def eval(X, Y, args, n_classes=10, exp=0, split=0):
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)
with tf.Graph().as_default():
dset = InputGenerator([None]+list(X.shape[1:]), n_classes, args.z_size, batch_size=args.batch_size, n_epochs=1)
aae = AAE("test", batch_size=args.batch_size, n_epochs=1, n_classes=n_classes, z_size=args.z_size, input_shape=x_shape)
iterador = dset.create_eval_generator()
x_input, y_input = iterador.get_next()
_, y_tilde = aae.encoder(x_input)
acc, acc_op = tf.metrics.mean_per_class_accuracy(tf.argmax(y_input, -1), tf.argmax(y_tilde, -1), n_classes)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(args.save_path + "exp_{}/".format(exp)))
sess.run(tf.local_variables_initializer())
sess.run(iterador.initializer, feed_dict={dset.x_input:X, dset.y_input:Y})
n_steps = (len(X) // args.batch_size)
with tqdm(n_steps, desc="Eval", unit="Steps") as pbar:
try:
while True:
accuracy, _ = sess.run([acc, acc_op])
pbar.update()
pbar.set_postfix(Test_Acc=accuracy)
except tf.errors.OutOfRangeError:
pass
out_string = "Split {} Accuracy: {:02.3f}% \n".format(split + 1, 100 * accuracy)
print(out_string)
return out_string
def get_train_fit(X, Y, args, n_classes=10, exp=0, split=0):
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,
args.z_size,
batch_size=args.batch_size,
n_epochs=1)
aae = AAE("test",
batch_size=args.batch_size,
n_epochs=1,
n_classes=n_classes,
z_size=args.z_size,
input_shape=x_shape)
iterador = dset.create_eval_generator()
x_input, y_input = iterador.get_next()
_, y_tilde = aae.encoder(x_input, supervised=True)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(
sess,
tf.train.latest_checkpoint(args.save_path +
"exp_{}/".format(exp)))
sess.run(iterador.initializer,
feed_dict={
dset.x_input: X,
dset.y_input: Y
})
n_steps = (len(X) // args.batch_size)
with tqdm(n_steps, desc="Novelty Train", unit="Steps",
leave=True) as pbar:
try:
train_logits = np.empty([0, n_classes], dtype=np.float32)
while True:
logit = sess.run(y_tilde)
pbar.update()
train_logits = np.append(train_logits, logit, axis=0)
except tf.errors.OutOfRangeError:
pass
assert len(train_logits) == len(Y.argmax(-1))
om = OpenMax(n_classes)
try:
om.fit(train_logits, Y.argmax(-1))
except ValueError:
print("No se pudo completar")
return None
return om
def check_dist_space(X, Y, args, n_classes=10, exp=0, split=0):
x_shape = [args.batch_size] + list(X.shape[1:])
create_dir("plots/")
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,
args.z_size,
batch_size=args.batch_size,
n_epochs=1)
aae = AAE("test",
batch_size=args.batch_size,
n_epochs=1,
n_classes=n_classes,
z_size=args.z_size,
input_shape=x_shape)
iterador = dset.create_eval_generator()
x_input, y_input = iterador.get_next()
z_hat, _ = aae.encoder(
x_input) #Nos interesa el espacio de distribución
y_class = tf.argmax(y_input, -1)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(
sess,
tf.train.latest_checkpoint(args.save_path +
"exp_{}/".format(exp)))
sess.run(iterador.initializer,
feed_dict={
dset.x_input: X,
dset.y_input: Y
})
n_steps = (len(X) // args.batch_size)
with tqdm(n_steps, desc="Dist", unit="Steps", leave=False) as pbar:
try:
Z_ = np.empty((0, args.z_size), dtype=np.float32)
Y_ = np.empty((0), dtype=np.int32)
while True:
z_, y = sess.run([z_hat, y_class])
Z_ = np.append(Z_, z_, axis=0)
Y_ = np.append(Y_, y)
pbar.update()
except tf.errors.OutOfRangeError:
pass
plot_dist(Z_, Y_, split)