train = np.eye(len(X_aux_list) + 1)[C_train - 1]
with tf.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(iterator.initializer,
feed_dict={
input_tensor: X_train,
cond_tensor: C_train,
label_tensor: X_train_t,
train_tensor: 1
})
for i in range(EPOCH):
tot_loss = 0
for _ in range(X_train.shape[1] // BATCH):
_, loss_value = sess.run([train_op, loss])
#feed_dict={input_tensor: X_train, cond_tensor: Y_tr, label_tensor: X_train, train_tensor: 1})
tot_loss += loss_value
#if i%100 == 0:
print("Iter: {}, Loss: {:.4f}".format(
i, tot_loss / (X_train.shape[1] // BATCH)))
yy = np.random.random_integers(0, 3, X_target.shape[0])
print(yy)
yy = np.eye(len(X_aux_list) + 1)[yy]
l = []
sess.run(iterator.initializer,
feed_dict={
input_tensor: X_target,
cond_tensor: yy,
label_tensor: X_target_t,
train_tensor: 1
})
for i in range(2000):
for img in sess.run(model['op_tensor']):
l.append(np.array(img))
l = np.vstack(l)
np.savetxt('abc.txt', l)
print(Y_te.shape)
input_tensor = tf.compat.v1.placeholder(dtype=tf.float32, shape=(None, 784))
cond_tensor = tf.compat.v1.placeholder(dtype=tf.float32, shape=(None, 10))
label_tensor = tf.compat.v1.placeholder(dtype=tf.float32, shape=(None, 784))
train_tensor = tf.compat.v1.placeholder(dtype=tf.float32, shape=())
train_ds = tf.data.Dataset.from_tensor_slices((input_tensor, cond_tensor, label_tensor)).shuffle(60000).batch(BATCH).repeat()
iterator = train_ds.make_initializable_iterator()
dat, c, lab = iterator.get_next()
#cvae = CVAE(100, hidden_layers=1, dims=[50, 20])
cvae = CVAE(784, hidden_layers=1, dims=[196, 100])
#cvae.init_model(input_tensor, cond_tensor, label_tensor, train_tensor)
cvae.init_model(dat, c, lab, train_tensor)
model = cvae.model_
loss = cvae.loss_
train_op = tf.train.AdamOptimizer().minimize(loss)
with tf.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(iterator.initializer, feed_dict={input_tensor: X_tr, cond_tensor: Y_tr, label_tensor: X_tr, train_tensor: 1})
for i in range(EPOCH):
tot_loss = 0
for _ in range(X_tr.shape[1]//BATCH):
_, loss_value = sess.run([train_op, loss])
#feed_dict={input_tensor: X_tr, cond_tensor: Y_tr, label_tensor: X_tr, train_tensor: 1})
tot_loss += loss_value
