def generate(args, n_classes=10, exp=0, split=0, n_generados=1000):
#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, 28, 28, 1],
n_classes,
args.z_size,
batch_size=args.batch_size,
n_epochs=1)
aae = AAE("test",
batch_size=n_classes,
n_epochs=1,
n_classes=n_classes,
z_size=args.z_size,
input_shape=[None, 28, 28, 1])
iterador = dset.create_gen_generator()
z_input, y_input = iterador.get_next()
x_recon = aae.decoder(z_input, y_input) #neutralizar los numeros
#x_bin = tf.cast(tf.greater_equal(x_recon, 0.5), tf.float32) #binarizar lo generado
_, y_tilde = aae.encoder(x_recon)
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)
X_gen = np.empty((0, 28, 28, 1), dtype=np.float32)
Y_gen = np.empty((0), dtype=np.int32)
with tqdm(desc="Generador", unit="Steps") as pbar:
while True:
x_p, y_p, y_clases, accuracy, _ = sess.run(
[x_recon, y_tilde, y_input, acc, acc_op])
pbar.update()
idx = (y_clases.argmax(-1) == y_p.argmax(-1)) & (
y_p.max(-1) >= args.gen_threshold)
X_gen = np.append(X_gen, x_p[idx], axis=0)
Y_gen = np.append(Y_gen, y_p[idx].argmax(-1), axis=0)
n_gen = np.bincount(Y_gen).mean()
pbar.set_postfix(Gen_acc=accuracy,
D_gen=n_gen,
refresh=False)
if n_gen >= n_generados:
break
assert len(X_gen) == len(Y_gen)
gen_str = "Generados:" + str(np.bincount(Y_gen)) + "\n"
print(gen_str)
# print("Elementos Generados por Clase:", np.bincount(Y_gen))
return X_gen, Y_gen, gen_str
def generate(X, Y, args, n_classes=10, exp=0, split=0, n_generados=1000):
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=n_classes,
n_epochs=1,
n_classes=n_classes,
z_size=args.z_size,
input_shape=x_shape)
iterador = dset.create_gen_generator()
z_input, y_input = iterador.get_next()
x_recon = aae.decoder(z_input, y_input) #neutralizar los numeros
_, y_tilde = aae.encoder(x_recon)
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)
X_gen = np.empty((0, 28, 28, 1), dtype=np.float32) #[]
Y_gen = np.empty((0, n_classes), dtype=np.float32) #[]
with tqdm(desc="Generador", unit="Steps") as pbar:
while True: #for step in pbar:
#y_clases = one_hot(np.random.randint(n_classes, size=args.batch_size), n_classes)
#Z = np.random.normal(0, 0.75, (args.batch_size, args.z_size))
x_p, y_p, y_clases, accuracy, _ = sess.run(
[x_recon, y_tilde, y_input, acc, acc_op])
pbar.update()
idx = (y_clases.argmax(-1) == y_p.argmax(-1)
) # & (y_p.max(-1) >= 0.9)
# idx = np.argwhere(y_clases.argmax(-1) == y_p.argmax(-1)).squeeze()
X_gen = np.append(X_gen, x_p[idx], axis=0) #+= [x_p[idx]]
Y_gen = np.append(Y_gen, y_p[idx], axis=0) #+= [y_p[idx]]
ngen = np.bincount(Y_gen.argmax(-1)).mean()
pbar.set_postfix(Gen_Acc=accuracy,
D_Gen=ngen,
refresh=False)
if ngen >= n_generados:
break
#print("Generated Acc={:02.3f}%".format(100*accuracy))
assert len(X_gen) == len(Y_gen)
#X_gen = np.concatenate(X_gen, axis=0)
#Y_gen = np.concatenate(Y_gen, axis=0).argmax(-1)
Y_gen = Y_gen.argmax(-1)
gen_str = "Elementos:" + str(np.bincount(Y_gen)) + "\n"
print(gen_str)
# print("Elementos Generados por Clase:", np.bincount(Y_gen))
return X_gen, Y_gen, gen_str