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q_script_v4.py
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q_script_v4.py
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# pylint: disable=not-callable
# pylint: disable=no-member
import numpy as np
import torch
import mnist_utils
import functions as F
from q_network_v3 import QCodingNetwork
"""
Precision term which weights influence - in theory a learnable parameter?
"""
class AttrDict(dict):
__setattr__ = dict.__setitem__
__getattr__ = dict.__getitem__
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 = []
init_errs = []
end_errs = []
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))
init_errs.append(init_err)
end_errs.append(end_err)
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))
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 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 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)
np.save(cf.init_errs_path, init_errs)
np.save(cf.end_errs_path, end_errs)
perm = np.random.permutation(img_train.shape[1])
img_train = img_train[:, perm]
label_train = label_train[:, perm]
if __name__ == "__main__":
cf = AttrDict()
cf.img_path = "imgs/epoch_{}.png"
cf.hybird_path = "data/h_accs_6"
cf.amortised_path = "data/q_accs_6"
cf.pc_path = "data/pc_accs_6"
cf.init_errs_path = "data/init_errs_6"
cf.end_errs_path = "data/end_errs_6"
cf.test_every = 1
cf.n_epochs = 100
cf.data_size = None
cf.batch_size = 128
cf.apply_inv = True
cf.apply_scaling = True
cf.label_scale = 0.94
cf.img_scale = 1.0
cf.td_neurons = [10, 500, 500, 784]
cf.bu_neurons = [784, 500, 500, 10]
cf.n_layers = len(cf.td_neurons)
cf.act_fn = F.TANH
cf.var_out = 1
cf.vars = torch.ones(cf.n_layers)
# TODO
cf.itr_max = 1000
cf.test_itr_max = 1000
# TODO change stuff here
cf.amortised_prec = 0.1
cf.generative_prec = 0.1
cf.beta = 0.1
cf.div = 2
# TODO TODO
cf.condition = 1e-6
cf.d_rate = 0
# optim parameters
cf.l_rate = 1e-5
# TODO q_l_rate low?
cf.q_l_rate = 1e-5
# TODO
cf.optim = "ADAM"
cf.eps = 1e-8
cf.decay_r = 0.9
cf.beta_1 = 0.9
cf.beta_2 = 0.999
cf.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
main(cf)