def main(cf):
print(f"device [{cf.device}]")
print("loading MNIST data...")
train_set = mnist_utils.get_mnist_train_set()
test_set = mnist_utils.get_mnist_test_set()
img_train = mnist_utils.get_imgs(train_set)
img_test = mnist_utils.get_imgs(test_set)
label_train = mnist_utils.get_labels(train_set)
label_test = mnist_utils.get_labels(test_set)
if cf.data_size is not None:
test_size = cf.data_size // 5
img_train = img_train[:, 0:cf.data_size]
label_train = label_train[:, 0:cf.data_size]
img_test = img_test[:, 0:test_size]
label_test = label_test[:, 0:test_size]
msg = "img_train {} img_test {} label_train {} label_test {}"
print(
msg.format(img_train.shape, img_test.shape, label_train.shape,
label_test.shape))
print("performing preprocessing...")
if cf.apply_scaling:
img_train = mnist_utils.scale_imgs(img_train, cf.img_scale)
img_test = mnist_utils.scale_imgs(img_test, cf.img_scale)
label_train = mnist_utils.scale_labels(label_train, cf.label_scale)
label_test = mnist_utils.scale_labels(label_test, cf.label_scale)
if cf.apply_inv:
img_train = F.f_inv(img_train, cf.act_fn)
img_test = F.f_inv(img_test, cf.act_fn)
model = PredictiveCodingNetwork(cf)
with torch.no_grad():
for epoch in range(cf.n_epochs):
print(f"\nepoch {epoch}")
img_batches, label_batches = mnist_utils.get_batches(
img_train, label_train, cf.batch_size)
print(
f"training on {len(img_batches)} batches of size {cf.batch_size}"
)
model.train_epoch(img_batches, label_batches, epoch_num=epoch)
img_batches, label_batches = mnist_utils.get_batches(
img_test, label_test, cf.batch_size)
print(
f"testing on {len(img_batches)} batches of size {cf.batch_size}"
)
accs = model.test_epoch(img_batches, label_batches)
print(f"average accuracy {np.mean(np.array(accs))}")
perm = np.random.permutation(img_train.shape[1])
img_train = img_train[:, perm]
label_train = label_train[:, perm]
def main(cf):
print(f"device [{cf.device}]")
print("loading MNIST data...")
train_set = mnist_utils.get_mnist_train_set()
test_set = mnist_utils.get_mnist_test_set()
img_train = mnist_utils.get_imgs(train_set)
img_test = mnist_utils.get_imgs(test_set)
label_train = mnist_utils.get_labels(train_set)
label_test = mnist_utils.get_labels(test_set)
if cf.data_size is not None:
test_size = cf.data_size // 5
img_train = img_train[:, 0:cf.data_size]
label_train = label_train[:, 0:cf.data_size]
img_test = img_test[:, 0:test_size]
label_test = label_test[:, 0:test_size]
msg = "img_train {} img_test {} label_train {} label_test {}"
print(
msg.format(img_train.shape, img_test.shape, label_train.shape,
label_test.shape))
print("performing preprocessing...")
if cf.apply_scaling:
img_train = mnist_utils.scale_imgs(img_train, cf.img_scale)
img_test = mnist_utils.scale_imgs(img_test, cf.img_scale)
label_train = mnist_utils.scale_labels(label_train, cf.label_scale)
label_test = mnist_utils.scale_labels(label_test, cf.label_scale)
if cf.apply_inv:
img_train = F.f_inv(img_train, cf.act_fn)
img_test = F.f_inv(img_test, cf.act_fn)
model = QCodingNetwork(cf)
q_accs = []
h_accs = []
p_accs = []
with torch.no_grad():
for epoch in range(cf.n_epochs):
print(f"\nepoch {epoch}")
img_batches, label_batches = mnist_utils.get_batches(
img_train, label_train, cf.batch_size)
print(
f"> training on {len(img_batches)} batches of size {cf.batch_size}"
)
end_err, init_err, its = model.train_epoch(img_batches,
label_batches,
epoch_num=epoch)
print("end_err {} / init_err {} / its {}".format(
end_err, init_err, its))
if epoch % cf.test_every == 0:
img_batches, label_batches = mnist_utils.get_batches(
img_test, label_test, cf.batch_size)
print("> generating images...")
pred_imgs = model.generate_data(label_batches[0])
mnist_utils.plot_imgs(pred_imgs, cf.img_path.format(epoch))
if cf.amortised:
img_batches, label_batches = mnist_utils.get_batches(
img_test, label_test, cf.batch_size)
print(
f"> testing amortised acc {len(img_batches)} batches of size {cf.batch_size}"
)
accs = model.test_amortised_epoch(img_batches,
label_batches)
mean_q_acc = np.mean(np.array(accs))
q_accs.append(mean_q_acc)
print(f"average amortised accuracy {mean_q_acc}")
img_batches, label_batches = mnist_utils.get_batches(
img_test, label_test, cf.batch_size)
print(
f"> testing hybrid acc on {len(img_batches)} batches of size {cf.batch_size}"
)
accs, its = model.test_epoch(img_batches,
label_batches,
itr_max=cf.test_itr_max)
mean_h_acc = np.mean(np.array(accs))
h_accs.append(mean_h_acc)
print(f"average hybrid accuracy {mean_h_acc} / its {its}")
img_batches, label_batches = mnist_utils.get_batches(
img_test, label_test, cf.batch_size)
print(
f"> testing PC acc on {len(img_batches)} batches of size {cf.batch_size}"
)
accs, its = model.test_pc_epoch(img_batches,
label_batches,
itr_max=cf.test_itr_max)
mean_p_acc = np.mean(np.array(accs))
p_accs.append(mean_p_acc)
print(f"average PC accuracy {mean_p_acc} / its {its}")
np.save(cf.hybird_path, h_accs)
np.save(cf.amortised_path, q_accs)
np.save(cf.pc_path, p_accs)
perm = np.random.permutation(img_train.shape[1])
img_train = img_train[:, perm]
label_train = label_train[:, perm]