trainer.compute_metrics_time = 0
trainer.n_epochs = n_epochs
trainer.compute_metrics()
n_epochs=20
lr=1e-3
eps=0.01
para = trainer.model.parameters()
params = filter(lambda p: p.requires_grad, para)
optimizer = torch.optim.Adam(params, lr=lr, eps=eps)
for epoch in range(n_epochs):
trainer.on_epoch_begin()
for tensors_list in trainer.data_loaders_loop():
loss = trainer.loss(*tensors_list)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if not trainer.on_epoch_end():
break
import sys
from tqdm import trange
with trange(
n_epochs,
desc="my training",
file=sys.stdout,
disable=False
if is_test_pragram:
pre_trainer.train(n_epochs=n_epochs, lr=lr)
torch.save(pre_trainer.model.state_dict(), '%s/pre_trainer6.pkl' % save_path)
if os.path.isfile('%s/pre_trainer6.pkl' % save_path):
pre_trainer.model.load_state_dict(torch.load('%s/pre_trainer6.pkl' % save_path))
pre_trainer.model.eval()
else:
#pre_trainer.model.init_gmm_params(dataset)
pre_trainer.train(n_epochs=n_epochs, lr=lr)
torch.save(pre_trainer.model.state_dict(), '%s/pre_trainer6.pkl' % save_path)
sample_latents = torch.tensor([])
samples = torch.tensor([])
sample_labels = torch.tensor([])
for tensors_list in pre_trainer.data_loaders_loop():
sample_batch, local_l_mean, local_l_var, batch_index, y = zip(*tensors_list)
temp_samples = pre_trainer.model.get_latents(*sample_batch) #check this expression
samples = torch.cat((samples, sample_batch[0].float()))
for temp_sample in temp_samples:
sample_latents = torch.cat((sample_latents, temp_sample.float()))
sample_labels = torch.cat((sample_labels, y[0].float()))
# end the pre-training
#multi_vae = Multi_VAE(dataset.nb_genes, len(dataset.atac_names), n_batch=dataset.n_batches * use_batches, n_centroids=n_centroids, n_alfa = n_alfa, mode="mm-vae") # should provide ATAC num, alfa, mode and loss type
multi_vae = Multi_VAE(dataset.nb_genes, len(dataset.atac_names), n_batch=256, n_centroids=n_centroids, n_alfa = n_alfa, mode="mm-vae") # should provide ATAC num, alfa, mode and loss type
# begin the multi-vae training
trainer = MultiTrainer(
multi_vae,
dataset,
