def train(X,
Y,
args,
increment=False,
n_classes=10,
exp=0,
split=0,
n_epochs=1):
x_shape = [args.batch_size] + list(X.shape[1:])
save_file = args.save_path + "exp_{}/model_split{}.ckpt".format(exp, split)
with tf.Graph().as_default():
dset = InputGenerator([None] + list(X.shape[1:]),
n_classes,
z_size=args.z_size,
batch_size=args.batch_size,
n_epochs=n_epochs)
aae = AAE("train",
batch_size=args.batch_size,
n_epochs=n_epochs,
n_classes=n_classes,
z_size=args.z_size,
input_shape=x_shape)
iterador = dset.create_train_generator()
(x_input, y_input), (z_real, y_real) = iterador.get_next()
# Estructura
z_hat, y_hat = aae.encoder(x_input)
x_recon = aae.decoder(z_hat, y_hat)
dz_real = aae.discriminator_z(z_real)
dz_fake = aae.discriminator_z(z_hat)
dy_real = aae.discriminator_y(y_input)
dy_fake = aae.discriminator_y(y_hat)
_, y_tilde = aae.encoder(x_input, supervised=True)
# Metricas
acc, acc_op = tf.metrics.mean_per_class_accuracy(
tf.argmax(y_input, -1), tf.argmax(y_tilde, -1), n_classes)
mse, mse_op = tf.metrics.mean_squared_error(x_input, x_recon)
# Costos
ae_loss = tf.losses.log_loss(x_input, x_recon)
clf_loss = tf.losses.softmax_cross_entropy(y_input, y_tilde)
dz_real_loss = tf.losses.sigmoid_cross_entropy(tf.ones_like(dz_real),
dz_real)
dz_fake_loss = tf.losses.sigmoid_cross_entropy(tf.zeros_like(dz_fake),
dz_fake)
dz_loss = dz_real_loss + dz_fake_loss
dy_real_loss = tf.losses.sigmoid_cross_entropy(tf.ones_like(dy_real),
dy_real)
dy_fake_loss = tf.losses.sigmoid_cross_entropy(tf.zeros_like(dy_fake),
dy_fake)
dy_loss = dy_real_loss + dy_fake_loss
gz_loss = tf.losses.sigmoid_cross_entropy(tf.ones_like(dz_fake),
dz_fake)
gy_loss = tf.losses.sigmoid_cross_entropy(tf.ones_like(dy_fake),
dy_fake)
gen_loss = gz_loss + gy_loss
# Training ops
all_vars = tf.trainable_variables()
dz_vars = [var for var in all_vars if "Discriminator_Z" in var.name]
dy_vars = [var for var in all_vars if "Discriminator_Y" in var.name]
enc_vars = [var for var in all_vars if "Encoder" in var.name]
ae_vars = enc_vars + [var for var in all_vars if "Decoder" in var.name]
if increment:
increment_vars = [
var for var in tf.global_variables() if "Y_" not in var.name
]
increment_vars = [
var for var in increment_vars
if "Discriminator" not in var.name
]
init_vars = [
var for var in tf.global_variables() if "Y_" in var.name
]
init_vars += [
var for var in tf.global_variables()
if "Discriminator" in var.name
]
else:
increment_vars = None
init_vars = None
step_tensor = tf.Variable(0, trainable=False, name="Step")
learning_rate = 0.001
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
ae_opt = tf.train.AdamOptimizer(learning_rate).minimize(
ae_loss, var_list=ae_vars, global_step=step_tensor)
dz_opt = tf.train.AdamOptimizer(learning_rate).minimize(
dz_loss, var_list=dz_vars)
dy_opt = tf.train.AdamOptimizer(learning_rate).minimize(
dy_loss, var_list=dy_vars)
gen_opt = tf.train.AdamOptimizer(learning_rate).minimize(
gen_loss, var_list=enc_vars)
clf_opt = tf.train.AdamOptimizer(learning_rate).minimize(
clf_loss, var_list=enc_vars)
train_ops = tf.group([ae_opt, dz_opt, dy_opt, gen_opt, clf_opt])
if increment:
saver = tf.train.Saver(increment_vars)
ckpt_saver = tf.train.Saver()
with tf.Session() as sess:
if increment:
sess.run(tf.global_variables_initializer())
saver.restore(
sess,
tf.train.latest_checkpoint(args.save_path +
"exp_{}/".format(exp)))
# sess.run(tf.variables_initializer(init_vars))
else:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
#Cargar los datasets
sess.run(iterador.initializer,
feed_dict={
dset.x_input: X,
dset.y_input: Y
})
n_steps = (len(X) // args.batch_size) * n_epochs
# Operacion de entrenamiento:
with tqdm(desc="Train", total=n_steps, unit="Steps",
miniters=10) as pbar:
try:
while True:
_, step, accuracy, msqer, _, _ = sess.run(
[train_ops, step_tensor, acc, mse, acc_op, mse_op])
pbar.update()
if step % 10 == 0:
pbar.set_postfix(Accuracy=accuracy,
MSE=msqer,
refresh=False)
except tf.errors.OutOfRangeError:
pass
ckpt_saver.save(sess, save_path=save_file)
print("Done!")
def train(X,
Y,
args,
increment=False,
n_classes=10,
exp=0,
split=0,
n_epochs=1):
x_shape = [args.batch_size] + list(X.shape[1:])
y_shape = [args.batch_size] + list(Y.shape[1:])
save_file = args.save_path + "exp_{}/model_split{}.ckpt".format(exp, split)
monitor_path = args.monitor + "exp_{}/split_{}".format(exp, split)
with tf.Graph().as_default():
dset = InputGenerator([None] + list(X.shape[1:]),
n_classes=n_classes,
args.z_size,
batch_size=args.batch_size,
n_epochs=n_epochs)
aae = AAE("train",
batch_size=args.batch_size,
n_epochs=n_epochs,
n_classes=n_classes,
z_size=args.z_size,
input_shape=x_shape)
iterador = dset.create_train_generator()
(x_input, y_input), (z_real, y_real) = iterador.get_next()
# Estructura
z_hat, y_hat = aae.encoder(x_input)
pz = tf.sigmoid(z_hat + 1e-8)
entropia = tf.reduce_mean(-tf.reduce_sum(pz * tf.log(pz), 1))
#entropia = tf.reduce_mean(0.5*tf.norm(z_hat - z_real, ord=1, axis=1))
x_recon = aae.decoder(z_hat, y_hat)
dz_real = aae.discriminator_z(z_real)
dz_fake = aae.discriminator_z(z_hat)
dy_real = aae.discriminator_y(y_real)
dy_fake = aae.discriminator_y(y_hat)
_, y_tilde = aae.encoder(x_input, supervised=True)
# Metricas
acc, acc_op = tf.metrics.mean_per_class_accuracy(
tf.argmax(y_input, -1), tf.argmax(y_tilde, -1), n_classes)
mse, mse_op = tf.metrics.mean_squared_error(x_input, x_recon)
# Costos
ae_loss = tf.losses.log_loss(
x_input, x_recon
) # tf.reduce_mean(aae.binary_crossentropy(x_input, x_recon))
clf_loss = tf.losses.softmax_cross_entropy(
y_input, y_tilde
) #tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_input, logits=y_tilde))
dz_real_loss = tf.losses.sigmoid_cross_entropy(
tf.ones_like(dz_real), dz_real
) #tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(dz_real), logits=dz_real))
dz_fake_loss = tf.losses.sigmoid_cross_entropy(
tf.zeros_like(dz_fake), dz_fake
) #tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(dz_fake), logits=dz_fake))
dz_loss = dz_real_loss + dz_fake_loss
dy_real_loss = tf.losses.sigmoid_cross_entropy(
tf.ones_like(dy_real), dy_real
) #tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(dy_real), logits=dy_real))
dy_fake_loss = tf.losses.sigmoid_cross_entropy(
tf.zeros_like(dy_fake), dy_fake
) #tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(dy_fake), logits=dy_fake))
dy_loss = dy_real_loss + dy_fake_loss
gz_loss = tf.losses.sigmoid_cross_entropy(
tf.ones_like(dz_fake), dz_fake
) #tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(dz_fake), logits=dz_fake))
gy_loss = tf.losses.sigmoid_cross_entropy(
tf.ones_like(dy_fake), dy_fake
) #tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(dy_fake), logits=dy_fake))
gen_loss = gz_loss + gy_loss
# Training ops
all_vars = tf.trainable_variables()
dz_vars = [var for var in all_vars if "Discriminator_Z" in var.name]
dy_vars = [var for var in all_vars if "Discriminator_Y" in var.name]
enc_vars = [var for var in all_vars if "Encoder" in var.name]
if increment:
increment_vars = [
var for var in tf.global_variables() if "Y_" not in var.name
]
init_vars = [
var for var in tf.global_variables() if "Y_" in var.name
]
else:
increment_vars = None
init_vars = None
step_tensor = tf.Variable(0, trainable=False, name="Step")
#learning_rate = tf.train.polynomial_decay(0.005, global_step=step_tensor, decay_steps=20000, end_learning_rate=0.000001, power=2, name="Learning_rate")
learning_rate = 0.001
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
ae_opt = tf.train.AdamOptimizer(learning_rate).minimize(
ae_loss, global_step=step_tensor)
dz_opt = tf.train.AdamOptimizer(learning_rate).minimize(
dz_loss, var_list=dz_vars)
dy_opt = tf.train.AdamOptimizer(learning_rate).minimize(
dy_loss, var_list=dy_vars)
gen_opt = tf.train.AdamOptimizer(learning_rate).minimize(
gen_loss, var_list=enc_vars)
clf_opt = tf.train.AdamOptimizer(learning_rate).minimize(
clf_loss, var_list=enc_vars)
train_ops = tf.group([ae_opt, dz_opt, dy_opt, gen_opt, clf_opt])
# summaries
tf.summary.scalar("Losses/AE_loss", ae_loss)
tf.summary.scalar("Losses/Dis_Y_loss", dy_loss)
tf.summary.scalar("Losses/Dis_Z_loss", dz_loss)
tf.summary.scalar("Losses/Gen_loss", gen_loss)
tf.summary.scalar("Losses/Clf_loss", clf_loss)
tf.summary.scalar("Metrics/Accuracy", acc)
tf.summary.scalar("Metrics/MSE", mse)
tf.summary.scalar("Metrics/Entropy", entropia)
#tf.summary.scalar("Metrics/LearningRate",learning_rate)
tf.summary.histogram("Z_pred", z_hat)
tf.summary.histogram("Z_real", z_real)
tf.summary.image("X_Real", x_input, 10)
tf.summary.image("X_Recon", x_recon, 10)
summary_op = tf.summary.merge_all()
if increment:
saver = tf.train.Saver(increment_vars)
ckpt_saver = tf.train.Saver()
with tf.Session() as sess:
if increment:
sess.run(tf.global_variables_initializer())
saver.restore(
sess,
tf.train.latest_checkpoint(args.save_path +
"exp_{}/".format(exp)))
# sess.run(tf.variables_initializer(init_vars))
else:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
summary_writer = tf.summary.FileWriter(monitor_path)
#Cargar los datasets
sess.run(iterador.initializer,
feed_dict={
dset.x_input: X,
dset.y_input: Y
})
n_steps = (len(X) // args.batch_size) * n_epochs
# Operacion de entrenamiento:
with tqdm(desc="Train", total=n_steps, unit="Steps",
miniters=10) as pbar:
try:
while True:
_, step, accuracy, msqer, _, _, summary = sess.run([
train_ops, step_tensor, acc, mse, acc_op, mse_op,
summary_op
])
summary_writer.add_summary(summary, step)
pbar.update()
if step % 10 == 0:
pbar.set_postfix(Accuracy=accuracy,
MSE=msqer,
refresh=False)
except tf.errors.OutOfRangeError:
pass
ckpt_saver.save(sess, save_path=save_file)
print("Done!")